Reviewer #1 (Public Review):

This manuscript describes a set of four passage-reading experiments which are paired with computational modeling to evaluate how task-optimization might modulate attention during reading. Broadly, participants show faster reading and modulated eye-movement patterns of short passages when given a preview of a question they will be asked. The attention weights of a Transformer-based neural network (BERT and variants) show a statistically reliable fit to these reading patterns above-and-beyond text- and semantic-similarity baseline metrics, as well as a recurrent-network-based baseline. Reading strategies are modulated when questions are not previewed, and when participants are L1 versus L2 readers, and these patterns are also statistically tracked by the same transformer-based network.

I should note that I served as a reviewer on an earlier version of this manuscript at a different venue. I had an overall positive view of the paper at that point, and the same opinion holds here as well.

Strengths:

- Task-optimization is a key notion in current models of reading and the current effort provides a computationally rigorous account of how such task effects might be modeled<br /> - Multiple experiments provide reasonable effort towards generalization across readers and different reading scenarios<br /> - Use of RNN-based baseline, text-based features, and semantic features provides a useful baseline for comparing Transformer-based models like BERT

Weaknesses:

- Generalization across neural network models seems, to me, somewhat limited: The transformer-based models differ from baseline models in numerous ways (model size, training data, scoring algorithm); it is thus not clear what properties of these models necessarily supports their fit to human reading patterns.<br /> - Inferential statistics are based on a series of linear regressions, but these differ markedly in model size (BERT models involve 144 attention-based regressor, while the RNN-based model uses just 1 attention-based regressor). How are improvements in model fit balanced against changes in model size? Also, it was not clear to me how participant-level variance was accounted for in the modeling effort (mixed-effects regression?) These questions may well be easily remedied by more complete reporting.<br /> - Experiment 1 was paired with a relatively comprehensive discussion of how attention weights mapped to reading times, but the same sort of analysis was not reported for Exps 2-4; this seems like a missed opportunity given the broader interest in testing how reading strategies might change across the different parameters of the four experiments.<br /> - Comparison of predictive power of BERT weights to human annotations of text relevance is limited: The annotation task asked participants to chose the 5 "most relevant" words for a given question; if >5 words carried utility in answering a question, this would not be captured by the annotation. It seems to me that the improvement of BERT over human annotations discussed around page 10-11 could well be due to this arbitrary limitation of the annotations.

Reviewer #2 (Public Review):

In this study, researchers aim to understand the computational principles behind attention allocation in goal-directed reading tasks. They explore how deep neural networks (DNNs) optimized for reading tasks can predict reading time and attention distribution. The findings show that attention weights in transformer-based DNNs predict reading time for each word. Eye tracking reveals that readers focus on basic text features and question-relevant information during initial reading and rereading, respectively. Attention weights in shallow and deep DNN layers are separately influenced by text features and question relevance. Additionally, when readers read without a specific question in mind, DNNs optimized for word prediction tasks can predict their reading time. Based on these findings, the authors suggest that attention in real-world reading can be understood as a result of task optimization.

The research question pursued by the study is interesting and important. The manuscript was well written and enjoyable to read. However, I do have some concerns.

1. In the first paragraph of the manuscript, it appears that the purpose of the study was to test the optimization hypothesis in natural tasks. However, the cited papers mainly focus on covert visual attention, while the present study primarily focuses on overt attention (eye movements). It is crucial to clearly distinguish between these two types of attention and state that the study mainly focuses on overt attention at the beginning of the manuscript.

2. The manuscript correctly describes attention in DNN as a mechanism to selectively extract useful information. However, eye-movement measures such as gaze duration and total reading time are primarily influenced by the time needed to process words. Therefore, there is a doubt whether the argument stating that attention in DNN is conceptually similar to the human attention mechanism at the computational level is correct. It is strongly suggested that the authors thoroughly discuss whether these concepts describe the same or different things.

Reviewer #1 (Public Review):

This manuscript describes extensive transcriptomic and epigenomic profiling for high-grade serous 'ovarian' cancer (HGSC) and its precancerous precursor the fallopian tube secretory epithelium cells (FTSEC). This study identifies MECOM, PAX8, SOX17 and WT1 as master transcription factors that regulate HGSC and FTSEC, as well as the transition from FTSEC to HGSC.

Overall, most the experiments described in the manuscript are well designed and executed. The data presented are of high quality, convincing, and in general support the conclusions made in the manuscript.

Given the complexity of the data and analysis, some clarification is needed to guide readers to better understand the results.

1) The definition of super enhancers should be clarified. In general, super enhancers are defined by large domains of enhancer clusters with high levels of H3K27ac, typically at least 10KB in size. The "super enhancers" presented in Figure 2 do not appear to be large clusters of enhancers.

2) Fig. 4D. Difficult to understand. Multiple bars seem to be represented by the same binding patterns by the four TFs. Need better description in both the text and figure legends.

3) "These data suggest that the antiproliferative effects of THZ1 and THZ531 in HGSC cells may be due to tumor-specific inhibition of MECOM, PAX8 and SOX17 expression by these drugs." Can the author expand the discussion on how CDK7/12 inhibitors could achieve tumor-specific inhibition of MECOM, PAX8 and SOX17?

Reviewer #2 (Public Review):

This study found that MECOM, PAX8, SOX17, and WT1, as the main regulators of high-grade serous ovarian cancer (HGSC), their transcriptional regulation related to the super-enhancer, were reconnected in the process of tumor development. These four TFS are essential for the clonality and survival of HGSC, while the absence of PAX8 and WT1 in non-cancerous fallopian tube secretory epithelium (FTSEC) can impair the survival of cells. These four TFS are only pharmacologically inhibited by transcriptional inhibitors in HGSCs, while not in FTSECs, making them potential targets for tumor-specific therapy.

I am thrilled to see such an exciting and scientific manuscript. The results will significantly impact the basic theory of cancer occurrence and clinical applications.

However, there were some issues with the data presentation. We hope that the author will carefully and rigorously review the data and visualization results. In addition, there is key information missing in the methods section, which does not meet the current requirements for the repeatability of scientific conclusions.

Reviewer #1 (Public Review):

The authors prepared several Acinetobacter baumannii strains from which an essential protein of known or unknown function can be depleted. They chose to study one of the proteins (AdvA) in more detail. AdvA is a known essential cell division protein that accumulates at cell division sites together with other such proteins. No clear homologs are present in model bacteria such as E.coli, and the precise role(s) of AdvA is still unclear. The authors rename AdvA here as Aeg1. The authors searched for suppressors of lethality caused by AdvA-depletion and recovered an allele of ftsA (E202K) that is capable of doing so. Based on similar superfission alleles previously recovered in other division genes in E.coli, they test several mutant genes and find that certain alleles in ftsB, L and W can also suppress lethality of AdvA-minus cells.

In addition, the authors perform bacterial two-hybrid assays and protein sublocalization studies of AdvA and of other division proteins, but the results of these studies are either not new (confirming previous work) or not convincing.

Reviewer #2 (Public Review):

In this study the authors confirm that one of the genes classified as essential in a Tn-mutagenesis study in A. baumannii is in fact an essential gene. It is also present in other closely related Gram negative bacteria and the authors designated it Aeg1. Depletion of Aeg1 leads to cell filamentation and it appears that the requirement for Aeg1 can be suppressed by what appear to be activation mutations in various genes. Overall, it appears that Aeg1 is involved in cell division but many of the images suffer from poor quality - it may be due to conversion to PDF. One of the main issues is that depletion of Aeg1 is carried out for such long times (18 hr) (Fig. 2, 4 and 5). Depleting a cell division protein for such long times may have pleiotropic effects on cell physiology. A. baumannii grows quite fast and even with a small inoculum, cells will probably be in stationary phase. If Aeg1 is that essential cells should be quite filamentous 2-3 hours after Ara removal when they are still in exponential phase. Also, it would be better to see the recovery to small cells if cells are not grown such a long time before Ara is added back. Overall, Aeg1 is potentially interesting but studies are needed to define its place in the assembly pathway. What proteins are at the division site when Aeg1 is depleted and what proteins are required for Aeg1 to localize to the division site. These experiments should be done when cell are depleted of proteins for only 1 -2 hours.

Reviewer #1 (Public Review):

Bolumar et al. isolated and characterized EV subpopulations, apoptotic bodies (AB), Microvesicles (MV), and Exosomes (EXO), from endometrial fluid through the female menstrual cycle. By performing DNA sequencing, they found the MVs contain more specific DNA sequences than other EVs, and specifically, more mtDNA were encapsulated in MVs. They also found a reduction of mtDNA content in the human endometrium at the receptive and post-receptive period that is associated with an increase in mitophagy activity in the cells, and a higher mtDNA content in the secreted MVs was found at the same time. Last, they demonstrated that the endometrial Ishikawa cell-derived EVs could be taken by the mouse embryos and resulted in altered embryo metabolism.

This is a very interesting study and is the first one demonstrating the direct transmission of maternal mtDNA to embryos through EVs.

Reviewer #2 (Public Review):

In Bolumar, Moncayo-Arlandi et al. the authors explore whether endometrium-derived extracellular vesicles contribute mtDNA to embryos and therefore influence embryo metabolism and respiration. The manuscript combines techniques for isolating different populations of extracellular vesicles, DNA sequencing, embryo culture, and respiration assays performed on human endometrial samples and mouse embryos.

Vesicle isolation is technically difficult and therefore collection from human samples is commendable. Also, the influence of maternally derived mtDNA on the bioenergetics of embryos is unknown and therefore novel. However, several experiments presented in the manuscript fail to reach statistical significance, likely due to the small sample sizes. Additionally, the experiments do not demonstrate a direct effect of mtDNA transfer on embryo bioenergetics. This has the unfortunate consequence of making several of the authors' conclusions speculative.

In my opinion the manuscript supports the following of the authors' claims:

1. Different amounts of mtDNA are shed in human endometrial extracellular vesicles during different phases of the menstrual cycle.<br /> 2. Endometrial microvesicles are more enriched for mitochondrial DNA sequences compared to other types of microvesicles present in the human samples.<br /> 3. Fluorescently labelled DNA from extracellular vesicles derived from an endometrial adenocarcinoma cell line can be incorporated into hatched mouse embryos.<br /> 4. Culture of mouse embryos with endometrial extracellular vesicles can influence embryo respiration and the effect is greater when cultured with isolated exosomes compared to other isolated microvesicles.

My main concerns with the manuscript:

1. The authors demonstrate that microvesicles contain the most mtDNA, however, they also demonstrate that only isolated exosomes influence embryo respiration. These are two separate populations of extracellular vesicles.<br /> 2. mtDNA is not specifically identified as being taken up by embryos only DNA.<br /> 3. The authors do not rule out that other components packaged in extracellular vesicles could be the factors influencing embryo metabolism.

Taken together, these concerns seem to contradict the implication of the title of the manuscript - the authors do not demonstrate that inheritance of maternal mtDNA has a direct causative effect on embryo metabolism.

Reviewer #1 (Public Review):

Huan Wang et al. analyzed more than 10 million sequences and find that T12I, T102I and A104V were the top 3 frequently occurring mutations. They verified whether these mutations affect the stability and binding ability of NSP10, and whether there are structural changes. They find that three mutations destabilize the NSP10 by NMA prediction and determine their prediction by TSA. In addition, the Kd values shows that variants have similar binding ability or slightly improved affinity to NSP14 and NSP16 than native NSP10. Even though crystallization of the two variants is missing, the comparison of the crystallization of the T102I crystalline protein with the native shows that there is no structural change. Simultaneously, the dihedral angles in the variants do not explore any additional minima than that observed in wild-type NSP10, and there is no major conformational change.

Reviewer #2 (Public Review):

The authors of this study levered large-scale genomics data on SARS-CoV2, and extracted non-synonymous mutations of NSP10. The overall frequency was little, compared to other significantly mutating Spike protein. Further they performed stability and binding analysis to report changes in three variants and found modest differences. However, crystallography and simulations study reported almost no changes.

The strength of the work clearly is merging genomics data and reporting quantitative frequencies with high-resolution structural data. Some open ended questions remain. For instance, The DynaMut2 and thermal shift assays point towards less stable variants than wild type, with Tm values slightly lower. On the other hand, the Kd value of variants reported stronger binding of NSP10 with NSP16. How do authors explain this, as the change due to point mutation may not fall within error range?

The crystal structures and the simulations have been under-analysed. For instance, the conformational ensemble could be utilized for docking with NSP16 and NSP14 . There could be a potential alternative pathway for explaining the above changes in Kd. This should be attempted for understanding the role in its functional activity.

Previous extensive EM work on Spike protein variants also displayed subtle differences locally. However, allosteric pathways with D614G have been reported. Therefore, more quantitative analysis is required to explain structural changes. The free energy landscape reported in the paper may not capture rare transition events or slight rearrangements in side chain dynamics, both these could offer better understanding of mutations.

Reviewer #1 (Public Review):

The manuscript by Goetz et al. takes a new perspective on sensory information processing in cells. In contrast to previous studies, which have used population data to build a response distribution and which estimate sensory information at about 1 bit, this work defines sensory information at the single cell level. To do so, the authors take two approaches. First, they estimate single cells' response distributions to various input levels from time-series data directly. Second, they infer these single-cell response distributions from the population data by assuming a biochemical model and extracting the cells' parameters with a maximum-entropy approach. In either case, they find, for two experimental examples, that single-cell sensory information is much higher than 1 bit, and that the reduction to 1 bit at the population level is due to the fact that cells' response functions are so different from each other. Finally, the authors identify examples of measurable cell properties that do or do not correlate with single-cell sensory information.

The work brings an important and distinct new insight to a research direction that generated strong interest about a decade ago: measuring sensory information in cells and understanding why it is so low. The manuscript is clear, the results are compelling, and the conclusions are well supported by the findings. Several contributions should be of interest to the quantitative biology community (e.g., the demonstration that single cells' sensory information is considerably larger than previously implied, and the approach of inferring single-cell data from population data with the help of a model and a maximum-entropy assumption).

Reviewer #2 (Public Review):

In this paper the authors present an existing information theoretic framework to assess the ability of single cells to encode external signals sensed through membrane receptors.

The main point is to distinguish actual noise in the signaling pathway from cell-cell variability, which could be due to differences in their phenotypic state, and to formalize this difference using information theory.

After correcting for this cellular variability, the authors find that cells may encode more information than one would estimate from ignoring it, which is expected. The authors show this using simple models of different complexities, and also by analyzing an imaging dataset of the IGF/FoxO pathway.

The implications of the work are limited because the analysed data is not rich enough to draw clear conclusions. Specifically,<br /> - the authors do not distinguish what could be methodological noise inherent to microscopy techniques (segmentation etc), and actual intrinsic cell state. It's not clear that cell-cell variability in the analyzed dataset is not just a constant offset or normalization factor. Other authors (e.g. Gregor et al Cell 130, 153-164) have re-centered and re-normalized their data before further analysis, which is more or less equivalent to the idea of the conditional information in the sense that it aims to correct for this experimental noise.<br /> - in the experiment, each condition is shown only once and sequentially. This means that the reproducibility of the response upon repeated exposures in a single cell was not tested, casting doubt on the estimate of the response fidelity (estimated as the variance over time in a single response).<br /> - another dataset on the EGF/EGFR pathway is analyzed, but no conclusion can be drawn from it because single-cell information cannot be directly estimated from it. The authors instead use a maximum-entropy Ansatz, which cannot be validated for lack of data.

Reviewer #3 (Public Review):

Goetz, Akl and Dixit investigated the heterogeneity in the fidelity of sensing the environment by individual cells in a population using computational modeling and analysis of experimental data for two important and well-studied mammalian signaling pathways: (insulin-like growth factor) IGF/FoxO and (epidermal growth factor) EFG/EFGR mammalian pathways. They quantified this heterogeneity using the conditional mutual information between the input (eg. level of IGF) and output (eg. level of FoxO in the nucleus), conditioned on the "state" variables which characterize the signaling pathway (such as abundances of key proteins, reaction rates, etc.) First, using a toy stochastic model of a receptor-ligand system - which constitutes the first step of both signaling pathways - they constructed the population average of the mutual information conditioned on the number of receptors and maximized over the input distribution and showed that it is always greater than or equal to the usual or "cell state agnostic" channel capacity. They constructed the probability distribution of cell state dependent mutual information for the two pathways, demonstrating agreement with experimental data in the case of the IGF/FoxO pathway using previously published data. Finally, for the IGF/FoxO pathway, they found the joint distribution of the cell state dependent mutual information and two experimentally accessible state variables: the response range of FoxO and total nuclear FoxO level prior to IGF stimulation. In both cases, the data approximately follow the contour lines of the joint distribution. Interestingly, high nuclear FoxO levels, and therefore lower associated noise in the number of output readout molecules, is not correlated with higher cell state dependent mutual information, as one might expect. This paper contributes to the vibrant body of work on information theoretic characterization of biochemical signaling pathways, using the distribution of cell state dependent mutual information as a metric to highlight the importance of heterogeneity in cell populations. The authors suggest that this metric can be used to infer "bottlenecks" in information transfer in signaling networks, where certain cell state variables have a lower joint distribution with the cell state dependent mutual information.

The utility of a metric based on the conditional mutual information to quantify fidelity of sensing and its heterogeneity (distribution) in a cell population is supported in the comparison with data. Some aspects of the analysis and claims in the main body of the paper and SI need to be clarified and extended.

1) The authors use their previously published (Ref. 32) maximum-entropy based method to extract the probability distribution of cell state variables, which is needed to construct their main result, namely p_CeeMI (I). The salient features of their method, and how it compares with other similar methods of parameter inference should be summarized in the section with this title. In SI 3.3, the Lagrangian, L, and Rm should be defined.<br /> 2) Throughout the text, the authors refer to "low" and "high" values of the channel capacity. For example, a value of 1-1.5 bits is claimed to be "low". The authors need to clarify the context in which this value is low: In some physically realistic cases, the signaling network may need to simply distinguish between the present or absence of a ligand, in which case this value would not be low.<br /> 3) Related to (2), the authors should comment on why in Fig. 3A, I_Cee=3. Importantly, where does the fact that the network is able to distinguish between 23 ligand levels come from? Is this related to the choice (and binning) of the input ligand distribution (described in the SI)?<br /> 4) The authors should justify the choice of the gamma distribution in a number of cases (eg. distribution of ligand, distribution cell state parameters, such as number of receptors, receptor degradation rate, etc.).<br /> 5) Referring to SI Section 2, it is stated that the probability of the response (receptor binding occupancy) conditioned on the input ligand concentration and number of receptors is a Poisson distribution. Indeed this is nicely demonstrated in Fig. S2. Therefore it is the coefficient of variation (std/mean) that decreases with increasing R0, not the noise (which is strictly the standard deviation) as stated in the paper.<br /> 6) In addition to explicitly stating what the input (IGF level) and the output (nuclear GFP-tagged FoxO level) are, it would be helpful if it is also stated what is the vector of state variables, theta, corresponding to the schematic diagram in Fig. 2C.<br /> 7) Related to Fig. 2C, the statement in the caption: "Phosphorylated Akt leads to phosphorylation of FoxO which effectively shuttles it out of the nucleus." needs clarification: From the figure, it appears that pFoxO does not cross the nuclear membrane, in which case it would be less confusing to say that phosphorylation prevents reentry of FoxO into the nucleus.<br /> 8) The explanations for Fig. 2D, E and insets are sparse and therefore not clear. The authors should expand on what is meant by model and experimental I(theta). What is CC input dose? Also in Fig. 2E, the overlap between the blue and pink histograms means that the value of the blue histogram for the final bin - and therefore agreement or lack thereof with the experimental result - is not visible. Also, the significance of the values 3.25 bits and 3 bits in these plots should be discussed in connection with the input distributions.<br /> 9) While the joint distribution of the cell state dependent mutual information and various biochemical parameters is given in Fig. S7, there is no explanation of what these results mean, either in the SI or main text. Related to this, while a central claim of the work is that establishing this joint distribution will allow determination of cell state variables that differentiate between high and low fidelity sensing, this claim would be stronger with more discussion of Figs. 3 and S7.<br /> 10) The related central claim that cell state dependent mutual information leads to higher fidelity sensing at the population level would be made stronger if it can be demonstrated that in the limit of rapidly varying cell state variables, the I_CSA is retrieved.

