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    1. Reviewer #2 (Public Review):

      The manuscript "Archaeal chromatin 'slinkies' are inherently dynamic complexes with deflected DNA wrapping pathways" by Bowerman and colleagues describes a study of archaeasome dynamics combining molecular simulations, cryo-EM, and sedimentation velocity analytical ultracentrifugation. How chromatin evolved is a fundamental question in biology, marking a striking departure from the bacterial nucleoid. Indeed, ever since the first description of archaeal nucleosomes and histones HmfA/B (Sandman and Reeves mid-80s) from thermophilic archaea, this question has fascinated and puzzled the field.

      Recent work from the Luger lab figured out the organization of these archaeal chromatin fibers as a continuous loop structure. Here, the authors extend this question further. MD analyses show that Arc90 has two preferred states (closed and flexible ends), but the same 5T5K structure on 120 or 180 bp of DNA prefer a single state (closed). Sedimentation velocity analytical ultracentrifugation showed that Arc207 sediments slower than the H3 mononucleosome, implying that that Arc207 has a shape with higher anisotropy, resulting in excessive drag compared to a mononucleosome. Subsequently, high-resolution cryoEM showed that at least two distinct classes for Arc207 exist, where one class represents a 5-mer and another class represent a 7-mer. The latter has a unique shape in that the 7-mer forms an L-shape (or open clam) with a 3-mer hinging on a 4-mer.

      Overall, these data provide exciting structural insights into how archaeal chromatin is folded up at its basic unit level, which the authors describe as most fittingly as a "slinkie". Because so little is known about how nucleosomes evolved during the transition from archaea to eukaryotes, we found this interdisciplinary report well written and with compelling data, that will be of interest to the chromosome biology field at large. We suggest a minor revision in which a few technical points are addressed.


      1) The cryoEM data showed two main groups of particles: 5-mer protecting 150 bp and a 7-mer protecting either 90bp or 120bp. A few times in the manuscript (both in the results and discussion section) the authors mention a 30-bp MNase digestion ladder is observed. The Mnase data should be included, as this provides evidence that the structures observed by cryoEM indeed represent physiological structures, especially if strong discrete bands are observed at 90, 120, and 150 bp.

      2) The two main classes found by cryoEM give the impression that adding dimers results in altered structures. The 7-mer shows an angled structure, which is interpreted as an open structure. The 5-mer shows a more uniform structure, which is interpreted as a closed structure. The former structure protects the full length of DNA on which HTkA histones were reconstituted, whereas the latter might be an incomplete reconstitution or a partially disassembled structure. It also raises the question if the length of the DNA is a limiting factor. What if HTkA was reconstituted on 170 bp or 307 bp instead? Would this in turn only permit the formation of the 5-mer on the 170 bp construct and two 5-mers on the 307 bp construct? The authors should consider addressing this point because the reconstitution might be constrained by the length of the DNA construct used. Indeed, a related topic might be AT content- what does archaeal DNA look like from the perspective of DNA sequence for chromatin (Jon Widom's group had a ChIPSeq paper on this a few years ago, just after his untimely passing).

      3) In the discussion the authors cite that in one archaeal species the Mg2+ concentration is ~120 mM, more than a magnitude greater than that tested in Figure 5. What happens to reconstituted archaeasomes at higher Mg+? This is relevant because in vivo, archaea are thought to have 10x the concentration of Mg+ (amongst other ions) relative to us humble eukaryotes who would probably die of kidney failure at those ionic concentrations. Indeed at high ionic conditions, eukaryotic chromatin can be made to precipitate out of solution (for e.g. 10mM Mg+, 3M NaCl). An AUC assay with higher Mg2+ concentrations seems a doable and physiologically relevant addition to the ms that would strengthen it. It is relevant to consider that in vivo structure in these halophilic and thermophilic organisms might be dependent on the concentration of various salts and temperature, it would be nice to read the authors' thoughts on this issue.

    1. Reviewer #2 (Public Review):

      This manuscript by Nicholas Strash et al. compares the effects of several potential mitogens on cell cycle of the two most used in vitro models of cardiomyocytes (CMs): neonatal rat ventricular myocytes (NRVMs) and human induced pluripotent stem cell (hiPSC)-derived CMs. In addition, they use a 3D model of NRVMs as a model that represents more mature, non-proliferating CMs. The work is interesting for researchers working in the field of cardiac regeneration and provides the first direct comparison of several potential mitogens. The inclusion of several in vitro models to account for potential species differences strengthens the data. The results support previously published findings and the main conclusions are supported by the data presented.

      The authors used a 3D model, cardiobundles made from NRVMs, as a more mature CM model. However, these cardiobundles still had a considerable number of CMs in active cell cycle in basal conditions. Whether this reflects true proliferation or the postnatal multinucleation process of rat cardiomyocytes, is unclear. Furthermore, post-mitotic human CMs were not studied. These can be obtained from hiPSC-CMs by prolonged culture or using metabolic stimuli as shown by Mills et al. 2017 (PNAS).

      The authors demonstrate that the known mitogenic pathway for CMs, Erbb2-mediated signalling, promotes cell cycle activation in 2D cultures or NRVMS and hiPSC-CMs as well as in 3D cardiobundles. Although cell cycle activity was clearly induced, no actual proof of cytokinesis has been presented. For the cardiobundle work, it remains unclear if the increase in cross-sectional size of cardiobundles induced by Erbb2 signalling is due to increased number of CMs or increased size of CMs. Both the physiological ligand of Erbb3, Neuregulin-1, and the downstream ERK pathway are known to induce CM hypertrophy (see for example Zurek et al. 2020 Circulation; Bueno and Molkentin 2002 Circ Res).

      The data analysis and statistics raise some concerns, which require clarification. First, the N numbers are really big and according to the Table 1 it is unclear if they all indeed represent independent samples. For example, one field in a monolayer (Table 1, definition of n in Figures 1J, 1P, 4C, 4E, 4G) should not be considered to represent n=1, if several images were analysed from the same sample and/or if several technical replicates (samples prepared from the same cell isolation or differentiation and treated similarly) were analysed. Only samples from separate differentiations or cell isolations should be considered as representatives of n and the results from technical replicates should be averaged to form the n=1 data. Second, the selection of statistical tests is a concern. It is unclear if the data were analysed for equal variances before selecting the test (parametric vs. non-parametric). It is also unclear why the authors carried out multiple t tests instead of using ANOVA or its variations, which are generally considered more suitable for multiple comparisons.

    1. Reviewer #2:

      The paper investigates the temporal signatures of single-neuron activity (the autocorrelation timescale and latency) in two frontal areas, MCC and LPFC. These signatures differ between the two areas and cell classes, and form an anatomical gradient in MCC. Moreover, the intrinsic timescales of single neurons correspond with their coding of behaviorally relevant information on different timescales. The authors develop a detailed biophysical network model which suggests that after-hyperpolarization potassium and inhibitory GABA-B conductances may underpin the potential biophysical mechanism that explains diverse temporal signatures observed in the data. The results appear exciting, as the proposed relationship between the intrinsic timescales, coding of behavioral timescales, and anatomical properties (e.g., the amount of local inhibition) in the two frontal areas is novel. The use of the biophysically detailed model is creative and interesting. However, there are serious methodological concerns undermining the key conclusions of this study, which need to be addressed before the results can be credited.

      Major Concerns:

      1) One of the key findings is the correspondence between the intrinsic timescales of single neurons and their coding of information on different behavioral timescales (Fig. 4). However, the method for estimating the intrinsic timescales has serious problems which can undermine the finding.

      1.1 The authors developed a new method for estimating autocorrelograms from spike data but the details of this method are not specified. It is stated that the method computes the distribution of inter-spike-intervals (ISIs) up to order 100, which was "normalized", but how it was normalized is not described. The correct normalization is crucial, as it converts the counts of spike coincidences (ISI distribution) into autocorrelogram (where the coincidence counts expected by chance are subtracted) and can produce artifacts if not performed correctly.

      1.2 The new method, described as superior to the previous method by Murray et al, 2014, appears to have access to more spikes than the Murray's method (Fig. 2). Where is this additional data coming from? While Murray's method was applied to the pre-cue period, the time epoch used for the analysis with the new method is not stated clearly. It seems that the new method was applied to the data through the entire trial duration and across all trials, hence more spikes were available. If so, then changes in firing rates related to behavioral events contribute to the autocorrelation, if not appropriately removed. For example, the Murray's method subtracts trail-averaged activity (PSTH) from spike-counts, similar to shuffle-correction methods. If a similar correction was not part of the new method, then changes in firing rates due to coding of task variables will appear in the autocorrelogram and estimated timescales. This is a serious confound for interpretation of the results in Fig. 4. For example, if the firing rate of a neuron varies slowly coding for the gauge size across trials, this will appear as a slow timescale if the autocorrelogram was not corrected to remove these rate changes. In this case, the timescale and GLM are just different metrics for the same rate changes, and the correspondence between them is expected. Before results in Fig. 4 can be interpreted, details of the method need to be provided to make sure that the method measures intrinsic timescales, and not timescales of rate changes triggered by the task events. This is an important concern also because recent work showed that there is no correlation between task dependent and intrinsic timescales of single neurons, including in cingulate cortex and PFC (Spitmaan et al., PNAS, 2020).

      2) The balanced network model with a variety of biophysical currents is interesting and it is impressive that the model reproduces the autocorrelation signatures in the data. However, we need to better understand the network mechanism by which the model operates.

      2.1 The classical balanced network (without biophysical currents such as after-hyperpolarization potassium) generates asynchronous activity without temporal correlations (Renart et al., Science, 2010). The balanced networks with slow adaptation currents can generate persistent Up and Down states that produce correlations on slow timescales (Jercog et al., eLife, 2017). Since slow after-polarization potassium current was identified as a key ingredient, is the mechanism in the model similar to the one generating Up and Down states, or is it different? Although the biophysical ingredients necessary to match the data were identified, the network mechanism has not been studied. Describing this network mechanism and presenting the model in the context of existing literature is necessary, otherwise the results are difficult to interpret for the reader.

      2.2 Does the model operate in a physiologically relevant regime where the firing rates, Fano factor etc. are similar to the data? It is hard to judge from Fig. 5b and needs to be quantified.

      2.3 The latency of autocorrelation is an interesting feature in the data. Since the model replicates this feature (which is not intuitive), it is important to know what mechanism in the model generates autocorrelation latency.

      3) HMM analysis is used to demonstrate metastability in the model and data, but there are some technical concerns that can undermine these conclusions.

      3.1 HMM with 4 states was fitted to the data and model. The ability to fit a four-state HMM to the data does not prove the existence of metastable states. HMM assumes a constant firing rate in each "state", and any deviation from this assumption is modeled as state transitions. For example, if some neurons gradually increase/decrease their firing rates over time, then HMM would generate a sequence of states with progressively higher/lower firing rates to capture this ramping activity. In addition, metastability implies exponential distributions of state durations, which was not verified. No model selection was performed to determine the necessary number of states. Therefore, the claims of metastable dynamics are not supported by the presented analysis.

      3.2 HMM was fit to a continuous segment of data lasting 600s, and the data was pooled across different recording sessions. However, different sessions have potentially different trial sequences due to the flexibility of the task. How were different trial types matched across the sessions? If trial-types were not matched/aligned in time, then the states inferred by the HMM may trivially reflect a concatenation of different trial types in different sessions. For example, the same time point can correspond to the gauge onset in one session and to the work trial in another session, and vice versa at a different time. If some neurons respond to the gauge and others to the work, then the HMM would need different states to capture firing patterns arising solely from concatenating the neural responses in this way. This confound needs to be addressed before the results can be interpreted.

    1. Reviewer #2:

      A state of the art imaging of the dynamics of astroglial glutamate transporters that certainly add novel perspective into this, quite important and hot field. Experiments and clean and convincing, the data obtained fully support conclusions.


      1) The authors mention the importance of efficient glutamate uptake in the development of neuropathological conditions, but do not discuss this in regards to their results. Such a discussion would seem relevant.

      2) Authors conclude that the membrane turnover pathway should be a particularly important GLT-1 resupply mechanism near excitatory synapses as some earlier studies have found the lowest lateral membrane mobility of GLT-1 there. In this context, it would be of interest to have some quantitative tips as to the relationship between the level of excitatory activity and the occupancy of local GLT-1.

      3) There is a recent work implicating the C-terminus in the surface assembly of GLT-1 (Peacy et al Mol Pharm 2020), which seems relevant to the present findings. Please discuss further.

      4) Functional activity of glutamate transporters is linked to (and is being regulated by) astroglial Na signalling; any suggestions how proposed turnover cycle may affect cytosolic Na+ dynamics

      5) The Fig. 5A imaging data seem to nicely provide both surface and cytosol labelling of the same cell. Perhaps the authors could thus assess the distribution of surface-to-volume ratios across live astroglia: to my knowledge, such data has not yet been available.

      6) Figure 1B: Please provide further detail regarding fast-exchange solution application, its physical arrangement, etc.

      7) Figure 2, whole-cell photobleaching: Please expand on what is 'tornado' mode scanning and how it has been applied.

      8) Figure 3, dSTORM data: Please provide further details regarding the numbers of sampled ROIs and/or individual molecules / distances analysed.

    1. Reviewer #2:

      This manuscript addresses the question of whether duplication of tumor suppressors occurred coincidently with the enlarged body size and reduced cancer risk evolved independently in Afrotherians. Using the human genome as reference, the authors systematically searched for gene duplications in 13 publicly available Afrotherian genomes, including 9 extant and 4 extinct species. The authors also reconstructed the ancestral body sizes, cancer risks and gene duplication events across the Afrotherian phylogeny. These data showed that both increased body sizes and reduced cancer risks are gradually evolved. Reactome pathway enrichment analysis for gene duplicates showed unexpectedly that gene duplicates in both lineages with or without major increases in body size/lifespan/decreases in cancer risk are enriched in many cancer related pathways. However, the authors found that 157 genes duplicated in Proboscidean stem-lineage, in which extremely large species evolved, were uniquely enriched in 12 cancer pathways. These genes might facilitate further body enlargement and cancer resistance evolution in Proboscidean. Most interestingly, the authors found that several genes both upstream and downstream of a famous tumor suppressor TP53 have also been duplicated, either before or after initial TP53 duplication. These genes are involved in transcriptional regulation of TP53 and may have facilitated re-functionalization of TP53 retroduplicates. Overall, this is an important and interesting study that can help us understand the evolution of body size, lifespan and cancer risk in mammals more deeply.

      Major comments:

      1) In general, the evolutionary fate of gene duplication includes: 1) Conservation of gene function; 2) Neofunctionalization; 3) Pseudogenization; 4) Subfunctionalization (doi:10.1016/S01695347(03)00033-8). To execute the function of tumor suppression, as this study focused on, gene duplicates were supposed to be functionally conserved or subfunctionalized. Gene duplicates that have been neofunctionalized or pseudogenized will not be helpful (also mentioned by authors in the Caveats section). Therefore, it might be more convincing to investigate the functional status of each gene duplicate, especially those in Fig 4C/D. In many cases, however, a related function, rather than an entirely new function, evolves by neofunctionalization after gene duplication, and also that to check new functions for a batch of genes is not realistic, the authors could simply check the coding sequences to ensure these genes duplicates are not pseudogenes and are functional. This is necessary because in Fig 4D, many genes have only 2 copies expressed. If one of them is a young pseudogene, it could be stochastically expressed and will encode a dysfunctional protein.

      2) In Results section 3, the cancer pathway frequency data of many nodes seems not consistent with data shown in Table 2. For example, Line 293-296: "55.8% (29/52) of the pathways that were enriched in the Tethytherian stem-lineage..., 27.8% (20/72) of the pathways that were enriched in the Proboscidean stem-lineage...were related to tumor suppression", the cancer pathway percentages shown in Table 2 for these 2 nodes are 63.4% and 38.81%, respectively. While the frequency data in Table 2 are consistent with Supplementary Data File S3: "Atlantogenata_Reactome_ORA.xlsx". It is possible that the frequency data shown in the main text are specific to pathways of tumor suppression, rather than cancer related pathways. If this is the case, more detailed data should be shown somewhere else.

      3) The titles of Results section 3 and section 4 are highly similar and actually the data in section 4 seems to be used to further solidify the conclusion of section 3. Therefore, is it possible to merge them into one single section?

    1. Reviewer #2:

      When animals are given a choice between drug and nondrug reinforcers, they will most often choose the nondrug alternative even when presented with highly reinforcing drugs of abuse. This is difficult to reconcile with known behavior in humans and for modeling aspects of addiction that are critical to the disorder, such as choosing to use drugs above all other reinforcers. Recent work by this same group has reported that responding for nondrug reinforcer is, surprisingly, insensitive to devaluation. This suggests that the choice for the nondrug reinforcer is under habitual, rather than the presumed goal-directed, control and may explain why animals most often choose the nondrug reinforcer over drug reinforcers. Moreover, because there is no devaluation procedure for determining whether drug choice is habitual or goal directed, it's not known if choice for drug is also habitual or remains goal-directed.

      The manuscript by Vandaele et al., therefore, sought to develop a procedure for determining whether behavior of rats making choices between saccharin and cocaine reinforcers was habitual or goal-directed based on reaction times (RT). Based on previous theories, the authors argue that goal-directed behavior should have slower RTs on choice trials versus sampling trials (e.g., because animals are deliberating between the alternatives) whereas habitual behavior should have similar RTs across both sampling and choice trials. The authors also present a third possibility in which options are evaluated sequentially, rather than simultaneously, resulting in RTs being longer in the sampling versus choice trials. The authors report that rats with minimal training and who are presumed to be goal-directed have slower RTs in choice trials compared to sample trials whereas rats that have had extensive training have similar RTs in the choice and sampling phases. These findings are consistent with their hypotheses. Moreover, they demonstrate that in the small subset of rats that prefer cocaine over saccharin, RTs in the sampling trials are longer than that in the choice trial suggesting that cocaine preferring rats are not evaluating each of the options. These data are the first to evaluate habitual responding for a drug reinforcer and suggest that comparing latencies across different task phases could be used to measure habitual and goal-directed behaviors.

    1. Reviewer #2:

      This manuscript by Mathsyaraja et al. studies the oncogenic loss of the Max-gene-associated (MGA) protein due to deletion or mutation in cell-lines, in mice and in human cancers (cell-lines and tumors). The authors knocked out MGA by aerosol-delivered, CRISPR-CAS expressing lentiviruses that simultaneously Cre-activated a Lox-stop Kras oncogene. The loss of MGA accelerated proliferation and oncogenesis, and shortened survival. Oncogenesis was further enhanced by enforced TP53 deletion in these lung tumors. RNA-seq and ChIP-seq of MGA+ or - cell-lines demonstrated the up and downregulation of various gene classes (thousands of genes) according to function and regulation including of PRC1.6 targets, meiosis regulators, TGF-beta signaling pathway components, EMT regulators, anti-tumor immunity, as well as of MYC, E2F, etc. Different cell lines exhibited both overlapping and distinct target sets. MGA knockout cells were more migratory and invasive and displayed actin-protrusions in accord with this behavior. They show that a Domain of Unknown Function in the mid-region of MGA engages PRC1.6 and is required to depress proliferation. The DUF is also required to limit actin-protrusions. Human colon organoids were studied since MGA mutations and deletions are also apparent in colon cancer. Again, shared and distinct targets of MGA action were inferred.

      The authors make a strong case that MGA is an important tumor suppressor that operates through PRC1.6 for some of its actions.

    1. Reviewer #2:

      1) The authors hint towards the involvement of c-di-GMP signaling via the YcgR protein. This hypothesis can be tested by knocking down the ycgr gene and repeating the assay, but this has not been done or reported. Addition of these data to the manuscript would make the paper significantly stronger.

      2) Do other chemoreceptors (Tar, Tsr, Tap) also act in the same way with their respective ligands? It would be useful to know if this effect is specific to Trg or if it is also found in the other chemoreceptors.

      3) In figure 3C, what is the reason that the GFP intensity and the speed do not have the same range? In other words, why is the slope not equal to 1? Since there is 1:1 correspondence between the number of MotB and the number of GFP, shouldn't the slope be 1?

      4) The authors do not cite or discuss the recent literature on load-dependent stator remodeling (e.g. PMIDs: 29183968, 31142644). It would be helpful to have a more in-depth discussion on how the observed stator unit recruitment relates to stator remodeling in response to load.

    1. Reviewer #2:

      This impressive manuscript describes a comprehensive, multifaceted analysis of the morphological and molecular changes that accompany photosynthetic establishment during seedling de-etiolation. Morphological data, focusing in particular on the photosynthetic thylakoid membranes, are derived using transmission electron microscopy (TEM), serial block face scanning electron microscopy (SBF-SEM), and confocal microscopy, while quantitative molecular data on the abundances of proteins and lipids are derived using mass spectrometry and western blotting. The various data are acquired over a time course between 0 h and 96 h post illumination, and with a high level of temporal resolution. The data allow the authors to develop a mathematical model for the expansion of the surface area of thylakoids (reaching 500-times the surface area of the cotyledon leaf), which matches well with experimental observations from the SBF-SEM analysis for earlier, but not later, stages of de-etiolation. Moreover, the data point to a two-phase organization of the de-etiolation process, with the first phase ("Structure Establishment") characterized by thylakoid assembly and photosynthetic establishment, and the second phase ("Chloroplast Proliferation") characterized by chloroplast division and cell expansion.

      The data are of a high standard, and the depth and breadth of analysis in a single, unified study is unprecedented. While it is arguable that there are few major, completely novel insights reported here (indeed, in the Discussion, the authors very helpfully point out how many of the parameters they have measured are consistent with data reported elsewhere by others), this should not detract from the overall value of the study; a major and unique strength here is that all of the data have been acquired together and so are directly comparable. I have no doubt that this dataset will be extremely interesting to many researchers, and prove to be an invaluable resource for the plant science community. Consequently, I am sure that it will attract many citations.

      I have a few specific comments that I would like the authors to consider carefully, as follows.

      1) Figure 3. The 3D reconstructions are undoubtedly useful for deriving quantitative data, as they enable the derivation of thylakoid surface area data to verify the mathematical model. However, it is very difficult to see anything clearly in the images shown in the Figure. I wonder if the authors can make the images clearer, and then also point to and describe some of the key features. The videos do help a bit, but even these are not that clear.

      2) Page 9, second paragraph. It is here that the "two phases" model is first proposed. I really could not see a clear basis for proposing this model here, using the data that had been presented thus far. As I see it (and based on the way the two phases are described in the Discussion), one can't really propose this model until after the chloroplast number and cell size data have been presented.

      Moreover, the description of the second phase here ("and a second phase...") seems a bit inconsistent with the statement in the paragraph above that thylakoid surface area increases dramatically between T4 and T24, and much less between T24 and T96.

      3) Figure 6, and the related supplementary figure. Loading controls are missing here, and should be added. Also, it is stated that a number of proteins (PsbA, PsbD, PsbO, Lhcb2) are "detectable" at T0 (line 348, page 11). To me, they look UNdetectable.

      4) Dividing chloroplasts. On page 13, line 412-413, it is stated that the volume of dividing chloroplasts was measured, and we are referred to Figures 8E and 4B in support of this statement. However, it is not explained how this was done. More clear and specific explanation is needed. Was it the case that the authors sought out and measured dumbbell-shaped organelles, and quantified those? If so, images are needed to illustrate this point. And, I don't see anything relevant in Fig. 4B - this callout apparently belongs in the following sentence. The statement that the average size of dividing chloroplasts was higher than that of all chloroplasts (lines 413-414) is not really surprising if the authors were measuring organelles just on the point of becoming two organelles.

      5) Page 13, beginning of modelling section. The motivation for this section needs to be better introduced. When I first read it, I could not understand why the authors wished to again "determine the thylakoid membrane surface area", as this had already been discussed earlier in the manuscript.

      Also related to the modelling: Did the authors take into account the existence of appressed membranes when calculating the surface area exposed to the stroma (lines 431-432). And, assuming it is clearly established that there is a 1:1 relationship between these proteins and the relevant complexes (lines 441-443), perhaps this should be stated and the relevant literature cited.

    1. Reviewer #2 (Public Review):

      In this manuscript, Anchimiuk et al reported that B. subtillis SMC can collide with each other, and that the collision is modulated by several factors including the number, strength, distribution of parS sites, the residence time of SMC on DNA, the translocation rate, and the cellular abundance of SMC. The authors suggested that these parameters are fine-tuned in the wild-type B. subtillis to minimize SMC collision. In my opinion, the finding is interesting, the experimental setup is creative, and the experiments were beautifully executed. Arguably, these experiments can only be performed in B. subtilis since parAB- and the insertion of another parS site at the mid-arm are not detrimental to cell viability (in Caulobacter crescentus, insertion of another parS mid-arm affects chromosome segregation, hence cell viability severely). Furthermore, the rare set of arm-modified SMCs from the Gruber lab also gives this manuscript a unique mechanistic angle. Given the available data, the conclusion of the manuscript is safe. I especially appreciate that the authors did not bias towards the model of SMC traversing each other by Z-loop formation.

    1. Reviewer #2 (Public Review):

      Parker et al attempted to show that the FPA protein functions to regulate the widespread premature transcription termination of the Arabidopsis NLR genes. Using in vivo interaction proteomic-mass spectrometry, FPA was shown to co-purified with the mRNA 3' end processing machinery. Metagene analysis was used to show that FPA co-localized with Pol II phosphorylated at Ser2 of the CTD heptad repeat at the 3' end of Arabidopsis genes. Using a combination of Illumina RNA-Seq, Helicos, and nanopore DRS technologies, FPA was found to affect RNA processing by promoting poly(A) site choice, and hence controls the processing of NLR transcripts whereas such process is independent of IBM1.

      Overall, it is a potentially important research. The data is rich and could be useful. However, the biological stories described are not thoroughly supported by the data presented, especially when the authors tried to touch on several aspects without some important validations and strong connections among different parts. Some special comments are provided below:

      1) The title of this manuscript is "The expression of Arabidopsis NLR immune response genes is modulated by premature transcription termination and this has implications for understanding NLR evolutionary dynamics". Therefore, the readers will expect some functional connections between the FPA and the novel NLR isoforms due to premature transcription termination. However, the transcript levels of plant NLR genes are under strict regulation (e.g. Mol. Plant Pathol. 19:1267). Since the functions of NLR genes are related to effector-triggered immunity, it is more important to study the function of FPA on premature transcription termination when the plants are challenged with pathogens. In this manuscript, most transcript analyses are based on samples under normal growth conditions. It is therefore a weak link between the genomic studies and the functional aspects. For instance, it is more important to identify unique NLR isoforms produced upon pathogen challenges that are regulated by FPA. The authors will need to provide some of these data to fill this gap.

      2) Since the function of FPA is to regulate NLR immune response genes, we should expect a change in plant defense phenotype in FPA loss-of-function mutants. Could the authors provide more information on this? On the contrary, in line 728 of this manuscript, the authors found that at least for some pathogens, "loss of FPA function does not reduce plant resistance". It is not consistent with the hypothesis that FPA is important to regulate NLR immune response genes.

      3) Furthermore, the authors mentioned in lines 729-731 "Greater variability in pathogen susceptibility was observed in the fpa-8 mutant and was not restored by complementation with pFPA::FPA, possibly indicating background EMS mutations affecting susceptibility." Does it mean that fpa-8 contains other mutations? Will these additional mutations complicate the results of the RNA processing? Could the authors outcross the fpa-8 mutation to a clean background?

      4) In line 318, the authors found 285 and 293 APA events in the fpa-8 mutant and the 35S::FPA:YFP construct respectively, but only 59 loci (line 347) exhibited opposite APA events (about one fifth). The low overlapping frequency suggests that some results could be false positive.

      5) In line 732-736: "In contrast, 35S::FPA:YFP plants exhibited a similar level of sporulation to the pathogen-sensitive Ksk-1 accession (median 3 sporangiophores per plant). This suggests that the premature exonic termination of RPP7 caused by FPA has a functional consequence for Arabidopsis immunity against Hpa-Hiks1." It is contradictory to the statement in line 728 that "loss of FPA function does not reduce plant resistance". Is it possible that overexpression of FPA:YFP had generated an artificial condition that is not related to the natural function of FPA?

      6) The fpa-8 mutant has a delayed flower phenotype (Plant Cell 13:1427). Could the 35S::FPA:YFP fusion protein construct reverse this phenotype and the plant defense response phenotype? It is important to interpret the data when the 35S::FPA:YFP construct was used to represent the overexpression of FPA.

      7) Under the subheading "FPA co-purifies with the mRNA 3' end processing machinery". The results were based on in vivo interaction proteomics-mass spectrometry. MS prompts to false positives and will need proper controls and validations. Have the authors added the control of 35S:YFP instead of just the untransformed Col-0? At least for the putative interacting partners in Figure 1A, could the authors perform validations of some important targets, using techniques such as reverse co-IP, or to show direct protein-protein interaction between FPA to a few of the important targets by in vitro pull-down, BiFC, or FRET, etc.

      8) In Fig. 3, the data show that the last exon of the FPA gene is missing in the FPA transcripts generated from the 35S::FPA:YFP construct. Will the missing of this exon affect the function of the transcript and the encoded protein?

      9) The function of FPA is still ambiguous. There was a quantitative shift toward the selection of distal poly(A) sites in the loss-of-function fpa-8 mutant and a strong shift to proximal poly(A) site selection when FPA is overexpressed (35S::FPA:YFP) in some cases (Fig. 3, Fig. 5, Fig. 8). But the situation could be kind of reversed in other cases (Fig. 6). What is the mechanism behind it?

      10) Under the subheading: "The impact of FPA on NLR gene regulation is independent of its role in controlling IBM1 expression". IBM1 is a common target of FPA and IBM2. Indeed, FPA and IBM2 share several common targets (Plant Physiol. 180:392). It may be more meaningful to compare the impact of FPA and IBM2 on NLR gene instead.

      11) In lines 423-425, the authors described "Consistent with previous reports, the level of mRNA m6A in the hypomorphic vir-1 allele was reduced to approximately 10% of wild-type levels (Parker et al., 2020b; Ruzicka et al., 2017) (Figure 4 - supplement 3)." This data could not be found.

      12) In line 426: "However, we did not detect any differences in the m6A level between genotypes with altered FPA activity." Which data is this statement referring to?

    1. Reviewer #2:

      The study investigates key components of the entorhinal circuits through which signals from the hippocampus are relayed to the neocortex. The question addressed is important but the stated claim that layer 5b (L5b) to layer 5a (L5a) connections mediate hippocampal-cortical outputs in LEC but not MEC appears to be an over-interpretation of the data. First, the experiments do not test hippocampal to L5a connections, but instead look at L5b to L5a connections. Second, the data provide evidence that there are L5b to L5a projections in LEC and MEC, which contradicts the claim made in the title. These projections do appear denser in LEC under the experimental conditions used, but possible technical explanations for the difference are not carefully addressed. If these technical concerns were addressed, and the conclusions modified appropriately, then I think this study could be very important for the field and would complement well recent work from several labs that collectively suggests that information processing in deep layers of MEC is more complex than has been appreciated (e.g. Sürmeli et al. 2015, Ohara et al. 2018, Wozny et al. 2018, Rozov et al. 2020). Major Concerns:

      1) An impressive component of the study is the introduction of a new mouse line that labels neurons in layer 5b of MEC and LEC. However, in each area the line appears to label only a subset (30-50%) of the principal cell population. It's unclear whether the unlabelled neurons have similar connectivity to the labelled neurons. If the unlabelled neurons are a distinct subpopulation then it's difficult to see how the experiments presented could support the conclusion that L5b does not project to L5a; perhaps there is a projection mediated by the unlabelled neurons? I don't think the authors need to include experiments to investigate the unlabelled population, but given that the labelling is incomplete they should be more cautious about generalising from data obtained with the line.

      2) For experiments using the AAV conditionally expressing oChIEF-citrine, the extent to which the injections are specific to LEC/MEC is unclear. This is a particular concern for injections into LEC where the possibility that perirhinal or postrhinal cortex are also labelled needs to be carefully considered. For example, in Figure 3D it appears the virus has spread to the perirhinal cortex. If this is the case then axonal projections/responses could originate there rather than from L5b of LEC. I suggest excluding any experiments where there is any suggestion of expression outside LEC/MEC or where this can not be ruled out through verification of the labelling. Alternatively, one might include control experiments in which the AAV is targeted to the perirhinal and postrhinal cortex. Similar concerns should be addressed for injections that target the MEC to rule out spread to the pre/parasubiculum.

      3) It appears likely from the biocytin fills shown that the apical dendrites of some of the recorded L5a neurons have been cut (e.g. Figure 4A, Figure 4-Supplement 1D, neuron v). Where the apical dendrite is clearly intact and undamaged synaptic responses to activation of L5b neurons are quite clear (e.g. Figure 4-Supplement 1D, neuron x). Given that axons of L5b cells branch extensively in L3, it is possible that any synapses they make with L5a neurons would be on their apical dendrites within L3. It therefore seems important to restrict the analysis only to L5a neurons with intact apical dendrites; a reasonable criteria would be that the dendrite extends through L3 at a reasonable distance (> 30 μm?) below the surface of the slice.

      4) Throughout the manuscript the data is over-interpreted. Here are some examples:

      • The title over-extrapolates from the results and should be changed. A more accurate title would be along the lines of "Evidence that L5b to L5a connections are more effective in lateral compared to medial entorhinal cortex".

      • "the conclusion that the dorsal parts of MEC lack the canonical hippocampal-cortical output system" seems over-stated given the evidence (see comments above).

      • Discussion, para 1, "Our key finding is that LEC and MEC are strikingly different with respect to the hippocampal-cortical pathway mediated by LV neurons, in that we obtained electrophysiological evidence for the presence of this postulated crucial circuit in LEC, but not in MEC". This is misleading as there is also evidence for L5b to L5a connections in MEC, although this projection may be relatively weak. Recent work by Rozov et al. demonstrating a projection from intermediate hippocampus to L5a provides good evidence for an alternative model in which MEC does relay hippocampal outputs. This needs to be considered.

      5) What proportion of responses are mono-synaptic? How was this tested?

    1. Reviewer #2:

      Overall, this is a very well written paper that presents software that fills an interesting niche: interactive, real-time simulations of complex multicellular systems that can run in a web browser, without any need for users to install or configure software. As the authors describe, this enables new modes of education, science communication, and multidisciplinary collaboration. The software itself is impressive, and the supplied examples are clean and beautifully fluid. It is eye-opening that Javascript can run these models so well. The authors also did a fantastic and complete job in sharing their full source code, from the overall software down to individual scripts used to generate figures.

      Some points that the authors should address in a revision:

      1) Suitability of the software for researchers:

      a. Artistoo simulations do not appear to have any method to save data for external manipulation and archival. This makes their use somewhat less applicable to robust simulation-driven investigations, particularly where postprocessing and further analyses are required.

      b. It is unclear if Artistoo-based models can be exported into other cellular Potts (CP) frameworks such as CC3D or Morpheus. This may leave researcher end users without a clear "upgrade path" after exploring model ideas in Artistoo and moving to larger simulations (e.g., larger or more complex domains), running simulations in high throughput on HPC resources, or adapting approximate Bayeseian techniques for parameter estimation that require automating many simulation runs. Without an upgrade path, such users may wish to immediately begin in research-focused platforms rather than start with Artistoo and re-implement in another framework later.

      c. Similarly, it is unclear if a model developed in Morpheus or CC3D can be directly imported into Artistoo. If such an import were possible rather than re-implementing models in Aristoo, research-focused users would be more likely to use Artistoo for scientific communication and outreach.

      2) Need for improved educational scaffolding: The examples provided in the paper are excellent. However, they lack context on what the parameters mean or do. (For example, what are max_act and lambda_act in the cell migration model?) This may limit the educational impact because users will be unclear on what to change, and how the parameters relate to cell biophysical processes.

      The authors should include more background information with each model, define parameters, and give end users some idea of what to expect when parameters are changed. We have also found it useful to help guide a new user's exploration of a model by suggesting parameter sets and describing what they should see. This can serve as an educational scaffolding to help learners build and grow.

      The authors' sample models should serve as a template to Artistoo users on best practices for communicating models to diverse audiences.

      3) New developments in online cellular Potts simulators: The authors should note that CompuCell3D has recently been ported to run interactively online in a web browser. See https://nanohub.org/resources/compucell3d. This recent development should be addressed in the paper.

      4) Narrow review of interactive, "zero install" simulation frameworks: The authors focus too narrowly by only comparing Artistoo with other cellular Potts frameworks, while the main use case for Artistoo is for interactively sharing and communicating complex simulation models online.

      The authors should discuss non-CP frameworks that worked towards this, such as CC3D on nanoHUB (see above), online Tellurium (https://nanohub.org/resources/tellurium), current practice to share R models online as Shiny apps, and recent work to use xml2jupyter to automatically convert research-focused (command line) PhysiCell models to interactive Jupyter notebooks that can be shared as interactive webapps on nanoHUB (e.g., https://nanohub.org/tools/pc4cancerimmune). All of these serve similar purposes of creating zero-install, interactive versions of models for science education and communication. The authors should briefly discuss these to further contextualize their work.

      5) While this is a more minor point, I would feel more comfortable if the supplementary information had convergence and accuracy testing. Are there limits on computational step sizes for numerically accurate simulations, particularly for large energies or when including diffusion processes?

      Overall, this is some fantastic work.

  2. Jan 2021
    1. Reviewer #2:

      This manuscript interrogates function of Ihog and Boi adhesion molecules in cytoneme-based transport of the Hedgehog morphogen in Drosophila. The cell biology of how cytonemes are regulated to deliver morphogen signals is not yet well understood, so the work addresses an important topic that will be of interest to a broad audience. However, much of the study refines previous work from the same group to provide only a modest advance in understanding of how Ihog impacts cytoneme behavior.

      The authors use genetic strategies in Drosophila to investigate how Ihog and Boi influence cytoneme dynamics. They find that the two proteins act differently with regard to cytoneme function. Boi effects are not exhaustively analyzed, but a number of genetic experiments are performed to interrogate Ihog. The authors reveal that the extracellular domains of Ihog interact with the glypicans Dally and Dlp to stabilize cytonemes that originate from Ihog over-expressing cells. Knockdown of Ihog does not alter cytoneme dynamics.

      The most novel aspect of the study - that Boi functions differently than Ihog in cytonemes - is, unfortunately, not expanded upon. Some experiments lack controls or are presented in a manner that prevents clear interpretation of results.

      Key points to be addressed:

      Figure 1: Null alleles and RNAi silencing are used interchangeably to reduce Ihog, Boi, Dally and Dlp function in vivo. Results between methods are directly compared. Oftentimes, controls are not included to confirm the level of knockdown following RNAi. If possible use null alleles due to consistency. However, if this is not possible due to experimental reasons, give an explanation and state impact in the discussion.

      Ihog levels decrease following loss of Dally or Dlp and Boi levels appear to increase following knockdown of Ihog, Dally, or Dlp. These stability changes have previously been reported. The mechanism is not clear, so should have been investigated here - especially the increased Boi protein level. How does this occur? Is stabilization occurring at the protein level or is gene expression changing? Is this a compensatory upregulation?

      Based upon the supplement for Figure 2, it looks like the Ihog truncation mutants show variable stability. Might this be affecting the extent to which they alter Dally or Dlp stability? The western blot data are presented as crops of single bands adjacent to crops of a molecular weight ladder. Blots should be shown as intact images, preferable with all variants compared across a single gel with a loading control. As presented, relative stability/expression levels are impossible to assess.

      Figures 3-4: Ihog mutant transgenes are tagged with either HA or RFP. Best to be consistent with tags when mutant function is being directly compared. Given that the HA tag is a small epitope and the RFP is a protein tag, they may differentially alter protein functionality. To be consistent it would be preferable to use the same tags. However, if this is not possible due to experimental reasons, the technical implication can also be mentioned in the discussion.

      Figure 5: Investigation of histoblast cytonemes reaching into ttv, botv mutant clones: The ability of cytonemes to invade double mutant clones is altered only under the engineered situation of glypican dysfunction combined with Ihog over-expression. From this, it is concluded that Ihog is acting with glypicans to stabilize cytonemes. This may be the case, but they ability to see it only under an engineered situation of compound mutation plus Ihog over-expression leads this review to question the physiological relevance of the observation. Of similar concern is that the authors state the ability of Ihog over-expressing cell cytonemes to cross small vs. large ttv, botv clones differs. The difference is very difficult to appreciate from the results presented.

      Figure 6: The apparent functional difference between Ihog and Boi in the ability to stabilize cytonemes is potentially very interesting, but is not investigated, which limits the advance of the current study.

    1. Reviewer #2 (Public Review):

      In "Evolution of cytokine production capacity in ancient and modern European populations", Dominguez-Andrés et al. collect a large amount of trait association data from various studies on immune-mediated disorders and cytokine production, and use this data to create polygenic scores in ancient genomes. They then use the scores to attempt to test whether the Neolithic transition was characterized by strong changes in the adaptive response to pathogens. The impact of pathogens in human prehistory and the evolutionary response to them is an intriguing line of inquiry that is now beginning to be approachable with the rapidly increasing availability of ancient genomes.

      While the study shows a commendable collection of association data, great expertise in immune biology and an interesting study question, the manuscript suffers from severe statistical issues, which makes me doubt the validity and robustness of their conclusions. I list my concerns below, in rough order of how important I believe they are to the claims of the paper:

      — In addition to the magnitude of an effect away from the null, P-values are a function of the amount of data one has to fit a model or test a hypothesis. In this case, the authors have vastly more data after the Neolithic Revolution than before, and so have much higher power to reject the null hypothesis of "no relationship to time" after the revolution than before. One can see this in the plots the authors provided, which show vastly more data after the Neolithic, and consequently a greater ability to fit a significant linear model (in any direction) afterwards as well.

      — The authors argue that Figure S2 makes their results robust to sample size differences, but showing a consistency in direction before and after downsampling in the post-neolithic samples is not enough, because:

      1) you still lack power to detect changes in direction before the Neolithic.

      2) even for the post-Neolithic, the relationship may be in the same direction but no longer significant after downsampling. How much the significance of the linear model fit is affected by the downsampling is not shown.

      — The authors chose to test "relationship between PRS with time" before and after the Neolithic as a way to demonstrate that "the advent of the Neolithic was a turning point for immune-mediated traits in Europeans". A more appropriate way to test this would be creating a model that incorporates both sets of scores together, accounts for both sample size and genetic drift in the change of polygenic scores, and shows a significant shift occurs particularly in the Neolithic, rather in any other time period, instead of choosing the Neolithic as an "a priori" partition of the data. My guess is that one could have partitioned the data into pre- and post-Mesolithic and gotten similar results, largely due to imbalances in data availability.

      — The authors only talk about partitions before and after the Neolithic, but plots are colored by multiple other periods. Why is the pre- and post-Neolithic the only transition that is mentioned?

      — Extrapolating polygenic scores to the distant past is especially problematic given recent findings about the poor portability of scores across populations (Martin et al. 2017, 2019) and the sensitivity of tests of polygenic adaptation to the choice of GWAS reference used to derive effect size estimates (Berg et al. 2019, Sohail et al. 2019). In addition to being more heavily under-represented, paleolithic hunter-gatherers are the most differentiated populations in the time series relative to the GWAS reference data, and so presumably they are also the genomes for which PGS estimates built using such a reference would have higher error (see, e.g. Rosenberg et al. 2019). Some analyses showing how believable these scores are is warranted (perhaps by comparing to phenotypes in distant present-day populations with equivalent amounts of differentiation to the GWAS panel).

      — In multiple parts of the paper, the authors mention "adaptation" as equivalent to the patterns they claim to have found, but alternative hypotheses like genetic drift are not tested (see e.g. Guo et al. 2018 for a review of methods that could be used for this).

      — 250 kb window is too short a physical distance for ensuring associated loci that are included in the score are not in LD, and much shorter than standard approaches for building polygenic scores in a population genomic context (e.g. see Berg et al. 2019, Berisa et al. 2016). Is this a robust correction for LD?

      — If one substitutes dosage with the average genotyped dosage for a variant from the entire dataset, then one is biasing towards the partitions of the dataset that are over-represented, in this case, post-Neolithic samples.

      — It seems from Figure 2, that some scores are indeed very sensitive to the choice of P-value cutoff (e.g., Malaria, Tuberculosis) and to the amount of missing data (e.g. HIV). This should be highlighted in the main text.

      — Some of the score distributions look a bit strange, like the Tuberculosis ones in Figure 2, which appear concentrated into particular values. Could this be because some of the scores are made with very few component SNPs?

    1. Reviewer #2 (Public Review):

      The authors present a compelling study that aims to resolve the extent to which synaptic responses mediated by metabotropic GABA receptors (i.e. GABA-B receptors) summate. The authors address this question by evaluating the synaptic responses evoked by GABA released from cortical (L1) neurogliaform cells (NGFCs), an inhibitory neuron subtype associated with volume neurotransmission, onto Layer 2/3 pyramidal neurons. While response summation mediated by ionotropic receptors is well-described, metabotropic receptor response summation is not, thereby making the authors' exploration of the phenomenon novel and impactful. By carrying out a series of elegant and challenging experiments that are coupled with computational analyses, the authors conclude that summation of synaptic GABA-B responses is linear, unlike the sublinear summation observed with ionotropic, GABA-A receptor-mediated responses.

      The study is generally straightforward, even if the presentation is often dense. Three primary issues worth considering include:

      1) The rather strong conclusion that GABA-B responses linearly summate, despite evidence to the contrary presented in Figure 5C.

      2) Additional analyses of data presented in Figure 3 to support the contention that NGFCs co-activate.

      3) How the MCell model informs the mechanisms contributing to linear response summation.

      These and other issues are described further below. Despite these comments, this reviewer is generally enthusiastic about the study. Through a set of very challenging experiments and sophisticated modeling approaches, the authors provide important observations on both (1) NGFC-PC interactions, and (2) GABA-B receptor mediated synaptic response dynamics.

      The differences between the sublinear, ionotropic responses and the linear, metabotropic responses are small. Understandably, these experiments are difficult – indeed, a real tour de force – from which the authors are attempting to derive meaningful observations. Therefore, asking for more triple recordings seems unreasonable. That said, the authors may want to consider showing all control and gabazine recordings corresponding to these experiments in a supplemental figure. Also, why are sublinear GABA-B responses observed when driven by three or more action potentials (Figure 5C)? It is not clear why the authors do not address this observation considering that it seems inconsistent with the study's overall message. Finally, the final readout – GIRK channel activation – in the MCell model appears to summate (mostly) linearly across the first four action potentials. Is this true and, if so, is the result inconsistent with Figure 5C?

      Presumably, the motivation for Figure 3 is that it provides physiological context for when NGFCs might be coactive, thereby providing the context for when downstream, PC responses might summate. This is a nice, technically impressive addition to the study. However, it seems that a relevant quantification/evaluation is missing from the figure. That is, the authors nicely show that hind limb stimulation evokes responses in the majority of NGFCs. But how many of these neurons are co-active, and what are their spatial relationships? Figure 3D appears to begin to address this point, but it is not clear if this plot comes from a single animal, or multiple? Also, it seems that such a plot would be most relevant for the study if it only showed alpha-actin 2-positive cells. In short, can one conclude that nearby, presumptive NGFCs co-activate, and is this conclusion derived from multiple animals?

      The inclusion of the diffusion-based model (MCell) is commendable and enhances the study. Also, the description of GABA-B receptor/GIRK channel activation is highly quantitative, a strength of the study. However, a general summary/synthesis of the observations would be helpful. Moreover, relating the simulation results back to the original motivation for generating the MCell model would be very helpful (i.e. the authors asked whether "linear summation was potentially a result of the locally constrained GABAB receptor - GIRK channel interaction when several presynaptic inputs converge"). Do the model results answer this question? It seems as if performing "experiments" on the model wherein local constraints are manipulated would begin to address this question. Why not use the model to provide some data – albeit theoretical – that begins to address their question?

      In sum, the authors present an important study that synthesizes many experimental (in vitro and in vivo) and computational approaches. Moreover, the authors address the important question of how synaptic responses mediated by metabotropic receptors summate. Additional insights are gleaned from the function of neurogliaform cells. Altogether, the authors should be congratulated for a sophisticated and important study.

    1. Reviewer #2:

      Using this new Trypanosoma carassii infectious model in larval zebrafish, Jacobs et al. have developed a new clinical scoring system to reliably separate high-and low-infected larvae in order to investigate their individual innate immune responses, with a special emphasis on macrophages and neutrophils.

      In summary the separation system used in this allows us i) to identify a strong macrophage and neutrophil proliferation response by high-and low-infected larvae, although happening a bit earlier, 5 dpi, for macrophages in low-infected larvae, and ii) to observe a differential distribution and morphology of macrophages, associated to the unique presence of more rounded foamy macrophages with a high pro-inflammatory profile into the vessels of high-infected zebrafish larvae. Together, this study constitutes the first report of the occurrence of foamy macrophages during an extracellular trypanosome infection.

      Although the paper is well-written and the findings are interesting as they bring new insights into the development of foamy macrophages in response to an extracellular pathogen, i.e. Trypanosoma carassii, using a zebrafish larvae model, I have a few concerns regarding the following:

      • The experimental infectious model in zebrafish: figure 2 summarizes that only 15% of the infected larvae, named low-infected larvae, are able to survive the infection. As an explanation the authors refer to the trypanosuceptible vs. trypanotolerant background of the host observed in non-zebrafish models. However, in this particular setting, all the larvae possess an identical genetic background. Therefore, why would the larvae behave differently in response to a similar pathogen? In addition, there is no clear differences in neither parasitic load at 2 dpi (figure 3F) nor myeloid cells accumulation at 3 dpi (figure 4AB), which could lead to a drastic difference in parasitic load based on mRNA expression at 4 dpi (figure 3F). The authors should discuss this shortly.

      • Figure 4: the representative pictures from Fig4B do not seem to clearly match the histograms depicted in Fig4C. For example, from the pictures in Fig4B, it seems that there is a decrease in red fluorescence in the representative pictures from 7 dpi to 9 dpi low-infected larvae, which is not reflected in the histogram. Also, a representative picture of 7 hi-infected larvae seems to show at least equal or even more red fluorescence compared to 9 dpi low-infected larvae.

      • Lines 494-496 states "No significant difference was observed between high-and low-infected fish, confirming that macrophages react to the presence and not to the number of trypanosomes.", reflecting that there is no differences in total macrophages nor in their proliferation between low- and high-infected zebrafish larvae (Figure 5B&C). Therefore it is not sufficiently clear on which basis the authors states a few lines later as a conclusion that "Altogether, these data confirm that T. carassii infection triggers macrophage proliferation and that proliferation is higher in low-infected compared to high-infected individuals, possibly due to a higher haematopoietic activity." Therefore the authors should revise this conclusion or bring stronger data to reinforce their results. Also, similar conclusions need to be adjusted in the discussion section and bring new elements to explain the higher number of macrophages observed in figure 4.

    1. Reviewer #2:

      This manuscript, "Lactobacilli in a clade ameliorate age-dependent decline of thermotaxis behavior in Caenorhabditis elegans," is focused on the impact of diet on age-dependent behavioral decline. The authors utilize a thermotaxis screen using different lactic acid bacteria (LAB) and identify strains of LAB with the ability to ameliorate age dependent decline in thermotaxis behavior. The study introduces some interesting results, including the finding that many LAB strains of the same clade can improve thermotaxis in older nematodes, despite disparate results on longevity. However, there were some questions remaining about methodology, and more importantly, there is very little evidence provided on what the molecular mechanism might be behind this phenomenon. Overall, this study contains interesting findings that are not developed thoroughly enough.

      Major Comments/Questions:

      1) How is LAB different from Ecoli? Does metabolic composition of LAB dictate its impact on thermotaxis behavior of worms? In the manuscript the authors argue that LAB are a "better" food source than E. coli. How does one define better for something as broad as a food source? There is a difference here but it is very unclear what aspects of LAB physiology may play a role.

      2) Does this phenomenon require eating LAB, or just perceiving it? The assays did not test whether perception of LAB diet is sufficient for its effect on thermotaxis, rather whether more time on LAB leads to better thermotaxis.

      3) Showing a potential daf-16 interaction is plausible, given that daf-16 interacts with many key pathways in the worm, but it is unclear whether this interaction is direct or indirect, or whether daf-16 is a major player in this pathway or just necessary for maintenance of health. What sensory pathways are activated when worms are fed on LAB diet, and how it finally interacts with daf-16?

      4) Similarly, the pha-4 and eat-2 data are interesting, but are not developed in any way. This is another avenue that could in principle lead toward a better mechanistic understanding.