Reviewer #1 (Public Review):

This manuscript sets out to implement a multi-stage fluorescence imaging essay to test two working models in understanding the folding states of RNA-binding proteins (RBPs) in stress-induced nuclear bodies. In conjunction with live-cell fluorescence lifetime imaging, the authors revealed and conformed a previously unclear phenomenon that the RBPs investigated in this work initially enter the nuclear bodies in native state in transient stress and then begin to misfold after prolonged stress. Comparing to conventional methods, the imaging strategy reported in this work is unique, comprehensive, and effective in surveying all three-stages (native, soluble oligomer, aggregates) of folding states for RBPs in one shot. Using this strategy, the authors then found that the heat shock protein 70 may protects RBPs from being degraded under stress. The manuscript is very well-written.

Reviewer #2 (Public Review):

The authors combine the use of fluorogenic tools with fluorescence bioimaging to visualize how changes in the folding states of the RBPs TDP-43, FUS and TAF15 affect their subcellular localization and recruitment inside nuclear bodies, as well as protein fate. While the development of SNAP-tag substrates coupled with confocal microscopy in living cells (including FLIM) to monitor changes in protein folding states represents an important conceptual and technical advance for the field, I am not convinced that the authors fully achieved their aim. The authors cannot conclude on protein fate only based on the experiments performed here. Showing a correlation between a decrease in TDP-43 levels upon Hsp70 inhibition and colocalization at nuclear bodies with Hsp70 and DNAJA2 is not supporting their conclusion about protein degradation. A number of additional control experiments are needed to support their claims.

Yet, the optimization of these methods has unlimited potential since it may provide new ways to visualize and monitor a large variety of fundamental intracellular processes, including protein aggregation and fate.

Reviewer #3 (Public Review):

This manuscript presents a novel fluorescence toolkit designed for investigating the folding states of RNA-binding proteins (RBPs) and their association with molecular chaperones during liquid-liquid phase separation (LLPS) in the formation of nuclear bodies under stress. The strategy is to use SNAP-tag technology including cell lines stably expressing three model proteins fused with SNAP tag and a series of environmentally sensitive fluorophores that can selectively label on the SNAP proteins. The changes in the microenvironment such as microviscosity and micropolarity can be well characterized by these fluorophores to reflect the conformational states of the RBPs.

The strength of this method is that the SNAP protein is smaller than classic fluorescent proteins like GFP and thus its impact on the conformation and behavior of the targeted proteins is much smaller. The experiment is carefully designed and well thought-out. Overall, this work is of very high quality.

This method can thus be adapted by other protein systems to study the LLPS process and thus I believe it will be of great interest to cell biologists and biophysicists.

Reviewer #1 (Public Review):

In the manuscript titled "GABAergic synaptic scaling is triggered by changes in spiking activity rather than transmitter receptor activation," the authors present an investigation of the role of GABAergic synaptic scaling in the maintenance of spike rates in networks of cultured neurons. Their main findings suggest that GABAergic scaling exhibits features consistent with a key homeostatic mechanism that contributes to the stability of neuronal firing rates. Their data demonstrate that GABAergic scaling is multiplicative and emerges when postsynaptic spike rates are altered. Finally, their data suggest that, in contrast to their prior data on glutamatergic scaling, GABAergic scaling is driven by spike rates. The authors set the paper up as an argument that GABAergic scaling, rather than glutamatergic scaling, serves as the critical homeostatic mechanism for spike rate regulation.

While the paper is ambitious in its rhetorical scope and certainly presents intriguing findings, there are several serious concerns that need to be addressed to substantiate the interpretations of the data. For example, the CTZ data do not support the interpretations and conclusions drawn by the authors. Summarily, the authors argue that GABAergic scaling is measuring spiking (at the time scale of the homeostatic response, which they suggest is a key feature of a homeostat) yet their data in figure 5B show more convincingly that CTZ does not influence spiking levels - only one out of four time points is marginally significant (also, I suspect that the bootstrapping method mentioned in line 454-459 was conducted as a pairwise comparison of distributions. There is no mention of multiple comparisons corrections, and I have to assume that the significance at 3h would disappear with correction). Then, the fact that TTX applied on top of CTZ drives a increase in mIPSC amplitude is interpreted as a conclusive demonstration that GABAergic scaling is sensing spiking. It is inevitable, however, that TTX will also severely reduce AMAP-R activation - a very plausible alternative explanation is that the augmentation of AMPAR activation caused by CTZ is not sufficient to overcome the dramatic impact of TTX. All together, these data do not provide substantial evidence for the conclusion drawn by the authors.

Specific points:

- The logic of the basis for the argument is somewhat flawed: A homeostat does not require a multiplicative mechanism, nor does it even need to be synaptic. Membrane excitability is a locus of homeostatic regulation of firing, for example. In addition, synapse-specific modulation can also be homeostatic. The only requirement of the homeostat is that its deployment subserves the stabilization of a biological parameter (e.g., firing rate).<br /> - Line 63 parenthetically references an important, but contradictory study as a brief "however". Given the tone of the writing, it would be more balanced to give this study at least a full sentence of exposition.<br /> - The authors state (line 11) that expression of a hyperpolarizing conductance did not trigger scaling. More recent work ('Homeostatic synaptic scaling establishes the specificity of an associative memory') does this via expression of DREADDs and finds robust scaling.<br /> - Supplemental figure 1 looks largely linear to me? Out of curiosity, wouldn't you expect the left end to be aberrant because scaling up should theoretically increase the strength of some synapses that would have been previously below threshold for detection? Given that figure 2B also shows warping at the tail ends of similar distributions, how is this to be interpreted?<br /> - The readability of the figures is poor. Some of them have inconsistent boundary boxes, bizarre axes, text that appears skewed as if the figures were quickly thrown together and stretched to fit.<br /> - I'm concerned about the optogenetic restoration of activity experiment. Cortical pyramidal neuron mean firing rates are log normally distributed and span multiple orders of magnitude. The stimulation experiments can only address the total firing at a network-level - given than a network level "mean" is meaningless in a lognormal distribution, how are we to think about the effect of this manipulation when it comes to individual neurons homeostatically stabilizing their own activities? In essence, the argument is made at the single-neuron level, but the experiment is conducted with a network-level resolution.<br /> - Line 198-99: multiplicativity is not a requirement of a homeostatic mechanism.<br /> - Line 264-265 - again, neither multiplicativity and synaptic mechanisms are fundamentally any more necessary for a homeostatic locus than anything else that can modulate firing rate in via negative feedback.<br /> - 277: do you mean AMPAR?<br /> - Example: Figure 1A is frustratingly unreadable. The axes on the raster insets are microscopic, the arrows are strangely large, and it seems unnecessary to fill so much realestate with 4 rasters. Only one is necessary to show the concept of a network burst. The effect of time+CNQX on the frequency of burst is shown in B and C.<br /> - Example: Figure 2 appears warped and hastily assembled. Statistical indications are shown within and outside of bounding boxes. Axes are not aligned. Labels are not aligned. Font sizes are not equal on equivalent axes.<br /> - The discussion should include mention of the limitations and/or constraints of drawing general conclusions from cell culture.<br /> - The discussion should include mention of the role of developmental age in the expression of specific mechanisms. It is highly likely that what is studied at ~P14 is specific to early postnatal development.

It is essential to ensure that the data presented in the paper adequately supports the conclusions drawn. A more cautious approach in interpreting the results may lead to a stronger argument and a more robust understanding of the underlying mechanisms at play.

Reviewer #2 (Public Review):

Synaptic scaling has long been proposed as a homeostatic mechanism for the regulation for the activity of individual neurons and networks. The question of whether homeostasis is controlled by neuronal spiking or by the activation of specific receptor populations in individual synapses has remained open. In a previous work, the Wenner group had shown that upscaling of glutamatergic transmission is triggered by direct blockade of glutamate receptors rather than by the concomitant reduction in firing rate (Nat Comm 2015). In this manuscript they investigate the mechanisms regulating scaling of GABA-mediated responses in cortical cell cultures using whole-cell recordings to detect GABAergic currents and multielectrode arrays to monitor global firing activity, and find that spiking plays a fundamental role in scaling.

Initially, the authors show that chronic blockade (24 h) of glutamatergic transmission by CNQX first reduces spontaneous spiking (at 2 h), but later (24 h) firing grows back towards higher frequencies, suggesting a compensatory mechanism. Then it is shown that either chronic CNQX treatment or TTX cause a reduction in the amplitude of GABAergic mIPSCs. Effects of CNQX on IPSCs are then reverted by replacing spontaneous network firing by chronic optogenetic stimulation of the entire culture, also indicating that GABAergic transmission is homeostatically regulated by global firing. Enhancing glutamatergic transmission with CTZ increases mIPSC amplitude, while addition of TTX in the presence of CTZ causes the opposite effect. Finally, increasing spiking activity using bicuculline also increases mIPSC amplitude, and the authors conclude that spiking activity rather than neurotransmission control homeostatic GABA scaling. The manuscript shows interesting properties in the regulation of global GABAergic transmission and highlight the important role of spiking activity in triggering GABA scaling. However, it is strongly recommended to address some caveats in order to better support the conclusions presented in the manuscript.

Major points:

1. The reason why CNQX does not completely eliminate spiking is unclear (Fig. 1). What is the circuit mechanism by which spiking continues, although at lower frequency, in the absence of AMPA-mediated transmission and what the mechanism by which spiking frequency grows back after 24h (still in the absence of AMPA transmission)?<br /> Is it possible that NMDA-mediated transmission takes over and triggers a different type of network plasticity?

2. A possible activation of NMDARs should be considered. One would think that experiments involving chronic glutamatergic blockade could have been conducted in the presence of NMDAR blockers. Why this was not the case?

Also, experiments with global ChR2 stimulation with coincident pre and postsynaptic firing might also activate NMDARs and result in additional effects that should be taken into consideration for the global scaling mechanism.

3. Cultures exposed to CTZ to enhance AMPA receptors generated variable results (Fig. 5), somewhat increasing spiking activity in a non-significant manner but, at the same time, strengthening mIPSC amplitude. This result seems to suggest that spiking might be involved in GABAergic scaling, but it does not seem to prove it.

Then, addition of TTX that blocked spiking reduced mIPSC amplitude. It was concluded here that the ability of CTZ to enhance GABAergic currents was primarily due to spiking, rather than the increase in AMPA-mediated currents. However, in addition to blocking action potentials, TTX would also prevent activation of AMPARs in the presence of CTZ due to the lack of glutamatergic release. Therefore, under these conditions, an effect of glutamatergic activation on GABAergic scaling cannot be ruled out.

4. The sample size is not mentioned in any figure. How many cells/culture dishes were used in each condition?

5. Cortical cultures may typically contain about 5-10% GABAergic interneurons and 90-95 % pyramidal cells. One would think that scaling mechanisms occurring in pyramidal cells and interneurons could be distinct, with different impact on the network. Although for whole-cell recordings the authors selected pyramidal looking cells, which might bias recordings towards excitatory neurons, naked eye selection of recording cells is quite difficult in primary cultures. Some of the variability in mIPSC amplitude values (Fig. 2A for example) might be attributed to the cell type? One could use cultures where interneurons are fluorescently labeled to obtain an accurate representation. The issue of the possible differential effects of scaling in pyramidal cells vs. interneurons and the consequences in the network should be discussed.

Reviewer #3 (Public Review):

This paper concerns whether scaling (or homeostatic synaptic plasticity; HSP) occurs similarly at GABA and Glu synapses and comes to the surprising conclusion that these are regulated separately. This is surprising because these were thought to be co-regulated during HSP and in fact, the major mechanisms thought to underlie downscaling (TTX or CNQX driven), retinoic acid and TNF, have been shown to regulate both GABARs and AMPARs directly. (As a side note, it is unclear that the manipulations used in Josesph and Turrigiano represent HSP, and so might not be relevant). Thus the main result, that GABA HSP is dissociable from Glu HSP, is novel and exciting. This suggests either different mechanisms underlie the two processes, or that under certain conditions, another mechanism is engaged that scales one type of synapse and not the other.

However, strong claims require strong evidence, and the results presented here only address GABA HSP, relying on previous work from this lab on Glu HSP (Fong, et al., 2015). But the previous experiments were done in rat cultures, while these experiments are done in mice and at somewhat different ages (DIV). Even identical culture systems can drift over time (possibly due to changes in the components of B27 or other media and supplements). Therefore it is necessary to demonstrate in the same system the dissociation. To be convincing, they need to show the mEPSCs for Fig 4, clearly showing the dissociation. Doing the same for Fig 5 would be great, but I think Fig 4 is the key.

The paper also suggests that only receptor function or spiking could control HSP, and therefore if it is not receptor function then it must be spiking. This seems like a false dichotomy; there are of course other options. Details in the data may suggest that spiking is not the (or the only) homeostat, as TTX and CNQX causes identical changes in mIPSC amplitude but have different effects on spiking. Further, in Fig 5, CTZ had a minimal effect on spiking but a large effect on mIPSCs. Similar issues appear in Fig 6, where the induction of increased spiking is highly variable, with many cells showing control levels or lower spiking rates. Yet the synaptic changes are robust, across all cells. Overall, this is not persuasive that spiking is necessarily the homeostat for GABA synapses.

The paper also suggests that the timing of the GABA changes coincides with the spiking changes, but while they have the time course of the spiking changes and recovery, they only have the 24h time point for synaptic changes. It is impossible to conclude how the time courses align without more data.

Reviewer #3 (Public Review):

This work shows how, in the formation of the immune synapse, the B cell controls the contraction phase, the formation and retraction of actin structures concentrating the antigen (actin foci), and, ultimately, global signal attenuation. The authors use a combination of TIRF microscopy and original image quantification to show that Arp2/3 activated by N-WASP controls a pool of actin concentrated in foci (situated in the synapse), formed and transported centripetally towards the center of the synapse through myosin II mediated contractions. These contractions concentrate the B cell receptors (BCRs) in the center, promote disassembly of the stimulatory kinase Syk as well as the the disassociation from the BCR of the inhibitory phosphatase SHIP, process which entails the attenuation of the BCR signal.

The author prove their claims by mean of thorough image analysis, mainly observing and quantifying the fluorescence and the dynamics of single clusters of antigen and actin foci and analyzing two-colors dynamical images. They perform their observation in control cells, on pharmacologically perturbed cells where the action of Arp2/3 or N-WASP is inhibited, and on modified primary cells (primary derived from genetically engineered mice) to silence N-WASP or WASP. The work is sound and complete, the experiments technically excellent and well explained. Some experiments and discussions are objectively harder to describe, and given the length of the work, the reader might find itself lost some times. A graphical abstract/summary of the main way NWASP ultimately control signal attenuation would solve this minor point.

This work adds an important information to the current view of B cell activation, in particular it links the contraction phase to the actin foci that have been recently characterized. Moreover, the late phase of the immune synapse formation is, in general, poorly investigated, but it is crucial for the fate of the cell: this work provides an explanation for the attenuation of the signal that might lead to the termination of the synapse.

Reviewer #1 (Public Review):

In this study, the authors demonstrated a new model that B cell contraction after antigen encountering was dependent on N-WASP-branched actin polymerization. This statement is achieved by a systemic comparison of genetic modified mice vs wild type mice or inhibitor treated cells vs control cells. By imaging how B cells interact with antigen-coated planar lipid bilayer, the authors further suggested that the contraction event may provide B cells a channel to dismiss downstream kinase for a purpose to attenuate B cell activation signaling. Even though this manuscript is well written and packaged, however there are a few points that should be carefully addressed and revised.

The first major issue is related to the imaging and tracking experiment to examine the formation and migration of F-actin foci as illustrated in figure 3. The formation and centripetally migration of F-actin foci is a significant finding of this MS for the promotion of B cells to switch from spreading to contraction response. Thus, I may suggest to recommend the authors to conduct one more rigorous fluorescent molecular tracking experiment to confirm this phenomenon. Molecular tracking usually requires low labeling density, and the lifeact-GFP labeling here do not meet this requirement which may cause misidentification of the moving molecules. Permeable dye-based fluorescent speckle microscopy is recommended here to track the actin foci if applicable (P. Risteski, Nat. Rev. Mol. Cell Biol., 2023, DOI: 10.1038/s41580-023-00588-w & K. Hu, et al, Science, 2007, 315, 111-115). Additionally, kymograph is used for foci tracking in figure3 and figure4. Kymograph is indeed a powerful tool for tracking cell protrusion and retraction but is fairly suitable here, since a F-actin focus is a concentrated point which may not move strictly along the selected eight lines generating kymograph. Other imaging processing method should be used to track the foci, for example, time series max projection is recommended if applicable.

The second major issue is about the relationship between actin foci formation and NMII recruitment in figure 5. The author concludes that 'N-WASP and Arp2/3 mediated branched actin polymerization promotes the recruitment and the reorganization of NMII ring-like structures by generating inner F-actin foci in the contact zone'. However, there is a lack of strong evidence to directly show the mechanism by which myosin is recruited and the up and down stream relationship between actin foci migration and myosin recruitment. Since myosin-induced actin retrograde flow is a classical model in adherent cells, is it possible that, here also in activated B cells, the recruited myosin driven the formation and migration of actin foci? This reviewer may recommend the author to investigate whether Myosin blocking (e.g., using Y27632) can eliminate the F-actin foci formation and migration.

Reviewer #2 (Public Review):

Bhanja et al have examined how actin polymerization switch B-cell receptor (BCR) signaling from amplification to attenuation. The authors have examined B cell spreading and contraction using lipid bilayers to assess the molecular regulation of BCR signalling during the contraction phase. Their data provide evidence for that N-WASP activated Arp2/3 generates centripetally moving actin foci and contractile actomyosin from lamellipodia actin networks. This generates BCR dense foci that pushes out both stimulatory kinases and inhibitory phosphatases. The study provides novel insight into how B cells upon activation attenuate BCR signalling by contraction of the actin cytoskeleton and clustering of BCR foci and this dynamic response is mediated by N-WASP and Arp2/3.