    1. Reviewer #2:

      Park et al. report on an analysis of existing semi-longitudinal NSPN 2400 data to learn how the projections of high-dimensional structural connectivity patterns onto a three dimensional subspace change with age during adolescence. They employ a non-linear manifold learning algorithm (diffusion embedding), thereby linking the maturation of global structural connectivity patterns to an emerging approach in understanding brain organization through spatial gradient representations. As might be expected based on the large body of literature indicating changes in structural connectivity in specific brain regions during adolescence, the authors find corresponding changes in the embedding of the structural connectivity patterns.

      While this work touches on an important topic, ties nicely with the increasing body of papers on global brain gradients, and its overall conclusions are warranted, I am not (yet) convinced that it offers fundamentally new insights that could not have been gleaned from previous work (after all, manifold learning simply displays a shadow of the underlying patterns; if the patterns change, so does their shadow). I am also not convinced by the rationale for employing diffusion embedding: the authors state that the ensuing gradients are heritable, conserved across species, capture functional activation patterns during task states, and provide a coordinate system to interrogate brain structure and function, but that would be true for any method that adequately captures biologically meaningful variance in the structural connectivity patterns.

      Other comments:

      The authors show that the maturational change of the manifold features predict intelligence at follow-up, but did not show that intelligence itself exhibited changes that exceeded the error bounds of the regression line. Why not predict IQ change?

      The slight improvements in prediction accuracy observed after adding maturational change and subcortical features to the features at baseline will necessarily happen by adding more regression parameters and may not be meaningful.

    1. Reviewer #2:

      The present study investigates how CSF-contacting neurons (CSFcNs) of the mouse spinal cord integrate and translate different synaptic inputs using distinct calcium-dependent spike mechanisms. Indeed two different types of voltage-gated calcium channels can be activated, resulting in the generation of spikes with different amplitudes. T-type Ca2+ channels would be involved in the generation of low amplitude spikes while HVA-Ca2+ channels participate in the generation of large amplitude spikes. Then these distinct spikes allow signaling different neurotransmitter systems. Consequently, the data provided here argue in favor of CSF-contacting neurons acting as a sensory system that uses Ca2+ channels-dependent spike activity with graded amplitude corresponding to the activation of different neurotransmitter receptors. This study is based on two-photon calcium imaging performed on spinal cord slices preparations obtained from young and adult mice. My comments are as follows:

      1) All data are based on calcium imaging. Therefore, traces correspond to calcium-dependent fluorescent changes in the cells of interest. Can the author provide at least one sample showing that these calcium events are indeed linked to the generation of spikes; i.e., electrophysiological recordings? In addition, is there any electrophysiological evidence for the existence of calcium-dependent conductances in the CSFcNs? In the same vein, the authors conclude that spontaneous activity of CSFcNs depends upon calcium- but not sodium-spikes as TTX has apparently no effect. But, are the authors sure that in their experimental conditions individual sodium spikes could be detected given the genetic encoded probe used, the kinetic of such spikes and the frequency of the sampling during image acquisition? Note that this does not preclude the conclusion that CSFcNs express calcium-dependent spikes. See also comment 4 below.

      2) Using the activation of different calcium channels to trigger spikes of different amplitude to code distinct signaling pathways associated with distinct neurotransmitter systems is a very attractive mechanism. I was wondering whether the authors ever observed the two processes in one single cell, meaning: did they ever try to apply Ach and ATP on the same cell? To my point of view, this would be an extremely elegant way to show that spikes of variable amplitudes imply the activation of distinct calcium-dependent conductances and are linked to different neurotransmitter signaling in one neuron. This should be possible as they said that 100% of the examined cells responded to Ach, suggesting that the only limitation would be to find a cell that also expresses purinergic receptors (should be highly feasible). In addition, this would strongly demonstrate how much this coding mechanism is valuable if this is present in a single cell, otherwise one could consider that the coding system just depends upon each cell, the neurotransmitter and its associated receptor signaling that by definition can involve distinct calcium-dependent channels. Then it would rather be a mechanism specific to each receptor than a sophisticated coding system.

      3) As a general comment on figures, I would suggest to the authors to provide samples that are more illustrative of the results they claim on. For example on Figure 3 they state that TTX has no effect on spike amplitude and frequency, but the two traces shown (in blue and green) rather indicate a decrease in spike frequency and even an increase in spike amplitude after a few minutes of recording (green trace). [See also comment 4 below]. Another example is in Figure 6 in which one important data is the distinct amplitude of spikes triggered by either Ach or ATP. While this is properly illustrated in panels C, D and E, in contrast the samples chosen for panels A and B show events with the exact same amplitude. Please choose other traces. By the way, panel C is not necessary because the same info are included in panels D and E. I would suggest removing panel C. Finally, in Figure 7 it is stated in the text that in some cells ATP induced first a decrease in fluorescence followed by a large Ca2+ spike, while this specific spike looks much smaller than all the other ones illustrated in the study (Fig 7G). Also, the spike triggered by UTP looks different than the one triggered by ATP. Is it a typical response?

      4) Several experimental details must be provided. First, the justification for the choice of VGAT promoter to drive the GCaMP6f indicator into PKD2L1 neurons is missing. Second, drug concentrations are not justified. This is important as the authors argue that Ach and ATP trigger Ca2+ spikes with different amplitudes, but isn't there the possibility that this is dose-dependent? Did the authors try different concentrations? Third, on TTX experiments (Fig 3), after how long under TTX exposure were measurements performed? While this is a crucial parameter, this is not indicated in the paper. Given the traces provided different conclusions could be reached depending on this timing.

      5) It remains unclear to me why only some of the data (for example Fig 7) make a distinction between dorsal and ventral CSF-contacting neurons. In the zebrafish it is established that ventral and dorsal CSFC neurons have different developmental origins and distinct types of projections related to different functions. Then, if these neurons are suspected to play different roles depending on their ventro-dorsal position also in mice, the entire study should take this into account.

    1. Reviewer #2:

      The manuscript "A naturalistic environment to study natural social behaviors and cognitive tasks in freely moving monkeys" describes a large-scale system of rooms allowing for non-human primates to, potentially, freely engage in several different behaviors and neuroscientific experiments to be performed. The study is well intended, but in its current form with many claims, but few if any results does not, in my view, meet scientific standards.

      The paper presents the testing environment consisting of different rooms. Compared to earlier work (e.g. Berger et al., 2018), the main innovation is the inclusion of an eye tracking system. Data supports the notion that this works in principle. But there is no analysis of data quality and accuracy. We also do not know whether the system works on every trial, or how often the eye is not detected or the tracker loses the signal.

      The authors claim novelty of this testing environment, but similar ones have been used in behavioral research for decades and in recent years in neuroscience.

      The authors claim that it is easier to place a testing system into a separate cage then in the home cage. It remains unclear what this claim is based on. Motivation of animals in these social settings should be more difficult than in the home cage environment. So, this is a potentially interesting result. It is also a conceptually important claim for the paper's logic, if the social setting should really be beneficial for training. But the claim needs to be substantiated.

      The authors claim that natural behavior can be analyzed because a CCTV camera is mounted in the cage. There are no results or analyses to demonstrate that.

      The authors mention neural recordings on multiple occasions, but do not show any. EM shielding is neither necessary nor new.

      Automatic training appears to be a one-to-one copy of that in Berger et al. 2018, but citation is missing, except for Supplemental Information.

      The authors report an anecdote of one animal (n=1) learning socially from others. There is no indication that this subject might have performed differently without social learning. The interpretation is a just-so story and appears rather anthropomorphic.

      There are no results in the manuscript.

      The manuscript is not organized well. The Methods section reads like a Discussion, important information on methods is distributed across Supplemental Information and Results. Results, as mentioned, does not contain any results or data.

    1. Reviewer #2 (Public Review):

      Bifulco et al. performed a large-scale in silico study to test whether the spatial fibrosis distribution measured via LGE-MRI in 45 patient with embolic stroke of undetermined source (ESUS) as compared to the distribution in 45 atrial fibrillation (AFib) patients without stroke leads to differences in reentrant arrhythmia inducibility of dynamics.

      1) This study comprises a high number of simulations and is one of the computational electrophysiology studies that covers the most anatomical and structural variability on the atrial level. In their comprehensive analysis, Bifulco et al. answered their question and found no pronounced differences in arrhythmia inducibility and dynamics between ESUS and AFib models. It would be interesting to learn how the spatial fibrosis distributions compare in terms of the previously suggested features density and entropy (Zahid et al.). This might also influence the statements in L170/L207.

      2) The authors chose to exclude patients with stroke from the AFib group, the reasons for this choice are not entirely clear. The same holds for the fact that the ESUS models included AFib-induced electrophysiological remodeling even though these patients have not been diagnosed with AFib (by definition).

      3) An acknowledged limitation of the study is the assumption of fixed conduction velocity and action potential duration/effective refractory period. Bifulco et al. base this assumption on previous studies by the group (e.g. L312), which, however, concluded that reentrant driver locations and inducibility are sensitive to changes of action potential and conduction velocity (Deng et al.). For conduction velocity, wider ranges have been reported since the publication of the supporting reference (35) in 1994, e.g. Verma et al.; Roney et al.

      4) The number of pacing sites is rather low for a comprehensive in silico arrhythmia inducibility test but likely a good balance of coverage and computational feasibility considering that the primary goal of this research was to check whether the two groups of models show differences when undergoing the same (but not necessarily exhaustive) protocol.

      5) The discussion does a good job in putting the results into context. Two interesting observations that deserve more attention are that i) the Inducibility Score was always higher for AFib vs. ESUS (Figure 6A, no statistical test performed). However, this did not translate to a difference in silico arrhythmia burden (inducibility). ii) Reentrant drivers were about twice as likely to localize to the left pulmonary veins than the right pulmonary veins in the AFib models (Figure 6D).

      6) The study succeeded in answering the question it posed in the sense that no marked difference was found between the ESUS and AFib models. This leads to the question what the stroke-inducing mechanism is in the ESUS patients. A hypothesis for future work could be that the fibrotic infiltrations in the ESUS patients reduce the hemodynamic efficacy of the left atrium and render clot formation (e.g. in the atrial appendage) more likely in this way.

      7) The negative finding in this study (no difference between groups) does not naturally allow us to draw clinical implications for diagnosis or stratification. Additional ways to put the hypothesis proposed by the authors (fewer arrhythmogenic triggers in the ESUS patients) to test could be to consider readouts/surrogate measures of the autonomic nervous system.

    1. This variant presents 14 non-synonymous mutations, 6 synonymous mutations and 3 deletions. The multiple mutations present in the viral RNA encoding for the spike protein (S) are of most concern, such as the deletion Δ69-70, deletion Δ144, N501Y, A570D, D614G, P681H, T716I, S982A, D1118H
    1. Reviewer #2:

      In this manuscript, Knight et al examine the genetic diversity in >12,000 publicly available C. difficile genomes in order to characterize genomic evidence of taxonomic incoherence among this genomically diverse pathogen. Their primary analysis employs average nucleotide identity thresholds to identify species boundaries, with secondary analyses examining core genome size changes, gene content, and estimated emergence dates. The authors' main conclusion is that the previously identified C. difficile cryptic clades CI-III are genomically divergent enough from the main clades C1-5 to warrant classification as different genomospecies. This paper is a useful contribution in benchmarking our understanding of the genetic diversity of C. difficile using all currently publicly available genomes, but the results are largely unsurprising given previous phylogenetic analyses involving clades 1-5 and CI-III, and is therefore probably best suited for a specialty journal. Additionally, in some instances, the methods lack details, reducing their interpretability and reproducibility.

      Major Comments:

      1) There are some claims that are too strong and not supported by the data or literature, including the claim that the rise of community-associated CDI is likely due to presence of C. difficile in livestock (Lines 53-54 - far too little evidence to make such a sweeping claim), the statement of apparent rapid population expansion into clades C1-4 (Lines 278-279 - only shown for certain sequence types and greatly impacted by observation bias), the statement that these findings "impacts the diagnosis of CDI worldwide" (Lines 37-38 -too grandiose given limited evidence of the clinical importance of the cryptic clades).

      2) Generally, it is hard to discern which sets of genomes and variants were used for each of the bioinformatic analyses that are described. If there are a limited number of genome sets it might be useful to define them in the results to allow the reader to more easily follow along and understand the scope of different analyses.

      3) The dated phylogenomic analyses methods would benefit from a more thorough assessment of model assumptions along with more description of the sources of bias and uncertainty at play. Specific questions are:

      • Was the temporal signal in the data evaluated?

      • What are the potential impacts of using a single clock model and demographic prior for such a diverse set of taxa?

      • Was the clock rate restricted to the cited 2.5x10-9 - 1.5 x 10-8 range? What clock prior distribution was applied?

      • Were relaxed clock priors explored?

      • What went into the selection of the demographic model prior in BEAST? Were alternative models evaluated?

      • The significant uncertainty in the divergence estimates should be emphasized/listed as a limitation.

      4) Similarly, the pangenome analyses could be more thoroughly described, and the relevance of the core-genome size changes more robustly explored. Specifically:

      • How did the core genome change when excluding any of C1-5? Were these changes much different than when excluding CI-III?

      • The differences between Roary and Panaroo are notable, and potentially important for the microbial genomics community. More details should be provided on these results and how sensitive they are to the input parameters of the respective programs (e.g. collapsing paralogs in Roary and percent identity for orthologs). In addition, it is important to know if any filtering was done with respect to the quality of assemblies, which could have a significant impact on Roary's behavior.

    1. Reviewer #2:

      Recombinant antibodies are the most common and powerful reagents in life science research to identify and study proteins. Yet, every single antibody should always be validated and carefully tested for its relevant application, to ensure constructive and reproductive scientific endeavor. I was thus extremely pleased to review the manuscript of Terkild Buus et al, as it provides a careful assessment of oligo-conjugated antibody signal in CITE-seq. The authors tested four variables (antibody concentration, staining volume, cell numbers and tissue origin) and clearly showed that antibody titration is a crucial step to optimize CITE-seq panel. The authors found that, as a general rule, concentration in the 0.625 and 2.5 µg/mL range provides the best results while recommended concentrations by vendors, 5 to 10 µg/mL range, increase background signal.

      In my opinion, the study is well-performed and may serve as a guideline to accurately validate antibodies for CITE-seq, as a consequence I have only minor comments.

      • As stated by the authors, the starting concentration used for each antibody was based on historical experience and assumptions about the abundance of the epitopes. This approach may not be ideal, and the optimal concentration may have been missed. Do the authors think that a proper titration would be an advantage? Maybe this could be discussed in the text.

      • The authors showed by testing four variables (see above) that they could define the optimal conditions to reduce background signal and increase sensitivity of antibodies and thus this way improves CITE-seq outcome. Nevertheless, the authors rely on the fact that all antibodies used in their panel are specific for their targeted antigens. I am not asking here to test the specificity of every single antibody used in the study as this would be a colossal amount of work. But I feel that this aspect should be discussed in the manuscript, especially when an "uncommon" antibody is intended to be used in the CITE-seq panel; the specificity of this antibody should be indeed tested prior to its use.

    1. Reviewer #2:

      In this paper, Numssen and co-workers focus on the functional differences between hemispheres to investigate the "domain-role" of IPL in different types of mental processes. They employ multivariate pattern-learning algorithms to assess the specific involvement of two IPL subregions in three tasks: an attentional task (Attention), a semantic task (Semantics) and a social task (Social cognition). The authors describe how, when involved in different tasks, each right and left IPL subregion recruits a different pattern of connected areas.

      The employed tasks are "well established", and the results confirm previous findings. However, the novelty of the paper lies in the fact that the authors use these results as a tool to observe IPL activity when involved in different domains of cognition.

      The methodology is sound, well explained in the method section, the analyses are appropriate, and the results clear and well explained in the text and in graphic format.

      However, a solid experimental design is required to provide strong results. To the reviewer's view, the employed design can provide interesting results about functional connectivity, but not about the functional role of IPL in the investigated functions.

      I think the study would be correct and much more interesting if only based on functional connectivity data. Note that rewriting the paper accordingly would lead to a thorough discussion about how anatomical circuits are differently recruited based on different cognitive demands and about the variable role of cortical regions in functional tasks. This issue is neglected in the present discussion, and this concept is in disagreement with the main results, suggesting (probably beyond the intention of the authors) that different parts of the right and left IPL are the areas responsible for the studied functions.

      Major points:

      1) The 3 chosen tasks explore functions that are widespread in the brain, and are not specifically aimed at investigating IPL. The results (see. e.g. fig 1) confirm this idea, but the authors specifically focus on IPL. This seems a rather arbitrary and not justified choice. If they want to explore the lateralization issue, they should consider the whole set of involved areas or use tasks showing all their maximal activation in IPL.

      2) The authors aims to study lateralization using an attentional task, considering the violation of a prevision (invalid>valid), a linguistic task, looking for an activation related to word identification (word>pseudoword) and a social task, considering correct perspective taking (false belief>true belief), but they do not consider that in all cases a movement (key press) is required. It is well known that IPL is a key area also for creating motor commands and guiding movements. Accordingly, the lateralization bias observed could be due more to the unbalance between effectors while issuing the motor command, than to a different involvement of IPL regions in the specific tasks functions.

      3) Like point 2, the position of keys is also crucial if the authors want to explore lateralization. This is especially important if one considers that IPL plays a major role in spatial attention (e.g. Neglect syndrome). In the Methods, the authors simply say "Button assignments were randomized across subjects and kept identical across sessions", this should be explained in more detail.

      4) The authors show to know well the anatomical complexity of IPL, however their results are referred to two large-multiareal-regions. This seems to the reader at odds with all the descriptions related to fig.2. If they don't find any more subtle distinction within these 2 macro-regions, they should at least discuss this discrepancy.

      5) The part about Task-specific network connectivity is indeed very interesting, I would suggest to the authors to focus exclusively on this part. (Note that the results of this part seems to confirm that only the linguistic task is able to show a clear lateralization).

    1. Reviewer #2:

      The main analyses of the study compare previously published experimental observations from Hi-C and ORCA to predictions of the author's "futile cycle" model. The predictions are derived from simulations and differential equations analysis of the model as a dynamical system. Given its centrality to the manuscript, we recommend describing this overall strategy in more detail in Results. For example, at line 124 (Pg. 4) the authors could talk about how the simulations are done, including where the variability comes from (e.g., random starting conditions vs. probabilistic events vs. different parameters).

      Xiao et al. make several key assumptions to dramatically simplify their model. Namely, it is assumed that promoter modification and transcription are equivalent and that enhancer-promoter contact influences transcription instead of transcription influencing structure. Steady-state equilibrium must also be assumed. It would be helpful if the authors explicitly stated these assumptions and provided references to support their being reasonable.

      It is not totally clear why the authors decide to call their proposed approach the futile cycle model. There are similarities to other well-known models in biochemistry and biophysics that should be noted. It might make sense to simply call this a mechanistic model of cooperative promoter activation. If the authors stick with "futile cycle", the relationship between promoter activation through tags and metabolic signaling should be described in more detail.

      There is also an opportunity to emphasize that the proposed model is not necessarily absolutely correct, but one of many plausible models that can produce a non-linear relationship between genome structure (enhancer-promoter contact) and transcription. Any thoughts on other models that could generate similar dynamics would be a useful discussion point. There are parallels to both sigmoidal dose-response curves, where drug concentration is plotted against response, and transcription factor binding curves, where free ligand concentration is plotted against the fraction bound. We recommend providing background context on these types of models or the Hill equation to illustrate why non-linear behavior is or is not surprising given the proposed model.

      For clarity, it would be helpful to discuss model parameters in greater detail. First, we suggest noting which parameters shift the location of the curve and which increase the steepness of the curve. Second, we recommend including a phase diagram exploring when sigmoidal behavior and any other key model predictions arise across parameter space. In what circumstances does hypersensitivity or time lag emerge? The authors demonstrate that a narrow set of parameters is sufficient to produce a super-linear relationship between enhancer-promoter contact and transcription in Figure 6. One potential dilemma is this model's ability to explain many experimental observations by indicating that minimal changes all occur in the sub-linear regime while observable changes occur in the super-linear regime. Given that one needs specific parameters to replicate an example of the hyper-linear regime (including at least three degrees of stimulation and increasing stimulation of the successive states), it could be valuable to demonstrate how large the plausible parameter space is. Without an exhaustive search across the space of minimal parameters, it is not clear when this property emerges or how common it is within the full parameter space. The authors could vary model parameters and plot a grid visualizing behavior (e.g., steepness of the curve or Hill coefficient).

      Images throughout the manuscript are low resolution, making the figures difficult to read. Increase the resolution of figures throughout, especially those containing text (Fig 6A).

    1. Reviewer #2:

      This manuscript by Diamanti et al. describes their study on how visual neurons responded to identical visual stimuli at two different locations along a virtual linear track. Extending their previous result that spatial location modulates the neuronal activities in the primary visual cortex (V1), they now demonstrate that similar spatial modulation also occurred in the higher visual areas (HVAs), but not so much in a lower visual area, the lateral geniculate nucleus (LGN). In addition, they show that the modulation, measured by a spatial modulation index (SMI), was stronger when animals had more experience in the track and when the animals were actively performing a task rather than passively viewing the same virtual track. The authors have been responsive to comments by previous reviewers at a different journal. Data are appropriately analyzed and clearly presented.

      Since the finding that visual neurons are spatially modulated similarly as hippocampal place cells in spatial navigation tasks (Ji and Wilson, 2007; Haggerty and Ji, 2015; Fiser at al, 2016; Saleem at al, 2018), there has been increasing interest in identifying the source(s) of this modulation. This study adds new evidence to this puzzle, suggesting that it is more likely either generated within the visual cortex or top-down propagated from higher brain areas, rather than bottom-up propagated from the thalamus. This is an important contribution. However, there are concerns, mainly on the data interpretation and the clarification of the main conclusion, as elaborated below.

      1) Because experience and task engagement enhanced spatial modulation, the authors concluded in the abstract that "Active navigation in a familiar environment, therefore, determines spatial modulation...". This conclusion is too strong and not well-supported by the data. First, spatial modulation on Day 1, when the task was novel, was lower than on later days, but it was already much higher than 0 (Fig. 1h). Also the individual neuron data (Fig. 1e) display clear spatial modulation on Day 1. Therefore, "familiar environment" is not a requirement. Second, spatial modulation during passive viewing was much higher than 0 and was correlated with that during active navigation, as shown in Fig. 4e - Fig. 4l. Therefore, "active navigation" is not a requirement either. It is true that both active navigation and familiar environment enhanced spatial modulation. They did not "determine" spatial modulation.

      2) Related to the point above, the presence of spatial modulation in passive viewing reminds us that these cells in the visual system were still mainly driven by visual stimuli. The data in Fig. 4e,f are especially telling: the modulation in V1 was similar and highly correlated between active navigation and running replay. In addition, it is clear from all the raw traces in Fig. 1 and Fig. 2 that these cells did respond to the two segments with identical stimuli reliably with two peaks. The spatial modulation was just a change in one of the peaks. So the nature of the modulation is a "rate remapping" of the expected, classical visual responses. I believe, in order to maintain the big picture of what drives the activities of these neurons, it is beneficial to clarify that the "spatial modulation" is a modulation on top of the expected visual responses. This message is not explicitly conveyed in the current manuscript.

      3) The authors stated that spatial modulation is "largely absent in the main thalamic pathway into V1". This was based on the significantly weaker SMIs in LGN than those in V1 and HVAs. However, it is unclear whether the SMIs in LGN were still significant. The SMI values for both LGN buttons (Line #100) and LGN units (Line# 130) might be statistically significant from zero. The statistical comparison p-values should be given in both cases. Second, Figure 3 - figure supplement 1 b,f show that the SMI values in LGN could be predicted by spatial modulation, but not by visual stimuli alone or behavioral variations, just like those in V1 and HVAs. This seems to me good evidence for the presence of spatial modulation in LGN. Therefore, it is my opinion that the data do not support the complete lack of spatial modulation in LGN, but do clearly demonstrate weaker spatial modulation in LGN than in V1 and HVAs.

    1. Reviewer #2:

      In this manuscript, the authors set out to measure participant's decisions about when an item occurred in a short list of 3 or 4 items, where the first and last items were always at the beginning and end, respectively. They report two behavioral studies that examine time judgments to items in the intermediate positions. They show that time judgments (when did you see X item using a continuous line scale) are always a little off but, more importantly, they tend to be anchored to other items presented. The results are interesting and add to our knowledge of the representation of time in the brain mainly by introducing a new paradigm with which to study time. Within the broader context of research on timing capacities, it should not be surprising that participants do not have a continuous representation of time that lasts beyond traditional time interval training of a few hundred milliseconds to a few seconds. Furthermore, research has also shown that 'events' that require attentional resources do morph our perception and memory for time. So while the paradigm is worth expanding on, the behavioral results are not surprising given this past literature. I do feel however that this work is an important first step in developing a more firm model of memory for time.

    1. Reviewer #2:

      Borghesani and colleagues aimed to understand how dysfunction in the ATL alters the dynamic activity during semantic categorization. To achieve this, they contrast MEG responses between patients with svPPA and age-matched healthy controls. Both groups show similar profiles of behavioural performance on the task, and broad similarities in MEG responses. Critically, svPPA patients show enhanced gamma synchronization in the occipital lobe compared to controls, while gamma synchronization was correlated to task RTs.

      In general, I found the manuscript interesting, and the major strength being the application of MEG analyses to a clinical population during a cognitive task. In terms of improvements, I think the results could be more fully characterized, which would allow for more expansive interpretations and inferences.

      Major comments:

      1) As the paper is about 'Neural dynamics', I felt this aspect could be developed, with the timing of the effects characterized further, and considered more in relation to the conclusions. For example, the main finding is the increased occipital gamma response in svPPA compared to controls. Looking at Figure 3, there is a peak in the svPPA group near 200 ms, and very little synchronized activity in the control group. This is interesting as there are many ways we could have seen svPPA > controls, but this suggests that the gamma synchronization response associated with compensation is specific to the svPPA group (and largely absent from controls - also from Supp fig 1), and is distinguished from an initial visual evoked response (peaking ~100 ms). I would recommend discussing and characterizing the dynamics of this effect more, such as what a later occipital effect could tell us about dynamics given ATL dysfunction? Is this increase a result of a lack of top-down effects from ATL? I think these kinds of issues could be explored and discussed more.

      2) The occipital gamma effect looks like the primary visual cortex, which might suggest the effects are not related to higher-level perceptual features (such as has eyes, teeth) as the authors suggest, but rather low-level visual effects. Do the authors perhaps think the effects could relate to enhanced processing of visual details (as related to the ideas of Hochstein and Asher's reverse hierarchy), or whether the effects relate to additional visual input following a visual saccade?

      3) The VBM results for the svPPA patients were surprising given that all the atrophy appeared in the left hemisphere. There can be hemispheric differences in svPPA, but is this a true lateral pattern (meaning the right ATL is intact) or a product of VBM being run so that the most atrophied hemisphere is shifted to the left side? If the VBM maps are correct, and the svPPA patients are only showing left hemisphere atrophy, then what does this suggest about the role of the right ATL, and the bilateral nature of occipital increased in svPPA?

      4) Both svPPA patients and healthy controls achieved around 80% accuracy in the categorization task. This seems surprisingly low given, (1) the task (living vs. nonliving after seeing the image for 2 seconds), (2) that all the images were pretested and had high name agreement, and (3) that items were repeated on average 2.5 times. Is there something that explains this low performance for all individuals?

    1. Reviewer #2:

      Overall I think the authors collected an interesting dataset. Analyses should be adjusted to include all cells rather than sub-selecting for stability. Additionally, the language needs to be adjusted to better reflect the data. I wish there was any behavioral data included, but if the authors compare their data to publicly available data in V1 for a single recording session during a visually guided task, these concerns could be quelled a bit.

      1) In general the language of this paper and title seem to mismatch the results. The fraction of cells that were 'stable' as the authors say on line 112 was very small, however the authors focus extensively on this small subset for the majority of analyses in the paper. Why ignore the bulk of data (line 119)? What happens if you repeat the same analysis and keep all cells in the dataset? The general language around stability of neural ensembles should be adjusted to better reflect the data (ex: lines 157, 225).

      2) There are claims in this paper about how ensembles 'implement long-term memories' in the introduction and conclusion and yet the authors never link the activity of ensembles to any behavioral or stimulus dependent feature. This language reaches far beyond the evidence provided in this paper. The introduction could provide some better framing for expectations of stability vs. drift in neural activity rather than focus on the link between ensembles and memory given that there isn't much focus on the ensembles' contribution to memory throughout. For example, the last sentence of the paper is not supported by data in the paper. Where is the link between ensembles and memory in the data? What is the evidence that transient ensembles are related to new or degraded memories? This reads as though it was the authors' hypothesis before doing the experiments and was not adjusted in light of the results.

      3) There is no discussion around the alternative to stability of neuronal ensembles. What are the current theories about representational drift? For example, in Line 34 the authors present an expectation for stability without any reasoning for why there need not be stability. This lack of framing makes their job of explaining results in line 217 more difficult. There is a possibility that the most stable cells aren't more important - what is the evidence that they are? Does an ensemble need a core? Would be interesting to include some discussion on the possibility of a drifting readout (Line 223). [https://doi.org/10.1016/j.conb.2019.08.005]

      4) How do activations in V1 in this dataset compare to other data collected from V1 while the animal is performing a task (where for example the angle of the gradings is relevant to how the mouse should respond)? I would be interested to know if the authors compared statistics of their ensembles to publicly available data recorded in V1 during a visually guided behavior. Are the ensembles tuned to anything in particular? Could they be related to movement? [http://repository.cshl.edu/id/eprint/38599/]

      5) The authors provide some hypotheses as to why fewer cells are active in the later imaging sessions (dead/dying cells?). This is worrisome in regards to how much it might have affected the imaged area's biology. One alternative hypothesis is that the animal is more familiar with the environment/ not running as much etc. Have the authors collected any behavioral data to compare over time?

      6) How much do the results change when you vary the 50% threshold of preserved neurons within an ensemble (Line 146)? Does it make sense to call an ensemble stable when 50% of the cells change? Especially given that the cells analyzed as contributing to an ensemble are already sub-selected to be within the small population of stable cells (Line 119)?

      7) Cells are referred to as 'stable' when they're active on 3 different sessions that are separated in time. However, the authors find a smaller number of cells are stable over extended time (43-46 days later). If we extrapolate this over more time, would we expect these cells to continue to be stable? Given these concerns, it might make more sense to qualify the language around stability by the timespan over which these cells were studied.

      8) Filtering frames to only coactive neurons for ensemble identification seems strange to me. Authors may be overestimating the extent of coactivation. What happens when you don't do this? How much do the results change when you don't subselect for Jaccard similarity? I would be interested to see how the results vary as you vary this threshold (Line 136).

      9) The term 'evoked activity' is misleading because the authors don't link these activations to the visual stimulus. There's no task, so the mice could be paying little attention to the stimulus. Should we really consider this activity to be visually driven? Could the authors provide any evidence of this?

      10) A method like seqNMF could reveal ensembles that are offset in time. This looser temporal constraint could potentially reveal more structure. This should be run on the entire dataset (without stability sub-selection). I suggest this as a potential alternative or supplement to the method described by the authors. [https://elifesciences.org/articles/38471]

    1. Reviewer #2:

      In this manuscript, Barondeau and co-workers test a hypothesis for the role of the protein frataxin in iron-sulfur cluster assembly, seeking, inter alia, to explain the observation that mutations in the gene encoding this protein are associated with the incurable neurodegenerative disease, Friederich's ataxia. Their notion is that, whereas the bacterial versions of the sulfur-providing cysteine desulfurase are stable homodimers - in which the interactions between the monomers help to organize the mobile loop harboring the key cysteine residue that serves as general acid and nucleophile in the C-S-cleavage reaction that mobilizes the sulfur for incorporation into the cluster - the human enzyme (i) has a dimer interface that has been weakened through evolution, (ii) can be monomeric or form non-optimal dimeric forms, and (iii) can be driven to adopt the optimally active dimer form by intervention of accessory proteins (e.g., frataxin). Their approach was to perturb a bacterial (E. coli) cysteine desulfurase (IscS) by structure-guided mutagenesis in an attempt to introduce into it the behavior of the human enzyme, specifically its activation by accessory proteins (here CyaA and FXN). The experiments were successful in this goal. I like this paper and believe that it is interesting and important. I would point out two aspects that perhaps leave room for improvement.

      1) In principle, it would have been a more powerful test of their hypothesis had they been able to perturb the human enzyme to get a constitutively active form, no longer dependent on the binding of the accessory proteins, either instead of, or in addition to, the converse perturbation of the bacterial system. Perhaps this approach was precluded by difficulties associated with the human enzyme?

      2) The second criticism is that the effects on quinonoid form decay and activity are rather modest. However, I believe that important biological effects can arise from even such modest regulation of enzyme activity levels.

    1. Reviewer #2:

      This manuscript by Dingens et al. develops a novel application of mutational antigenic scanning to identify dominant neutralizing antibody epitopes in polyclonal sera from vaccinated animals, and compares the findings of such techniques with those from cryo-EM based unbiased mapping of binding antibodies and from conventional mutational mapping of neutralizing epitopes. Overall, I find the experiments and analyses to be of high quality, thorough and of sound reasoning, and the manuscript to be well written. I also commend the authors for the development of a facile and easy-to-use interactive viewer for exploring the mutational scanning data. I think the dual approach of mutational scanning and cryo-EM based mapping has the potential to be a powerful approach for dissecting antibody content of polyclonal sera post-vaccination or in infected hosts.

      The only major concern I could identify is the following. One of the main advantages of the mutational scanning approach is that it can identify novel epitopes targeted by antibody responses in a high-throughput manner. It is a little disappointing that this advantage was not leveraged in the current manuscript, perhaps due to the choice of the vaccine (BG505 SOSIP trimers where the epitopes have been thoroughly mapped in the literature) and the selection of vaccinated animals. Looking at Fig. 2, animal 5727 was the only animal whose serum showed some selection signatures outside of the regions considered in depth (at sites 507 and 509) - have the authors analyzed these escape mutations? If not, and only if possible within reasonable workload, I urge the authors to pursue this example or any other example where a potential novel epitope discovery could be possible.

    1. Reviewer #2:

      The Hydra, in the phylum cnidaria, is a near microscopic freshwater animal that has recently resurfaced as an attractive model organism in neuroscience due to its optically accessible transparent body, sparsely distributed neural network, and simple behaviors. In this manuscript, Badhiwala and colleagues use calcium imaging of the Hydra neural network, combined with surgical resection and microfluidics pressure stimulation to identify body regions indispensable for mechanosensory activity. They report that while resection of the aboral region did not abolish the mechanical response, resection of the oral region attenuated this response, while combined resection of oral and aboral regions showed the greatest effect. They also find a correlation between reduced stimulated activity and spontaneous activity, suggesting a common mechanism that gives rise to both activities. While this study takes on an innovative approach by using a microfluidics device to mechanically stimulate the hydra under optical recording there are a number of conceptual and technical limitations. Perhaps my biggest reservation is that despite real potential, the data are rather low resolution (body transections and bulk calcium responses) and as such the conclusions that can be reasonably drawn do not extend what is known in a significant way.

      Major comments:

      1) The authors have designed a microfluidic device that allows them to simultaneously mechanically stimulate, monitor movement and functionally image a hydra. The highly quantifiable nature of the microfluidic device is a great asset, although this potential is not deeply explored. While I can see how the microfluidic stimulation could offer benefits over fluid jet or blunt probe, more in-depth characterization is needed.

      2) What is the spatial distribution of the pressure pulse stimulus on the Hydra body? How far does the mechanical force spread from the region directly touching the pressure valve?

      3) The use of the microfluidic device was limited. Have the authors attempted to map mechanical sensitivity across the Hydra body by stimulating different sites?

      4) The authors have not attempted to record calcium responses from single neurons, but rather spatially average a population response from a large region of interest. This should be specifically stated in the results section. More importantly, to provide insight into network function much smaller ROIs over multiple sites are needed instead of the bulk activity of the entire peduncle. This seems like a real lost opportunity as the lure of the optically clear and small hyda is that neural representation and coding can be tracked over large portions of the network at cellular resolution.

      5) It is unclear where the recorded signals are coming from and if movement is creating artifacts. Have authors made any attempts to correct for movement? The supplemental movies show a stationary region of interest and moving animal, in some cases parts of animal moving in and out. Furthermore, is background subtracted and how? There is a large fluorescent signal coming from the entire body/ middle columnar part of the body and spontaneous firing that makes interpretation of the data difficult.

      6) Contraction is a behavioral response of the animal; however, the authors use 'contraction' do describe calcium imaging responses throughout the figures and text. This should be avoided.

      7) I am unsure if the title of the paper is accurate. I do not think this work has demonstrated "multiple nerve rings" are important for coordinating mechanosensory behavior.

      8) Furthermore, the claim that the observed "linear relationship" between the spontaneous contraction probability and resection type is evidence for shared neural pathways is a stretch. These data are fairly coarse resolution and include only 3 animals in each group with highly variable responses (Figure 4C). Additionally, they do not provide evidence to distinguish the motor circuits they hypothesized these neural nets converge upon.

    1. Reviewer #2:

      The authors asked how the brain uses different sensory signals to estimate self-motion for path integration in the presence of different movement dynamics. They used a new paradigm to show that path integration based on vision was mostly accurate, but vestibular signals alone led to systematic errors particularly for velocity-based control.

      While I really like the general idea and approach, the conclusions of this study hinge on a number of assumptions for which it would be helpful if the authors could provide better justifications. I also have some clarification questions for certain parts of the manuscript.

      1) Lines 26-7: "performance in all conditions was highly sensitive to the underlying control dynamics". This is hard to really appreciate from the residual error regressions in Fig 3 and seems to be contradicting Fig 5A (for vestibular condition). A more explicit demonstration of how tau affects performance would be helpful.

      2) One of the main potential caveats I see in the study design is the fact that trial types (vest, visual, combined) were randomly interleaved. In the combined condition, this could potentially result in a form of calibration of the vestibular signal and/or a better estimate of tau that then is used for a subsequent vestibular-only trial. As such, you'd expect a history effect based on trial type more so (or in addition to) simple sequence effects. This is particularly true since you have a random walk design for across-trial changes of tau. In other words, my question is whether in the vestibular condition participants simply use their previous estimate of tau, since that would be on average close enough to the real tau?

      3) I thought the experimental design was very clever, but I was missing some crucial information regarding the design choices and their consequences. First, has there been a psychophysical validation of GIA vs pure inertial acceleration? Second, were GIAs always well above the vestibular motion detection threshold? In other words could the worse performance in the vestibular condition be simply related to signal detection limitations? Third, how often did the motion platform enter the platform motion range limit regime (non-linear portion of sigmoid)?

      4) Lines 331-345: it's unclear to me why you did not propose a more normative framework as outlined here. Especially, a model that would "constrain the hypothesized brain computation and their neurophysiological correlates" would be highly desirable and really strengthen the future impact of this study.

      5) I would highly recommend all data to be made available online in the same way as the analysis code has been made available.

    1. Reviewer #2:

      The article by Gould et al breaks new ground by demonstrating a role for lysosomal-mediated degradation in the mechanosensitive repression of Sclerostin levels in bone. Though the post-translational repression of Sclerostin has long been apparent, no one has yet unraveled the mechanisms. Therefore, this discovery is important to the skeletal biology community - both because of the findings themselves, and because the conditions/models used by this team to make these discoveries will be useful for other investigators, including their ability to manipulate and observe the rapid lysosome-dependent control of Sclerostin levels in vitro and in vivo in response to PTH or mechanical stimulation. In addition to the importance within this field, the work has broad impact on multiple levels including a) the clinical relevance for understanding and potentially treating osteoporosis and the skeletal phenotypes in individuals with lysosomal disease, and b) the mechanoregulation of lysosomal function and its relationships to crinophagy, which has implications not only for the regulation of Sclerostin, but also for other factors in and beyond the skeleton (RANKL, insulin).

      The study is elegantly designed, clearly communicated, and rigorously conducted. The conclusions drawn in the manuscript are mostly supported by the data provided. In general, it is important to elaborate on what gives the authors confidence that the inhibitors were effective and act as expected throughout the study - but especially Bafilomycin A1 and Apocynin in vivo. If BafA1 and Apocynin treatment in vivo work as expected, they should prevent the rapid load-dependent repression of Sclerostin levels (shown in Figure 1D).

      Other revisions or additions, described below, would improve the quality of the study:

      1) Are Sclerostin levels insensitive to FSS or PTH in Gaucher cells (though it understandably may not be feasible to differentiate these cells in microfluidic devices)?

      2) Since a sex-specific effect of exercise on bone anabolism has previously been described, and TRPV4 also has a sexually dimorphic effect on bone, were any differences observed between male and female animals here?

      3) Can the authors discuss where the pathway used by PTH diverges from that activated by FSS/load?

      4) Is it possible to detect load dependent changes in sclerostin localization in lysosomes in vivo?

      5) Given the non-specific effects of hydrogen peroxide, Figure 6D may not add a great deal in light of the other data that was gathered with more rigorous approaches. Additional controls would give more confidence in the efficacy/specificity of this approach.

      6) Please include how long the OCY454 cells were differentiated prior to the treatments applied.

      7) Please identify the route by which inhibitory agents were administered to the mice (i.e. subcutaneous, intraperitoneal).

      8) Please increase the N for experiments in Figure 4A and 5D, or remove these data and the corresponding conclusions.

    1. Reviewer #2:


      This paper predicts intelligence using either coarse-grained functional connectivity (based on 360 ROIs) or fine-grained functional connectivity (vertex-wise) after hyperalignment. The results show a two-fold increase of variance explained in general intelligence between coarse-grained and fine-grained connectivity.


      This is a very clearly-written paper that presents an important result, which has the potential of great impact on the field of behavioral prediction. My comments below are relatively minor and primarily aimed at clarifying a few details in the article. Please find my detailed comments below, approximately in order of importance.

      Major comments:

      1) The fine-grained functional connectivity has richer features than coarse-grained, leading to higher dimensionality in the PCA step (supplementary figure S5). I wonder if this might contribute to improved prediction accuracy. Related to this, it appears that there may also be a relationship between PCA dimensionality and regularization parameter, such that more regularization may be needed when more PCs are used in the model. It would be interesting to test the effect of fixing the PCA dimensionality (and perhaps also the regularization) across all models to control model complexity.

      2) The Glasser 360 parcellation was used throughout this work. There are subject-specific parcels and group-level parcels available for this parcellation. Please clarify which of these were used. If the group-level parcels were used, it might be interesting to see how the coarse-grained prediction accuracies might improve when using subject-specific parcels.

      3) The residuals of fine-grained connectivity profiles were obtained after subtracting coarse-grain connectivity. Why was subtraction used here, rather than regressing out (i.e., orthogonalizing with respect to) the coarse-grained connectivity?

    1. Reviewer #2:

      This manuscript from Allio is an interesting mix of approach demonstration (population genomic sampling via roadkill) and application (demographic analyses, questions about taxonomic status, and phylogenomics). There are some valuable results from the application component of the paper. In particular, I appreciate the comparative approach for studying patterns of intra- vs. inter-species genetic diversity. However, there is some rework to fully normalize those comparisons, that I feel is required.

      I would suggest the authors be more immediately forthcoming about the sizes of their samples, and perhaps consider changes to the introductory text to avoid giving any mis-impressions to readers about what data are ultimately presented in the manuscript. I had envisioned more of a landscape genetics-level sample and analyses, rather than n=3 individuals per each of the two species. Furthermore, while I think the reporting of the genome assembly qualities is important from confirmatory and quality control perspectives, and while presenting the new assemblies, in my view this shouldn't be set up to be a surprising result. These are very high-quality DNA samples, so we expect to be able to achieve DNA assembly qualities to whatever the invested level using current best-practices data generation and analytical methods.

      On the general genetic diversity and taxonomic questions, from my own experience I know that genetic differentiation metrics are not necessarily precisely comparable between a new study based on genome sequence data and an existing published dataset. Sample size affects false positive and false negative SNP calling error rates and sequence coverage and the variation among samples can also make a difference. Thus, especially since this leads to a key result/conclusion (i.e. that the two subspecies of aardwolf may deserve species status), it isn't sufficient that "similar individual sampling was available" for the carnivoran comparative datasets. The datasets should be equalized with sample number and individual sequence coverage (using downsampling) and then SNPs re-called using the same approach, before making the comparison. From the methods it wasn't clear to me the extent to which this all was done. It does appear that the same number of samples were used, and that the SNP calling approach was likely re-done from the read data (although please be more explicit about this, in the description). However, it doesn't appear that the sequence read data were subsampled for equivalency across the samples, which should be incorporated. Hopefully the results are similar, but there can be big changes that affect interpretation, so a careful approach is required.

      The study design and proposed expanded use of roadkill samples in population genomics led me to think of this study, one of my all-time favorites: Brown & Bomberger Brown (2013), Where has all the road kill gone?, Current Biology. For the present study, the question of potential biases in the sample for similar or related reasons is beyond the scope of investigation; this is not relevant for the sample sizes collected and analyses conducted. However, the importance of keeping this possibility in mind should at least be noted given the more expansive promotion of the wider inclusion of roadkill samples in population genomic studies. E.g. could the sample be biased towards individuals with genetically-mediated and/or culturally learned behavioral tolerance of human-disturbed habitats, etc., rather than a truly random sample representative of the overall landscape.

      In the methods section, the collection process and permits for the four samples from South Africa are described in detail. (Could you preemptively explain that the IUCN status for these two species is Least Concern, and thus that CITES permits are not required for the international transport of the samples?). However, the same information is not provided for the two East African samples that were included in the study (also, I think that there should not be two separate sampling sections in the manuscript). Please provide these details or expanded explanations.

    1. Reviewer #2:

      In their manuscript, Kim et al address the role of PIE-1 sumoylation during C. elegans oogenesis. The authors favour a model in which sumoylated PIE-1 acts as a sort of E3-like factor 'enhancing' HDA-1 sumoylation. While the results are indeed very interesting, it is unclear to me whether there is enough data to support the author's model. I have list of comments, suggestions, questions, and concerns, which are listed below, which I hope will help the authors strengthen the manuscript:

      Figure 1)

      I) As with the accompanying manuscript, the extremely low level of SUMO modification should be factored in the model.

      II) Is sumoylation also observed in untagged pie-1? As judged by figure 3A, the authors have a very good antibody to test this.

      III) While the authors claim that PIE-1 sumoylation is not observed in embryos, that panel shows a lower exposure than the corresponding one in Adult (as judged by the co-purified unmodified PIE-1::FLAG). A longer exposure and/or more loading would be helpful.

      IV) Their strategy and optimisation for purification of sumoylated proteins is excellent and will be useful for future research (along with other reagents the authors developed here). Is the 10xHis::smo-1 functional? Could this be tested in vitro and/or in vivo?

      V) In vitro PIE-1 sumoylation would be a desirable addition to this figure.

      VI) In addition to germline PIE-1 localisation, it would be interesting to see embryos and PIE-1(K68R).

      VII) MW markers are missing in the blots.

      Figure 2)

      I) The generation of the ubc-9 ts allele is an exceptional tool. Could the authors show SUMO conjugation levels at permissive vs restrictive temperature? Just out of curiosity, is this a fast-acting allele?

      II) The authors mention that gei-17 alleles are viable, could the authors mention any thoughts on why the tm2723 allele is lethal/sterile?

      Figure 3)

      I) Panel C is mentioned in the text in the wrong place. Also in C, what do the authors think about the big increase in MEP-1 sumoylation in the PIE-1(K68R) background?

      II) I have the same comment for panel D as I had for figure 1 comment III: the exposure/loading for the embryo WB seems lower, as judged by the co-purifying, unmodified HDA-1. A positive control for sumoylated protein coming from embryos would be nice.

      III) In general, the model of PIE-1 acting as a SUMO machinery recruiter should be tested with recombinant proteins. Even if compatible with some results in vivo, showing that this is a plausible mechanism in vitro would be extremely helpful and greatly support the authors' claim.

      Figure 4)

      I) The authors make a quantitative comparison of the HDA-1/MEP-1 interaction in the text. I think this is not correct. Even if these have been run in the same gel, this could just be a lower exposure. In this line, the HDA-1 blot in the 'Adult' IP would benefit from a longer exposure to better appreciate what seems a rather small difference between PIE-1 and PIE-1(K68R).

      II) Since there still seems to be interaction between MEP-1 and HDA-1 in the PIE-1(K68R) background, does smo-1(RNAi) or ubc-9(G56R) reduce this further?

      III) In panel B, the LET-418 blot on the right is massively overexposed.

      IV) Once again, in vitro binding experiments to get some indication that the authors' model is plausible would be a great addition.

      Figure 5)

      I) Could the authors make some quantitation of the immunofluorescence data?

      Overall, I think this manuscript proposes a very interesting model and the results support this model, although I am not convinced these are sufficient to strongly back the authors' claims. I would very much like to see a revised version with some in vitro data backing the authors' model.

    1. Reviewer #2:

      In their manuscript, Kim et al describe a role for HDAC1 (HDA-1) sumoylation in Argonaute-directed transcriptional silencing. The authors suggest that sumoylation of HDA-1 is important for proper assembly of the NuRD deacetylase complex. The role of SUMO modification in heterochromatin has been extensively documented and it is a very interesting topic. The current manuscript provides a very interesting set of results on this topic. I have list of comments, suggestions, questions, and concerns, which are listed below, especially related to the first half of the results:

      1) A general question would be how can HDA-1 sumoylation, which is barely detectable, account for such a big 'positive' effect on complex assembly? HDA-1 SUMO modification seems around 10% after enriching for SUMO-modified proteins, which means that stoichiometry will be way lower than this. While this is common for SUMO-modified proteins, it does make it difficult to associate with a 'simple' model.

      2) In Figure 1, a schematic of the sensor used throughout the study would benefit the reader.

      3) In Figure 1, have the authors checked if the 10xHis::tagged smo-1 has the same effect as the 3xflag::smo-1 (i.e. is it also a partial loss of function allele)?

      4) In Figure 1 it would be nice to see the global SUMO conjugation levels in the different conditions, particularly in the smo-1(RNAi), 3xflag::smo-1, and ubc-9(G56R).

      5) Also Figure 1, was gei-17 depletion/deletion checked in any way (i.e. WB)? Did the authors consider other SUMO E3 ligase, such as the mms-21 orthologue?

      6) While I am not a big fan of fusing SUMO to proteins, in this case it seems like a very reasonable thing to do, considering the modification sites are located very close to the C-terminal end of the protein. Did the authors check an N-terminal fusion?

      7) In Figure 2B, it becomes very clear that the level of SUMO modification of HDA-1 is extremely small, barely detectable after an enrichment method. I also wonder why the gels were cropped so tightly, especially considering that in Figure 3 there is an additional band corresponding to ubiquitylated, sumoylated HDA-1. In vitro modification assays would be helpful. HDA-1 alongside a known and characterised SUMO substrate would indicate how good a substrate HDA-1 is.

      8) In Figure 2D, is the difference between HDA-1(KKRR)::SUMO and HDA-1::SUMO significant?

      9) In Figure 3A-C, it would be useful to control whether the GFP::HDA-1 fusion behaves as the untagged one in the sensor assay (wt vs. KKRR).

      10) I have a few questions regarding Figure 3D:

      I. Considering the extremely low level of HDA-1 sumoylation, did the authors detect SUMO and ub conjugated HDA-1 (not the SUMO usion)?

      II. Is ub conjugated to SUMO or to HDA-1?

      III. Does MEP-1 contain any obvious SIMs and or UIMs?

      IV. To make a stronger case for the SUMO-dependent interaction model, in vitro interaction assays with recombinant proteins would be extremely useful.

      11) In the discussion, the authors compare the lack of requirement for GEI-17 in their manuscript with the requirement for Su(var)2-10 in flies. It is very important to back this claim that the authors control GEI-17 depletion (as pointed out in 5).

      Overall, I think this manuscript provides a very interesting set of results and I believe that, with the addition of some simple biochemical experiments, the quality and impact of the overall work would be much greater.

    1. Reviewer #2:

      Soucy and colleagues developed a thermoplastic microphysiological system (MPS) to investigate the mechanisms regulating adrenomedullary innervation. This system consists of 3D cultures of adrenal chromaffin cells and preganglionic sympathetic neurons within a contiguous bioengineered microtissue. Using this model, they report that adrenal chromaffin innervation is critical for hypoxia-induced catecholamine release. They also show that opioids and nicotine affect adrenal chromaffin cell response to hypoxia without impairing neurogenic control mechanisms. In addition to providing mechanistic insights on adrenomedullary catecholamine release, this study represents an elegant proof-of-concept that the MPS have the potential to become useful tools to study organ innervation.

      Disclosure: I do not have the expertise to review the engineering aspects of this manuscript and will therefore share some concerns I have regarding the accuracy of this technology to mimic native tissues.