Strengths: The manuscript is well written and results, methods, figures and legends described in detail making it easy to follow the experimental setup, analysis, and conclusions. The authors achieved their aims, and the results support their conclusions.

Weaknesses: Minor as listed below. The working hypothesis of molecular crowding as a way to push out signalling molecules from the BCR dense foci is interesting. The authors provide evidence for that this is an active process mediated by N-WASP - Arp2/3 induced actin foci. Another possibility is that BCR dense foci formation is an indirect consequence of lamellipodia retraction. Future works should define the specific role of N-WASP, Arp2/3 and actin in the process to form BCR dense foci, especially as the BCR continue to signal in the cytoplasm.

Reviewer #1 (Public Review):

The authors sought to address the longstanding question of which cell types are infected during congenital or perinatal rubella virus infection. They used brain slice and organoid-microglia experimental models to demonstrate that the main cell types targeted by rubella virus are microglia. The authors further show that infection results in augmented interferon responses in neighboring neuronal cells but not in the microglia themselves. The data convincingly support the conclusions, with major strengths being the sophisticated primary cell models and single-cell RNA-Seq used to pinpoint microglia as the main cellular targets of rubella virus, and neurons as the bystander targets of immune signaling. This study reveals a new cellular target that will have important implications for basic studies on rubella virus-host interactions and for the potential development of therapies or improved vaccines targeting this virus. As rubella virus is a pathogen of high concern during human pregnancy, this study is also relevant in the field of neonatal infectious diseases.

Reviewer #2 (Public Review):

Maternal infection by Rubella virus (RV) early during pregnancy is a serious threat to the health of the fetus. It can cause brain malformation and later expose the newborn to a constellation of symptoms collectively named Congenital Rubella Syndrome (CRS). In this manuscript, the authors provide novel exciting findings on the pathophysiological mechanisms of RV infection during human brain development. By combining analyses of human fetal brain material and cerebral organoids, Popova and colleagues uncovered a selective tropism of RV for microglial cells. Their results suggest that the infection of microglia by RV relies on the presence of diffusible factors secreted by neighboring brain cells. Moreover, the authors showed that RV infection of human cerebral organoids leads to a strong interferon response and dysregulation of neurodevelopmental genes, which both may contribute to brain malformation. Altogether, these data shed some new light on the cellular tropism and the pathophysiological mechanisms triggered by RV infection in the developing brain. This study also raises the importance of using human cell-based models to further understand the pathophysiological mechanisms of CRS. Identifying the cellular and molecular targets of Rubella virus will also pave the way to develop therapies against CRS.

Joint Public Review:

As nucleoporins can function at intact nuclear pore complexes (NPCs) or outside of NPCs as individual proteins or subcomplexes, it remains challenging to molecularly define the pool of molecules that exert a specific function. To address this challenge, here the authors develop a new method for specifically mapping NPC-associated loci by DamID with a recombinant fusion protein of the Dam methylase and the nuclear transport receptor, importin b (Dam-Impb), in permeabilized cells. The authors demonstrate that Dam-Impb is active, accumulates at the NPC and, using super-resolution microscopy, methylates NPC-adjacent regions; other observations further support the assertion that the approach is specific for NPC-associate chromatin regions. Furthermore, NPC-DamID does not require genetic manipulation and they show that it can be applied to both diverse cell lines as well as tissues. The authors confirm the association of nucleoporins with super-enhancers (SEs) in line with their prior work, now confirmed to occur at NPCs based on this study. Among SEs categorized as hierarchical enhancers (Nat Commun 9, 943 (2018)), hub enhancers are over-represented for methylation by Dam-Impb. The association of such enhancers with cohesin and CTCF suggests these regions could have a critical role in chromatin folding; enhancer-associated factors and marks such as H3K27 acetylation, RNA polymerase II, P300, CTCF and BRD4 also enrich at Dam-Impb methylation peaks. Using proximity ligation, the authors provide further evidence that Tpr, which interacts with the NPC basket, colocalizes with CTCF, BRD4 and P300. Based on these observations, the authors hypothesized that nucleoporin phase separation at SEs might potentiate phase separation of other factors at these elements. Consistent with previous work, over-expression of the intrinsically disordered region (IDR) of Nup153, a component of the NPC basket, forms nuclear droplets that are largely dispersed by 1,6-hexanediol. In this same condition, colocalization RNAPII and both Tpr and BRD4 is reduced, although some interactions between IDRs were not sensitive to this treatment. Last, using a lac operator array as a tethering site, the authors show that tethered Nup153 IDR recruits the carboxy terminal domain of RNAPII and Med1. However, whether the biology of how nucleoporins at NPCs influence SEs depends on biomolecular condensation will require future study.

Overall, the reviewers agree that this is an excellent manuscript that will impact our understanding of nuclear pore complex-genome interactions and how nucleoporins impact super enhancer function. The data are generally of high quality and are reasonably interpreted. There are, however, several important controls or analyses that would strengthen the conclusions of the paper, as outlined below.

1. NPC vs nucleoplasmic interactions: One of the main claims of the paper is that it provides a way to study specifically the NPC-associated loci and contrast them to the nucleoplasmic Nup-associated loci. Unfortunately, the authors do not devote much space to this comparison and many of the manipulations involve proteins that are in both locations (see below). This seems like an important, missed opportunity. The choices of Tpr or Nup153 should be more clearly justified. The Dpn8 staining appeared in regions outside of the nuclear envelope, which is inconsistent with the text. This should be addressed.

2. The PLA experiments: Although Tpr exists both at the NPC and in the nucleoplasm, the authors interpret these experiments as if they are exclusively reporting on proximity of enhancer proteins to the NPC. The images (e.g. Figure 5a, supplementary Figure 5) make it clear that the foci are throughout the nucleus. Where are the antigens recognized by the antibodies to Tpr and what this may mean for the findings? Further, PLA experiments are prone to artefacts and, while the authors have included a knockdown of Tpr as a negative control, additional controls would strengthen their conclusions. For example, what is the result when Tpr colocalization with NPC-specific proteins is assessed? How is that affected by hexanediol? A better PLA experiment might be to assess colocalization of Dam-ImpB or Dpn8 (bound to Dam-ImpB methylated sites) with super enhancer proteins such as Med1, CTCF, Brd4, etc. With regards to the PLA with

3. Analysis of genomic data: The normalization of the DNA sequencing tracks is not sufficiently explained. Moreover, some of the correlations using meta-site plots are not convincing. For example, the peaks of Nup153 or Nup98 methylation over Imp-B peaks are apparently weak. Although the authors report local maxima, these may not be strong associations. This raises the possibility that the stronger Nup153 or Nup98 peaks are not ImpB peaks. A better way to test for this would be to correlate the ImpB peak intensity to the Nup153 or Nup98 peak intensity globally. The expectation is that there will be both correlated peaks that show strong methylation by Nup153/Nup98 and ImpB, as well as peaks that do not (i.e. those in the nucleoplasm). Along these lines, the Dam alone control can be used for comparison. Peaks identified by Dam alone should not be correlated with ImpB, Nup153, Nup98, CTCF, RNAPII, Cohesin, H3K27Ac, Brd4, Mediator, super enhancers, hubs, etc. Also, what is the source of the Nup93 CUT&RUN data? It was unclear if it was from this study or a prior publication.

4. FISH experiments: these should be in the main figures of the paper and better described. How many loci were assessed in each category? Are the differences between the three classes significant? Also, the order of the legend is the opposite of the order of the bar segments, which is confusing to the reader. Related to Figure 2j: What are the FISH probes used here? How many cells were quantified?

5. The focus on IDRs as the primary functional mechanism for the NPC-SE connection was felt to be the least well-justified of the authors' conclusions. In particular, the quantitative effects in Fig. 6 are over-stated while caveats including possible over-expression artifacts and changes in the nuclear concentration of the IDRs due to efflux out of the nucleus in response to 1,6 hexanediol treatment as a consequence of the effect on the barrier of NPCs are not addressed. Additional experimental follow-up - for example does critical depletion of Nup153 (now possible with auxin degrons) disrupt the NPC-DamID profile? - would strengthen the support for the model.

6. Recent evidence points to the fact that 1,6-HD treatment probes the presence of hydrophobic interactions, rather than distinguishing between LLPS and interactions with spatially clustered binding sites (ICBS). These possibilities should be taken into account when interpreting the data, and should be discussed more thoroughly.

Joint Public Review:

This study is concerned with the general question as to how pools of synaptic vesicles are organized in presynaptic terminals to support different types of transmitter release, such as fast synchronous and asynchronous release. To address this issue, the authors employed the classical method of loading synaptic vesicle membranes with FM-styryl dyes and assessing dye destaining during repetitive synapse stimulation by live imaging as a readout of the mobilization of vesicles for fusion. Among other findings, the authors provide evidence indicating that there are multiple reserve vesicle pools, that quickly and slowly mobilized reserves do not mix, and that vesicle fusion does not follow a mono-exponential time course, leading to the notion that two separate reserve pools of vesicles - slowly vs. rapidly mobilizing - feed two distinct releasable pools - reluctantly vs. rapidly releasing. These findings are valuable to the field of synapse biology, where the organization of synaptic vesicle pools that support synaptic transmission in different temporal and stimulation regimes has been a focus of intense experimentation and discussion for more than two decades.

On the other hand, the present study has limitations, so that the authors' key conclusions remain incompletely supported by the data, and alternative interpretations of the data remain possible. The approach of using bulk FM-styryl dye destaining as a readout of precise vesicle arrangements and pools in a population of functionally very diverse synapses bears problems. In essence, the approach is 'blind' to many additional processes and confounding factors that operate in the background, from other forms of release to inter-synaptic vesicle exchange. Further, averaging signals over many - functionally very diverse - synapses makes it difficult to distinguish the dynamics of separate vesicle pools within single synapses from a scenario where different kinetics of release originate from different types of synapses with different release probabilities.

Reviewer #1 (Public Review):

This well written and designed study by Broca-Brisson et al describes the generation of a new in vitro model for creatine transporter deficiency (CTD), making use of human brain organoid cultures derived from CTD patients. This new model will certainly prove itself very useful to better understand this genetic disease essentially affecting CNS. As CTD has no satisfactory treatment so far (despite more than 20 years of research), this new model will also be very useful to design and develop new treatments.

In particular, through the use of immunohistochemistry, real time PCR, and proteomics combined with integrative bioinformatic and statistical analysis, authors provide very interesting new informations on the brain pathways affected in CTD (e.g. neurogenesis with down-regulation of SOX2 and PAX6 but up-regulation of GSK3b; and proteins involved in autistic spectrum, epilepsies or intellectual disabilities).

While the CTD human brain organoids show a decrease in Cr (in absence of Cr in the culture medium) as compared to control organoids (4 times less), they are not devoid of Cr. Do these organoids express the two enzymes allowing Cr synthesis (AGAT and GAMT), and in which brain cell types? If yes, how to explain the decrease in Cr in the CTD organoids?

The rescue experiment, re-establishing a functional Cr transporter (CRT or SLC6A8) in the CTD human brain organoids, is very interesting, as this may help the design and development of new treatments for CTD. However, authors claim that the functional CRT expressed in the rescued CTD organoids was expressed in each cell. This may be a difficulty in the development of new CTD treatments, as CRT should be expressed in neurons and oligodendrocytes, but not in astrocytes. Authors may want to comment on this point.

Reviewer #2 (Public Review):

In their recent manuscript, Broca-Brisson et al. deliver a multidisciplinary approach to investigate creatine transporter deficiency (CTD) using human-derived brain organoids. The authors have provided a compelling CTD human brain organoid model using induced pluripotent stem cells (iPSCs) derived from individuals with CTD. This model shows distinct differences in creatine uptake between organoids originating from CTD patients and their healthy counterparts. Furthermore, the researchers effectively restored creatine uptake by reintroducing the wild-type CRT in the iPSCs.

The team used advanced molecular biology techniques and sophisticated mass spectrometry to identify changes in protein regulation within these CTD brain organoids. They propose an intriguing theory linking reduced creatine uptake to abnormalities in the GSK3β kinase pathway and mitochondrial function, which might underlie intellectual disability seen in CTD patients.<br /> This study is well-structured and easy to follow, with clear and concise explanations of the experiments. The authors present an important idea: a dysfunction in just one protein transporter (CRT) can cause significant biochemical changes in the brain. Their findings are well-presented and backed by high-quality figures and comprehensive data analysis.

Reviewer #1 (Public Review):

Qin et al., demonstrate, convincingly, that plasticity of ocular dominance of binocular neurons in the visual thalamus persists in adulthood. The adult plasticity is similar to that described in critical period juveniles in that it is absent in transgenic mice with the deletion of the GABA a1 receptor in thalamus, which also blocks ocular dominance plasticity in primary visual cortex. However, the adult plasticity is not dependent on feedback from primary visual cortex, an important difference from juveniles. These findings are an important contribution of a growing body of work identifying plasticity in the adult visual system, and identifies the visual thalamus as a potential target for therapies to reverse adult amblyopia.

Reviewer #2 (Public Review):

In this work, the authors found in the mouse line of GABAA a1 subunit KO in thalamic neurons, which was previously reported lacking ocular dominance (OD) plasticity in juvenile V1 and dLGN (Sommeijer et al., 2017), the adult V1 and dLGN OD plasticity was also missing. Through muscimol inhibiting the V1 feedback, thalamic OD plasticity was unaffected in both WT and KO adult mice. However, during the critical period, the thalamic OD plasticity was dependent on V1 feedback in WT mice.

Strengths:

1. The experiments were well designed. The authors used both MD and No MD controls with both WT and KO mice. The authors used in vivo SU recording, which is broadly accepted as the major method for evaluating OD plasticity.

2. The data analysis was solid. The authors used proper statistical tests for non-parametric data set.

Weaknesses:

1. The current work was basically a follow-up of a previous study in juvenile mice, and the results were also very similar to the juvenile results (Sommeijer et al., 2017). One possible interpretation of the results is that the lack of OD plasticity in adult V1 and dLGN was caused by an early blockade of the development of the inhibitory circuit in dLGN, which retains the dLGN in an immature stage till adulthood. The authors indeed claimed in the discussion that the 2-day OD shift is intact in juvenile dLGN and V1 in KO mice, and provided evidence in supplementary figure that GABAergic and cholinergic synapse amount are similar between WT and KO mice. However, the 7-day OD shift is indeed defected in juvenile V1 and dLGN in KO mice (Sommeijer et al., 2017), and it is possible that this early functional deficit didn't induce a structural remodeling in adulthood. To better support the author's claim that the lack of adult V1 OD plasticity is specifically due to reduced dLGN synaptic inhibition, the author should generate conditional KO mice that dLGN synaptic inhibition was only interfered in adulthood.

2. The authors found that in juveniles, dLGN OD shift is dependent on V1 feedback, but not in adults. However, a recent work showed that the effects of V1 silencing on dLGN OD plasticity could differ with various starting points and duration of the V1 silencing and MD (Li et al., 2023). Could the authors provide more details of the MD and V1 silencing for an in-depth discussion?

References<br /> Li, N., Liu, Q., Zhang, Y., Yang, Z., Shi, X., and Gu, Y. (2023). Cortical feedback modulates distinct critical period development in mouse visual thalamus. iScience 26, 105752.<br /> Sommeijer, J.P., Ahmadlou, M., Saiepour, M.H., Seignette, K., Min, R., Heimel, J.A., and Levelt, C.N. (2017). Thalamic inhibition regulates critical-period plasticity in visual cortex and thalamus. Nat Neurosci 20, 1715-1721.

Reviewer #1 (Public Review):

This manuscript tackles an important question, namely how K+ affects substrate transport in the SLC6 family. K+ effects have previously been reported for DAT and SERT, but the prototypical SLC6-fold transporter LeuT was not known to be sensitive to the K+ concentration. In this manuscript, the authors demonstrate convincingly that K+ inhibits Na+ binding, and Na+-dependent amino acid binding at high concentrations, and that K+ inside of vesicles containing LeuT increases the transport rate. However, outside K+ apparently had very little effect. Uptake data are supplemented with binding data, using the scintillation proximity assay, and transition metal FRET, allowing the observation of the distribution of distinct conformational states of the transporter.<br /> Overall, the data are of high quality. I was initially concerned about the use of solutions of very high ionic strength (the Km for K+ is in the 200 mM range), however, the authors performed good controls with lower ionic strength solutions, suggesting that the K+ effect is specific and not caused by artifacts from the high salt concentrations.

The major issue I have with this manuscript is with the interpretation of the experimental data. Granted that the K+ effect seems to be complex. However, it seems counterintuitive that K+ competes with Na+ for the same binding site, while at the same time accelerating the transport rate. Even if K+ prevents rebinding of Na+ on the inside of vesicles, it would be expected that K+ then stabilizes this Na+-free conformation, resulting in a slowing of the transport rate. However, the opposite is found. I feel that it would be useful to perform some kinetic modeling of the transport cycle to identify a mechanism that would allow K+ to act as a competitive inhibitor of Na+ binding and rate-accelerator at the same time.

This ties into the second point: It is not mentioned in the manuscript what the configuration of the vesicles is after LeuT reconstitution. Are they right-side out? Is LeuT distributed evenly in inside-out and right-side out orientation? Is the distribution known? If yes, how does it affect the interpretation of the uptake data with and without K+ gradient?

Finally, mutations were only made to the Na1 cation binding site. These mutations have an effect mostly to be expected, if K+ would bind to this site. However, indirect effects of mutations can never be excluded, and the authors acknowledge this in the discussion section. It would be interesting to see the effect of K+ on a couple of mutants that are far away from Na+/substrate binding sites. This could be another piece of evidence to exclude indirect effects, if the K+ affinity is less affected.

Reviewer #2 (Public Review):

To characterize the relationship between Na+ and K+ binding to LeuT, the effect of K+ on Na+- dependent [3 H] leucine binding was studied using a scintillation proximity assay. In the presence of K+ the apparent affinity for sodium was reduced but the maximal binding capacity for this ion was unchanged, consistent with a competitive mechanism of inhibition between Na+ and K+.

To obtain a more direct readout of K+ binding to LeuT, tmFRET was used. This method relies on the distance-dependent quenching of a cysteine-conjugated fluorophore (FRET donor) by a transition metal (FRET acceptor). This method is a conformational readout for both ion- and ligand-binding. Along with the effect of K+ on Na+-dependent [3 H] leucine binding, the findings support the existence of a specific K+ binding site in LeuT and that K+ binding to this site induces an outward closed conformation.