      I understand that one advantage of the MPS over microfluidic devices using micro-posts or micro-tunnels is the presence of an unobstructed interface between the compartments that is similar to tissue interfaces. However, how better is it compared to other organs-on-chips constructs for reaching the biological complexity of an intact organ?

      The system consists of adrenal chromaffin cells and preganglionic sympathetic neurons. I wonder if in this format it could lack the normal cellular heterogeneity of the adrenals. Can the absence of adrenal cortex cells producing aldosterone, androgens and glucocorticoids with important autocrine functions on chromaffin cells interfere with the ability of chromaffin cells to respond normally to a stimulus? Authors discuss that future efforts will incorporate additional adrenal cortical cell populations to better mimic the native physiology. Could they extend this discussion by highlighting the potential weaknesses of the model in its current format? Was any observations made that would suggest caveats?

      In vivo, do all fibers innervating the medulla target the chromaffin cells or do some/most innervate the blood vessels or pericytes? If a majority of the innervation is to blood vessels, how does this system take into account potential changes in blood flow and perfusion of the adrenals that could occur and affect the oxygenation?

      Early work suggests that adrenergic terminals innervate chromaffin cells and that the adrenal medulla receives a sympathetic and parasympathetic efferent and an afferent innervation (J Anat. 1993 Oct; 183: 265-276). How would this system allow to study such complex innervation? Is it possible to add additional neuronal types to this MPS?

      In addition to the nicotinic cholinergic receptors, chromaffin cells express muscarinic receptors that may also be involved in catecholamine release. A quick profiling and comparison of the expression of the different receptors could reinforce the representative nature of the technology to model a biological system.

      One important caveat of MPS is the challenge of delivering a drug in a physiologically realistic manner. Could the author comment on the doses of the different drugs used and how they are representative of what a chromaffin cell would normally "see" in vivo?

      Could the authors comment on the culture media/conditions and how they are representative of a biological system? Would the use of blood or blood components be a better alternative to the system?

    1. Completing this survey is a course requirement, but answers will not count as part of your grade. All individual answers will be kept confidential, although de-identified data might be discussed in class.

      Momments Annotation 2

    1. Reviewer #2:

      The manuscript introduces a computational account of meta-control in value-based decision making. According to this account, meta-control can be described as a cost-benefit analysis that weighs the benefits of allocating mental effort against associated costs. The benefits of mental effort pertain to the integration of value-relevant information to form posterior beliefs about option values. Given a small set of parameters, as well as pre-choice value ratings and pre-choice uncertainty ratings as inputs to the model, it can predict relevant decision variables as outputs, such as choice accuracy, choice confidence, choice induced preference changes, response time and subjective effort ratings. The study fits the model to data from a behavioral experiment involving value-based decisions between food items. The resulting behavioral fits reproduce a number of predictions derived from the model. Finally, the article describes how the model relates to well-established accumulator models, such as the drift diffusion model or the race model.

      Before I get into more detailed comments, I would like to highlight that this work addresses a timely and heavily debated subject, namely the role of cognitive control (or mental effort) in value-based decision making (see Shenhav et al., 2020). While there are plenty of models explaining value-based choice, and there is a growing number of computational accounts concerning effort-allocation, little theoretical work has been done to relate the two literatures (but see Major Comment 1). This work contributes a novel and interesting step in this direction. Moreover, I had the impression that the presented model can account for a broad range of behavioral phenomena and that the authors did a commendable amount of work to validate the model (but see Major Comments 2 and 3). The manuscript is also well written in that it seems accessible to a broad audience, including non-technical readers. However, while I remain curious about what the other reviewers have to say, the manuscript misses to address a few issues that I elaborate below.

      Major Comments:

      1) Model Comparison(s): While the manuscript compares the presented computational approach to existing accumulator models, it could situate itself better in the existing literature, ideally in the form of formal model comparisons. For instance, as someone less familiar with choice-induced preference changes in value-based decision making, I wonder how the model compares to existing computational work on this matter, e.g. the models described in Izuma & Murayama (2013) or the efficient coding account of Polanía, Woodford, & Ruff (2019). I do understand that the presented model can account for some phenomena that the other models cannot account for, at least without auxiliary assumptions (e.g. subjective effort ratings), but the interested reader might want to know how well the presented model can explain established decision-related variables, such as decision confidence, choice accuracy or choice-induced preference changes compared to existing models, by having them contrasted in a formal manner. Finally, it would seem fair to compare the presented account to emerging, more mechanistically explicit accounts of meta-control in value-based decision making (e.g. Callaway, Rangel & Griffiths, 2020; Jang, Sharma, & Drugowitsch, 2020). As these approaches are still in preprint, it may not be necessary to relate them in a formal model comparison. However, the manuscript might benefit from discussing how these approaches differ from the presented model in the text.

      2) Fitting Procedure: This comment concerns the validation of the described model based on its fits to behavioral data. If I understand correctly, the authors first fit the model to each participant while "[a]ll five MCD dependent variables were [...] fitted concurrently with a single set of subject-specific parameters" and then evaluate whether model fits match the predicted qualitative relationship between experimental variables (e.g. pre-choice value ratings and pre-choice confidence ratings) and dependent variables (e.g. choice accuracy). I'm happy to be convinced otherwise, but it appears that the model's predictions could be tested in a more stringent manner. That is, it doesn't appear compelling to me that the model, once fitted, matches the behavior of participants -- please note that this is not to diminish the value of the results; I still think that these results are valuable to include in the manuscript. Instead, rather than fitting the model to all dependent variables at once, it would be more compelling to fit the model to a subset of established decision-related variables (e.g. accuracy, choice confidence, choice induced preference changes) and then evaluate how the fitted model can predict out-of-sample variables related to effort allocation (e.g. response time and subjective effort ratings). Again, I am happy to be convinced otherwise but the latter would seem like a much more stringent test of the model, and may serve to highlight its value for linking variables related to value-based decision making to variables related to meta-control.

      3) Parameter Recoverability: Given that many of the results rely on model fits to human participants, it would seem appropriate to include an analysis of parameter recoverability. That is how well can the fitting procedure recover model parameters from data generated by the model? I apologize if I missed this, but the manuscript doesn't appear to report this kind of analysis.


      Callaway, F., Rangel, A., & Griffiths, T. L. (2020). Fixation patterns in simple choice are consistent with optimal use of cognitive resources. PsyArXiv: https://doi.org/10.31234/osf.io/57v6k

      Izuma, K., & Murayama, K. (2013). Choice-induced preference change in the free-choice paradigm: a critical methodological review. Frontiers in psychology, 4, 41.

      Jang, A. I., Sharma, R., & Drugowitsch, J. (2020). Optimal policy for attention-modulated decisions explains human fixation behavior. bioRxiv: 2020.2008.2004.237057.

      Polania, R., Woodford, M., & Ruff, C. C. (2019). Efficient coding of subjective value. Nature neuroscience, 22(1), 134-142.

      Shenhav, A., Musslick, S., Botvinick, M. M., & Cohen, J. D. (2020, June 16). Misdirected vigor: Differentiating the control of value from the value of control. PsyArXiv: https://doi.org/10.31234/osf.io/5bhwe

    1. Reviewer #2:

      This is a nice study that is clearly written and makes use of several datasets. The authors show that a gene signature associated with increased myelopoiesis in utero is associated with increased risk of pediatric asthma. Furthermore they show that cord blood serum PGLYRP -1 is associated with reduced risk of pediatric asthma and increased FEV1/FVC. Interestingly sIL6ra which is derived from neutrophils but not associated with neutrophil granules did not show any association with pulmonary outcomes. This suggests that it is the neutrophil granules rather than the neutrophils per se that are the problem association. The following should be addressed:

      1) While the manuscript is clearly written, the message regarding PGLYRP-1 is at times confusing. The manuscript is clear that PGLYRP -1 is inversely associated with mid childhood asthma risk. The discussion however refers to animal models where PGLYRP -1 is proinflammatory and is associated with increased airway resistance and allergen sensitization. The apparent disparity should be clarified.

      2) What is the proposed role of neutrophil degranulation in the pathogenesis or long term susceptibility to asthma?

      3) While it was not the focus of the current study and maybe beyond the scope of the data it would be interesting to know if there is any association with the subsequent development of adult asthma.

    1. Reviewer #2:

      The paper submitted by Girard-Buttoz and colleagues asks whether and how early maternal loss affects cortisol levels and diurnal slopes among wild chimpanzees at Tai Forest, Côte d'Ivoire. The major claim of the paper is that, like humans, chimpanzees experience altered HPA functioning after maternal loss, including alterations to both diurnal slope and overall cortisol levels. However, their chimpanzee orphans exhibited patterns in diurnal slope that were opposite to their predictions (predicted blunted slopes, observed steeper slopes). The authors should be commended for their efforts in collecting a large number of samples for this analysis. However, I am not convinced that it is sufficient for investigating the hypotheses put forth here and, therefore I am also not convinced that their results are solid. I also have concerns about the theoretical grounding for the paper.

      1) My principal concerns with this paper, as written, revolve around the methods/results. First and foremost, I am not convinced that the authors have the sufficient sample size to evaluate the predictions/hypotheses outlined in the introduction. While 849 urine samples is a large number, and again, their efforts here should be commended, the sample spread is actually quite thin once it is spliced up into appropriate categories, especially considering how many samples were collected per individual year, on average. As the authors indicate throughout and especially when describing their modeling approach, cortisol is inherently a very noisy hormone impacted by myriad factors- including age in at least one other densely-sampled chimpanzee community. I'm also surprised that time of day was modeled quadratically. It is my understanding that humans, other populations of chimpanzees, and other mammals follow a sigmoidal curve which should be modeled with a third-order term as well. For these reasons, it's difficult to tell whether model 1A is not significant because of insufficient sample or a true lack of predictive power. Additionally, I'm concerned that the paper seems to focus so much on the results from a single model term in a model that did not reach significance.

      2) Despite acknowledging that the "significance of these predictors should be interpreted with caution" because model 1a did not reach significance, the authors make very strong claims about the results in the discussion- and also feature the finding of that model in the title of the paper. That seems problematic to me- especially because the insignificant model results (more intense diurnal slopes among immature orphans) diverge from the expectations set forth by other works in humans and non-humans. The finding that this is to do with higher-than-expected morning cortisol is puzzling given that evening levels are generally considered more responsive or plastic. However, this could also be an artefact of fitting the models without the third-order term for time.

      3) The introduction needs refinement to help clarify and specify the authors' arguments.

      (a) Does the biological embedding model always lead to negative fitness outcomes? Or is it possible that phenotypic adjustments might be adaptive, or even just making the best of a bad job (e.g. earlier death, but not death today)?

      (b) Throughout the introduction it is unclear whether and where the authors refer to the human clinical literature as opposed to animal literature. It is also unclear how human patterns are similar versus different from those observed in animals. Further, I would recommend that the authors include a deeper review of the animal literature (e.g. early experimental work with macaques, cortisol at other chimpanzee field sites/captivity). It's also unclear whether and where the authors refer more broadly to early life adversity (and what this means for humans vs. animals) versus more specifically to maternal loss. Additionally, there should be further discussion specifically related early maternal loss (rather than "early life adversity" which can include a lot of different factors) focused on the nutritional and social obstacles associated with early maternal loss, how these related to HPA functioning, and how these effects are expected to change during development (Plasticity? Flexibility? The role of HPA in responding to changing environmental conditions?). What about the adaptive calibration model which posits that the HPA can readjust during particular periods of developmental reorganization?

      4) It is difficult to assess the discussion without first dealing with the problems in the introduction/methods. However, despite their claims in the results section, it does not seem that the authors interpreted the results of model 1a with caution.

    1. Reviewer #2:

      The manuscript addresses an interesting question: whether genetic effects of common variants on educational attainment (EA) differ between individuals with and without psychiatric diagnoses. The dataset they use is ideally suited for such an analysis. The authors find evidence that the influence of common variants on EA is attenuated in individuals with a diagnosis of autism spectrum disorder (ASD) or ADHD.

      My main concern with the paper is the statistical analyses used to support the authors' conclusions. The authors draw conclusions from dividing individuals into subgroups and comparing the R^2 of the EA PGS between those subgroups. This analysis is liable to bias due to range restriction: if the subgroups have been selected based on low/high education, then the R^2 of a predictor will tend to be lower in the subgroups than in the overall sample. Furthermore, here the selection into the subgroup (here diagnosis with ASD or ADHD) itself is related to both education and the EA PGS, which could be contributing to the differences in R^2 the authors observe between subgroups.

      A more powerful and robust analysis would be to fit an interaction model in the full sample. The authors could regress individual's EA jointly onto their EA PGS, their diagnoses coded as binary variables, and the interactions between the EA PGS and the diagnoses codings. The authors could do this jointly for all diagnoses in the full sample, which would account for comorbidities between psychiatric disorders. If the influence of the EA PGS is truly weaker in ASD and ADHD cases, there should be a negative interaction effect between the EA PGS and ASD and ADHD diagnoses, which can be tested with a simple statistical test for a non-zero interaction effect.

      It could also be worth first regressing the EA PGS onto the psychiatric diagnoses, and taking the residuals before assessing whether there are interactions between the EA PGS and ADHD/ASD diagnosis. It is possible that correlation between the EA PGS and ADHD/ASD diagnosis could generate a spurious interaction effect in the above analysis.

      It is interesting that controlling for SES appears to mediate the (potential) interaction between EA PGS and ADHD diagnosis. However, I worry again that this could be a function of SES influencing ADHD diagnosis. SES and its interaction with both EA PGS and ADHD diagnosis could also be included in a full interaction model that could help interpret this finding.

      The authors construct the PGS by using a pruning and thresholding approach. This is known to be suboptimal, which may explain why their R^2 is lower than in other studies. The authors could use LD-pred or other methods that account for linkage disequilibrium and non-infinitesimal genetic architectures. In the EA GWAS from which the score was constructed, the best R^2 was found by applying LD-pred to all variants without p-value thresholding.

      The hypothesis that indirect genetic effects differ between psychiatric cases and controls is interesting. Do the authors have sufficient sibling data within their samples to test this?

      Line 581: Closely related individuals were removed from the analysis. Why? How many were removed? Could inclusion of these help assess the hypothesis about indirect genetic effects and improve power? The authors could use a mixed model regression to control for relatedness without having to throw individuals out of their sample.

      The grammar in the writing of the paper is a little odd at times. Often, definite or indefinite articles are omitted preceding nouns, such as in 'association of EA-PGS' in the abstract, which should be 'association of the EA-PGS'.

      line 54: 'strongly influences', I think this is a little overconfident in its assignment of causality to highest level of education, perhaps 'strongly associated' would be better

      Paragraph 3 of the introduction: the authors should mention population stratification and assortative mating as possible mediators of the association between EA PGS and EA, especially when referencing the drop in association strength in within-family designs

      I found the decile based analyses a bit pointless. By arbitrarily dividing a continuous outcome into discrete subgroups, the authors are losing power and not gaining much compared to simply performing linear regression, which they already do. I would relegate these to supplementary figures.

      Line 452: I think that the stated equivalence between low EA PGS and learning difficulties goes a bit too far here. I understand the point the authors are trying to make, but I think it should be phrased more carefully.

      The authors used an MAF threshold of 5% for construction of the score. Typically, a threshold of 1% is used for construction of PGS from summary statistics by software such as LD-pred.

      Line 580: the authors state that an EA PGS based on summary statistics from European samples cannot be used to predict EA in non-European samples. This is not true. It is true that the prediction accuracy is attenuated, but it is not zero.

  3. Dec 2020
    1. Reviewer #2:

      General assessment and major comments:

      The study addresses a timely and important question of the role of potential modulations in perceptual decision-making in the atypicalities observed in perceptual processing of individuals diagnosed with autism. The manuscript is important, and the methods used are sound.

      There are however some issues to consider:

      Thresholds, or other indications of sensitivity and precision of performance in the task are not detailed (although judging by the individual psychometric functions presented in the figures, slopes seem less steep in ASD). Was sensitivity considered in any way in the analysis? wondering how the model fitting would look like and how it would interact with the biases. Bias magnitude could vary as a factor of noise or sensitivity.

      Also, could larger consistency bias in the ASD group result from weaker performance, more lapses of attention etc.?

      Age range is quite large. Did you check for age-related differences? I understand the sample size is not big enough to analyze data across different age groups but maybe as a covariate? (there is also the problematic issue of determining sample size of children based on the study in young adults).

      Not sure why the effects of prior stimuli are considered adaptation effects, particularly in the first experiment where stimuli were briefly presented. Also, regarding the argument in the Introduction about Bayesian priors producing positive effects -- there are other prior effects that may cause 'negative effects' in relation to prior expectations (for example, in perceptual illusions such as the weight-brightness illusion).

      Can you think of a reason why controls did not show significant consistency bias in their responses in the heading discrimination?

      There is some wording in the reports of the statistics such as 'more significant' or 'more marginally' that needs to be rephrased.

      Were the analyses corrected for multiple comparisons?

      Usually RTs in this sort of perceptual task are longer in ASD. Wonder how this is not the case here, although instructions for the subjects emphasize speed and accuracy.

      I agree with the authors. It is interesting to look at correlations between the effects of prior choices and clinical scores of repetitiveness and flexibility in ASD. Did you look at the correlation between the effects of prior choices and SCQ scores across the two groups? Previous work documenting correlation between autistic traits (AQ) and modulated perception provided important information about the generalization of the findings to the broader spectrum of autism in the wider, nonclinical population (see Lawson, Mathys, & Rees, 2017; Hadad, Scwartz, & Binur, 2019).

    1. Reviewer #2:

      Nonlinear microscopy is in the unique position that high-resolution images of cells and other tissue components can be obtained in live tissue. However, scattering and absorption limit the penetration depth. The impact of nonlinear microscopy in biomedicine and biology would be much improved if higher imaging depths can be achieved. Lately a few key studies have appeared achieving this. This manuscript contains a well-motivated extension of this research, in particular on the benefits of high-pulse-energy low-duty-cycle infrared excitation near 1300 and 1700 nm over 2-photon excitation, in heterogenous and dense tissue. The authors compare three types of excitation, at 1650 and 1300 nm at 1 MHz and at 1100 (or 1270) nm at 80MHz. They characterize photodamage in the tissue and determine the limits for power densities to stay below that. They study the achieved resolution at high depth for each of the processes and show a deeper imaging depth is resolved in bone and tumor core with 3P and 4P than with 2P. The article is a very solid and extensive study.

      Though I have no major concerns with article, I do have some minor points:

      l.57: Are the resolutions reported for 2-, 3-, or 4- photon processes. Do you not expect these to differ for the different processes? l.60 It is not explained that power is increased from X to Y, instead the peak power of 87 nJ in L 67 is not found back in fig. S2.

      L. 103 Given is the power at the sample surface, after which the readout for cell stress via Ca imaging is done (very elegant). Is not the imaging depth of the readout relevant too, as it is probably the power density at the focus which matters. What imaging depths can be reached with this low power? This comes back later, but would be good to mention here.

      L.110 The phrase 'Furthermore' confuses me. I guess the authors mean to say that with their 2.8-8.7 nJ of power they were well below the 100 mW level? Which is kind of obvious at 1 MHz?

      L. 126 Some words are missing, 'but 1180'.

      Why do some signals show a peak in intensity in fig. 3C and G rather than a slope?

    1. Reviewer #2:

      The authors quantify virulence factors in Cryptococcus neoformans and C. gattii in a large number of clinical isolates and correlate these virulence factors to survival in a g. mellonella infection model and to the clinical outcome. The authors found a correlation between secreted laccases and disease outcome in patients. In addition, the authors show that a faster melanization rate in C. neoformans correlated with phagocytosis evasion, virulence in the g. mellonella model and worse prognosis in humans.

      The manuscript is well structured with an appropriate abstract summing the main findings, a clear introduction, well described methods section and appropriate number of figures and tables. The results are clearly described.

      1) The authors identify and acknowledge the most important limitation of the study: line 365-366 the patients were treated with different regimens in distinct health services. This reviewers agrees this is a limitation. However, to get a feeling about the impact of these differences the authors should indicate how the patients were treated and whether there were differences in patients that died and survived. Without this information clearly presented, I cannot interpret the correlations between virulence factors and outcome found in this study. Perhaps the authors can show how many patients that were included in the phenotype-survival analysis, that died and survived were treated according to Brazilian guidelines.

      2) The melanin production evaluation assay is an important tool that the authors use in this study and the measurements from these assays were correlated with G. mellonella and patients survival and thus are essential to the conclusions of the study. The method is well standardized, and the authors show elegantly that the outcomes are highly reproducible. Can the authors describe when melanization occurs: does it occur in mature colonies and may growth rate itself may influence the measurements? Do isolates with a high growth rate/colony maturation have a low T-HMM or high melanization Top. Have the final colonies of different species have a different final cell number after 7 days incubation and how does this correlate to melanization? And how does the growth rate/ budding rate/ colony maturation/ correlate to G. mellonella survival?

      3) The figures 1-5 give a clear picture of the wide distribution and variation of virulence parameters e.g. the distribution of melanization kinetics parameters, the distribution of capsule sizes, GXM secretion and LC3 phagocytosis. But what does this distribution mean, it only shows that the isolates are not the same but does not contribute majorly to the final conclusion. Can the authors think of a way to give more meaning to these figures: e.g. indicate with colors which isolates were retrieved from patients that eventually died and which survived (although this may be inappropriate as not all clinical information is available. Figure 6 really gives meaning to the numbers displayed in figure 1-5. Perhaps move some figures to the supplementary file.

    1. Reviewer #2:

      The topic of this manuscript is the basis of continuous and episodic bursting electrical activity in developing spinal cords. The approach used is to employ a simple mathematical model as a representation of the central pattern generator underlying the bursting pattern, and examine how the properties of bursting change with variation in three key system parameters. Some of the model predictions are tested in an actual in vitro spinal cord preparation. Although I enjoyed reading the manuscript, I have some serious concerns about the model that is employed, which I discuss below.

      Major concerns:

      1) The model is a half-center oscillator (HCO) in which one cell inhibits the other, resulting in anti-phasic electrical activity of the two cells. (Each "cell" actually represents a cell population, so the model is a mean field model.) This is certainly one way to get electrical bursts. However, it is not at all clear that such a HCO structure exists in the developing spinal cord, or that there are neural populations with this anti-phasic activity. If such data exists, it is not mentioned in the paper or cited. Indeed, the recordings in Supp. Fig. 1 show extracellular neurogram recordings from ventral roots in different lumbar segments and in which the bursting appears to be synchronous. So I see no evidence that the HCO model reflects the actual neural circuit, other than the fact that it can produce bursting and episodic bursting. This does not mean that such a phenomenological model is without value, but it should be made clear to the reader that that is what the model is. Also, the next two points below do appear to cast doubt on the utility of this model.

      2) In Fig. 3 it is shown that the inter-episode interval (IEI) is increased in the model when the conductance g_h is reduced. Because of this, the episode period (EP) also increases. The data, also in Fig. 3, show the opposite. They show that blocking the h-type current decreases the EP. This seems like a flaw in the model, since it is the h-type current that is responsible for episode production (at least I think it is, see point 4 below). The discrepancy is mentioned in the manuscript, but only briefly and it should be fully addressed.

      3) In Fig. 5 it is shown that, in the model, there is a very small interval of g_NaP where episodic bursting is produced. Otherwise, the model produces continuous bursting (for larger g_NaP values) or silent cells (for smaller g_NaP values). However, the data that is also shown in the figure indicates that blocking the NaP channels has little effect on episodic bursting. This is another serious discrepancy between the model and the experimental data.

      Points for clarification:

      4) It appears from Fig. 1 that episodes stop when h-type current activation slowly moves to an insufficient level to kick off a new burst. Logically, a new episode would start once that activation grows back to a sufficiently large value. Is this right? The mechanism for episode production is never discussed, and it should be.

      5) The model is deterministic, yet there is variation in burst duration and episode duration (see Fig. 3). What is the source of the variation? Does this mean that the episodes are not periodic?

      6) The model has a multistable region in parameter space, and much is made of this in the Results and the Discussion. In Fig. 6, it was demonstrated that hyperpolarizing pulses could switch the system from one behavior to another. Can this be done experimentally in the in vitro prep? If so, was it tried?


      7) Discussion is too long and touches on things that were far from the focus of the manuscript. For example, there is about a page and a half of text discussing short term motor memory (STMM) although the Results section did not focus at all on homeostatic functions of the circuit or STMM. Furthermore, some points were made several times during the Discussion, where one time would have been sufficient.

      8) Almost two pages of the Discussion was dedicated to multistable zones, yet in the model the multistable zone was tiny, and there was no evidence that the experimental prep lies in or near that zone. The authors state that in actual neural circuitry there could be a much larger multistable zone, which is true, but there also may be none at all. This discussion appears irrelevant.

    1. Reviewer #2:

      Amyotrophic lateral sclerosis (ALS) used to be considered primarily a disease of motoneurons. Recent work using mouse models of ALS has revealed that pathological changes can also be detected in spinal interneurons, particularly inhibitory interneurons, and that some of these changes can be detected before birth. The present paper is the first to directly examine the electrical properties of spinal inhibitory interneurons in a mouse model of ALS and show that some of these are altered in the neonatal period well before the mice start to exhibit symptoms. The authors show that SOD1 Lamina IX neurons are smaller than the Lamina IX WT neurons whereas no differences were found between WT and SOD1 neurons outside Lamina IX. They also use whole cell recordings to reveal that putative 'Renshaw cells' are less excitable in SOD1 mice than wild type animals whereas non-Renshaw inhibitory SOD1 neurons are more excitable.

      Major Comments

      1) The authors claim that Renshaw cells are in lamina IX, when they have been shown to be located mostly in the ventral part of lamina VII, ventromedial to the motor nucleus (Alvarez and Fyffe, 2007). In addition, not all calbindin+ neurons in lamina VII are Renshaw cells. From the location of the whole cell recordings shown in fig.2, it seems likely that most of the recorded neurons are not Renshaw cells because they are outside the classical 'Renshaw' area. It is not clear why the authors are focusing on glycinergic neurons in lamina IX, as there is no evidence that they belong to a unique class or that they are presynaptic to motoneurons.

      2) The concern about the identity of the Renshaw cells obviously undermines the statistical modeling to segregate Renshaw versus non-Renshaw cells. Furthermore, it was not clear from the text whether the model used both WT and SOD1 calbindin-positive neurons to define 'Renshaw cells'. Assuming it did, and given that there were changes in the electrical and morphological properties of the calbindin+ SOD1 neurons, is it not surprising that they could be grouped with the WT 'Renshaw cells'?