It was previously shown that in liposomes inlaid with LeuT by reconstitution, intra-vesicular K+ increases the concentrative capacity of [ 3 H] alanine. To obtain insights into the mechanistic basis of this phenomenon, purified LeuT was reconstituted into liposomes containing a variety of cations, including Na+ and K+ followed by measurements of [ 3 H] alanine uptake driven by a Na+ gradient. The ionic composition of the external medium was manipulated to determine if the stimulation of [3 H] alanine uptake by K+ was due to an outward directed potassium gradient serving as a driving force for sodium-dependent substrate transport by moving in the direction opposite to that of sodium and the substrate. Remarkably it was found that it is the intra-liposomal K+ per se that increases the transport rate of alanine and not a K+ gradient, suggesting that binding of K+ to the intra-cellular face of the transporter could prevent the rebinding of sodium and the substrate thereby reducing their efflux from the cell. These conclusions assume that the measured radioactive transport is via right-side-out liposomes rather than from their inverted counterparts (in case of a random orientation of the transporters in the proteoliposomes). Even though this assumption is likely to be correct, it should be tested.

Since K+- and Na+-binding are competitive and K+ excludes substrate binding, the Authors chose to focus on the Na1 site where the carboxyl group of the substrate serves as one of the groups which coordinate the sodium ion. This was done by the introduction of conservative mutations of the amino acid residues forming the Na1 site. The potassium interaction in these mutants was monitored by sodium dependent radioactive leucine binding. Moreover, the effect the effect of Na+ with and without substrate as well as that of potassium on the conformational equilibria was measured by tmFRET measurements on the mutants introduced in the construct enabling the measurements. The results suggest that K+-binding to LeuT modulates substrate transport and that the K+ affinity and selectivity for LeuT is sensitive to mutations in the Na1 site, pointing toward the Na1 site as a candidate site for facilitating the interaction between K+ in some NSS members.

The data presented in this manuscript are of very high quality. They are a detailed extension of results by the same group (Billesbolle et. al, Ref. 16 from the list) providing more detailed information on the importance of the Na1 site for potassium interaction. Clearly this begs for the identification of the binding site in a potassium bound LeuT structure in the future. Presumably LeuT was studied here because it appears that it is relatively easy to determine structures of many conformational states. Furthermore, convincing evidence showed that the stimulatory effect of K+ on transport is not because of energization of substrate accumulation but is rather due to the binding of this cation to a specific site.

Reviewer #1 (Public Review):

Astrocytes are known to express neuroligins 1-3. Within neurons, these cell adhesion molecules perform important roles in synapse formation and function. Within astrocytes, a significant role for neuroligin 2 in determining excitatory synapse formation and astrocyte morphology was shown in 2017. However, there has been no assessment of what happens to synapses or astrocyte morphology when all three major forms of neuroligins within astrocytes (isoforms 1-3) are deleted using a well characterized, astrocyte specific, and inducible cre line. By using such selective mouse genetic methods, the authors here show that astrocytic neuroligin 1-3 expression in astrocytes is not consequential for synapse function or for astrocyte morphology. They reach these conclusions with careful experiments employing quantitative western blot analyses, imaging and electrophysiology. They also characterize the specificity of the cre line they used. Overall, this is a very clear and strong paper that is supported by rigorous experiments. The discussion considers the findings carefully in relation to past work. This paper is of high importance, because it now raises the fundamental question of exactly what neuroligins 1-3 are actually doing in astrocytes. In addition, it enriches our understanding of the mechanisms by which astrocytes participate in synapse formation and function. The paper is very clear, well written and well illustrated with raw and average data.

Reviewer #2 (Public Review):

In the present manuscript, Golf et al. investigate the consequences of astrocyte-specific deletion of Neuroligin family cell adhesion proteins on synapse structure and function in the brain. Decades of prior research had shown that Neuroligins mediate their effects at synapses through their role in the postsynaptic compartment of neurons and their transsynaptic interaction with presynaptic Neurexins. More recently, it was proposed for the first time that Neuroligins expressed by astrocytes can also bind to presynaptic Neurexins to regulate synaptogenesis (Stogsdill et al. 2017, Nature). However, several aspects of the model proposed by Stogsdill et al. on astrocytic Neuroligin function conflict with prior evidence on the role of Neuroligins at synapses, prompting Golf et al. to further investigate astrocytic Neuroligin function in the current study. Using postnatal conditional deletion of Neuroligins 1, 2 and 3 specifically from astrocytes, Golf et al. show that virtually no changes in the expression of synaptic proteins or in the properties of synaptic transmission at either excitatory or inhibitory synapses are observed. Moreover, no alterations in the morphology of astrocytes themselves were found. The authors conclude that while Neuroligins are indeed expressed in astrocytes and are hence likely to play some role there, this role does not include any direct consequences on synaptic structure and function, in direct contrast to the model proposed by Stogsdill et al.

Overall, this is a strong study that addresses an important and highly relevant question in the field of synaptic neuroscience. Neuroligins are not only key regulators of synaptic function, they have also been linked to numerous psychiatric and neurodevelopmental disorders, highlighting the need to precisely define their mechanisms of action. The authors take a wide range of approaches to convincingly demonstrate that under their experimental conditions, no alterations in the levels of synaptic proteins or in synaptic transmission at excitatory or inhibitory synapses, or in the morphology of astrocytes, are observed.

One caveat to this study is that the authors do not directly provide evidence that their Tamoxifen-inducible conditional deletion paradigm does indeed result in efficient deletion of all three Neuroligins from astrocytes. Using a Cre-dependent tdTomato reporter line, they show that tdTomato expression is efficiently induced by the current paradigm, and they refer to a prior study showing efficient deletion of Neuroligins from neurons using the same conditional Nlgn1-3 mouse lines but a different Cre driver strategy. However, neither of these approaches directly provide evidence that all three Neuroligins are indeed deleted from astrocytes in the current study. In contrast, Stogsdill et al. employed FACS and qPCR to directly quantify the loss of Nlgn2 mRNA from astrocytes. This leaves the current Golf et al. study somewhat vulnerable to the criticism, however unlikely, that their lack of synaptic effects may be a consequence of incomplete Neuroligin deletion, rather than a true lack of effect of astrocytic Neuroligins.

Reviewer #3 (Public Review):

This study investigates the roles of astrocytes in the regulation of synapse development and astrocyte morphology using conditional KO mice carrying mutations of three neuroligins1-3 in astrocytes with the deletion starting at two different time points (P1 and P10/11). The authors use morphological, electrophysiological, and cell-biological approaches and find that there are no differences in synapse formation and astrocyte cytoarchitecture in the mutant hippocampus and visual cortex. These results differ from the previous results (Stogsdill et al., 2017), although the authors make several discussion points on how the differences could have been induced. This study provides important information on how astrocytes and neurons interact with each other to coordinate neural development and function. The experiments were well-designed, and the data are of high quality.

Reviewer #1 (Public Review):

In this paper, Scholz and colleagues introduce a new paradigm aimed to bridge the gap between two domains that rely on hierarchical processing: language and memory. They find that, generally in line with their hypotheses, hierarchical processing is associated with activation in hippocampus (especially anterior), medial prefrontal cortex (mPFC), posterior superior temporal sulcus (pSTS), and inferior frontal gyrus (IFG). They also report that these effects in IFG are particularly strong late in the task, once participants have had a lot of experience and processing is presumably more automatic.

This work has many strengths. The goal to bridge these literatures by developing a new task is commendable. I appreciate also that the authors separately validated their new task behaviorally by comparing it to another accepted as tapping hierarchical processing. I also liked that the authors were transparent about their hypotheses, and certain analyses like the grid coding one that was planned but did not work out. I do however have a number of concerns about the interpretations of the findings, such as whether some patterns are ambiguous as to the true underlying effects. I also have a number of clarification questions. All concerns are described below.

1. Broadly, I would like to see the authors provide more information and logic on why hierarchical processing should be associated with a big reduction in univariate activation between P1 and P2-why would this signify item in contexts binding? How does this relate to existing work using other methods (e.g., like animal studies, which seem to make predictions more about representational structures)?

2. There are many differences between what kind of information participants are processing between Position 1 and Position 2 for the HIER but not ITER conditions, and these may not be related to the hierarchical structure specifically. Related to but I think distinct from some of the limitations mentioned in the Discussion is the fact that in the HIER condition, what is happening cognitively between Position 1 and Position 2 items is more distinct (attending to color for position 1, and shape for position 2), whereas the two positions are equivalent in the ITER condition. This is a bit different from the authors' intended manipulation of hierarchy, because it involves a specific dimension. A stronger design might have been to flip the dimensions with respect to position specifically, to make shape sometimes important for position 1, and color for position 2 (perhaps by counterbalancing across subjects, so half would see the current P1=color and P2=shape rules, and the other half P1=shape and P2=color rules). Another important difference between color and shape is that while color is a simple binary distinction that participants can make based on their preexisting knowledge of red versus green, and to which they can assign a verbal label; whereas, the shape distinction was something novel they acquired during the experiment, has no real-world validity or meaning, and would presumably rely more on visuospatial processing. The shape dimension was also much more variable, I believe. I should say that I do find comfort in a few things - (1) that behavior on this task is correlated with another one that also indexes hierarchy processing, and (2) that the results show regional specificity in a pattern at least not easily explained by this distinction. However, I do think future work will be needed to ask whether it is hierarchy processing per se or rather something to do with the particular cognitive states engaged during each phase in this particular task that is eliciting activation in this set of regions. It would strengthen the paper to discuss this issue directly so readers are alerted to the caveat.

3. I did not understand what data went into creating the schematic in Figure 2E. First, I think this depiction of a gradient might be easily misinterpreted because it seems to imply that the authors have a higher resolution analysis than they actually do. I believe the data were just analyzed in three subregions of hippocampus - head, body, and tail. Variability within each subregion (as seems to be implied by certain parts of a region being more grey and others more red/orange), is not something that could be assessed in this analysis. For example, why does the medial part of the head seem to be more "unspecific" whereas lateral regions look more HIER Pos1 specific? This type of depiction would only make sense in my mind if the authors had performed something like a voxelwise analysis to determine where specifically the interaction "peaks." I would recommend this visualization be cut or significantly changed to do away with the gradient.

4. I believe the authors have not reported enough information for us to know that hippocampus involvement indeed does not change with experience. It is interesting that hippocampus in the task x experience ROI analysis shows, if anything, bigger differentiation between the two tasks (numerically) for the late trials. This seems to go against the authors' hypothesis, and a lot of existing data, that hippocampus is preferentially involved in early (vs. late) learning. Given that the key signature in this region, though, is that it differentiates between position 1 and position 2 in HIER but not ITER, and doesn't show a big difference in magnitude across the two tasks, it makes me wonder whether the task x experience interaction collapsing across the two positions makes sense for this region. Did the authors consider a similar task x experience interaction within hippocampus, but additionally considering position? I think there are multiple ways to look at this question (e.g., either looking for a task x experience x position interaction, a task x experience within position 1, a task x position interaction separately in early vs. late portions of the task, or even a position x experience interaction only within the HIER task), and I'm sure the authors would be in a better place to decide on a specific path forward. The same logic might go for mPFC, which shows an interaction but no main effect of task. This relates to claims in the discussion as well, such as that "hippocampus was equally active in early and late trials," but given this analysis is collapsing across the dimension hippocampus (and mPFC) seem to be sensitive to (position), it seems like this could be masking an underlying effect in which hippocampus/mPFC might still be differentially involved early vs. late (i.e., they might show the task x position interaction preferentially during some task phases).

5. For the IFG regions, the task x experience interaction seems to be driven mainly by change (decrease in activation) for the ITER, rather than change in the HIER. The authors are at times careful to talk about this as "sustained" activity in IFG, which I appreciated, but other times talk about a "relative increase." I am not sure how I feel about that. I see the compelling evidence that there are task differences by experience, and that there is reduction for ITER that is interestingly not present for HIER, but I think I am still feeling uncomfortable with the term "increase" or even "relative increase" for HIER. For example, couldn't it simply be that the ITER task is requiring less processing with experience, whereas the HIER does not (perhaps because it requires more processing to begin with)? i.e., we do not know whether the reduction for ITER is simply a neural signal thing (i.e., activations diminish over time/experience) or a cognitive thing, specific to the ITER task. I think the authors are wanting to interpret the reductions as the former, but perhaps it would be more powerful to demonstrate if there was a baseline task that also showed reductions but for which not much would be expected in the way of cognitive change. Can the authors provide more justification for their choice of terminology (through either more logic or analyses), or if not, simply talk about it as sustained activity for HIER-which is especially interesting in the face of reductions for the ITER task?

6. Please define what is meant by the term "automaticity" in the introduction. A clearer definition of the concept would make the paper generally easier to follow, and it would also help foreshadow the hypotheses about mPFC activity in the introduction. To this end, it could be useful to elaborate on how learning takes place in this task, how it could foster increasing automaticity, and how automaticity maps onto behaviour (e.g., is it RT decrease alone, which happens for both conditions in this task?) the brain regions discussed.

7. There was no association between brain and behavior, which the authors interpret as a positive (as therefore task difficulty differences could not explain the effects). However in light of these null findings, it is on the flip side hard to know whether this neural engagement carries any behavioral significance. It seems to me as though the authors' framework makes predictions about brain-behavior correlations that were not tested in the manuscript. For example, I believe the authors asked whether behavior overall was correlated with activation. However, wouldn't the automaticity in IFG explanation for example predict that more engagement or an increase in engagement from early to late should be associated with e.g., faster RTs-not necessarily a relationship overall?

8. On p. 8, it is stated that "In the hippocampus, this effect is driven by higher betas for the presentation of the first object (H1 > I1) and lower betas for the second object (H2 < I2) when comparing across tasks." Can the authors confirm whether the pairwise comparisons following up on the interaction here are significant, or rather if they are referring to a numerical difference in the betas? It looked like the same (numerically) would be true for mPFC; is there a reason why the same information is not included for the mPFC ROI? Also, might the authors provide more speculation as to why one might see both enhanced and reduced activation for P1 and P2, respectively?

9. I was expecting some discussion of how hippocampus does not seem to show preferential involvement early, given that its potential role being restricted to early in learning (i.e., during acquisition only) was one of the primary motivators for using this task. As noted in my above comment (#4), I am not quite sure that I think there is evidence that the hippocampal role remains constant over this task, given the analyses provided (i.e., that they did not look at the position effect for early vs. late). However upon further analysis if it does seem to be more stable, and/or if it even increases over experience, the authors might want to talk about that in the Discussion.

10. The fact that the hierarchies in this paradigm unfolded over time makes them distinct on some level from the hierarchies present in the VRT task that was used to validate the HIER task's hierarchical processing demands. For example, there might be additional computations required to processes these temporally ordered structures, support online maintenance, and so on. It may be worth considering this aspect of the task, and whether/to what extent the results could be related to it, in the paper.

11. I also have many methodological and analytic clarification questions, which I detail in the recommendations for authors.

Reviewer #2 (Public Review):

In this manuscript, Scholz et al., adopt a set of tasks to study how brain regions are differentially activated with temporal context clues. In one task, the first item in a two item sequence will dictate the value of the second. In another task, there is no hierarchy in temporal order, though subjects must still maintain information across the delay to add the value of the two presented items. Using univariate analyses, the authors found many regions that showed an interaction between item position and task, including: the mPFC, anterior hippocampus and the left prefrontal and posterior temporal cortices. The results are interpreted as evidence for a dedicated system for understanding hierarchical relationships across domains as various as spatial cognition, music, and language.

The question raised by the authors is important and fMRI may be an appropriate means of studying the neural basis for hierarchical computations. The main limitation of the manuscript, and one that is briefly mentioned and dismissed in the discussion is the task design, which confounds whether or not a hierarchical relationship must be formed, and the content of the information that must be held across working memory (color in the hierarchy task and number in the iterative task).

The authors also report an interesting difference between the activation observed in the head and tail of the hippocampus during the different tasks. However, the authors compare each region independently, show one is significant and the other is not, and then conclude "the effect of hierarchical context representation in the hippocampus is specific to its anterior regions." Such a conclusion requires direct comparison of the regions.

Finally, it isn't clear if the motivating prior work makes a simple univariate prediction. A strong prediction however is that the representational similarity should be very different for objects in the first versus second position in the hierarchy task and much less so in the iterative task. Such a representational similarity analysis would better connect this study to prior research and to the hypothesis that hierarchical processing affects the coding of items in sequence.

Reviewer #3 (Public Review):

My biggest concern is that I am not convinced that the HIER task is indeed hierarchical. Based on Figure 1B, it seems that the rules of the task can be listed as "Green and same = 2", "Green and different = 4", "Red and same = 1", "Red and different = 3". If so, the hierarchical organisation intended by the authors can be trumped by simply memorising these 4 options. The rote memory explanation is even more likely given that the other, ITER task, clearly required rote memory. Hence the two tasks may vary simply in the amount of difficulty/WM involvement.

Reviewer #2 (Public Review):

The manuscript of Duewell et al has made critical observations that help to understand the mechanisms of activation of the class IA PI3Ks. By using single-molecule kinetic measurements, the authors have made outstanding progress toward understanding how PI3Kbeta is uniquely activated by phosphorylated tyrosine kinase receptors, Gbeta/gamma heterodimers and the small G protein Rac1. While previous studies have defined these as activators of PI3Kbeta, the current manuscript makes clear the quantitative limitations of these previous observations. Most previous quantitative in vitro studies of PI3Kbeta activation have used soluble peptides derived from bis-phosphorylated receptors to stimulate the enzyme. These soluble peptides stimulate the enzyme, and even stimulate membrane interaction. Although these previous studies showed that the release of p85-mediated autoinhibition unmasks an intrinsic affinity of the enzyme for lipid membranes, they ignored what would be the consequence of these peptide sequences being present in the context of intrinsic membrane proteins. The current manuscript shows that the effect of membrane-conjugated peptides on the enzyme activity is profound, in terms of recruiting the enzyme to membranes. In this context, the authors show that G proteins associated with the membranes have an important contribution to membrane recruitment, but they also have a profound allosteric effect on the activity on the membrane, These are observations that would not have been possible with bulk measurements, and they do not simply recapitulate observations that were made for other class IA PI3Ks.

An important observation that the authors have made is that Gbeta/gamma heterodimers and RAc1 alone have almost no ability to recruit PI3Kbeta to the membranes that they are using, and this is central to one of the most profoundly novel activation mechanisms offered by the manuscript. The authors propose that the nSH2- and Gbeta/gamma binding sites partially overlap, so that Gbeta/gamma can only bind once the nSH2 domain releases the p110beta subunit. This mechanism would mean that once the nSH2 is engaged by membrane-congugated pY, the Gbg heterodimer can bind and increase the association of the enzyme with membranes. Indeed, this increased membrane association is observed by the authors. However, the authors also show that this increased recruitment to membranes accounts for relatively little increase in activity, and that the far greater component of activation is due to an allosteric effect of the membrane association on the activity of the enzyme. The proposal for competition between Gbg binding and the nSH2 is consistent with the behavior of an nSH2 mutant that cannot bind to pY and which, consequently, does not vacate the Gbg-binding site. In addition to the outstanding contribution to understanding the kinetics of activation of PI3Kbeta, the authors have offered the first structural interpretation for the kinetics of Gbg activation in synergy with pY activation. The proposal for an overlapping nSH2/Gbg binding site is supported by predictions made by John Burke, using alphafold multimer. Although there is no experimental structure to support this structural model, it is consistent with HDX-MS analyses that were published previously.