      In addition, the characteristics the 'Renshaw cell' population used for the model are not clear. On line 186 that it states that 15/23 of the whole cell recorded interneurons were positive for calbindin. Does this refer to 15 WT and 23 SOD1 neurons? Thus 38 neurons were calbindin positive. Of the remaining 21 neurons how many were calbindin-negative and how many were not tested? How many of the 38 calbindin-positive neurons had their dendrites reconstructed sufficiently from the intracellular fill to be used in the model? The model predicted that 80% of the 59 patched interneurons were Renshaw cells. How many of these were in the calbindin-negative group and how many were in the not-tested group? The spatial distribution of these groups should also be plotted. However, it seems very unlikely that 80% of the recorded cells are Renshaw based on their location as shown in fig.2B.

      Second it would have been useful apply the model to known non-Renshaw cells, to establish that it was not generating too many false positives. Another way the authors could test the model is to establish if it could distinguish WT and SOD1 neurons based on their morphology.

      3) The authors suggest that the reduced excitability of 'Renshaw cells' might contribute to the excitability changes seen in motoneurons. However, based on their own data, this is not a straightforward conclusion. They find that 'non-Renshaw cells' are hyperexcitable and since this population would include 1a inhibitory interneurons and other premotor inhibitory interneurons, it is not clear what the overall effect on motoneuron excitability would be. Additionally, because the authors suggest that 'Renshaw cells' are less excitable this would presumably lead to reduced inhibition of 1a inhibitory interneurons counteracting a potential loss of inhibition onto motoneurons from Renshaw cells.

    1. Reviewer #2:

      Kandul et al. present an interesting study that could lead to important improvements on the use of homing-based gene drives. However, there are a number of things that should be addressed to improve the manuscript for better comprehension by readers.

      Overall the manuscript presents a load of data. But the presentation of these data could be made in a better digestible way. The authors should go over their manuscript with a reader in mind, that is interested but not necessarily knows all the relevant literature in the very detail.

      Abstract (line 18): Please remove "inherently confinable" from the abstract. The drive is indeed designed in a split drive design, however, all the experiments were done in a homozygous Cas9 background. Therefore, there are no experimental data for a split drive provided in this manuscript. The split situation seems to be here more for a practical reason to be allowed to do the experiments in a less stringent laboratory environment. Thus there are no experimental data that would support the confineable nature of this drive. Actually there are not even modelling data to this. Thus, such a statement should not be put in the abstract. This manuscript is not a demonstration of a confineable drive.

      Results (line 124): How was Pol-gamma35 identified? It would be interesting to the reader to get to know about the exact reasoning, why this gene was chosen. Or were there several ones chosen before and this turned out to work the best or was the easiest to design. This could be very interesting considerations important to the field.

      Results (lines 147-148 Fig. 1B; lines 155-156 Fig. 1C) and Methods (lines 698 and 706) and Figure 1 (both Figure and legend): The addressing of the Figure panels and the writing to it don't fit! Has there been a rearrangement of the Figure that was not worked through the text? When referring to "B" in the text, it is still about Act5C-Cas9 and the nos-Cas9 data are in the text referred to Fig1C! But Fig1C is BLM! In current panel Fig1B, what does "all" mean below the X-axis? This is not comprehensible. Panel C is not really described in the Figure legend!

      Results (line 253), Discussion (lines 526-527), and supplementary Figure 1 (line 1101). "converting recessive non-functional resistant alleles into dominant deleterious /lethal mutations" is completely misleading! There is no "conversion" and how should that be done molecularly. There is a continuous removal of such alleles from the population because of lethal transheterozygous conditions caused in the drive. However, there is no active conversion of such alleles into dominant lethal ones. This needs to be clearly rewritten to avoid the misleading idea. Supplementary Figure 1 also seems to have a slight conceptual problem. What are "cells" (rectangles) with a red frame and a green core? Green means at least one wt allele (this must include the recoded rescue allele!). Red means biallelic knock-out: thus a red cell cannot have a wt allele. Thus what is a red-framed green core cell? To explain the removal of R2 alleles, a depiction of yellow framed red core cells in the germ line would be helpful, since this would explain how R2 alleles are selected against and might be continuously removed from the population!

      Results (from line 424 to end of results): Before going into the modelling, the reader should be clearly informed about all the different approaches that are now to be compared. This is currently not done well, if at all! Thus moving current Fig 6 before current Fig. 5 might clearly help! Also a better explanation of the panels in Figure 6 is necessary as well as a correction of Fig6 Panel E! A comparison of a great number of the currently approached toxin-antidote (gene destruction - rescue, but not killer-rescue!) systems is greatly appreciated. However, the authors cannot expect the general reader to know about the small detailed differences between the systems that are compared here. Thus the authors need to provide some explanations and categorization of the different approaches here and also cite all the respective literature.

      -First subdivision: Non-homing (interference-based drives) VERSUS Homing (thus overreplication-based drives). This will also help them to better understand why the interference-based drives (TARE and ClvR) are more sensitive to fitness parameters than overreplication drives.

      -Second subdivision: same-site VERSUS distant site. This is important to understand the difference between the here modelled TARE and the CLvR. Actually ClvR is a TARE, but you use TARE here more specifically as the results in the respective paper are demonstrating only a same-site TARE! But this needs to be clearly stated here!

      -Third subdivision: viable VERSUS haplosufficient VERSUS haploinsufficient. This also needs to be clearly depicted in labelling panels C to F of Figure 6, which are currently hard to grasp what the essential differences are, before looking at the panels in detail: C: HGD of viable gene (HGD) D: HGD of viable gene with rescue (HGD+R) E: HGD of haploinsufficient gene with rescue (HGD-hi+R). THIS PANEL NEEDS MAJOR CORRECTION!! F: HGD of haplosufficiant (essential) gene with rescue (HomeR)

      -Forth subdivision: split VERSUS non-split. Here for the split HGD situation, the respective papers of which the current authors are co-authors should be cited: Kandul et al. 2020 (actually published end of 2019 and still cited as biorxiv Archives article 2019a!?) and Li et al. 2020, Elife). In addition, it is also important to state clearly that "split or two locus" is completely independent of the "distant site" concept! The reader needs to understand the differences of the systems that are compared here, without having the reader to go to the respective publications themselves and then try to find out what the differences really are. This is not so obvious and the current authors have a clear chance here to do that and help the reader in the mists of all this similar but still distinct approaches.

      Figure 6 Panel E: This depiction is not consistent within itself, not consistent with the legend, and not consistent with the cited literature!

      -Why should the rescuing drive construct over the wt allele be lethal as indicated in the right two boxes?

      -The cited paper Champer et al. 2020b, which is by now also published in PNAS! clearly states that there is maternal carry over, which actually makes it so hard to use and is probably only working via male propagation. In the Figure legend, it is said that "maternal carryover and somatic expression ... are empirically unavoidable", which is in contrast with the depiction! The legend then also states that this is "unachievable". This should be better replaced by "hard to achieve", since the approach is published and seems to drive, even though probably just via the males! Thus the depiction of panel E needs to be thoroughly revised.

      Discussion (lines 499-500): The haplolethal HGD works (admittingly poorly) despite the maternal carryover (Champer et al 2020b). Therefore, your statement needs to be refined or deleted: "requires germline-specific promoter that lacks maternal carryover" is not consistent with the published paper! The drive could go via the males because then you do not have maternal carry over! And homing based drives can go via males and do not necessarily have to be promoted through females, see also KaramiNejadRanjbar et al. 2018.

      Discussion (lines 540 to 543). This sentence is based on an old but clearly overruled idea! NHEJ repair is not restricted to a time before the fusion of the paternal and maternal genetic material. It has been clearly demonstrated that R1 and R2 alleles are also generated in the early embryo after the zygote state (Champer et al 2017, KaramiNejadRanjbar et al. 2018). Actually, all of the authors' Figure 1C and Supplementary Figure 1 are about NHEJ mutation in the early embryo causing "BLM". Thus this sentence is inconsistent with current beliefs and also with the authors' own writing!

      Figure 4: Panel C graph: Why is, in the controls, the transgene consistently and significantly higher inherited to the next generation (0). It is about 75% progeny sired by the transgenic fathers compared to the wild type fathers? Was there an age advantage of the transgenic ones or whatever other fitness factor? This is surprising and no explanation is given at all! In contrast, in the Cas9 background in generation 0, less than 50% carry the drive allele, which is probably due to induced lethality. But this should also be commented upon. In the legend it is stated that 7 of 9 flies carried an R1 allele heterozygous to an R2 allele. What about the other two?

    1. Reviewer #2:

      This work from Beier et al. elegantly dissects the rod circuits contributing to the mouse pupillary light reflex through ipRGCs. The authors combine multiple genetic mouse models with electrophysiology and behavior to demonstrate that the primary rod pathway is the primary driver of the dim light pupillary light reflex, and that the secondary pupillary light reflex cannot effectively compensate for this pathway if it is lost. My technical comments are minor. This will be a welcome addition to the field of ipRGC research. My main concern, which I will leave to the editors, is that the actual advance may not be substantial enough.

      This is the first study to attribute the rod contribution to the PLR to the primary rod pathway. Though elegant, the fact that the primary rod pathway through ipRGCs is the major contributor in low light and that both primary and secondary pathways contribute to the photopic light PLR is not particularly surprising given the previous clear demonstration by the Hattar group that the rod pathway itself is required for the pupillary light reflex (Keenan et al., 2016).

      The authors do convincingly show that the OFF pathway cannot drive the PLR, but again this is in agreement with data showing ipRGCs are the sole conduit for light to drive the PLR (Güler 2008; Chew 2015) and that all ipRGCs get info primarily or solely from the ON pathway (Dumitrescu et al. 2009 and Schmidt 2010, etc.).

      Is 1 lux of mixed wavelength light truly in the scotopic regime? How was this calculation/determination made?

      Was there any difference in the dark adapted pupil diameter between each of the mouse lines?

    1. Reviewer #2:

      In this study, Zhang et al. use a semi-novel method to track acyltransferase activity using fluorescently labeled palmitic acid (NBC-16:0) to track where specific lipids are incorporated with subcellular specificity. They show that NBD-16:0 can be incorporated into different lipid classes that segregate based previously known on subcellular specificity. While this is an interesting technique, it is difficult to determine how much fidelity this method has in recapitulating biological function without additional experimentation, orthogonal measurements, and a more descriptive methods section.


      1) The authors do not specify which lysophospholipids were used in their study. In the method section they specify that they came from Avanti, but there are >100 different lPLs in their catalog. Also, the authors give a range of lPL concentrations in the methods, but do not specify which concentration was used for which experiment. Without this information and other unspecified aspect of their studies, interpreting subsequent experiments is difficult.

      2) One potential advantage of this method is that it is a method to track endogenous lipids in live cells, however the authors show the NBD-16:0 transporting to lipid species where palmitate is almost never measured. For example, the use of the transport of NBD-16:0 to CL as evidence this is working. However natural cardiolipins are almost completely devoid of 16:0. In mammalian cells >80% of the fatty acids in CL is 18:2 with most of the remaining being 18:1 and 18:3. Similarly (assuming you are using sn-1 lPL-16:0), phospholipids with two 16:0 are extremely rare in mammalian cells with the exception of lung surfactant. Further, 16:0 composes <5% of cholesteryl esters in typical cells. The authors should be clearer about how this discrepancy between natural sorting of palmitate and the sorting of NBD-16:0 supports this as an accurate model of acyltransferase activity and intracellular transport.

      3) The authors state that PA is primarily remodeled in the ER and transported to the mitochondria as a precursor to CLs (lines 108-111). This statement needs a source. In most studies I am aware of, the vast majority of both PA and 16:0 are primarily converted to TGs or PC/PE with only a small fraction going towards the CDP-DAG pathway required for CL biosynthesis. Are C2C12 cells unique in this regard? Does lPA stimulation specificity induce CL production? Does any of the NBD get into the TG or phospholipid fractions?

      4) This study would be much stronger if another fluorescently labeled fatty acid was added. A comparison of the sorting of 20:4 and 16:0 would be very informative. This is especially true if the studies were done in the context of a known acyltransferase, for example LPCAT3.

      5) This study would also be strengthened by an orthogonal technique showing similar sorting. For example, separation of the organelles and measurement of labeled fatty acids by MS or nano-SIM analysis would greatly strengthen these studies.

      6) In figure 1A, the authors draw a schematic with an sn-2 lyso-PL in the figure. Sn-2 lyso-PLs as labile and will acyl migrate to the sn-1 position without careful handling of the PL in a basic solution. The authors make no mention of this type of handling in the method section. This figure should either be corrected or more details of how they handled their lysophospholipids should be provided.

    1. Reviewer #2:

      I think this is a superb manuscript - it is written in a clear way, such that the story starts at the historical understanding of lipoprotein trafficking and builds up convincingly using various experimental methods to show that a new class of lipoproteins is trafficked via acylation of glycine, through the Aat secretion system.

      It is highly exciting that a protein that does the acylation AND the secretion from the periplasm to the cell surface has been identified! Next step is to get a structure.

      The data are convincing and the paper is extremely well-written. My comment is that I am not convinced by the argument that CexE would not be recognised by the lol system, when it is acylated it likely would be as the hydrophobic pocket of LolA and LolB are fairly indiscriminate - see e.g. the binding of small hydrophobic molecules to these proteins. The authors should comment on this aspect.

      It is intriguing how glycine in particular is recognised for acylation.

      Overall, a great paper - authors should be commended.

    1. Reviewer #2:

      The manuscript prepared by Kim and Colleagues provides a solid attempt at understanding the neural correlates associated with self-reassurance and self-criticism in relation to what they term neural pain. While it is well written and there is a clear story presented here, there appears to be insufficient details in the introduction and discussion. The methodology appears sound for the most part, but I have some concerns relating to stimuli and gender effects that I believe would make the findings more compelling if addressed.


      1) The example items of the neutral statements appear to involve an external agent (i.e., a reference to a friend), while the neural pain is purely about the self. Are there also references to other people in the neural pain condition? If not, how have the authors ensured that the neutral condition is actually neutral. It seems likely that the inclusion of an external agent for many of the neutral statements could pose problems with interpretation, especially when talking about self-criticism and self-reassurance. The presence of an external agent in the neutral statements changes the meaning from a purely self-oriented experience to a shared experience.

      2) I am curious as to why the inverse contrasts (i.e., reassurance - criticism) were not run? Knowing whether there was a unique network associated with self-reassurance would provide a more comprehensive understanding of the authors' findings.

      3) I am wondering why the authors did not accommodate for gender differences in their study? Given recent evidence (See citation) it seems likely that this may play a part in self-compassion. The authors report an almost equal distribution of males and females, so it should be possible. If the authors did explore this and found no difference with gender as a regressor then this should be noted in the manuscript. Mercadillo, R. E., Díaz, J. L., Pasaye, E. H., & Barrios, F. A. (2011). Perception of suffering and compassion experience: brain gender disparities. Brain and cognition, 76(1), 5-14.

      4) It seems as though a whole body of literature is being very lightly touched on here but would benefit from inclusion. I think it would be useful to have some information in the introduction regarding moral emotions (i.e., compassion) and the link with empathy and emotion regulation (see work by Jean Decety). This would also be beneficial for the discussion as the authors are in essence describing empathy.

    1. Reviewer #2:

      The authors report on a model system to study the infiltration of growing thrombi by leukocytes using fluorescent microscopy. Such a system will facilitate studies on the role of leukocytes in the context of immuno-thrombosis and thrombus growth. I like the proposed model and I'm convinced about the utility of such a system. However, although the model is sound, I do have a couple of major concerns:

      1) The authors describe crawling neutrophils; which methodology has been applied to guarantee that the crawling of the neutrophil is a general phenomenon in this system and not a feature of selected neutrophils? Has there been used a method of quantification (e.g. 86% of the neutrophils are crawling)?

      2) The authors identify two types of granulocytes (uniform DiOC6-type A vs cluster-like DiOC6-type B granulocytes. Although the pictures provided (fig 2) do represent nice and clear examples of type A vs type B granulocytes, the experiment will reveal for sure staining patterns which have been less clear. What kind of systematic methodology has been applied to delineate type A from Type B neutrophils?

      3) Figure 3 and 4 are nice figures showing differences in granulocytes/FOV and velocity- but it is not clear to the reader what percentage of the neutrophils visible in the FOV do move or not. Was there a minimal amount of neutrophils being in motion?

      4) The authors want to point out the synergistic effects of neutrophils and platelets in the thrombus growth. To finally make the point they use whole blood of a WAS patient. However, since WAS affects neutrophil motility as well as platelet morphology/number the individual role of platelets and neutrophils in this process remains open. Therefore control experiments targeting either platelets (whole blood of a thrombocytopenic patient and/or platelet depletion) may contribute to identify the role of platelets in the model. On the other hand, inhibition of neutrophil activation/motility may illustrate the individual contribution of neutrophils in this setting.

    1. Reviewer #2:

      Summary and comments:

      This study presents an analysis of five large datasets of cancer cell viability including both genetic and chemical perturbations and find that RNA-seq expression outperforms DNA-derived features in predicting cell viability response in nearly all cases, and the best results are typically driven by a small number of interpretable expression features. The authors suggest that both existing and new cancer targets are frequently better identified using RNA-seq gene expression than any combination of other cancer cell properties.

      Overall, none of the main conclusions in the paper are surprising, and begs the question whether sequencing more cancer exomes is really meaningful? This is a question that deserves serious debate as major resources are being diverted to large-scale exome sequencing projects with low information content returns.

      The paper is well written. And at first glance, the results seem to support the provocative title. Improved clarity around what the predictor and response variables are earlier in the paper would improve readability, particularly around what a "genomic" variable is. Most of the helpful details are buried in the methods.

      The main benefits of the manuscript: (1) emphases on simplistic (i.e. few features) predictors that are themselves easily interpretable; and (2) the choice of random forest classifiers also makes interpretation of the predictions pretty straightforward. One concern is whether the breadth or depth (i.e. completeness) of the genomic predictor variables somehow unfairly bias the findings against the ability to predict with those variables compared to expression variables, which are quite easy to encode and interpret. This concern is alluded to in the discussion when reviewing the findings of previous, related publications, and could be further explored. For instance, while variations in mutant RAS (H, K or N) or B-RAF were the only dependencies noted to be predicted better by genomics (i.e. mutations), are all driver mutations known and represented in the data? One would expect that amplified EGFR or HER2 would be predicted well from genomics, but these are notably missing, presumably because they do not meet the filtering criteria.

      A notable finding was that a single genes' expression data produced notably better results than gene set enrichment scores overall, despite having many more presumably irrelevant features. Predictive models for many vulnerabilities exhibit relationships expected to be specific to a single genes' expression (e.g. los of a paralog's expression predicts dependency on its partner). There is no biological validation for any of the predictions in the manuscript.

      Specific comments:

      What was the thought process for choosing 100 perturbations in each dataset to label as SSVs? Why not 82, or 105? Was there a systematic analysis done to pick this number (e.g. harmonic mean)?

      Did the authors estimate the effect size they are measuring across the 100 selected SSVs? In other words, was there an estimation of fitness effect, single mutant fitness or degree of essentiality for the 100 SSVs and what range of effects are they exploring? One possible way to measure the fitness effect of each of the 100 vulnerabilities is to examine the dropout rate in pooled screens at the guide RNA level, looking for consistency in gRNA behaviour.

      Did the authors include essential and non-essential genes as reference points in their analyses? This wasn't clear from the methods.

      The authors describe a clear gradation of response to either TP53 or MDM2 knockout according to the magnitude of EDA2R expression observed in multiple datasets (i.e. Achilles, Project Score, RNAi, GDSC17, PRISM). Using EDA2R expression to infer TP53 activity could have clinical benefit and deserves more attention (i.e. validation).

    1. Reviewer #2:

      Asprosin, as identified by the authors' group, is reported to stimulate glucose release from the liver and also centrally act as an orexigenic hormone. The present study developed monoclonal antibodies for asprosin and demonstrated that antibody-based asprosin depletion lowered food intake, prevented diet-induced body weight gain, and lowered blood glucose levels in mice. Overall the data are supportive of the conclusion; however, several concerns were identified as follows:

      1) One of the central issues is the specificity of the antibody action. The authors should demonstrate if the effect of the asprosin antibodies is blunted in mice that lack either asporosin or its receptor OR4M1.

      2) Previous studies from the authors' group show that asprosin acts on hepatocytes and triggers cAMP signaling. The authors should examine if the neutral antibodies blunt the cAMP signaling in DIO mice.

      3) Similarly, asprosin was shown to stimulate AgRP+ neurons. The authors need to demonstrate the effect of asprosin antibodies on AgRP+ neuronal activity.

      4) A recent paper (von Herrath et al. Cell Metabolism 2019) challenged the author's observation of the metabolic action of asprosin. The authors claim that this is due to "due to use of poor quality recombinant asprosin". However, no scientific evidence was presented. This study needs a more rigorous assessment of data reproducibility.

      5) Most of the bodyweight data are presented as "body-weight change". However, the authors should present them as whole-body weight.

      6) Some of the data points and stat analyses require further clarification. e.g.) lack of SE in Fig.1c, statistical analysis of Fig.3, Sup Fig.1

    1. Reviewer #2:

      The authors investigate the neural correlates of second order conditioning in carefully designed behavioural experiments coupling multivariate fMRI and functional connectivity. They found that the lateral OFC in connection with the amygdala, plays an important role. I think the paper represents a valuable addition to the human cognitive literature, where second order conditioning is surprisingly under-investigated. I have only a few suggestions to make.

      I encourage the authors to complement the multivariate analyses with a standard univariate analysis. To be clear, I am not without seeing the added value of the multivariate approach, however, given the extensive literature on the neural bases of conditioning using univariate analyses and the strong prediction about directionally of the effects in the OFC (which should positively encoded expected values and rewards), I think the paper would definitely benefit from including the univariate results for the main contrasts / variables.

      I am also curious to see the reaction times in the attentional control task analyzed to check if they were affected by the underlying conditioning procedure. Following the Pavlovian-to-Instrumental transfer theory, we should observe that the reaction times are slower for negative (aversive) stimuli and faster for positive (appetitive) stimuli.

    1. Reviewer #2:

      In this work, Sims and colleagues use resting-state functional connectivity and diffusion tractography in human connectome project data to examine the connectivity of the central and peripheral aspects of the primary visual cortex. They find that central V1 connects more strongly to regions of the prefrontal cortex interpreted as the Fronto-parietal network than does peripheral V1.

      The idea that central V1 may be directly connected to control-related networks is an interesting one, and has fascinating implications for the study of top-down modulation of visual cortex function. However, I must say I am somewhat skeptical of these findings, for several reasons.

      First, I find the a priori anatomical basis for these proposed connections to be dubious. The authors themselves describe how Markov et al. explicitly conducted tract tracing with central V1 and found connections with posterior frontal and parietal cortex, but nothing with areas classically associated with the fronto-parietal cortex. The authors propose that the inferior fronto-occipital fasciculus may connect V1 with lateral prefrontal regions only in humans. However, they provide no evidence for this suggestion. Indeed, my understanding of the iFOF is that it connects to inferior and lateral occipital cortex (see e.g. figures from the Takemura study cited in this work). Can the authors better support the idea that the iFOF might be the route of connection between V1 and frontal cortex?

      Second, I am concerned that both 1) the Central V1 ROI employed in this work and 2) the inferior frontal cortex region showing strong FC with that Central V1 ROI overlap very closely with regions where we have seen poor BOLD signal in our own fMRI data (I would like to attach a figure if possible).

      We are not confident what the source of the poor signal might be in posterior occipital or inferior frontal cortex; we suspect the presence of large veins (possibly the transverse sinus in V1; see Winawer et al., 2010, Journal of Vision). In any case, the data quality is low enough that we believe our data should not be considered to represent actual neural function in those regions. Can the authors demonstrate convincingly that this is not the case in their HCP data?

      Third, I have an issue with the localization of effects in this paper. The paper describes effects in the fronto-parietal network throughout the manuscript, including the title. How surprising, then, that the strongest effects are not in the FP network at all! Figure 4A makes it very clear that the largest effects are in the IFG, which is outside the green outlines describing the extent of the fronto-parietal network, but inside the Default network. Figure 3A also supports this Default-centric localization, with Central V1 effects in posterior lateral parietal, medial parietal, and superior frontal cortex, all outside FP but inside Default. Since the FC effects are not actually primarily in FP, I see no reason why FP should be used as a mask in Figure 5. Indeed, the authors should show the localization of SC effects throughout the cortex, not just in FP. I also see no reason why these V1-Default connections should be characterized in any way as "attention" or "control".

      Fourth, I feel that these FC and SC differences are wildly over-interpreted. From the scale, the actual strength of FC and SC between central V1 and lateral parietal cortex is extremely weak (around Z(r) = .1 for FC and p-track = .1 for SC). Under no circumstances would I believe that either of those values represents any sort of real connection. Cortical regions with direct structural connections have much stronger FC values, as do regions that influence each other indirectly via multi-step connections. Further, very large portions of the brain probably have both stronger FC and SC to central V1 than these FP regions (the authors show this for FC but exclude this info for SC). Most glaringly, I certainly don't believe there is a "direct structural connection" as is claimed in the discussion--a claim based, strangely, on the spatial correspondence between the structural and functional maps, which really has nothing to do with any evidence for a direct connection.

      Finally, the authors must note that p values may not be used for spatial correlations between brain maps. This is because these maps are always highly autocorrelated, which violates the independence assumption of the correlation procedure.

    1. Reviewer #2:

      In the manuscript, "Structural characterization of an RNA bound antiterminator protein reveals a successive binding mode," the authors present the solved crystal structure of Enterococcus faecalis EutV by itself as well as bound to its RNA substrate. In previous work, the RNA substrate was proposed to consist of a dual hairpin and the genetics strongly suggested that both hairpins of this feature were crucial to functional antitermination in vivo. The finding revealed by the crystal structure in this work is that the EutV dimer does not appear to bind both hairpins simultaneously. The structure shows one EutV chain binding a hairpin in one RNA molecule and the second binding a second hairpin in a separate RNA molecule. The orientation of the two ANTAR domains is such that it is not possible to bind one RNA molecule simultaneously. Based on their findings, the authors propose a model of successive antitermination in which EutV binds to the first hairpin as it is generated by RNA polymerase and then this somehow favors binding to the second hairpin overlapping the terminator sequence as soon as it is made to prevent terminator formation. My overall assessment is that this is potentially an important and interesting contribution to the fields of transcription termination/antitermination and RNA/protein structural biology. However, there are concerns with how conclusive the data is, how exactly the model can work, and a lack of experimental evidence for the model.