Reviewer #1 (Public Review):

The manuscript by Aguirre et al. describes an elegant approach for developing selective inhibitors of inositol hexakisphosphate kinases (IP6Ks). There are 3 IP6K isozymes (IP6K1-3) in humans, which catalyze the synthesis of inositol pyrophosphates. The lack of isozyme-selective inhibitors has hampered efforts to understand their individual physiological roles. While several inhibitors of IP6Ks have been described, their either lack isozyme selectivity or inhibit other kinases. To address this gap, Aguirre et al. used an analog-sensitive approach, which involves the identification of a mutant that, in an ideal world, doesn't impact the activity of the enzyme but renders it sensitive to an inhibitor that is absolutely selective for the engineered (analog-sensitive) enzyme. Initially, they generated the canonical gatekeeper (Leu210 in IP6K1) mutations (glycine and alanine); unfortunately, these mutations had a deleterious effect on the enzymatic activity of IP6K1. Interestingly, mutation of Leu210 to a valine, a subtly smaller amino acid, didn't affect enzymatic activity. The authors then designed a clever high-throughput assay to identify compounds that show selectivity for L210V IP6K1 versus WT IP6K1. The assay monitors the reverse reaction catalyzed by IP6Ks, monitoring the formation of ATP using a luminescence-based readout. After validating the screen, the authors screened 54,912 compounds. After culling the list of compounds using several criteria, the authors focused on one particular compound, referred to as FMP-201300. FMP-201300 was ~10-fold more potent against L210V IP6K1 compared to WT IP6K1. This selectivity was maintained for IP6K2. Mechanistic studies showed that FMP-201300 is an allosteric inhibitor of IP6K1. The authors also did a small SAR campaign to identify key functional groups required for inhibition.

Overall, this manuscript describes a unique and useful strategy for developing isozyme-selective inhibitors of IP6Ks. The serendipitous finding that subtle changes to the gatekeeper position can sensitize the IP6K1 mutant to allosteric inhibitors will undoubtedly inspire other analog-sensitive inhibitor studies. The manuscript is well-written and the experiments are generally well-controlled.

Reviewer #2 (Public Review):

Fiedler and colleagues set out to establish an analog-sensitive approach for selective inhibition of the mammalian IP6K isozymes. IP6Ks are inositol hexakisphosphate kinases, and the authors found that the classic glycine and alanine gatekeeper mutation (established by Kevan Shokat as the "bump and hole approach" for various protein kinases) resulted in limited catalytic efficiency. Therefore, the authors decided to use a leucine-to-valine mutation, which did not affect kinase activity, but, unfortunately, was less amenable to any of the well-established analog-sensitive kinase inhibitors such as PP1 and naphthyl-PP1. To overcome this limitation, the authors performed an elegant HT screen and identified a benzimidazole-based mutant-selective small molecule inhibitor. A focused SAR analysis combined with detailed kinetic studies revealed the hit molecule FMP-201300 as an allosteric inhibitor of IP6K mutants. While co-crystallization experiments failed, the authors used high-end HDX-MS measurements to gain insight into the structural and conformational determinants of mutant selectivity.

Overall, this is an excellent study of high quality. The identified FMP-201300 has the potential for further compound and probe development. My only minor comment is that the authors could spend more time discussing the proposed allosteric binding mode of FMP-201300 and provide more detailed figures to highlight the proposed interactions with the protein and the conformational changes that must ultimately take place to accommodate the allosteric modulator. I appreciate that the co-crystallization experiments did not yield bound inhibitor structures, but perhaps the authors could consider MD simulations to complete their study.

Reviewer #1 (Public Review):

Transcriptional readthrough, intron retention, and transposon expression have been previously shown to be elevated in mammalian aging and senescence by multiple studies. The current manuscript claims that the increased intron retention and readthrough could completely explain the findings of elevated transposon expression seen in these conditions. To that end, they analyze multiple RNA-seq expression datasets of human aging, human senescence, and mouse aging, and establish a series of correlations between the overall expression of these three entities in all datasets.

While the findings are useful, the strength of the evidence is incomplete, as the individual analyses unfortunately do not support the claims. Specifically, to establish this claim there is a burden of proof on the authors to analyze both intron-by-intron and gene-by-gene, using internal matched regions, and, in addition, thoroughly quantify the extent of transcription of completely intergenic transposons and show that they do not contribute to the increase in aging/senescence. Furthermore, the authors chose to analyze the datasets as unstranded, even though strand information is crucial to their claim, as both introns and readthrough are stranded, and if there is causality, than opposite strand transposons should show no preferential increase in aging/senescence. Finally, there are some unclear figures that do not seem to show what the authors claim. Overall, the study is not convincing.

Major concerns:

1. Why were all datasets treated as unstanded? Strand information seems critical, and should not be discarded. Specifically, stranded information is crucial to increase the confidence in the causality claimed by the authors, since readthrough and intron retention are both strand specific, and therefore should influence only the same strand transposons and not the opposite-strand ones.

2. "Altogether this data suggests that intron retention contributes to the age-related<br /> increase in the expression of transposons" - this analysis doesn't demonstrate the claim. In order to prove this they need to show that transposons that are independent of introns are either negligible, or non-changing with age.

3. Additionally, the correct control regions should be intronic regions other than the transposon, which overall contributed to the read counts of the intron.

4. Furthermore, analysis of read spanning intron and partly transposons should more directly show this contribution.

5. "This contrasts with the almost completely even distribution of randomly permuted transposons." How was random permutation of transposons performed? Why is this contract not trivial, and why is this a good control?

6. Fig 4: the choice to analyze only the 10kb-20kb region downstream to TSE for readthrough regions has probably reduced the number of regions substantially (there are only 200 left) and to what extent this faithfully represent the overall trend is unclear at this point.

7. Fig. 5B shows the opposite of the authors claims: in the control samples there are more transposon reads than in the KCl samples.

8. "induced readthrough led to preferential expression of gene proximal transposons (i.e. those within 25 kb of genes), when compared with senescence or aging". A convincing analysis would show if there is indeed preferential proximity of induced transposons to TSEs. Since readthrough transcription decays as a function of distance from TSEs, the expression of transposons should show the same trends if indeed simply caused by readthrough. Also, these should be compared to the extent of transposon expression (not induction) in intergenic regions without any readthrough, in these conditions.

Reviewer #2 (Public Review):

In this manuscript, the authors examined the role of transcription readout and intron retention in increasing transcription of transposable elements during aging in mammals. It is assumed that most transposable elements have lost the regulatory elements necessary for transcription activation. Using available RNA-seq datasets, the authors showed that an increase in intron retention and readthrough transcription during aging contributes to an increase in the number of transcripts containing transposable elements.

Previously, it was assumed that the activation of transposable elements during aging is a consequence of a gradual imbalance of transcriptional repression and a decrease in the functionality of heterochromatin (de repression of transcription in heterochromatin). Therefore, this is an interesting study with important novel conclusion. However, there are many questions about bioinformatics analysis and the results obtained.

Major comments:

1. In Introduction the authors indicated that only small fraction of LINE-1 and SINE elements are expressed from functional promoters and most of LINE-1 are co-expressed with neighboring transcriptional units. What about other classes of mobile elements (LTR mobile element and transposons)?

2. Results: Why authors considered all classes of mobile elements together? It is likely that most of the LTR containing mobile elements and transposons contain active promoters that are repressed in heterochromatin or by KRAB-C2H2 proteins.

3. Fig. 2. A schematic model of transposon expression is not presented clearly. What is the purpose of showing three identical spliced transcripts?

4. The study analyzed the levels of RNA from cell cultures of human fibroblasts of different ages. The annotation to the dataset indicated that the cells were cultured and maintained. (The cells were cultured in high-glucose (4.5mg/ml) DMEM (Gibco) supplemented with 15% (vol/vol) fetal bovine serum (Gibco), 1X glutamax (Gibco), 1X non-essential amino acids (Gibco) and 1% (vol/vol) penicillin-streptomycin (Gibco). How correct that gene expression levels in cell cultures are the same as in body cells? In cell cultures, transcription is optimized for efficient division and is very different from that of cells in the body. In order to correlate a result on cells with an organism, there must be rigorous evidence that the transcriptomes match.

5. The results obtained for isolated cultures of fibroblasts are transferred to the whole organism, which has not been verified. The conclusions should be more accurate.

6. The full pipeline with all the configuration files IS NOT available on github (pabisk/aging_transposons).

7. Analysis of transcripts passing through repeating regions is a complex matter. There is always a high probability of incorrect mapping of multi-reads to the genome. Things worsen if unpaired short reads are used, as in the study (L=51). Therefore, the authors used the Expectation maximization algorithm to quantify transposon reads. Such an option is possible. But it is necessary to indicate how statistically reliable the calculated levels are. It would be nice to make a similar comparison of TE levels using only unique reads. The density of reads would drop, but in this case it would be possible to avoid the artifacts of the EM algorithm.

Reviewer #1 (Public Review):

The manuscript entitled: "TCR-pMHC complex formation triggers CD3 dynamics" by Van Eerden et al. mainly uses coarse-grained molecular dynamics to probe the dynamic changes, in terms of CDε spatial arrangements around 226 TCRs, before and after the engagements of MCC/I-Ek. The broader distributions of CDε iso-occupancies after pMHC binding correlate with the decreases of TCR-CD3 contacts and extensions of TCR conformations. Given the observed release of motion restrictions upon antigen recognition, the authors proposed a "drawbridge" model to describe the initial triggering processes from pMHC association to TCR straightening, FG-loop getaway, and CD3 enhanced mobility. In addition, the authors briefly investigated the functional effects of the rigidified connecting peptide (CP) in T-cell activation using in silico and in vitro mutagenesis. The manuscript raises an important and exciting hypothesis about the allostery of TCR-CD3 during TCR triggering; however, due to current not-yet-convincing evidence, both computationally and experimentally, in supporting their conclusions.

1. As mentioned by the authors, the TCR triggering and T cell activation have been illustrated by a number of models, such as mechanosensing and kinetic proofreading, "in which TCRs discriminate agonistic from antagonistic pMHCs." However, the critical feature of antigen discrimination is lacking in the drawbridge model. So far, the CDε movements qualitatively distinguish on and off states. The simulation of the antagonist or weaker binder would strengthen the manuscript by demonstrating the relevance of CDε mobility in the triggering mechanism. 226 TCR associated with K99E/I-Ek has been resolved in Ref (DOI: 10.4049/jimmunol.1100197), which can potentially serve as the "intermediate" system to formulate the gradual increase of CDε dynamics.

2. The linkage between conserved motifs in CP and CDε mobility is less apparent to this reviewer. The notion of the rigidified hinge (PP) requires further clarification. Computationally, the details of fine-grained simulations are required to justify the origin of the apparent mobility increase in PP. The direct comparison between Fig. 2 and Fig. 7 can help assess the relevance of CP through the alignment by FG-loop at a fixed direction in polar coordinates. Experimentally, anti-CD3 positive experiments and, ideally, another antagonist on 3A9 TCRs can strengthen the current functional assay. The baseline level of TCR expression (after positive selection) and 0h activation (Fig. S8) is missing.

3. Regarding the section "The TCRβ FG loop acts as a gatekeeper," besides contact analysis, additional motion analysis, such as RMSF or PCA, can further establish the importance of FG loops.

4. The discussion on anti-CD3 antibody effects and their potential contribution to CD3 mobility is highly recommended.

Reviewer #2 (Public Review):

In this research article a new allosteric mechanism for T cell receptor (TCR) triggering upon peptide-MHC complex binding is presented in which conformational change in the TCR facilitates activation by controlling CD3 dynamics around the TCR. The authors find that the Cb FG loop acts as a gatekeeper and Cb connecting peptide acts as a hinge to control TCR flexibility.

As an initial result, the authors set up two sets of simulations - TCR-CD3 and pMHC-TCR-CD3 in POPC bilayers and identified that the CD3e chains exhibit a wider range of mobility in the pMHC-TCR-CD3 system as compared to the TCR-CD3 system. Next, they examined the contacts between all subunits during the course of both simulations and established that CD3g and CD3eg made far fewer contacts with TCRb in the pMHC-TCR-CD3 simulations. Next, they identified that the TCR is extended in the pMHC-TCR-CD3 simulations with larger tilt angle of 150º and FG loop acts as gatekeeper that allows CD3 movements upon pMHC binding. Finally, Mutations in Cb connecting peptide regions indicated rigidified TCR leading to hypersensitive TCR, proved both by simulations and in vitro experiments.

The following major concerns must be addressed.

Major concerns:

1) The simulations were performed with intracellular regions unfolded and free from the membrane. A more complete system should have the intracellular regions embedded in the membrane. An NMR structure of CD3e is available (Xu et al., Cell, 2008) and could have been modeled into the TCR-CD3 system before the simulation. Prakaash et al., (PLoS, Comput Biol, 2021) studied cytoplasmic domain motions during in their simulation experiments.

2) Comparing Fig. 2C and Fig.7C, the movement of CD3eg is more restricted in Fig.7C. Is this because the simulation time is lower in the mutation experiments?

3) Only TCR-CD3 simulation were performed for PP and AA mutants. A simulation with pMHC (pMHC-TCRmutants-CD3) should be performed to show increased CD3 mobility.

4) Using CD3e antibody, OKT3, for activation instead of pMHC as a separate experiment would add more value to this study. They can look at CD3 mobility and TCR elongation in the system with OKT3 antibody and compare it to the CD3 mobility and TCR elongation with the pMHC system. They can also use OKT3 with AA and PP mutants and perform both simulation and in vitro activation experiments.

5) The activation experimental data is rather underwhelming. The difference between WT and PP in 2hr experiment at 0.016 ug/mL looks exceedingly low. A stronger TCR-pMHC system should be considered for the in vitro activation experiments.

6) There is some concern that the scientific work lacks solid experimental functional data and lack of novelty due to earlier TCR-CD3 simulation studies (Pandey et al., 2021, eLife) that already reported flexibility and elongation of the complex.

Reviewer #3 (Public Review):

The authors first explore structural differences of unbound TCR-CD3 complexes and pMHC-bound TCR-CD3 complexes with coarse-grained simulations. In the simulations with pMHC-bound complexes, the transmembrane (TM) domains of the TCR-CD3 complex and of pMHC are embedded in two opposing membrane patches. In the pMHC membrane patch, a pore is created and stabilised in the simulation setup with the aim to allow water transport in and out of the compartment between the membranes. The authors report a more upright conformation of the TCR extracellular (EC) domain in the simulations in which this EC domain is bound to pMHC, compared to simulations with unbound TCR, and postulate an allosteric signalling model based on these apparent conformational changes and associated changes in TCR-CD3 quaternary arrangements. Subsequently, the authors identify a GxxG motif in the TCRbeta connecting peptide between EC domain and TM domain as putative hinge in allosteric signalling, and explore the effect of double proline and double alanine substitutions in atomistic simulations and experiments.

While these simulation and experimental setups and the addressed questions are of interest in the field, the following weaknesses prevail in my overall assessment of the work:

(1) I am not convinced that the reported coarse-grained simulation results are sound or allow to draw the conclusions stated in the work. In the simulations with a pMHC-bound TCR-CD3 complex, the intermembrane distance in the setup shown in Figure S1 appears excessively large and likely leads to a rather strong force in the membrane-vertical direction and to the reported upright conformation of the TCR EC domain. This upright confirmation thus appears to be a consequence of force from the simulation setup, rather than a consequence of pMHC binding alone as suggested by the authors. While the membrane pore in principle allows water exchange, the relaxation of the intermembrane distance resulting from this water exchange in the 10 microsecond long simulation trajectories is not (but needs to be) addressed. This relaxation eventually would lead to an equilibrated membrane separation, in which essentially no force is exerted on the TCR-pMHC EC complex. However, I suspect that this computationally demanding equilibration is not achieved in the simulations, with the consequence that forces on the TCR-pMHC EC complex in the membrane-vertical direction remain.

In addition, I am not convinced that the Martini force field of the coarse-grained simulations allows a reliable assessment of the quaternary interactions between the TCR and CD3 EC domains. Getting protein structures and interactions right in coarse-grained simulations is notoriously difficult. In simulations with the coarse-grained Martini force field, secondary protein structures are constrained as a standard procedure, and the authors also use a recommended Go-potential procedure, likely to stabilise tertiary protein structures. The quaternary interactions between the TCR EC domain and the pMHC EC domain are modelled by rather strong harmonic constraints to prevent dissociation. While the treatment of the quaternary interactions between the TCR EC domain and the CD3 EC domains in the simulations is not (but needs to be) addressed in detail, I suspect that there are no additional, or only weak constraints to stabilise these interactions. In any case, I think that a faithful representation of these quaternary interactions is beyond the reach of the Martini force field, as is the reported diffusion of the CD3 EC domains around the TCR EC domain, which plays a central role in the allosteric mechanism proposed by the authors (see Fig 2 and 5).

(2) The allosteric model suggested by the authors is motivated in an introduction that appears to omit central controversial aspects in the field, as well as experimental evidence that is not compatible with allosteric conformational changes in the TCR. These aspects are:

- The mechanosensor model is controversial. In original versions of this model, a transversal force has been postulated to be required for T cell activation. However, more recent single-molecule force-sensor experiments reported in J Goehring et al., Nat Commun 12, 1 (2021) provide no evidence for a scenario in which transversal forces beyond 2 pN are associated with T cell activation.

- The role of catch bonds is controversial. Evidence for TCR catch bonds has been mainly obtained in experimental setups using the biomembrane force probe, in which force is applied to TCRs on the surface of T cells, but is not reproduced in experimental setups using isolated TCRs, see e.g. L Limozin et al., PNAS 116, 16943 (2019)

- Ref. 1 of the manuscript prominently discusses the kinetic segregation model of T cell activation, which is not (but needs to be) addressed in the introduction. In this model, exclusion of CD45 from close-contact zones around pMHC-bound TCRs triggers T cell activation. The model is supported by evidence from diverse experiments, see for example M Aramesh et al., PNAS 118, e2107535118 (2021) and Ref. 1. At least part of this evidence is not compatible with mechanosensing or allosteric models of T cell activation.

Reviewer #2 (Public Review):

• The central component of the Nuclear Pore Complex (NPC) that controls nucleocytoplasmic transport is the assembly of the intrinsically disordered proteins (IDPs) that line its passageway. Nanopore based mimics functionalized with these IDPs have been an important tool in understanding the mechanisms of protein transport through the NPC. This paper develops a new type of nanopore NPC mimic that acts as Zero Mode Waveguide enabling optical detection of protein translocations on the single molecule level in pores of different diameters. This is a significant improvement over previous mimics, where optical detection was used only for measurement of bulk fluxes, while single molecule detection relied on electrochemical methods that potentially introduce substantial artifacts. Studying the dependence of transport on the pore diameter is interesting because of its important connections to mechanosensitivity of protein partitioning in cells, which can be difficult to directly control and study in live cells.

• The authors study the transport of individual transport proteins in the dilute regime, and compare the transport of the transport proteins that naturally carry cargoes through the NPC with the transport of BSA that serves as a neutral control. The paper confirms the insights of previous work by the same and other authors - IDP functionalized nanopores are selective in a sense that they conduct the transport proteins well while blocking the passage of BSA. As reported in the paper, the selectivity disappears at large pore diameters which become similar to empty pores because the IDPs don't stretch far enough to cover the pore cross-section.

• The authors use one-bead-per-amino acid coarse grained modeling of the IDPs that they developed and validated previously, to model the distribution of the IDPs in the pores. Combining the simulations with the recently developed "void" model of transport through IDP network and phenomenological transport models, they provide an explanation for the observed reduction in the flux of the neutral control proteins compared to that of transport proteins. The translocation of transport proteins is not modeled directly.

• Together, the experimental and the computational results constitute convincing evidence that points toward the correctness of our current understanding of the physical mechanisms of NPC transport.

• The authors study interference between the transport proteins and the neutral control proteins at high concentrations of the latter, where the pore is occupied by multiple transport proteins. The results appear to be different from previous observations (but more study is needed). I think more discussion of how the results seem with the previous work and what are the potential implication for NPC transport would be welcome.

• The authors use simulations and phenomenological models of transport to analyze the crowded regime. It appears there are some inconsistencies in the application of these models in the dilute and crowded regimes, that should be clarified.

• Some details of the experimental system and the appropriateness of the transport models should be explained more - such as the role of the hydrodynamic pressure gradient.

Reviewer #1 (Public Review):

The contribution of Klughammer et al reports on the fabrication and functionalization of zero-mode waveguides of different diameters as a mimic system for nuclear pore complexes. Moreover, the researchers performed molecular transport measurements on these mimic systems (together with molecular dynamic simulations) to assess the contribution of pore diameter and Nsp functionalization on the translocation rates of BSA, the nuclear transport protein Kap95 and finally the impact of different Kap95 concentrations on BSA translocation and overall selectivity of the mimicked pores as a function of their diameter. In order to assess the effect of the Nsp1 on the coated pores to the translocation rates and molecular selectivity they also conducted separated experiments on bare nano-pores, i.e., without coating, and of different diameters. One of the most novel aspects of this contribution is the detection scheme used to assess the translocation rates & selectivity, i.e., the use of an optical scheme based on single molecule fluorescence detection as compared to previous works that have mostly relied on conductance measurements. The results are in general convincing, the experiments carefully performed and the procedures explained in detail. Some weaknesses are identified on the FDTD simulations and interpretation of the single molecule data since they might be affected by quenching of the dye in close proximity to the pore. These weaknesses should be clarified and discussed properly.

Importantly, this study provides new insights on the mechanisms of nuclear transport contributing to further our understanding on how real nuclear-pore complexes (i.e., in living cell) can regulate molecular transport. The recent findings that the nuclear pore complexes are sensitive to mechanical stimulation by modulating their effective diameters, adds an additional level of interest to the work reported here, since the authors thoroughly explored different nano-pore diameters and quantified their impact on translocation and selectivity. There are multiple avenues for future research based on the system developed here, including higher throughout detection, extending to truly multicolor schemes or expanding the range of FG-Nups, nuclear transport proteins or cargos that need to be efficiently l transported to the nucleus through the nuclear pore complexes. As a whole, this is an important contribution to the field.

Reviewer #3 (Public Review):

In this study, a minimalistic setup was used to investigate the selectivity of the nuclear pore complex as a function of its diameter. For this an array of solid-state pores was designed in a free-standing palladium membrane and attached to a PDMS-based fluidic cell, which could be mounted on a confocal microscope. In this way, the frequency of translocation events could be measured in an unbiased manner, i.e., no voltage was applied in this setup to facilitate them as it was done previously (Kowalczyk et al., 2011; Ananth et al, 2018; Fragasso et al., 2021, 2022), and therefore they can be considered as spontaneous. Moreover, the pores exhibited the key properties of the nuclear pore complex: (i) the size of the pore, (ii) disordered FG Nups specifically attached in the central channel; (ii) transport receptors that can shuttle through the central channel by binding to the FG Nups. Additionally, the properties of such minimalistic system could be well controlled. This gave the authors an advantage to monitor the translocation of multiple fluorescently labeled molecules (e.g. Kap95 and BSA) simultaneously, in real time and under well controlled conditions.

Strength:<br /> By being able to adjust each system parameter independently, the authors were able to monitor a reciprocal influence of active transporters, such as Kap95, and passive diffusion (using BSA as passive cargo) at different pore sizes and protein concentrations. It was discovered that up to a certain pore size (ca. 50-60 nm, which is close to the diameter of the physiological nuclear pore complex) and the Nsp1 density, Kap95 binding in the pore significantly increases selectivity as it was previously predicted by 'Kap-centric control' model (Kapinos, et al, 2014, Wagner et al, 2015). However, in pores larger than 60 nm, this effect was fading and becoming negligible in very large pores (> 60 nm), showing that the pores could become leaky and less selective due to stretching, as has been previously suggested (Andreu et al., 2022). It was also shown that passive molecules, such as BSA, had no effect on the Kap95 translocation frequency through the pore.

The experimental data were also supported by coarse-grained modelling of Nsp1-coated pores, and the theoretical prediction correlates qualitatively with the experimentally obtained data. These simulations show that there is a relationship between pore diameter and Nsp1 conformation. Based on these simulations, the authors suggest that in small pores (<60 nm) Kap95 increases selectivity by interacting with the Nsp1-FG domains across the pore, whereas this is less likely for larger pore diameters and Kap95 may collapse the Nsp1-FG domains along the pore walls, making them more permeable.

Weaknesses:<br /> However, the simulations did not consider an effect of Kap95 on the conformation of the Nsp1 layer within the pore, which weakens the conclusion of Kap95-induced collapse, even though it seems very plausible.<br /> In addition, there is a discrepancy in the frequency of translocation events in different experimental setups reported in different studies. The authors suggest that this may be due to differences in the sensitivity of detection methods.

Strength of evidence:<br /> However, this does not detract from the results obtained in this work, as the conclusions are based on the relative changes compared to the numerous controls within the same experimental setup and a careful evaluation of all possible sources of error.

Reviewer #1 (Public Review):

Full activation of T cells requires not only antigen recognition through the T cell receptor, but also engagement of co-stimulation by the T cell. There are multiple co-stimulatory receptors that can be engaged by the T cell; yet, the downstream effects of signaling through these different receptors on T cell gene programs and function and are not yet fully understood. These questions are clinically important because genomic variants associated with immune and inflammatory disease map onto these different co-stimulatory receptors and, potentially, their downstream gene programs.

Based on these observations, the authors hypothesize that different modes of co-stimulation engage different genes and pathways that may be differentially associated with risk for inflammatory disease. To ask this question, the authors performs a comparative analysis of different co-stimulatory receptors, both CD28 - the most widely used form of co-stimulation for in vitro assays - as well as alternative modes of co-stimulation involving ICOS, CD6, CD27. They analyzing their effects on their T cell activation in vitro for human naive and memory CD4 cells, on gene expression using RNA-seq (at 24 hrs), on chromatin accessibility using ATAC-seq, and on specific proteins identified from transcriptomic data using flow cytometry.

From these experimental analysis, the authors conclude the following (1) alternative co-stimulation (ICOS, CD6, Cd27) can induce a *qualitatively* different gene and cellular program compared to canonical co-stim (CD28), resulting not only in less proliferation and cytokine production, as expected, but also in higher lysosome production and different metabolic programming. They also found that risk variants for inflammatory bowel disease mapped onto genes that were both shared across different modes of co-stimulation, as well as onto targets of specific co-stimulation.

This study and the authors' experimental system is well-designed to precisely identify genomic effects of co-stimulation, employing sorted subsets of human CD4 cells, as well as a in vitro setting that can effectively eliminate many confounding variables associated more complex scenarios. The transcriptome/chromatin accessibility measurements were also robustly analyzed and offer some support for the author's conclusion. However, there were two main weaknesses that limit that, if overcome, would enhance the authors' argument:

(1) It is not clear whether the qualitatively different effects of alternate co-stimulation compared to canonical CD28 co-stimulation, e.g. increased OXPHOS or lysosomal abundance for CD6, or heightened expression of genes or represent truly unique effects, or whether they simply represent effects of having quantitatively weaker strengths of CD28 co-stimulation. This concern would be addressed by an experiment doing a dose response curve for CD28 co-stimulation while measuring these variables (Fig. 6) or, more systematically, while performing RNA-seq. Also, to strengthen this argument, the authors would benefit from further in-depth literature discussion/analysis of the signaling pathways downstream of co-stimulation, to discuss molecular bases for different signaling, if any.

(2) There is no functional evidence to link differential activation of risk variant-associated genes by alternate co-stimulation with inflammatory disease. To show this, the authors can examine the activation of these genes (e.g. Bach2, Il18R1, from Table 2) using their assay, either using T cells from humans containing disease-associated variants at these gene loci, or by using T cells with a genetic disruption of the associated loci.

While providing insights for the pathogenesis of IBD, this study's main impact would be in the enhancing our understanding of how different modes of co-stimulation differ to activate T cells and prompt broader consideration of use of different co-stimulatory ligands in these in vitro assays and evaluation of their function in vivo.

Reviewer #2 (Public Review):

Voda et al examined the role of multiple co-stimulations on gene expression and chromatin accessibility of T cells. They further linked the roles of co-stimulatory proteins to genetic variants associated with IBD. They reported a shared effect of co-stimulatory proteins on gene expression and chromatin accessibility. In particular they reported the induction of genes associated with lysosome production with alternative co-stimulatory proteins. In linking human genetics to the effect of costimulation, they reported the largest enrichment of IBD risk variants in open chromatin regions shared by all costimulatory molecules.

The question that is being investigated in this manuscript is significant considering the requirement of costimulatory proteins in controlling T cell responses. However, the data presented and analyzes performed remain exploratory and it is not clear how it can advance our understanding of the link between IBD risk association and immune responses. At least one locus ( a target of shared/unique costimulatory molecules) should be selected and mechanistic investigation of the locus, transcription factors involved, and perturbation studies for understanding gene regulation should be performed.

Reviewer #1 (Public Review):

This manuscript provides an important case study for in-depth research on the adaptability of vertebrates in deep-sea environments. Through analysis of the genomic data of the hadal snailfish, the authors found that this species may have entered and fully adapted to extreme environments only in the last few million years. Additionally, the study revealed the adaptive features of hadal snailfish in terms of perceptions, circadian rhythms and metabolisms, and the role of ferritin in high-hydrostatic pressure adaptation. Besides, the reads mapping method used to identify events such as gene loss and duplication avoids false positives caused by genome assembly and annotation. This ensures the reliability of the results presented in this manuscript. Overall, these findings provide important clues for a better understanding of deep-sea ecosystems and vertebrate evolution.

Reviewer #2 (Public Review):

This paper presents improved, chromosome level assemblies of the hadal snailfish and Tanaka's snailfish. This is an extension and update of previous work from the group on the hadal snailfish genome. The chromosomal assemblies allow comparisons of genome architecture between a shallow water snailfish and the hadal snailfish to aid inference on timing of colonization of trenches and genomic changes that may have been adaptive for that move.

The comparisons in genomic architecture are compelling: genes present in Tanaka's snailfish that are lost in hadal snailfish that involve whole regions of the genome that no longer map even though adjacent regions do map between the species and across a large evolutionary distance to stickleback. Or genes that are duplicated in hadal snailfish but only appear as single copy in other fishes. The paper focuses on genes in the eye, in hearing, in circadian rhythms, and in ROS scavaging. These are all functions that could play a role in adapting to the hadal environment.

The genomic comparisons all seem sound. Stylistically I would prefer if the authors could introduce the gene product and protein function every time they introduce a gene locus. They introduce a gene and general function, but don't usually note what the protein encoded by the gene is and what it's specific function is.

I found the paper generally well written, and the data compelling and creatively displayed. There is room for improvement in places where additional details could be added (e.g. the choice to show expression data as TPMs) and the writing could be clarified.

Reviewer #1 (Public Review):

In the manuscript "Long‐read single‐cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells", S. Liu et al present a protocol combining 10x Genomics single-cell assay with Element LoopSeq synthetic long-read sequencing to study single nucleotide variants (SNVs) and gene fusions in Hepatocellular carcinoma (HCC) at single‐cell level. The authors were the first to combine LoopSeq synthetic long‐read sequencing technology and 10x Genomics barcoding for single cell sequencing. For each cell and each somatic mutation, they obtain fractions of mutated transcripts per gene and per each transcript isoform. The manuscript states that these values (as well as gene fusion information) provide better features for tumor-normal classification than gene expression levels. The authors identified many SNVs in genes of the human major histocompatibility complex (HLA) with up to 25 SNVs in the same molecule of HLA‐DQB1 transcript. The analysis shows that most mutations occur in HLA genes and suggests evolution pathways that led to these hypermutation clusters. Yet, very little is said about novel isoforms and alternative splicing in HCC cells, differences in isoform ratio between cells carrying different mutations, or diversity of alternative isoforms across cells. While the manuscript by Liu et al. presents a promising combination of technologies, it lacks significant insights, a comprehensive introduction, and has significant problems with data description and presentation.

Major comments:

1. The introduction section is scarce. It lacks description of important previous works focused on clustered mutations in cancers (for example, PMID35140399), on deriving the process of cancer development through somatic evolution (PMID32025013, from single cell data PMID32807900). Moreover, some key concepts e.g. mutational gene expression and mutational isoform expression are not defined. The introduction and the abstract contain slang expressions e.g. "protein mutation', a combination of terms I teach my students not to use.

2. In the results section, to select the mutations of interest, the authors apply UMAP dimensionality reduction to the mutation isoforms expression and cluster samples in UMAP space, then select the mutations that are present only in one cluster, then apply UMAP to the selected mutations only and cluster the samples again. The motivation for such a procedure seems unclear, could it be replaced with a more straightforward feature selection?

3. As I understand, the first "mutated isoform"-based UMAP clustering was built from expression levels of 205 "mutational isoforms". What was the purpose and outcome of the second "mutated isoform"-based UMAP clustering (Figure 2E)? In the manuscript the authors just describe the clusters and do not draw any conclusions or use the results of the clustering anywhere further.

4. The authors just cluster the data three times based on expression levels of different sets of "mutational isoforms" and describe the clusters. What do we need to gather from these clustering attempts besides the set of 113 mutations used for further analysis? What was the point of the re-clusterings? Did the authors observe improvement of the classification at each step?

5. The alignment of short reads generated from hypermutated transcriptomes is non-trivial. The proposed approach could address the issue without need for whole genome sequencing and offer insights about the cancer development through somatic evolution. Why didn't the authors use modern phylogenetic approaches in the "Evolution of mutations in HLA molecules" section or at least utilize the already performed clustering to infer cell lineages?

6. I am not sure I understood the definition of "mutated gene expression levels" and "mutated isoform expression levels" in the "Mutational gene expression and fusion transcript enhanced transcriptome clustering of benign hepatocytes and HCC" section. The authors mention that gene lists included all the isoforms within the same range of standard deviation. If I understand it correctly, they are equal if there is only one expressed transcript isoform. In that case, this overlap is not surprising at all.

7. "To investigate the roles of gene expression alterations that were not accompanied with isoform expression changes, UMAP analyses were performed based on the non‐overlapped genes." Venn diagrams (Sup Figure 8) show that there are much less "non-overlapped genes" than "genes that showed both gene and isoform level changes" for each SD threshold (for example, for SD>=0.8 59 vs 275). Could that be the reason why clustering based on the former group is worse i.e the cancer and normal cells are separated less clearly?

Reviewer #2 (Public Review):

In the present study, Liu et al present an analysis of benign and HCC liver samples which were subjected to a new technology (LOOP-Seq) and paired WES.  By integrating these data, the authors find isoforms, fusions and mutations which uniquely cluster within HCC samples, such as in the HLA locus, which serve as candidate leads for further investigation.  The main appeal of the study is in the potential of LOOP-Seq as a method to present isoform-resolved data without actually performing long-read sequencing.   While this presents an exciting new method, the current study lacks systematic comparisons with other technologies/data to test the robustness, reproducibility and utility of LOOP-Seq.  Further, this study could be further improved by giving more physiologic context and examples from the analyses, thus providing a new resource to the HCC community.  A few suggestions based on these are below:    

A primary consideration is that this seems to be the first implementation of LOOP-Seq, where the technology, while intriguing, has not been evaluated systematically.  It seems like a standard 10x workflow is performed, where exons are selectively pulled down and amplified.  Subsequent ultra-deep sequencing is assumed to give isoform-resolution of the sc-seq data.  To demonstrate the utility of the approach it would benefit the study to compare the isoform-resolved results with studies where long-read sequencing was actually performed (ex: https://journals.lww.com/hep/Fulltext/2019/09000/Long_Read_RNA_Sequencing_Identifies_Alternative.19.aspx, https://www.jhep-reports.eu/article/S2589-5559(22)00021-0/fulltext,  https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1010342).  Presumably, a fair amount of overlap should occur to justify the usage.  

Related to this point, the sc-seq cell types and benign vs HCC genes should be compared with the wealth of data available for HCC sc-seq  (https://www.nature.com/articles/s41467-022-32283-3, https://www.nature.com/articles/s41598-021-84693-w).  These seem to be important to benchmark the technology in order to demonstrate that the probe-based selection and subsequent amplification does not bias cell type definition and clustering.  In particular, https://www.nature.com/articles/s41586-021-03974-6 seems quite relevant to compare mutational landscapes from the data.<br /> <br /> From the initial UMAP clustering, it will be important to know what the identities are of the cells themselves.  Presumably there is quite a bit of immune cells and hepatocytes, but without giving identities, downstream mechanistic interpretation is difficult.  

In general, there are a fair amount of broad analyses, such as comparisons of hierarchical clustering of cell types, but very little physiologic interpretations of what these results mean.  For example, among the cell clusters from Fig 6, knowing the pathways and cell annotations would help to contextualize these results.  Without more biologically-meaningful aspects to highlight, most of the current appeal for the manuscript is dependent on the robustness of LOOP-seq and its implementation.  

Many of the specific analyses are difficult and the methods are brief.  Especially given that this technology is new and the dataset potentially useful, I would strongly recommend the authors set up a git repository, galaxy notebook or similar to maximize utility and reproducibility 

The authors claim that clustering between benign and HCC samples was improved by including isoform & gene (Suppl fig 8).  This seems like an important conclusion if true, especially to justify the use of long-read implementation.  Given that the combination of isoform + gene presents ~double the number of variables on which to cluster, it would be important to show that the improved separation on UMAP distance is actually due to the isoforms themselves and not just sampling more variables from either gene or isoform

SQANTI implementation to identify fusions relevant for the HCC/benign comparison. How do the fusions compare with those already identified for HCC?  These analyses can be quite messy when performed on WES alone so it seems that having such deep RNA-seq would improve the capacity to see which fused genes are strongly expressed/suppressed.  This doesn't seem as evident from current analysis.  There are quite a bit of WES datasets which could be compared:  https://www.nature.com/articles/ng.3252, https://www.nature.com/articles/s41467-018-03276-y

Figure 4 is fairly unclear.  The matrix graphs showing gene position mutations are tough to interpret and make out.  Usually, gene track views with bars or lollipop graphs can make these results more readily interpretable.  Also, how Figure 4 B infers causal directions from mutations is unclear.