      Major Comments:

      1) One major concern about the structure is that it is of non-phosphorylated EutV bound to its RNA substrate. Two-component system regulators almost always undergo conformational changes upon phosphorylation and therefore I think it is still an open question whether the structure truly represents active EutV bound to RNA. Perhaps the ANTAR binding domains of the EutV dimer change orientation upon phosphorylation such that binding to both hairpins can occur.

      2) If binding does only occur with one hairpin, then why are two necessary for activation? If it is impossible for one dimer to bind both hairpins simultaneously, how does binding to the first hairpin help binding to the second? This is not clearly explained. Also, no experimental evidence is presented to support the model.

      3) Wording of the abstract does not well reflect the final model presented. The abstract makes it sound like the second hairpin is not important, which is not what is shown here or in the previous work. I think the authors should say a bit more about what the actual model is in the abstract to eliminate this misconception.

      4) Ramesh et al. (2012) observed that EutV bound the eutP UTR with a higher KD (less efficiently) when just the P1 loop was used in an EMSA assay compared to P1/P2. This study found the same KD, whether P1, P2, or both are used in a SPR assay. Could the difference in these findings be related to the different techniques or the fact that slightly different versions of the EutV protein were used?

    1. Reviewer #2:

      The findings presented in this manuscript are really exciting. They show that selection is happening at multiple scales - among viruses within a cell - and between their host cells within a population. The conflict between these levels of selection results in evolved populations that are less fit than the ancestors. This result is exciting because it happens repeatedly in independently-evolving populations, showing that it can be a general result. It is also an example of how a non-transitive interaction can evolve de novo, as the authors claim in the manuscript. The experiments seem to rule out most alternative hypotheses. However, the authors could explain their reasoning more clearly in some cases.

      1) In particular I found it difficult to understand some of their conclusions on page 9, in the first paragraph around lines 210 - 219, without rereading, rewriting results, and lots of thinking. On lines 211-213, they state that production of active toxin or maintenance of the virus has no detectable fitness cost to the host". There are a lot of comparisons to think through here to get to that conclusion, and I think the average reader needs to be taken through that. Even though I have some experience thinking about costs and how they can be estimated, I still spent quite a lot of time trying to follow the logic from figure A to that statement. In fact, I still do not understand how they are distinguishing between 'production of active toxin' and 'maintenance of the virus'. I also had to spend a lot of time thinking through the results in figure 3 and the conclusion stated on line 217.

      2) I think it would be helpful to the reader, and interesting, if there were more of an explanation about WHY K+|+ cells have positive frequency-dependent fitness relative to K-|- cells. Why is the presence of an active virus and immunity more beneficial at higher frequencies?

    1. Reviewer #2:

      This is an interesting study that provides convincing evidence that a Draxin mutation underpins forebrain commissure phenotypes in BTBR mice and crosses.

      The use of BTBR x C57 N2 crosses where commissure phenotype is correlated with the Draxin mutation (Figure 5) is a nice illustration of unpicking variable penetrance. The phenocopy of the BTBR/c57 phenotype to Draxin mutants is a nice confirmatory experiment.

      Further, analysis of midline fusion shows that problems in MZG proliferation and hemisphere fusion are prevalent in BTBR mice supporting the hypothesis that Draxin is needed for midline fusion.

      MRI scans of human subjects with a spectrum of CC abnormalities show that commissure abnormalities correlate with midline fusion defects.

      Major comments.

      1) As a central contention of this study is that variable penetrance of the commissure phenotypes in the BTBR x C57 mice stems from an earlier midline fusion phenotype is would have been useful to see if the (embryonic) midline fusion phenotype also showed the same partial penetrance in BTBR x C57 mice, perhaps also correlated with the WT/MUT Draxin alleles (as in Figure 5). This would be a testable prediction of the hypothesis that midline fusion (and not something else) mediates the Draxin phenotype.

      2) I am not sure the human data adds substantially to the paper as it is not related to Draxin mutations. It is already well known that corpus callosum phenotypes are variable in humans (and mice).

      Minor comments:

      Some of the data are not normally distributed (particularly clear for pink data points in Fig 5a,e,i,m) so it is not appropriate to show standard errors (the SEM bars could simply be removed), a non-parametic Kruskal-Wallis ANOVA has been used which is appropriate.

    1. Reviewer #2:

      This paper presents the results of a study of optogenetic visual restoration. ChR2 was targeted either to a subset of ganglion cells (GCs) or to a subset of ganglion cells-not necessarily the same ones-plus starburst amacrine cells (SACs) using an intersectional genetic strategy. Photoreceptors were ablated using MNU in animals expressing ChR2, and then retinal and whole animal responses to visual stimuli were assessed. Interestingly, co-expression of ChR2 in SACs and GCs resulted in different, potentially more "naturalistic" responses than expression in GCs alone. This is an interesting result, but given the number of possible explanations for it, the lack of any rigorous investigation of the underlying mechanism is problematic. Results presented by the authors indicate that ACh release from stimulated SACs acts upon some network(s) containing electrical synapses and presynaptic to the GCs to alter GC responses, but the identities of these network(s) remain unknown. Given that ACh is considered to act in a paracrine manner within the retina, the affected cells could be any number of amacrine or bipolar cells.

      There are a number of lines of investigation that the authors could pursue to identify-or at least, rule out-specific presynaptic networks. While too numerous to discuss individually, potential lines of investigation could differentiate nicotinic from muscarinic effects and effects on inhibitory and excitatory inputs to ganglion cells. As well, it would be important to express ChR2 in SACs alone to see if this drives changes in GC spiking.

      In all, the authors here have the opportunity to examine the effects of paracrine signaling by SACs on inner retinal network excitability and function using a nice model system, and they should take advantage of it.

    1. Reviewer #2:

      This is an important piece of work addressing in-vivo measurements where two coupled components are to be measured ideally in the same time and space components. Unfortunately, the impact of this work is likely to be minimized due to its poor organization and the attempt to deal with a number of separate but related issues in the same manuscript. Accordingly, it is suggested that this work be divided into two manuscripts to be published together. The focus of the two might be:

      A) A Tetrode-Based Microsensor (TACO) - This work would focus on the criteria for performance that would be expected for a new sensor, presumably with new and unique properties. This work would include differential plating of electrodes (It is unclear whether some or all of this work has been previously reported), dimension considerations, and the simulation of sensor response. (An important consideration but frequently overlooked is that a sensing element with a 17um diameter will exhibit hemispherical diffusion (Eq. 4)). Other issues such as interferences, stability, sensor response time and linearity need to be more fully explained. Presumably such a sensor configuration would be useful in other applications involving oxidase-based sensors.

      B) Effects of Local Field Potential and Oxygen-evoked ChOx transients in the In-Vivo Measurement of Acetylcholine in Freely Moving Rodents (A better title can surely be presented!) - Here the focus should be on the in-vivo measurements including a qualitative explanation of the LFP and O2 response and how the TACO sensor corrects for this. Presumably the detection of REM and NREM sleep will be detected by EEG. This is not well explained. Also unclear is how the improved performance of the sensor affects the conclusions of the in-vivo studies.

    1. I also would like to see any hidden blocks or dev shortcuts.
    2. I'm more bothered by the fact that you can't practice other people's levels, just certain parts of the level, before actually going back and trying to beat it completely.
    1. Reviewer #2:

      The manuscript represents a lot of hard work on an interesting topic. Understanding how threatened populations are impacted by human-derived processes is critical, and requires more study. However, as it stands, the study suffers from some logical flaws that detract from the scientific insights that can be gained from this study. These are:

      1) The authors argue that older individuals are important repositories of ecological knowledge, which is now well-established knowledge. However, the authors then build their study around the consequences of poaching in terms of the effects on network metrics that are assumed to correspond to transmission properties. The logical problem here is that removing ecological knowledge from a network leaves nothing to transmit-hence the transmission properties of the network are inconsequential.

      2) Linked to this point is the issue that the results and discussion focus a lot on the concept of network transmission, but the study uses network metrics (e.g. diameter) as proxies of transmission properties. It is pretty well known that there are many factors (e.g. clustering coefficient) that contribute to transmission dynamics, and it is unlikely that any one network metric alone can capture the ability for a network to transmit information.

      3) The authors note that continuous data on the reorganization of the network after poaching are not existent, and that they justify using a static approach (i.e. the network does not change after a removal/simulated poaching event) by focusing on the consequences immediately after deletion. However, the simulations involve removing up to 20% of the individuals in the population, meaning that their model assumes that poaching events are occurring substantially faster than the network is reorganizing itself. This seems too unrealistic an assumption.

      4) A further issue with using a static approach is that the networks captured in the study may not represent the network structure that is in place when an event takes place in which ecological knowledge is important. For example, studies from other multilevel societies, e.g. hamadryas baboons (from Kummer's work), suggest that units come together when conditions necessitate forming larger groups. So, the network measured in the empirical data may not be the network through which ecological knowledge is transferred when an event necessitates it.

      5) Finally, the results and the conclusions drawn from the study seem in conflict. On the one hand, the main summary of the results are that removing older individuals has little, if any, impact on the network's capacity to transmit information. On the other, the conclusions seem to be slanted towards removal of older individuals as a conservation issue (e.g. L662). Thus, there is tension in the manuscript that, unfortunately, reduces both the clarity of the findings and the clarity of the take-home messages.

      Overall, the study was enjoyable to read, with lots of biology, which is a strength for a modelling study. However, some of its construction, and the reliance on simple node deletions, really limits the capacity to gain substantial new insights from this study.

    1. Reviewer #2:

      In this study, Ortuste Quiroga et al. showed that the mechanosensitive ion channel Piezo1 promotes myoblast fusion during the formation of multinucleated, mature myotubes. The authors show that Piezo1 knockdown suppressed myoblast fusion during myotube formation and maturation. This was accompanied by a decrease in Myomaker expression. In addition, Piezo1 knockdown lowered Ca2+ influx in response to stretch. In contrast, the agonist (Yoda1)-mediated activation of Piezo1 increased Ca2+ influx and enhanced myoblast fusion, but only under certain conditions. Over-activation of Piezo1 resulted in the loss of myotube integrity. Surprisingly, the myotubes were thinner in Yoda1-treated cells compared to the control. Furthermore, the authors showed that Piezo1 activation enhanced Ca2+ influx in cultured myotubes and the influx of Ca2+ increased in response to stretch. However, it is unclear how this is related to myoblast fusion.

      Overall, the authors made several interesting observations in this study, such as Piezo1's role in myoblast fusion and Piezo1-mediated Ca2+ influx, etc. However, how these phenomena are linked and what is causal remain largely unclear. Another issue is the discrepancy between this study and Tsuchiya et al. Nature Communication (2018) on the function of Piezo in myoblast fusion.

      Major comments:

      1) In this study, the authors uncovered a positive role for Piezo1 in myoblast fusion. This is in contrast to Tsuchiya et al., which demonstrated an inhibitory role of Piezo1 in this process. While this study used an RNAi approach to knock down Piezo1 and found a decrease in myoblast fusion, Tsuchiya et al. used CRISPR/Cas9 to knock out Piezo1 in muscle cells and observed a significant increase in myoblast fusion. These two opposite results are difficult to interpret and made the role of Piezo1 in myoblast fusion confusing. It is necessary that the authors make some effort to bring clarity to this issue. First, the authors need to perform rescue experiments in their RNAi cells to make sure that the fusion defect is not due to off-target effects caused by the siRNAs. Second, the authors should design an siRNA that causes a more significant knockdown of Piezo1 than the current siRNAs and test if myoblast fusion is enhanced as in the knockout cells (Tsuchiya et al.). Third, the authors could make their own CRISPR/Cas9 knockout cells and examine the resulting fusion index.

      2) How does Ca2+ influx regulate fusion? Tsuchiya et al. provided evidence that Piezo1-mediated Ca2+ influx activates actomyosin activity and inhibits myoblast fusion. This current study suggests that Ca2+ influx increases fusion, but without providing mechanistic explanations. What are the effects of Ca2+ influx that lead to an increase in myoblast fusion? Does it cause more IL4 secretion? Or transcription upregulation of Myomaker? How? Does the Ca2+ influx level correlate with Myomaker expression level? If Ca2+ influx indeed leads to upregulation of Myomaker, why would Piezo1 knockout cells (low Ca2+ influx) show increased levels of fusion (Tsuchiya et al.)?

      3) Is Piezo1 required in myoblasts or myotubes or both cell types for fusion? Is it localized to the fusion sites?

    1. Reviewer #2:

      Naetal et al. studied the effect of Lap2a on lamin A/C dynamics-of-assembly and mobility as well as telomere movements. This study indicated that lamin A/C are first assembled into the lamina, before some of the lamin A/C is re-localized to the nucleoplasm. Interestingly, the amount of nucleoplasmic lamins is independent of Lap2a although its physical properties are different. The results indicated that Lap2a contributes to the dynamics of lamin A/C in the nucleoplasm while its absence reduces nucleoplasmic lamin and telomere dynamics. These results reveal the function of Lap2a as regulator of lamin anchorage in the nucleoplasm but it has no major role in recruiting lamins into the nucleoplasm. Since the impact of lamins on the nuclear organization is critical for nuclear functions and important for nuclear integrity, these results are fundamental for the understanding of both lamin A/C and Lap2a.

      The authors also identified two pathways in which nucleoplasmic lamin emerged. First, lamin can be localized to the lamina and then relocated to the nucleoplasm, and second, from the pool of mitotic lamins which are not associated with the lamina.

      The authors may consider some textual changes, in particular regarding the state of nucleoplasmic lamin polymerization:

      1) The nuclear lamina filaments are typically 200-400 nm in length, but they are very flexible. A 200 nm filament would have a molecular weight of <1.4MDa ( ~50% of a ribosome) and can be bent and curved. That would mean that a single filament has a reasonably high diffusion coefficient. At the lamina, lamins are less mobile, however, it is likely to be due to binding partners that anchor lamins to the INM and chromatin (e.g. emerin is a membrane protein that binds lamin A) - the diffusion of 1.4 MDa protein complexes is quite fast. The above is mentioned because nucleoplasmic lamins may be polymerized but more mobile (less anchored) than their lamina-hosted lamins population.

      2) The authors show that nucleoplasmic lamins are first localized to the lamina, where they can polymerize. Isn't it possible that filaments can be released into the nucleoplasm?

      3) In vitro assembly assays of lamin A in the presence of Lap2a indicated that lamin A assembly is inhibited by Lap2a. Based on these results the authors suggest that Lap2a keeps lamin in a less polymerized state. Previous work by Zwerger et al. 2015, showed that inhibitors of in vitro lamin A assembly, have no impact on incorporation and localization of lamin A into the lamina, while incorporation of lamin A into the nuclear lamina was abolished when other lamin binders that have no effect on lamin assembly in vitro were used. That would suggest that either in vitro assembly is not representing the cellular lamin assembly or assembly of lamin into the lamina is independent of polymerization states of lamins. The authors may want to discuss these views.

    1. Reviewer #2:

      This manuscript asks how learners solve the problem of continuous motor control. The authors find qualitatively distinct components of learning under continuous tracking conditions: the adaptation of a baseline controller and the formation of a new task-specific continuous controller. These learning components were differentially engaged for rotation-learning and mirror-reversal. Further, the authors present a methodological advance in motor control and learning analysis that relies on frequency-based system identification techniques.

      Overall, this paper presented a valuable third perspective on the learning processes that underlie motor performance and provided an impressive analysis of continuous control data. Furthermore, the system identification technique that they developed will likely be of great value to the study of motor learning. However, I believe that there are some issues with the framing of the de novo learning mechanism and in their interpretation of the results.

      1) Positing a de novo learning mechanism as the absence of established learning process signatures.

      The authors introduce the concept of de novo learning in contrast to both error-driven adaptation and re-aiming: 'a motor task could be learned by forming a de novo controller, rather than through adaptation or re-aiming.' However, the discussion reframes de novo learning as purely in contrast with implicit adaptation: '[...] de novo learning refers to any mechanism, aside from implicit adaptation, that leads to the creation of a new controller'. While this apparent shift in perspective is likely due to their results and realistically represents the scientific process, this shift should be more explicitly communicated.

      As explicitly raised in the discussion and suggested in the introduction, the authors have categorized any learning process that is not implicit adaptation as a de novo learning process. To substantiate this conceptual decision, the authors should further explain why motor learning unaccounted for by established learning processes should be accounted for by a de novo learning process.

      2) The distinction between de novo learning and re-aiming is unclear.

      Participants could not learn mirror-reversal under continuous tracking without the point-to-point task, which the authors interpret to mean that re-aiming is important for the acquisition of a de novo controller. This suggests that re-aiming may not be important for the execution of a de novo controller.

      However, the frequency-based performance analysis presented in the main experiment would seem to suggest otherwise. As mentioned in the introduction, low stimulus frequencies allow a catch-up strategy. Both rotation and mirror groups were successful at compensating at low frequencies but the mirror-reversal group was largely unsuccessful at high frequencies. Assuming that higher frequencies inhibit cognitive strategy, this suggests to me that catch-up strategies might be essential to mirror-reversal, possibly not only during learning but also during execution.

      Further, the authors note that, in the rotation group, aftereffects only accounted for a fraction of total compensation, then suggest that residual learning not accounted for by adaptation was attributable to the same de novo learning process driving mirror reversal. This framing makes it unclear to me how the authors think re-aiming fits into the concept of a de novo learning process (e.g. Is all learning not driven by implicit adaptation de novo learning? What about the role of re-aiming?)

      3) Interpretation of spectral linearity as support for the absence of a catch-up strategy.

      Using linearity as a metric for mechanistic inference has limitations.

      • The absence of learning (errors) would present as nonlinearity.
      • The use of cognitive strategy could present as nonlinearity.
      • It doesn't seem possible to parse the two mechanisms, especially as you might expect both an increase in error at the beginning of learning and possibly an intervening cognitive strategy at the beginning of learning.

      Given these issues, a more grounded interpretation is that linearity simply represents real-time updating. If the relationship between the cursor and the hand is nonlinear, then updating is not in real time.

      The data shown in Fig 4B do not appear to provide clear evidence that the relationship between the cursor and the hand was approximately linear. Currently, it seems equally plausible to say that the data are approximately non-linear. Establishing a criterion for nonlinearity would be useful (e.g. shuffling a linear response for comparison).

      4) The presentation of mean-squared error in Figure 2 seems to have limited utility. As the authors mention, it does not arbitrate between mechanisms or represent the aftereffects observed in rotation learning. I suggest removing panel 2C altogether and magnifying panel 2B so that the reader can better appreciate the raw data.

    1. Reviewer #2:

      While much independent progress has been made in the development of RL models for learning and DDM-like models for decision-making, only recently have people begun to combine the two (e.g. Pedersen et al., 2017). In this paper, Miletić et al. develop a new set of combined reinforcement learning (RL) and evidence-accumulation models (EAM) in an attempt to account for learning/choice data and reaction time data in a series of probabilistic selection tasks (Frank et al., 2004). While previous developments have provided proof-of-concept that these models can be joined, here the authors present a new model, Advantage Racing Diffusion, which additionally captures stimulus difficulty, speed-accuracy trade-offs, and reversal learning effects. Using behavioral experiments and Bayesian model selection techniques, the authors demonstrate a superior fit to choice/RT data with their model relative to similar alternatives. These results suggest that the Advantage framework may be a key element in capturing choice/RT behavior during instrumental learning tasks.

      I think this paper asks some really interesting questions, the methods are quite sound, and it is written nicely. I do think that the central focus of the Advantage learning element is key to the study's novelty. However, I feel that the framing of the paper and the implementation are somewhat at odds, and thus additional experiments (or re-analyses of extant data sets) may be needed to transform the paper from a welcome, modest incremental improvement to a qualitative theoretical advance. I outline my major concerns/suggestions below:

      Major Points:

      In the abstract, the authors allude to both learning tasks with >2 options and to the role of absolute values of choices in characterizing the limitations of the typical DDM. However, in the manuscript the former is not addressed (and actually does not appear to be amenable to the current model implementation; see below), and the latter is addressed via modest improvements to model fits rather than true qualitative divergence between their model and other models' ability to capture specific behavior effects. Thus, I think the authors' could greatly strengthen their conclusions if they extend their model to RL data sets with a) >2 options, and b) variations in the absolute mean reward across blocks of learning trials. For instance, does their model predict set size effects during instrumental learning? Does their model predict qualitative shifts in choice and RT when different task blocks have different µ rewards? At the moment the primary results are improved fits, but I think it would be important to show their model's unique ability to capture more salient qualitative behavior effects.

      Moreover, I'm not sure I understand how the winning model would easily transfer to >2 options. As depicted in Equation 1, the model depends on the difference between two unique Q-values (weighted by w-d). How would this be implemented with >2 options? I see some paths forward on this (e.g., the current Q relative to the top Q-value, the current Q minus the average, etc.) but they seem to require somewhat arbitrary heuristics? Perhaps the authors could incorporate modulation of drift rates by policies? Or use an actor-critic approach? I may be missing something, but I think if the model in its current form doesn't accurately transfer to >2 options, the primary contribution is the utility of urgency, which has been presented in earlier studies.

      I appreciate the rigorous parameter recovery experiments in the supplement, but I think the authors could also perform a model separability analysis (e.g., plot a confusion matrix) - it seems several of the models are relatively similar and it could be useful to see if they're confusable (though I imagine they're mostly separable).

      I may be missing something, but I do not think the authors are implementing SARSA. SARSA is: Q(s,a)[t+1] = Q(s,a)[t] + lr(r[t+1] + discount(Q(s,a)[t+1]) - Q(s,a)[t]. However, this is a single-step task...isn't it just 'SAR' (aka, the standard Rescorla-Wagner delta rule)?

    1. Reviewer #2:

      In this manuscript, Lee and Usher study choices between two options, and model how such choices are affected by the certainty with which the decision-maker evaluates the two options. They insist that this value certainty should be incorporated in current models, and compare ways to do so within the framework of the drift-diffusion model (DDM).

      My main concern is that I find the main contribution a bit light. Mathematically, we know that in a DDM higher noise leads to shorter RTs. Empirically, we already know that options rated with low certainty lead to longer RTs (e.g. as demonstrated by the first author in Lee & Coricelli, 2020). So it is not surprising that low certainty cannot correspond to higher noise in a DDM, and might be captured by a lower drift instead. Then, the specific way it can be done deserves to be investigated, but the authors should explore in more details the different classes of models, and the ways in which value certainty could affect other parameters of the model as well.


      I would suggest presenting in the introduction more details about how DDM is currently used in studies of value based decisions, to better explain the context of the present work and highlight the specific contribution of the study.

      The authors consider a number of models in the discussion (effects of uncertainty on the bounds, balance of evidence, collapsing bounds, etc.) but do not give the full details of these models. I would suggest including these models in the analyses presented in the result section. Maybe the authors could capitalize on the amount of data they have to do some model fitting, to estimate how the parameters of the DDM would change with value certainty. Parameters of interest are the drift and the drift variability (in the extended version of the DDM) but the authors could also explore the bounds and the variability in the starting point. A basic approach would be to split the data based on value certainty: using a median-split for both options, they could fit separately the choices between 2 options rated with high certainty, and the choices between 2 options rated with low certainty, etc. A more involved approach would be to estimate the effect of value certainty on the parameters in a single analysis across all the data (e.g. using a hierarchical ddm).

      Minor points:

      The motivation for model 5, which includes an additional component for accumulating certainty, should be more detailed. This approach is not standard, and would deserve more details and some references to prior work offering the same approach, if it exists.

      A figure would be helpful to present the typical experimental paradigm, and including the notations of the variables.

      In Figure 2, the variable C1 and C2 are not properly defined.

    1. Reviewer #2:

      Chromatin remodelers use the energy derived from ATP hydrolysis to reposition or evict nucleosomes, thus shaping the chromatin landscape of the cell. In this study, the McKnight lab use creative genetic and genomic approaches to understand how the apparently nonspecific biochemical activity of one such chromatin remodeler, Isw2, is targeted to specific nucleosomes in the budding yeast genome. The use of an isw1/chd1 mutant is a nice approach to remove the effects of spacing factors, and the SpyTag/SpyCatcher approach is a novel idea for artificial recruitment of factors. The bottom line of the study is that small, conserved epitopes in transcription factors act as recruiting elements for Isw2, allowing precise targeting of a nonspecific biochemical activity to specific genomic loci. From a larger perspective, the results lend support to an interacting barrier model of nucleosome positioning, wherein positioning of specific nucleosomes defines the borders of nucleosomal arrays. The data appear to be of high quality and soundly interpreted, and I believe that the results will be of great interest to those interested in chromatin and transcription. There are many questions raised by the results that I believe will drive further investigation into specificity in chromatin remodeling. My one major criticism (not that major in the scheme of things) is that the authors analyze the interesting subsets of their sites, as detailed below. One example is the analysis of the Isw2/Itc2 co-bound sites to the exclusion of the Isw2-alone sites. I think some exploration of these sites would be warranted, as discussed below.

      1) In Fig. S1C, there is nice correspondence between strong Isw2 K215R binding and Isw2-dependent nucleosome remodeling. However, at PICs where there is no apparent Isw2 remodeling, there does seem to be some Isw2 K215R ChIP-seq signal, albeit at a lower level. Does this potentially represent capture of transient sampling-type interactions, or something else?

      2) In Fig. S1D, Ume6 ChIP (WT and DBD alone) is shown at 202 intergenic Ume6 motifs. It is stated that the rows are linked with Fig. 1B - it would be nice to see the nucleosome data next to the ChIP data in this panel, as it appears that Ume6 is bound to at some level to the majority of these 202 sites, while Isw2 seems only to be active at the 58 sites of cluster 1. Germane to this point, I of course understand why the authors focused on the cluster 1 sites, but it would be nice to have some speculation on why Isw2 only seems to function at a fraction of Ume6-bound loci. Also, the lengths of the cluster-denoting bars appear to be off here relative to Fig. 1B.

      3) In Fig. 5C, it appears that only a subset of Isw2 sites are bound by Itc1 as well. Again, as with the selection of the 58 Ume6 sites, I understand why the Isw2/Itc1 co-bound sites are selected for further analysis, but the Isw2 sites without Itc1 could be discussed as well. Are these sites non-functional? How does Itc1 ChIP-seq data compare to the Isw2 remodeling activity shown in Fig. 1A? How does it compare to Ume6 binding? Does it specify the Isw2-remodeled nucleosomes?

      4) Did the authors perform western blots to ensure that their various truncation constructs were stable? This is important for interpretation of the results vs deletions.

      5) To summarize the above points, a major thing missing from the discussion is why only subsets of TF binding sites recruit Isw2. For instance, as mentioned above, 58 Ume6 sites seem to specific Isw2 remodeling - what is special about those sites versus the other ~150 sites that appear to be bound by Ume6? It's mentioned briefly in the discussion that only three Swi6 sites were identified as Isw2-recruiting and that this may be tuned by cellular context, but this is quite vague and superficial. More speculation on what differentiates these sites from the TF-bound but non-Isw2 recruiting sites could be included.

    1. Reviewer #2:

      In this manuscript, the authors examine the neural correlates of perception and memory in the human brain. One issue that has plagued the field of memory is whether the neural processes that underlie perception can be dissociated from those that underlie memory formation. Here the authors directly test this question by introducing a behavioral paradigm designed to dissociate perception from mnemonic binding. In brief, while recording MEG data, they present subjects with a sequence of visual stimuli. Following the sequence, the subjects are instructed to bind the three stimuli together into a cohesive memory, and then are tested on their memory for which pattern was associated with an object, and which scene. The authors investigate changes in alpha/beta power and theta/gamma phase amplitude coupling during two separate epochs - perceptual processing and mnemonic binding. Overall, this is a well written and clear manuscript, with a clear hypothesis to be tested. Using MEG data enables the authors to draw conclusions about the neurophysiological changes underlying both perception and memory, and establishing this dissociation would be an important contribution to the field. I think the conclusions are justified, but there are several issues that should be addressed to improve the strength and clarity of the work.

      The fundamental premise of the task design is that subjects view a sequence of stimuli, and then separately at a later time actively try to bind those visual stimuli together as a memory. However, it is entirely possible, and even likely, that memories are being formed and even bound together as the subjects are still viewing the sequences of objects. How would the authors account for this possibility? One possible way would be if there were a control task where subjects were just asked to view items and not remember them.

      Another possibility would be to examine the trials that the participants failed to remember correctly. Presumably, one would still see the same decreases in alpha power. Yet it seems from the data, and the correlations, that during those trials that were not remembered properly, alpha power changed very little. Of course, it is unclear in these trials if failed memory is due to failed perception, but one concern would be that this would imply that decreases in alpha power are relevant for memory too. It would be helpful to see how changes in alpha power break down as a function of the number of actual items remembered. It would also be helpful to know how strong these correlations actually are.

      A related issue is with respect to hippocampal PAC. The authors investigate this during the mnemonic binding period. Yet they also raise the possibility in discussion that this could also be happening during perception, which goes back to the point above. Did they analyze these data during perception, and are there changes with perception that correlate with memory? This would suggest that binding is actually occurring during this sequence of visual stimuli.

      The authors perform a whole brain analysis examining the correlation between alpha power and memory to identify cluster corrected regions of significant. However, the PAC analysis focuses only on the hippocampus, raising the question of whether these results can account for the possible comparisons one could make in the whole brain. They do look at four other brain regions for PAC, which it would be helpful to account for. In addition, are there other measures of mnemonic binding that are significant? For example, theta power, or even gamma power?

      The authors note in the discussion that the magnitude of hippocampal gamma synchrony has been shown to be related to the decreases in alpha power. Is this also true in their data?

  4. Nov 2020
    1. Reviewer #2:

      This paper is the second in a series of landmark studies from the Richards lab that re-assess the molecular and cellular mechanisms that permit the corpus callosum (CC) to cross the interhemispheric midline in the telencephalon. The Richards lab previously showed key role for a specialized population of fetal astrocytes, the midline zipper glia (MZG), establishing this substrate when the MZG migrate into the interhemispheric fissure (IHF), intercalate with one another and degrade the intervening leptomeninges. In this manuscript, the authors now assess the requirement for the Ntn1/Dcc pathway in remodeling the IHF. In an elegant series of experiments, they show that Ntn1/Dcc regulate the migration pathway of the MZG, potentially by directly controlling cytoskeletal dynamics. This mechanism is conserved between humans and rodents; the authors show that Dcc mutations that cause CC dysgenesis in humans, cause striking changes in the morphology of astroglial-like cells, consistent with the regulation of MZG migration. Thus, Dcc appears to have two roles first, remodeling the MGZ and then guiding CC axons towards the telencephalic midline. Together, these studies continue the overgoing re-evaluation of the role of netrin1/Dcc in establishing neural circuitry, and shed further understanding on a fascinating and beautiful piece of biology.

      This is a very beautiful manuscript, the authors are to be congratulated for the very high quality of their images, and detailed quantifications. Would that all studies were so thorough! These studies will be of great interest to the developmental neuroscience research and clinical communities.

      Major comments

      The authors should be congratulated by including what was clearly a difficult conditional analysis to assess whether Dcc is required in the callosal axons, or in the MZG radial fibers. This analysis was confounded a) by the low efficiency of the shRNA to knock down Dcc and b) the mosaic nature of Emx::cre line, which appears to be variably expressing cre in both callosal neurons and MZG, given that TDT/Dcc are present in both axons (Fig 5B), and the MZG (Fig 5O) in the less severely affected animals.

      As currently presented, however, the analysis (sadly) does not greatly add to the paper, since technical issues beyond the authors' control, have made it difficult to assess specifically where Dcc is required with much confidence. Would the authors could consider removing the shRNA approach from the manuscript, and re-focusing the cKO data on a description of a Dcc phenotypic series? This analysis might fit better with the initial description of lack of interhemispheric remodeling observed in Dcc/Ntn mutant mice, and how they relate to (variable?) phenotypes observed in patients.

      Minor Points:

      1) Fig 3C, D. The failure of the MZG radial fibers to extend along the IHF in Dcc mutant at E15 is very striking, and well described in the text. However, there appears to be an additional more punctate Glast/Nestin signal immediately above the radial fibers in IHF in the E15 mutants, what is that?

      2) Fig 4E. Could the increased numbers of migrating MGZ cells seen on the surface of the IHF in E16 Dcc mutants be because there is no "stop" signal created when the IHF is remodeled?

      3) Fig 5B. The failure of the GFAP cells to move away from the third ventricle in Dcc mutants seems profound in both the figures and the quantification. Can the authors elaborate more on why the 0-400 um measurement doesn't rise to being significant in the Dcckanga mutants? Perhaps spell out (p=0.0?) where the trend lies on Fig 5B. ?

    1. Reviewer #2:

      This manuscript represents a very considerable amount of work, both wet lab and analytic, constituting excellent science. This may be the best paper yet produced on Bdelloids. Despite this glowing recommendation I have some very significant concerns about certain parts, their conclusions section, and the evidence for "enhanced cellular defence mechanisms" in the abstract. Some parts are very rigorous, but others give in to excess speculation. This paper does not really need additional work, it needs some re-writing. Afterwards this important manuscript would be a welcome addition to the field, even without the supposedly unique defence mechanisms.

      Substantive concerns:

      1) Line 273 onwards: There is a comparison in the manuscript between Bdelloids and Monogonants. It wasn't clear to me however that these groups had been sampled sufficiently. The Monogonants are represented by 5 species (8 genomes) within a single genus in no way representing the diversity of Monogonants and the sampling of Bdelloids is also small. The authors should take a more cautious tone to any conclusions.

      2) Line 276-278: The rationale for focussing on this specific group of TEs did not appear robust. The authors say "this class of TEs is thought to be least likely to undergo horizontal transfer and thus the most dependent on sex for transmission". But other groups are not evolving predominantly by horizontal transfer, transposons can change without meiotic sex and this section needs writing a little more clearly. The following lines make a case that some transposon groups increase, and some decrease in frequency. The obvious hypothesis is drift, but the writing was unclear, I always felt that some other mechanism was being proposed but never really stated clearly.

      3) Lines 288-300, comparison of TE abundances across animals; this section was very poorly done. I thought the authors could delete this comparison and have a better manuscript. How were these other species chosen? Is C. elegans a good representative of the entire phylum Nematoda? Are the tardigrades representatives of their phylum? Assembly and annotation methods vary enormously across datasets so what can the authors conclude without standardising assembly and annotation for these other animal groups? The authors say "as expected, both the abundance and diversity of TEs varied widely across taxa" This was indeed expected, Figure 2b seems to show noise, and suggests to me that the inclusion of this data was not a good idea. I suggest it is removed, or a very substantive analysis and discussion of the way in which it is an accurate and representative sample of animal transposon loads is written.

      4) Line 350-353: This section is weak and needs to be improved. The authors need to make it very clear that this is not a test, it is a single observation. The phrase "as predicted by theory for elements dependent on vertical transmission" seems rather unsupported. Does this relate to the argument put forward in lines 276-278? It was unconvincing at this point also. The current description that some families increase and some decrease is couched in what sounds like too meaningful sounding language, which could be improved to be more consistent with the results. Lines 353-355 here seem to make an argument that the variation of TEs in bdelloids is purely a phylogenetic effect variably present in some bdelloid lineages and related groups. If this is their view (and it seems very reasonable indeed) then the manuscript would be improved considerably if they stated it more clearly.

      5) Lines 533-535 "consistent with a high fit of the data to the phylogeny under a Brownian motion model as would be expected if TE load evolves neutrally along branches of the phylogeny." I felt that this was a truly excellent result that needed to be put forward more strongly in other areas of the manuscript. In this area, and some others in this manuscript the authors have truly unique data dramatically improving our understanding of bdelloids. The manuscript would be improved if authors concentrated much less throughout on ideas this data is exceptional and different from other animals, and instead followed their own analysis that this fits with current biological thought.

      6) Lines 621-632: "no significant difference between monogononts and bdelloids, or between desiccating and non-desiccating bdelloids" It is not clear to me here what statistical test is being carried out. All tests require phylogenetic control of course. I do agree that they are quite similar, perhaps this should be rewritten to reflect only that?

      7) 705-706 The authors look at 3 gene families concerned with transposon control to examine copy number. In one of them they say "the RdRP domain in particular is significantly expanded". I am unclear of what test of significance was carried out and where to find this analysis. Unlike the query concerning desiccating and non-desiccating above I think this analysis is essential. The authors make a really big thing about the expansion of this gene family, including it in the abstract. If they wish to keep its prominence then they need to clearly show whether there is evidence that the size of this domain family is significantly expanded along the branch leading to bdelloids. I understand that this is illustrated in Figure 7 but this is not a test. This needs to be made much clearer in a quantitative rather than descriptive way. There is a need for broad taxonomic sampling, standardisation of assembly and annotation, and a phylogenetic design for this analysis. Else it should be removed or at the least described more conservatively.

      8) Line 725: "Why do bdelloids possess such a marked expansion of gene silencing machinery?" There is no evidence presented that they do. There may be a hypothesis that they do it differently, rather than more, but that also needs testing. There is a lot of speculation in this paragraph, and I think removing this whole paragraph would improve the manuscript.

      9) If there is an expansion of this family what can we then conclude? The authors say in the abstract "bdelloids share a large and unusual expansion of genes involved in RNAi-mediated TE suppression. This suggests that enhanced cellular defence mechanisms might mitigate the deleterious effects of active TEs and compensate for the consequences of long-term asexuality" yet they also review that animal groups can utilize different gene families for transposon control. Is there evidence that clade 5 nematodes with PIWI have a quantitatively different transposon defence mechanism? No, they just use a different pathway to some other groups, and the default position surely has to be the same for bdelloids, there is no evidence presented that their defence is enhanced. I would strongly recommend that the authors reduce the strength of their claims about the significance of bdelloid transposon control gene families in this manuscript.

      10) I felt that the Conclusions (and Abstract) were too speculative and not fully supported by the existing data, though this can easily be addressed by a substantial re-write.

    1. Reviewer #2:

      This is a beautiful and clever paper, expanding the authors' tracking method for fast psychophysics to the domain of interocular delay. They find that it is possible to measure interocular delay quite accurately by comparing 1D tracking (in x) in each eye. The tracking technique is exciting because it potentially makes psychophysics much more accessible, and this paper demonstrates that it can be used to measure interocular timing differences.

      The authors also examine whether it's possible to estimate interocular delay in a single binocular experiment where people track in depth (x and z). The answer at this point is no - while some aspects of the depth tracking are beautifully accounted for in this way, other factors clearly contribute.

      I don't have any substantive concerns at all but I would be interested to see some quantification of the advantage of tracking over button-press psychophysics. It's clear from the error bars in Fig 6B that button-press results are considerably more precise, but presumably they take a lot longer. Could the authors quantify this for us? E.g. button-press psychophysics: 95% confidence interval is 1ms after 100 minutes of experimentation; tracking : 95% CI is 5ms after 10 minutes, or similar.

      Could you select a subset of the button-press psychophysics (fewer trials per data point) in order to say what precision could be achieved after the same time as the tracking? This would really help readers assess the costs & benefits of the two approaches.

    1. Reviewer #2:

      Generally, this paper is excellent. It explores many characteristics of Brachypodium distachyon population genetics and demography, many of which have been assumed or hypothesised by less data-rich papers over the last two decades. The authors do so with whole-genome sequencing of both a pre-existing global collection and some novel "gap-filling" sampling. The authors appear to have conducted all analyses using best practices, and the conclusions are largely not over-interpreted. I have only a few minor comments.

      L68: Ideally a more detailed summary of the work summarised in Supp File 1 would be brought into the main manuscript. The introduction in and of itself largely skims over the quite large amount that is already known or assumed about the population genomics and dynamics of B. distachyon, especially the ~4 other recent WGS popgen papers which cover adjacent/overlapping collections and topics to this manuscript.

      L165: with regards random sequence subsets for BPP: does this include sequence only from genes, or from intergenic space? what about TE or other repeat loci? How do you ensure subset regions are single-copy orthologs in all accessions? I'm no expert on BPP, but I'm largely aware of BPP being used on exon capture data (i.e. genic sequence and flanking introns), admitted at different evolutionary scales with a greater expectation that assumptions of orthology are not met.

      L338: the speculation about heterozygosity being induced "in the lab" is very interesting. If you have the data which allows investigating this, could you test if the maternal/paternal haplotypes in heterozygous regions match implausibly distant accessions, suggesting in-lab outcrossing?

      L364-365: wouldn't a decrease in diversity as one moves east imply an eastwards migration? I'm not sure if I'm misreading this sentence or there is a typo which switches the direction of the decrease. In any case perhaps reword this sentence for clarity.

      L403: typo: distance is week -> distance is weak

      L405: typo: descent -> descend. Also, a suggestion: did not descend from a single recent colonization (add "single")

      L410: Seed dispersal then ensures OR "would then ensure" (delete would, or ensures -> ensure).

      L421: While human-commensal seed dispersal likely explains most recent migration, surely the estimated branch times (fig 5) predate significant human movement? Or, phrased alternatively, were there other/additional historical agents of migration?

      L433: are pathogens not a potentially strong selective pressure on (nearly) all plants? How then do pathogens relate uniquely to the reproductive strategy/population structure and dynamics of B. distachyon?

      L435: Is a concluding paragraph required? I feel the discussion ends somewhat abruptly.

      L539: (optional suggestion): given the non-linearity in the IBD plots you present, it would be interesting to apply Generalised Dissimilarity Modelling to test for/examine IBD.

      L567: Please give light measurements in uE PAR (umol photos /m2/sec; 400-700nm) in addition to/instead of klux.

    1. Reviewer #2:

      This is a fascinating study demonstrating the role of KIF21B in control of T cell microtubule network required for T cell polarization during immunological synapse formation. The authors show that knockout of KIF21B results in longer microtubules that result in an inability to move the polarise the MT network by a mechanism consistent with dynein motor function at the immunological synapse to capture long MT and center the MT aster at the synapse. They use the Jurkat cell line, which is a classical model for this step in immune synapse function and fully appropriate. They show that KIF21B-GFP can rescue the knockout phenotype and then use this as a way to follow KIF12B dynamics in the Jurkat cells. KIF21B works by binding to the + end and inducing pausing and catastrophe, thus, more MT that are shorter when present. They also rescue the defect in the KIF21B Kos with 0.5 nM vinblastine, that directly increases catastrophes, shortens the MT and restores MT network polarization to the synapse. As a functional surrogate they investigate lysosome positioning at the synapse, which is one of the proposed functions of this cytoskeletal polarization. The use of expansion microscopy in this system is relatively new and clearly very powerful. The modelling component adds to the story and supports the sliding model proposed by Poenie and colleagues in 2006, but cannot say that there is no component of end capture and shrinkage as proposed by Hammer and colleagues more recently.

    1. Reviewer #2:

      This short study highlights the complexity of the octopaminergic system in insect behavior. This aspect of neuromodulation has received little attention in comparison with the role of dopamine in learning and motivation. The main question being addressed is whether, how and where octopamine modulates the generation of rhythmic behavior (peristalsis) upon noxious sensory stimulation (touch and pain). Using a combination of functional imaging and behavioral inspections, the authors explore the role of octopamine released by the VUM neurons on the escape crawling behavior of the Drosophila larva.

      The specific observations reported in the study are:

      1) Isolated larval CNS preparations that do not receive sensory input (deafferented preps) show spontaneous rhythmic wave patterns of neuronal activity in octopaminergic VUM neuron cluster.

      2) In vivo preps that receive sensory input did not show spontaneous rhythmic patterns in the neural activity of the VUM neuron cluster.

      3) The VUM neurons show weaker responses in clusters that get sensory input from physically stimulated body segments and stronger responses in clusters that get input from segments further away from stimulated segments.

      4) In functional (GCaMP) imaging experiments, repeated gentle (rod) touch stimulations led to decreased VUM response intensities. Repeated harsh (brush) stimulations resulted in increasing VUM intensities. The authors correlate these physiological observations of the VUM activity with an increase in crawling speed upon repeated harsh stimulations, and a decrease in crawling speed upon repeated gentle touch stimulations.

      Based on observations (4), the authors propose that the differences in the behavior elicited by series of gentle touch and harsh stimulations are due to differences in adaptation of two classes of mechanosensory neurons. The class III da neurons responsible for detecting gentle touch would quickly adapt, whereas the class IV da neurons responsible for detecting harsh touch would integrate neural activity over time. The authors also conclude that (i) the octopaminergic system is strongly coupled to the CPG underlying peristalsis and (ii) "it is simultaneously activated by physical stimulation, rather intensity than sequential coded" (line 53). The first conclusion is supported by observations (1-2). While the involvement of octopamine in the modulation of a key CPG of the larva is a certainly interesting result, it represents the starting point of a mechanistic inspection. The problem is that the rest of the study falls short of testing or establishing any concrete mechanism.

      Although the topic of this study is exciting and its results are generally promising, the work is largely inconclusive. In addition, some conclusions are phrased in a way that is cryptic. For instance, I found it difficult to decipher the meaning of "the octopaminergic system is simultaneously activated by physical stimulation, rather intensity than sequential coded" (line 53). This conclusion appears to contradict the observation that repeated gentle touch stimulations produce a gradual decrease in the overall activity of VUM neurons. In the discussion section, the authors nicely refer to published findings in stick insects, honey beers and locusts. Compared to these systems, the advantage of Drosophila is that it offers the neuro-genetic tools to shed mechanistic insights into the molecular and cellular bases of neuromodulation.

      Questions and mechanisms that the authors might have wanted to address at a mechanistic level:

      Re. observations (1-2): What explains the observation that sensory inputs present in in-vivo preps abolish the spontaneous rhythmic pattern in the VUM activity? How does this relate to the VUM activity elicited by the tactile stimulations presented in Fig 3?

      It would be important to establish the importance of the VUM activity on peristalsis through loss of function experiments. Expression of Tdc2 could be restrictive to the VNC by using tshirt-Gal4. These experiments would support the authors' proposal that octopamine is released to facilitate motor coordination (in lines 474-478).

      Technical concerns:

      -How can you rule out that the mini-stage featured in the in-vivo prep (Fig 2A) does not sever nervous fibers innervating the VNC? The plate placed under the CNS is very large. It is difficult to believe that this plate can be inserted while leaving all nerves (afferent and efferent neurons) intact on both sides. The integrity of the preparation should be controlled anatomically.

      -In Fig 2, a statistical analysis should be performed to establish a lack of correlation between the VUM activity and patterns of crawling. Trial 2.2 suggests the existence of some correlation. This correlative analysis would be important to back up the statement that "unstimulated larvae showed no consistent VUM neuron responses correlated to crawling movements" (lines 228-229; see also lines 235-236).

      -Lines 234-236: How can "movements" be assessed in an isolated deafferented prep?

      Re. observation (3): Do the mechanosensory inputs have an inhibitory effect on the VUM activity patterns? If so, how does the inhibition come about?

      How do you explain that harsh stimulation at the posterior end inhibits activity of both the most abdominal and thoracic segments? Does this imply that the t1 and a8 segments are somehow coupled?

      In line 400, the authors propose that "VUM neurons as one possible system to modulate either indirectly the endogenous input or directly the central pattern generating neurons as a response to external tactile stimulation of the body wall." How does this model and subsequent discussion fit with the observations of Fig 3? It would be helpful to test the validity of the two alternatives described in line 400.

      Technical concerns:

      -Line 292: The segments displaying highest activity upon tactile stimulations are said to be consistent across consecutive simulations. Are they consistent across preparations as well? Were the data of Fig 3 generated on more than one prep?

      -Are the results of Fig 3 dependent on the strength of the tactile stimulations? More than one intensity should be tested to rule out intensity coding, as is stated in the abstract (lines 53 and 55).

      Re. observation (4): One of the observations reported in Fig 3 is that posterior harsh stimulations produce an overall increase in VUM activity whereas anterior harsh stimulation produce a decrease in activity. In Fig 4, larvae undergo harsh physical stimulations. However, it is unclear whether the harsh stimulations are applied to the posterior or anterior end of the larva. Based on the physiological results of Fig 3, wouldn't the authors expect that harsh stimulations of the head/neck region should lead to a deceleration of the larva, as was observed for gentle touch? Couldn't this prediction be tested experimentally? For the same reason, stating in line 512 that the same stimulation is used to activate the VUM neurons in Fig 3 and Fig 4 is misleading.

      The discussion about the adaptive nature of the class III and IV da neurons is compelling. However it ought to be supported by more direct experimental evidence that could be collected in the Drosophila larva.

    1. Reviewer #2:

      This is a super interesting exploration of the dynamic allosteric changes in the SARS-CoV-2 S protein upon engagement with the angiotensin 2 converting enzyme 2 (ACE2) receptor (and vice versa). It also represents a tour de force for HDX-MS since the S protein is almost 1200 amino acids long and the ACE2 is also very large. The data are beautiful and the analysis is well-done. The S protein consists of two sub-domains S1 and S2 with the S1 needing to be cleaved-off so the S2 can become the fusion protein responsible for getting the SARS-CoV-2 into the cell. Structures are available but they do not shed light on how the protease furin can access the cleavage site between S1 and S2 in order to begin the process of fusion. In this paper, the Anand group shows that when ACE2 binds to the S protein, a conformational change occurs near the S1/S2 cleavage site exposing it and likely making it more susceptible to furin cleavage. It also dampens exchange in the stalk region. They call these regions "dynamic hotspots in the pre-fusion state".

      There are some things that need to be addressed:

      1) The manuscript appears to have been hastily written, it would benefit from a scientific editor making it more readable. For example, line 90 ff "Average deuterium exchange at these 91 reporter peptides was monitored for comparative deuterium exchange analysis of S protein, ACE2 receptor and S:ACE2 complex, along with a specific ACE2 complex with the isolated RBD." Presumably "reporter peptides" refers to the 321 peptides mentioned two sentences earlier...Why is the ACE2 complex with the isolated RBD qualified as "specific" while none of the others are? Then the article continues with more information about glycosylation…

      2) Figure S1B the concentrations should be reported in molar not ng/ml

      3) Line 90 and Figure S2: A bit more should be said about the glycosylation sites. If only non-glycosylated peptides are observed in the pepsin digestion, the coverage map (Fig. S2), shows expected lack of coverage for only a few sites (17, 122, 149, 165, 234, 282, 709, 1134) whereas many other sites are covered by peptides. Does this indicate that these sites are mostly not glycosylated?

      4) Fig. S3 legend seems to indicate that uptake of each peptide is plotted, whereas uptake per residue should be plotted because overlapping peptides make these data misleading. The peptides are shown in the other relative uptake graphs, but then there is more than one data point per peptide. Can the authors explain a bit more in the legend how they got the data in these figures?

      5) Fig. S4 seems to indicate that the cleavage site is already very dynamic. Can the authors explain this?

      6) Line 98-99 "... Mapping the relative deuterium exchange across all peptides onto this S protein model showed the greatest deuterium exchange at the stalk region" seems to contradict lines 105-106 "The deuterium exchange heat map showed the highest relative exchange in the S2 subunit (Fig. S3) and helical segments," Please clarify.

      7) Fig. 2 A and B look like the same molecular structure (nice that they are in the same orientation) but the domains are labeled differently. Yet a third domain listing is used in panel E. Comparing panels A and B, it's a little strange that some of the least dynamic spots in the Head/ECD are the highest exchanging, do the authors want to comment on this?

      8) I thank the authors for the details provided in the Methods section regarding the HDX-MS data. If it wouldn't slow things down too much, it would be great if the RFU data were calculated after back exchange correction. Even an imperfect correction (such as a global correction for the back exchange during analysis) would make the data more meaningful.

      9) Fig. 3C and 3D look remarkably different considering that they both are reflecting the RBD:ACE2 interaction. Did the authors attempt to find a convergent set of peptides to do this analysis? Perhaps if the binding site were labeled it would help make the differences look less important (overall the top part of the molecule is blue and the bottom more-or-less has some red and if that's all we are supposed to get out of this figure then it is ok).

      10) Fig. 4. The authors state that the significance cut-off for difference in deuterium exchange is 0.3 D but I don't see where they explain how they derived this value.

    1. Tg(fli:nls-mcherry)ubs10

      DOI: 10.1016/j.celrep.2020.108404

      Resource: (ZFIN Cat# ZDB-ALT-160726-2,RRID:ZFIN_ZDB-ALT-160726-2)

      Curator: @Naa003

      SciCrunch record: RRID:ZFIN_ZDB-ALT-160726-2

      Curator comments: allele name: ubs10Tg Danio rerio ZFIN Cat# ZDB-ALT-160726-2