Reviewer #3 (Public Review):

The Liu, et al. manuscript focuses on the interesting topic of evaluating in an almost genome-wide-scale, the number of transcriptional isoforms and fusion gene are present in single cells across the annotated protein coding genome. They also seek to determine the occurrences of single nucleotide variations/mutations (SNV) in the same isoform molecule emanating from the same gene expressed in normal and normal and hepatocellular carcinoma (HCC) cells. This study has been accomplished using modified LoopSeq long‐read technology (developed by several of the authors) and single cell isolation (10X) technologies. While this effort addresses a timely and important biological question, the reader encounters several issues in their report that are problematic.:

1) Much of the analysis of the evolution of mutations results and the biological effects of the fusion genes is conjecture and is not supported by empirical data. While their conclusions leave the reader with a sense that the results obtained from the LoopSeq has substantive biological implications. However, they are extended interpretations of the data. For example: The fusion protein likely functions as a decoy interference protein that negatively impacts the microtubule organization activity of EML4.(pg 9)... and other statements presented in a similar fashion.

2) LoopSeq has the advantage of using short read sequencing analyses to characterize the exome capture results and thus benefits from low error rate compared to standard long-read sequencing techniques. However, there is no evidence obtained from standard long read sequencing that the isoforms observed with LoopSeq are obtained with parallel technologies such as long read technologies. It is not made clear how much discordance there is in comparing the LoopSeq results are with either PacBio or ONT long read technologies.

3) There is no proteome evidence (empirically derived or present in proteome databases) from the HCC and normal samples that confirms the presence or importance of the identified novel isoforms, nor is there support that indicate that changes in levels HLA genes translate to effects observed at the protein level. Since the stability and transport differences of isoforms from the same gene are often regulated at the post-transcriptional level, the biological importance of the isoform variations is unclear.

4) It is unclear why certain thresholds were chosen for standard deviation (SD) <0.4 (page 5), SD >1.0 (pg 11).

5) HLA is known to accumulate considerable somatic variation. Of the many non-immunological genes determined to have multiple isoforms what are the isoform specific mutation rates in the same isoform molecule? Are the HLA genes unique in the number of mutations occurring in the same isoform?

Reviewer #1 (Public Review):

The authors sought to craft a method, applicable to biobank-scale data but without necessarily using genotyping or sequencing, to detect the presence of de novo mutations and rare variants that stand out from the polygenic background of a given trait. Their method depends essentially on sibling pairs where one sibling is in an extreme tail of the phenotypic distribution and whether the other sibling's regression to the mean shows a systematic deviation from what is expected under a simple polygenic architecture.

Their method is successful in that it builds on a compelling intuition, rests on a rigorous derivation, and seems to show reasonable statistical power in the UK Biobank. (More biobanks of this size will probably become available in the near future.) It is somewhat unsuccessful in that rejection of the null hypothesis does not necessarily point to the favored hypothesis of de novo or rare variants. The authors discuss the alternative possibility of rare environmental events of large effect. Maybe attention should be drawn to this in the abstract or the introduction of the paper. Nevertheless, since either of these possibilities is interesting, the method remains valuable.

Reviewer #2 (Public Review):

Souaiaia et al. attempt to use sibling phenotype data to infer aspects of genetic architecture affecting the extremes of the trait distribution. They do this by considering deviations from the expected joint distribution of siblings' phenotypes under the standard additive genetic model, which forms their null model. They ascribe excess similarity compared to the null as due to rare variants shared between siblings (which they term 'Mendelian') and excess dissimilarity as due to de-novo variants. While this is a nice idea, there can be many explanations for rejection of their null model, which clouds interpretation of Souaiaia et al.'s empirical results.

The authors present their method as detecting aspects of genetic architecture affecting the extremes of the trait distribution. However, I think it would be better to characterize the method as detecting whether siblings are more or less likely to be aggregated in the extremes of the phenotype distribution than would be predicted under a common variant, additive genetic model.

Exactly how the rareness and penetrance of a genetic variant influence the conditional sibling phenotype distribution at the extremes is not made clear. The contrast between de-novo and 'Mendelian' architectures is somewhat odd since these are highly related phenomena: a 'Mendelian' architecture could be due to a de-novo variant of the previous generation. The fact that these two phenomena are surmised to give opposing signatures in the authors' statistical tests seems suboptimal to me: would it not be better to specify a parameter that characterizes the degree or sharing between siblings of rare factors of large effect? This could be related to the mixture components in the bimodal distribution displayed in Fig 1. In fact, won't the extremes of all phenotypes be influenced by all three types of variants (common, rare, de-novo) to greater or lesser degree? By framing the problem as a hypothesis testing problem, I think the authors are obscuring the fact that the extremes of real phenotypes likely reflect a mixture of causes: common, de-novo, and rare variants (and shared and non-shared environmental factors).

To better enable interpretation of the results of this method, a more comprehensive set of simulations is needed. Factors that may influence the conditional distribution of siblings' phenotypes beyond those considered include: non-normal distribution, assortative mating, shared environment, interactions between genetic and shared environmental factors, and genetic interactions.

In summary, I think this is a promising method that is revealing something interesting about extreme values of phenotypes. Determining exactly what is being revealed is going to take a lot more work, however.

Reviewer #1 (Public Review):

In their manuscript entitled: "Is tumor mutational burden predictive of response to immunotherapy?", Gurjao and colleagues discuss the use of tumor mutational burden (TMB) as a predictive biomarker for cancer patients to respond to immune checkpoint blockage (ICB). By analyzing a large cohort of 882 patient samples across different tumor types they find either little or no association of TMB to the response of ICB. In addition, they showed that finding the optimal cutoff for patient stratification lead to a severe multiple testing problem. By rigorously addressing this multiple testing problem only non-small cell lung cancer out of 10 cancer types showed a statistically significant association of TMB and response to ICB. Nevertheless, it is clearly shown that in any case the rate of misclassification is too high that TMB alone would qualify as a clinically suitable biomarker for ICB response. Finally, the authors demonstrate with a simple mathematical model that only a few strong immunogenic mutations would be sufficient for an ICB response, thereby showing that also patients with a low TMB score could benefit from immunotherapy. The manuscript is clearly written, the results are well presented and the applied methods are state-of-the-art.

Reviewer #2 (Public Review):

The manuscript points out that TMB cut-offs are not strong predictors of response to immunotherapy or overall survival. By randomly shuffling TMB values within cohorts to simulate a null distribution of log-rank test p-values, they show that under correction, the statistical significance of previously reported TMB cut-offs for predicting outcomes is questionable. There is a clinical need for a better prediction of treatment response than TMB alone can provide. However, no part of the analysis challenges the validity of the well-known pan-cancer correlation between TMB and immunotherapy response. The failure to detect significant TMB cut-offs may be due to insufficient power, as the examined cohorts have relatively low sample sizes. A power analysis would be informative of what cohort sizes are needed to detect small to modest effects of TMB on immune response.

The manuscript provides a simple model of immunogenicity that is tailored to be consistent with a claimed lack of relationship between TMB and response to immunotherapy. Under the model, if each mutation that a tumor has acquired has a relatively high probability of being immunogenic (~10%, they suggest), and if 1-2 immunogenic mutations is enough to induce an immune response, then most tumors produce an immune response, and TMB and response should be uncorrelated except in very low-TMB tumors. The question then becomes whether the response is sufficient to wipe out tumor cells in conjunction with immunotherapy, which is essentially the same question of predicting response that motivated the original analysis. While TMB alone is not an excellent predictor of treatment response, the pan-cancer correlation between TMB and response/survival is highly significant, so the model's only independent prediction is wrong. Additionally, experiments to predict and validate neoepitopes suggest that a much smaller fraction of nonsynonymous mutations produce immune responses1,2.

A key idea that is overlooked in this manuscript is that of survivorship bias: self-evidently, none of the mutations found at the time of sequencing have been immunogenic enough to provoke a response capable of eliminating the tumor. While the authors suggest that immunoediting "is inefficient, allowing tumors to accumulate a high TMB," the alternative explanation fits the neoepitope literature better: most mutations that reach high allele frequency in tumor cells are not immunogenic in typical (or patient-specific) tumor environments. Of course, immunotherapies sometimes succeed in overcoming the evolved immune evasion of tumors. Higher-TMB tumors are likely to continue to have higher mutation rates after sequencing; increased generation of new immunogenic mutations may partially explain their modestly improved responses to therapy.

Reviewer #1 (Public Review):

This work presents findings on the cellular and ultrastructural organization of the nervous system in the freshwater polyp Hydra. Although the work presents potentially important data, there are several points that need to be addressed:

1) The antibody has to be properly validated as a tool for detecting all neurons. As it stands, the antibody might not recognize a cadherin and it is not clear whether it is specific and labels all neurons.

2) The lack of communication between the two nerve nets is an interesting observation, but its implications are limited due to technical reasons. This should be investigated further.

3) The apparent lack of typical terminal synaptic contacts and the predominant presence of "en passant" contacts in the neurite bundles could be the central element of the paper but this would have to be supported by more thorough observations and experiments.

4) The authors should highlight the novelty of the findings as compared to previous work that had already addressed some of these points.

Reviewer #2 (Public Review):

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

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

The manuscript reports a remarkably meticulous study and presents stunning imaging results. However, the manuscript would benefit from a more thorough presentation of immunochemical and electron microscopy data. The work would also greatly benefit from a more straightforward presentation of truly novel findings and a more concise summary of already-known aspects.

Major comments:

1) The novelty of findings.<br /> The authors present a lot of findings and illustrate them with numerous very impressive images. However, most observations have been actually reported before, and genuinely novel discoveries are obscured. For instance, the findings on the elongated morphology of the endodermal sensory cell (entire passage starting with "Figure 2B shows..."), qualitative ("Figure 3 shows..."), and quantitative estimation of neuronal densities in the different body compartments of Hydra - all these observations do not provide novel insights. Some co-authors of this manuscript or other authors have previously published all these features. A substantial advance would be performing in vivo experiments, addressing directly, for instance, the question of what is the function of sensory neurons reaching into the gastric cavity. What signals do they detect there? If the authors have access to such functional assays, any additional in vivo experiments will substantially improve the study.

2) The utility of the hyCADab as a pan-neuronal antibody.<br /> Most of the analysis in the manuscript relies on immunostaining of fixed polyps with a novel polyclonal antibody. The authors claim that this antibody recognizes a neuron-specific cadherin protein of Hydra and stains all neurons in the nerve net. However, a brief search in the publicly available resources (such as the Hydra Genome Portal: https://research.nhgri.nih.gov/HydraAEP/) indicates that the gene encoding a protein with a sequence similar to the epitope used by Keramidioti and co-authors is, in fact, not a neuron-specific. It is strongly expressed in nematocytes. Furthermore, the cytoplasmic staining hyCADab is puzzling. Given that the target Cadherin protein is a membrane-associated protein, one would anticipate the immunochemical signal to be localized on the cell's periphery, under the surface.

The authors compare the density of neurons related to epithelial cells detected in whole mounts by the antibody with counts on macerates. Perhaps, a more direct and accurate approach would be to stain macerates with the antibody. In this way, one would be able to identify neurons by their morphology and validate whether 100% of them are hyCADab-positive.

The nGreen strain used by the authors is a mosaic one (see Materials and Methods). Hence, not all neurons are, in fact, labeled by GFP. Therefore, the argument that 51/51 GFP-positive cells are also hyCADab-positive is not convincing and insufficient to claim that hyCADab is a pan-neuronal antibody.

Finally, it is truly surprising that transgenic GFP-positive neurons are, in most cases, hyCADab-negative. (It is particularly evident in Fig. 11B. If the hyCADab antibody is indeed a pan-neuronal one, the red signal in the transgenic neurons should be as high as in the surrounding cells, and the cells would appear yellow).

3) The apparent absence of contact between the ectodermal and endodermal nerve nets.<br /> A central claim of the manuscript is that there are no contacts between the nervous networks in the ectoderm and the endoderm. Therefore, the activities of these networks appear to be not coordinated. In support of these claims, the authors provide images of sections from the polyps' body column (Fig. 4). However, the mesoglea itself is not visible in these images.

Another limitation of the study by Keramidioti and co-authors is that they investigate sections only from the gastric region of a polyp. Earlier studies (for instance, Westfall, 1973) using TEM provided compelling evidence for communication between the ectodermal and endodermal nerve networks via neurites that cross the mesoglea. These neurites traversing mesoglea have been detected specifically in the hypostome of Hydra - the region not thoroughly investigated by Keramidioti et al. It is also surprising that transmesogleal bridges between ectodermal and endodermal epithelial cells, abundantly present not only in the hypostome but in the body column as well, can not be detected on any of the images provided by the authors. This suggests that their approach overall might be in general not suitable for addressing the question of connection and communication between the ectodermal and endodermal structures.

4) Formation of neurite bundles<br /> The most intriguing finding of the study by Keramidioti et al. is that neurites of nerve cells often run parallel to each other, forming conspicuous bundles in both ectodermal and endodermal nerve nets. The formation of such bundles per se is not surprising. It has already been documented by Takahashi-Iwanaga et al.,1994 (this study definitely did not escape the authors' attention) in Hydra's body column. Moreover, neurite bundles have been previously described in the hypostomes of other Hydra species (e.g., Davis et al., 1968; Grimmelikhuijzen, 1985; Yaross et al., 1986) and in other cnidarians (e.g., Mackie 1973, 1989; Garm et al., 2007). Hence, this appears to be a common, universal principle of the nervous system architecture in Cnidaria. I agree with the authors that such an organization of the nerve net is surprising and contrasts the neuronal architecture of most Bilateria. Could these observations, taken together, lead to a view of an alternative design of a nerve system? (a recently published description of the syncytial nerve net in Ctenophora is another revolutionary example of a nervous system architecture). The authors might compare the organization of the Hydra nerve plexus with the architecture of the vertebrate enteric nervous system - where bundles of neurites are also highly abundant, stimulating some thoughts on the evolutionary roots of the peripheral NS.

Another aspect worth discussing in this context is whether the nerve system of Hydra can be organized in any other way. Given the architecture of epithelia in Hydra, there's virtually no other way for the neurites to run other than to form bundles - they occupy the narrow spaces between the epithelial cells and between their muscular fibers. The growth of the neurites thus appears constrained.

Finally, the functional implications of such bundle formation appear extremely interesting. Do neurons really form contacts in these bundles? Unfortunately, the authors provide no evidence for synaptic contacts within the bundles. This is somehow surprising given that numerous studies have effectively localized chemical and electric synapses in Hydra cells (e.g., Westfall et al., 1971). Overlapping of neurites may suggest an alternative, non-synaptic mechanism of signal propagation - via ephaptic coupling. It would be beneficial if the authors provided more TEM data on the presence or absence of synapses between neurites in the body column of Hydra. Some experiments, such as the dye coupling approach, may also help probe the existence of synaptic connections between the neurons forming a bundle.

Reviewer #3 (Public Review):

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

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

Joint Public Review:

Chen and collaborators first analysed in sheep embryonic gene editing using CRISPR-Cas9 technology to invalidate the two alleles of Mstn and Fgf5 genes by using different ratios of Cas9 mRNA and sgRNA. They showed that a ratio of 1:10 had highest efficiency and they successfully generated two sheep with biallelic mutations of both genes. Materials and Methods on the generation of gened edited sheep is entirely missing. The data on these gene edited sheep have been already published twice by the authors in different contexts. Other groups reported on gene editing of Mstn or Fgf5 in sheep embryos and the resulting phenotypes.

Although the findings are interesting, they do not provide sufficiently new scientific information or advancements in producing genetically modified livestock with improved production characteristics. While the MSTNDel273 sheep exhibited an increased number of muscle fibers, the data provided did not demonstrate a significant improvement in meat productions, quality or quantity in the MSTNDel273 sheep vs WT.

The authors indicate that sgRNA design changes in addition to changing the molar ratio of Cas9MRNA:sgRNA improved the ability to generate biallelic homozygous mutant sheep; however, the data provided to not demonstrate any significant difference. Given the small number of sheep that were actually produced and evaluated,it is extremely difficult to demonstrate anything that was analyzed to be significantly (statistically) different between MSTNDel273 sheep and WT, yet the authors seem to ignore this in much of their discussion. There is no explanation as to why the authors started with sheep that were FGF5 knockouts. The reviewer assumes that this was simply a line of sheep available from previous studies and the goal was to produce sheep with both improved hair/wool characteristics in addition to improved muscle development. However, the use of FGF5 knockout sheep complicates the ability to accurately decipher the unique aspects associated with targeting only myostatin for knock-out. At minimum, this is a variable that has to be considered in the statistical analysis. No information is provided on the methods used to produce the MSTNDel273 sheep, which is fundamentally important. It is assumed they were produced by injecting one-cell zygotes then transferring these into surrogate females. The methods employed might have a profound effect on the outcome.

Authors genotyped one sheep with a biallelic three base pair deletion in Mstn exon 3 and a compound heterozygote mutation in Fgf5 with a 5 nucleotides deletion on one allele and 37 nucleotides deletion on the other allele, partially spanning over the same region. This sheep developed a double muscle phenotype, which was documented using photography and CT scan. The hair phenotype was not further addressed, but authors referred to a previous publication.

Authors performed morphometric studies on two distinct muscles, longissimus dorsi and gluteus medius, and found a profound fiber hypotrophy in the Mstn-/-;Fgf5-/- double mutants, with a shift from larger fiber diameter to smaller fiber sizes. Morphometric studies showed only a low percentage of fibers in wt and mutant sheep had fiber cross sectional areas larger than 800 µm2, whereas about 30% in wt and about 60% in the mutant had CSA of <400 µm2. The report of one case, without reproducing the phenotype in other sheep, is scientifically insufficient. The fiber sizes in wt sheep remains far below previously published reports in sheep (about 3-5 times smaller) and as compared to other species, which suggests a methodological error in morphometric methods.

The authors also investigated the influence of Fgf5 mutation on muscle development. They determined fiber cross sectional area in heterozygous Fgf5 mutant (number of investigated animals not given) and conclude that Mstn mutation but not Fgf5 mutation caused the double muscle phenotype. Results are insufficient to support this conclusion. Firstly, authors investigated heterozygous FGF5 sheep and not homozygous mutants. Secondly, FGF5 has previously been shown to stimulate expansion of connective tissue fibroblasts and to inhibit skeletal muscle development during limb embryonic development (Clase et al. 2000). Of note, Mstn is also expressed during embryonic development. A combined knockout could therefore entail synergistic effects and cause muscle hyperplasia that is not found in individual knockout, a hypothesis that was not addressed by the authors.

The authors generated and studied an F1 generation of mutant sheep with heterozyogous mutation in Mstn and Fgf5. In Mstn+/-;Fgf5+/-, gluteus medius muscle was found to be larger compared to wt sheep, whereas other muscles were smaller, and overall meat quantity did not change. Morphometric studies revealed a similar muscle fiber hypotrophy and muscle hyperplasia as in the Mstn-/-;Fgf5-/- gluteus muscle.

In the next part of results, authors investigated the presence of myostatin protein in homozygous Mstn muscle using immunohistochemistry and found no differences compared to wt, however, positive and negative controls are missing. The also determined Mstn transcription and protein quantity using WB in heterozygous Mstn muscle and found no difference. The authors did not provide data to explain of why the herein generated Mstn mutation causes muscle fiber hypotrophy, whereas most work on myostatin abrogation demonstrated fiber hypertrophy.

Authors then isolated myoblasts from hind limbs of 3-month-old sheep fetuses and cultured in presence of 20% fetal bovine serum before switching to differentiation medium containing 2% horse serum. The cultures showed increased proliferation of Mstn+/-;Fgf5+/- myoblasts as well as downregulation of genes associated with muscle differentiation as well as reduced fusion index. No experiments were performed to assure whether the myostatin and FGF5 pathways were inhibited. No control experiments using supplementation with recombinant proteins and using growth factor depleted culture supplements were performed. As FGF5 and myostatin are secreted factors, evidence is missing whether this led to conditioning of the culture medium. Of note, previous work in mice demonstrated that the double muscle phenotype developed independent of satellite cells activity (Amthor et al. 2009).

Authors then performed RNA seq from Mstn+/-;Fgf5+/- muscle and found a number of differentially expressed genes, but none has been previously reported being involved in the myostatin signaling pathway, so the authors chose to only focus on FOSL1 and associated genes. Authors then demonstrated that Pdpn and Ankrd2 were upregulated during myogenic differentiation, whereas FOPSL1 was downregulated. Moreover, Fosl1 transcription was upregulated in myoblasts and myotubes from Mstn+/-;Fgf5+/- muscle. Authors showed an interaction between Fosl1 and Myod1. Moreover, authors demonstrated that Polsl1 directly binds to the Myod1 promoter. Authors also found decreased p38 MARPK protein levels in proliferating myoblasts from Mstn+/-;Fgf5+/- muscle and increased p38 MARPK in differentiating myotubes.

Furthermore, gain-of-function by overexpressing FOSL1 promoted cell proliferation and inhibited differentiation, and tert-butylhydroquinone, an indirect activator of FOSL1 also inhibited myogenic differentiation. The findings do not support the idea that FOSL1 is not involved, but neither do they strongly support the involvement of FOSL1. The observations made by the authors could be co-incidental and not causative in nature.

The manuscript by Chen et al. demonstrated successful gene editing in sheep embryos to obtain biallelic mutation of Mstn and FGF5. The resulting double muscle phenotype resulted from fiber hypotrophy and hyperplasia, which contradicts findings in the literature. Chen et al. generated F1 heterozygous offsprings, in which Mstn transcription and translation did not change. Myoblasts from these animals showed increased proliferation and decreased differentiation, which authors interpreted as the underlying cellular mechanism of the double muscle phenotype. However, no work on muscle development in these animals is presented. Important in vitro control experiments are missing. Chen and collaborators found Fosl1 as a differentially expressed gene in Mstn+/-;Fgf5+/- muscle. Fosl1 drives myoblast proliferation and has direct regulatory effect on the Myod1 promoter. The cellular and molecular mechanism of Fosl1 during myogenesis is novel and solid evidence. However, data remain inadequate to conclude whether Fosl1 indeed acts downstream of myostatin.

As the significant findings are minimal, the amount of text provided, figures and tables are disproportionally excessive. A large number of different molecular techniques are employed to try and decipher the mechanism(s) that result in the observed phenotype = double muscling. The authors focus on the MEK-ERK-FOSL1 pathway an suggest this the key pathway/mechanism resulting in the phenotype observed in MSTNDel273sheep. However, they provide very little solid evidence to support this notion.

The manuscript is very long, complicated and difficult to read, given the minimum amount of significant information that is provided. Further, it misses information in material methods, on the generation of animals, on histological techniques and morphometric studies. There is no information provided on the sex of the animals produced and then analyzed. There are also a number of editorial mistakes e.g. the authors refer to tables S1-S4 in the materials and methods and results section, but and there is no table S1-S4 provided.

Reviewer #2 (Public Review):

The work presented here by Morgun et al is performed in the context of vaccine development, a field especially active in the context of tuberculosis (TB). The generation of a new vaccine either enhancing or replacing the 100-year-old BCG is urgently needed.

Most subunit vaccines integrate protein antigens formulated with adjuvants and there are few examples on the performance of subunit vaccines integrating lipid antigens. Considering the hydrophobic and lipid nature of the mycobacterial cell envelope studies assessing the suitability of mycobacterial lipids in vaccine formulations may contribute to generate new vaccines to tackle the disease.

The mycobacterial lipid antigens under study are mycolic acids (MA), which are located at the cell wall covalently linked to arabinogalactan. These lipids carry extremely long chain fatty acids of up to 60-90 carbons.

The group has previously shown that formulating MA into micellar nanocarriers and vaccinating mice intranasally it could activate CD1-restricted T cells. However, this formulation did not allow for the incorporation of protein antigens.

This work is novel, and it brings new data of high relevance for the TB vaccine field pointing to alternative formulations and antigens and immune mechanisms.

Authors assay different routes of vaccination but the main results are obtained using non-conventional vaccination routes. Although, it maybe out of the scope of the paper, no protection studies are provided.

Several recommendations are given to improve the quality and the readability of the manuscript.

1. Authors elaborate the introduction solely highlighting the relevance of antigen persistence in the context of vaccination. However, it is well known that several mycobacterial antigens (Lipids and proteins) can cause detrimental responses when overexposed to the immune system. In this regard, it would be appropriate to introduce the possibility of the occurrence of exhaustion when prolonged exposure to antigens is happening, which is the main theme of this paper.

2. Authors need to provide more information about the source of MA. It is briefly mentioned in the materials and methods section that it was obtained from Sigma. If that is the case, it would be ideal to show the integrity of the polysaccharide in term of balance and abundance between different MA species.

3. Building up on the previous comment, MA is a complex mixture of polysaccharides including multiple lengths of fatty acids and modifications. Could the authors comments on the potential variability of MA structure and potential impact on immune responses?

4. How do the authors explain the lack of stimulation of cell proliferation induced by MA-PLGA formulation? Does this result contradict previous findings?

5. Fig 3. Authors switch to IT administration simply arguing against the limitation of IN delivery regarding its low volume. However, administration via IN could be done in an iterative manner. According to this change, this reviewer asks whether the performance of MA-PLGA could now be comparable to BCN-MA using IT instead.

6. What would be the reasons of the no role of encapsulating NP in the persistence of MA?

7. Authors need to discuss to what extent the MA location into AM is route dependent.

8. Also, AM are programmed to sustain low immune responses because of their unique location in the lung. In fact, Mtb uses this to replicate while immune response is mounted. In this regard, accumulation of MA into this compartment may not be relevant for the overall immune response. In other words, what would be the contribution of this population to the T cell activation?

9. Could the T cells responses measured be due to the reduced fraction of DC loaded with BCN-MA at initial time points?

Reviewer #1 (Public Review):

It is well established that tuberculosis (TB), which is caused by Mycobacterium tuberculosis (Mtb), is a leading cause of mortality and morbidity worldwide. However, the only vaccine licensed against tuberculosis is Bacille Calmette Guerin (BCG), has been around for nearly a century, and has limited efficacy in adults. Herein, the authors sought to investigate the effectiveness of a nanoparticle-based formulation of a subunit vaccine composed of Mtb lipid and protein antigens. The authors found that they were able to load the lipid, mycolic acid, into their nanoparticles without disrupting the architecture, and that the loaded particles activated T cells both in vitro and in vivo. Moreover, when they vaccinated with particles loaded with both lipid and protein antigens, they found that the lipid antigen persisted, and mycolic acid-specific T cells were able to be activated 6 weeks post-vaccination, in contrast to peptide-specific T cells. The authors investigated further and found that persistence required the nanoparticle encapsulation, rather than free lipid, and that it was independent of route (intratracheal, intravenous, or subcutaneous) of administration. To address the mechanisms underlying antigen persistence, the authors loaded the nanoparticles with a dye and demonstrated that the nanoparticle encapsulated lipid antigen was primarily stored in lung alveolar macrophages and that CD1b+ dendritic cells presented the antigen to mycolic acid specific T cells. Finally, the authors conducted mixed bone marrow chimera studies to examine the phenotype of the mycolic acid specific T cells and found that the memory T cell population phenotypically resembled T follicular helper, regulatory T cells, and exhausted T cells. Interestingly, while a large percentage of these lipid antigen specific T cells in the lymph nodes, lung and spleen were CXCR5+PD1+, the cells were still proliferating (Ki67+). Overall, this is a comprehensive study that has the potential to significantly enhance the field.

Reviewer #1 (Public Review):

The authors investigate the roles of ACOT12/8 in the production of acetate by the liver. They observe that acetate concentration parallels ketone concentrations during fasting and T1DM. They show that acetate is produced from fatty acids in hepatocytes. They also provide data from human subjects who were classified as either "healthy" or "diabetic," but there is no other characterization or description of these people, making it difficult to ascertain the context by which they were studied. Nevertheless, these findings could be gleaned from the literature, and yet there remains surprising uncertainty regarding the mechanism of acetate production by the liver. The authors use ShACOT12/8 and liver-specific ACOT12/8 knockout mice to demonstrate that these acetyl-CoA hydrolases are largely necessary for acetate production. There is data on this role for ACOTs in the literature, but they have yet to be widely studied. Using a 3H-palmitate assay, the authors then find that loss of these ACOTs inhibit fatty acid oxidation and propose that the mechanism involves scavenging CoA, analogous to the canonical role of ketogenesis. The idea is plausible but only partially proven. A related finding is that loss of these ACOTs inhibit ketogenesis, which the authors attribute to the loss of function of HMGC2S, partially through acetylation. These mechanisms suffer some limitations based on the cytosolic and mitochondrial compartmentation of the two processes, but the observations appear sound. Finally, the authors try to demonstrate that hepatic ACOT-mediated acetate production is necessary for normal motor function. The tracer data used to support the importance of acetate metabolism do not include loss of function models and generally need to be reported more transparently. Conceptually, one may be skeptical of the rather dramatic loss of motor function in the context of a relatively minor circulating nutrient. This may be a significant finding but requires more supporting evidence. Overall, the authors convincingly show that ACOT12/8 are critical for hepatic acetate production in mice, which will be helpful for the field, but the ramifications will require further investigation.

Reviewer #2 (Public Review):

Catabolic conditions lead to increased formation of ketone bodies in the liver, which under these conditions play an important role in supplying energy to metabolically active organs. In this manuscript, the authors explore the concept of whether and to what extent hepatic formation of acetate might contribute to energy supply under metabolic stress conditions. The authors show that patients with diabetes have increased acetate levels, which is explained as a consequence of the increased fatty acid flux from adipose tissue to the liver. This is confirmed in a preclinical model for type 1 diabetes, where acetate concentrations are in a similar range to ketone bodies. Acetate concentrations also increase under physiological conditions of fasting. Using stable isotopes, the authors show that palmitate is used as the primary source for acetate production in primary hepatocytes. Using cell culture studies and adenoviral-mediated knockdown in mice, it can be shown that the conversion of acetyl-CoA to acetate is catalyzed in peroxisomes by acyl-CoA thioesterase8 (ACOT8) and after transport of citrate from mitochondria and subsequent conversion to acetyl-CoA in the cytosol by ACOT12. Remarkably, ACOT8/12 not only regulate the formation of acetate but play a crucial role in the maintenance of cellular CoA concentration. Accordingly, depletion of ACOT8/12 activity leads to a reduction of other CoA derivatives such as HMG-CoA, which resulted in the inhibition of ketone body synthesis. In diabetic mice, ACOT 8 or ACOT12 knockdown appears to lead to some limitations in strength and behavior.

In summary, the authors clearly demonstrate that hepatic release-mediated by ACOT8 and ACOT12-determines the plasma concentration of acetate. This is a very remarkable observation, since most studies assume that short-chain fatty acids in plasma are primarily generated by fermentation of dietary fiber by intestinal bacteria. The authors demonstrate in very well performed studies the metabolic changes that result from impaired thiolysis. On the other hand, the ACOT12 phenotype has been demonstrated in a recently published study (PMID: 34285335). In this study, ACOT12 deficiency caused NAFLD, thus it would be worth to determine whether deficiency of ACOT12 and/or ACOT8 promotes de novo lipogenesis under the conditions of the present study. As a further limitation, it should be noted that the relevance of acetate production for the energy supply of peripheral organs including the central nervous system could not be clearly demonstrated. For instance, impaired ketone body production due to impaired CoA availability could affect the metabolic activity of various organs. Moreover, the human cohort is not very well described, e.g. it is unclear whether the patients have type 1 or type 2 diabetes.

Reviewer #3 (Public Review):

Wang et al. investigated the role of acetate production, a byproduct of fatty acid oxidation, in the context of metabolic stressors, including diabetes mellitus and prolonged fasting. Mechanistically, they show the importance of the liver enzymes ACOT8 (peroxisome) and ACOT12 (cytoplasm) in converting FFA-derived acetyl-CA into acetate and CoA. The regeneration of CoA allows for subsequent fatty acid oxidation. Inhibiting the generation of acetate has negative motor and behavioral consequences in streptozocin-treated mice, which are mitigated with acetate injection.

This paper's strengths include using multiple mouse models, metabolic stressors (db/db-/-, streptozocin, and prolonged starvation), numerous cell lines, precise knockout and rescue experiments, and complimentary use of mass spectrometry and nuclear magnetic resonance analytical platforms. The presented data support the conclusions of this paper, but some aspects need to be clarified.

For example, for all animal studies, please list the age and sex of the animals at the time of the experiments. Sex and age are important biological variables that can affect metabolism, and such characteristics are needed when comparing results from different research groups.

In clinical medicine, common ketones that are measured are acetoacetate, beta-hydroxybutyrate, and acetone. However, the data presented here suggest the importance of measuring acetate when patients present with ketoacidosis in uncontrolled diabetes or starvation.

Reviewer #1 (Public Review):

Hermanns et al., investigated the virus diversity and prevalence patterns in conjunction with mosquito community compositions in natural and disturbed ecosystems (5 habitats) within the Tai National Park in Cote d'Ivorie. The ultimate aim was to analyse the interplay between viral biodiversity and prevalence with mosquito host biodiversity and prevalence. Pools of morphologically identified mosquitoes from pristine forest habitats through to habitats of high human disturbance were analysed for the presence of viruses of 12 major mosquito-borne virus taxa. While 15 of the viruses detected have been published previously, 34 potentially new viruses were detected of which the full genome of 5 was completely elucidated for phylogenetic analysis and temperature-dependent replication of 4 was performed. Via comprehensive analyses of the biodiversity of the viruses detected in mosquitoes collected within each habitat, it was shown that i) the highest virus richness was observed in the intermediately disturbed habitats, ii) that the prevalence of viruses corresponded to the relative abundance of the main mosquito host species that carried them, but iii) when just the main host mosquito for each virus was analysed alone in each habitat, that there was no trend in increasing or decreasing virus prevalence.

The conclusions within the paper were generally well-justified, but a caveat of the study is that the (likely) mechanisms of transmission of the viruses identified in the paper were not discussed. Many of these viruses are most likely maintained in nature via vertical transmission and thus, this information needs to be taken into consideration. Due to the fact that it is likely that many of these insect-specific viruses evolve with their mosquito host, it was not surprising that if there was an increased abundance of a particular mosquito species, that there was also increased prevalence of the virus which it hosts. Of course, this may differ depending on the viral family, but requires comment in the context of what is known. Furthermore, there requires clarification as to why analysing insect-specific virus prevalence and diversity will serve as a model for the study of typical arboviruses due to the differences in their maintenance in nature.

For many of the putative new viruses, only small sequences of less than 1200 nt were analysed. Granted that the RdRp is the most conserved gene, how was the 5% demarcation for a new species determined when established criteria differ to this.

Reviewer #2 (Public Review):

In this manuscript authors make an important contribution to the diversity of mosquito specific viruses, describing the genetic diversity of RNA viruses from the family Culicidae, along an anthropogenic-disturbance gradient in Côte d'Ivoire in 2004.<br /> The manuscript is methodologically rigorous from the virologic perspective; molecular techniques were standardized to perform virus detection, increasing the detection potential from a previous published work by the team from five to 49 viruses (331 viral sequences pertaining to 49 viruses of ten RNA-virus families).<br /> It is rich in terms of the genetic diversity of mosquito specific viruses, but not as strong from the entomological and ecological perspectives. Mosquito specific viruses are analyzed under the lens of pathogens with public health importance, which is confusing.<br /> One of the major information gaps are the potential transmission routes or sources of infection of the detected viruses. Mosquito specific viruses can be transmitted vertically or horizontally, and are in general strongly associated with the environment, but not related with other hosts such as vertebrates. From this perspective, the ecology of transmission of these viruses should not be compared to pathogens that use vertebrate hosts. The authors found 49 viruses, but emphasize the ecological relevance of their findings to five viruses with increased prevalence from pristine to disturbed habitats, to show a dilution effect.<br /> Another suggested important contribution is the finding of an "abundance effect", suggesting that higher prevalence in degraded ecosystems is the result of host abundance, but additional ecological information is missing on the potential mechanisms leading to this effect. Breeding sites may be a main source of variation in species composition and abundances among habitats, but no comments on this are found on the manuscript.<br /> Some additional useful information could be provided to better understand mosquito sampling, for instance: the number of traps used, duration of sampling in each locality, and sampling dates to understand if there could be seasonal variation.<br /> In conclusion the manuscript is interesting and well written. The virologic component is strong, but its relation to the ecological determinants should be improved.

Reviewer #3 (Public Review):

One challenge with this study doing descriptive mosquito and virus work in a remote location is the uncertainly with species identification for both mosquitoes and viruses. It appears that nearly half of the mosquitoes in three of the study sites could not be identified to species. This appears problematic for the estimation of host (mosquito) richness and diversity along the anthropogenic gradient. Viral taxonomy is also complicated and this study is presenting many new viruses which, based on partial or whole sequencing, are putative novel viruses. It is not clear how many of these novel viruses would be accepted by current practices endorsed by the International Committee on Taxonomy of Viruses. The viral taxa uncertainty add complexity for the current analysis. How many of these viral lineages that cluster together are variants of the same virus? How many are unique taxonomic units? This has important consequences on the application of these data to the analyses conducted in this study.

On a related front, many of these viruses the authors are documented are mostly Insect-specific viruses (ISVs). But it also appears that several could be amplified by vertebrate hosts with poorly understood natural history and for the purposes of this study, all of the viral taxa appear to be grouped together. The inclusion of all viruses is therefore somewhat confounding given the very different natural history associated with these viruses. You frequently refer to 'hosts' throughout the MS and for ISVs, the host would likely only be mosquitoes but for arboviruses involving vertebrate amplification hosts, the hosts would be both the mosquitoes and the vertebrates. This study did not quantify any aspect of vertebrate host abundance, diversity, or richness across the gradient. Since most of this study focuses just on the ISVs as a unique system to test the hypotheses, it would be interesting if the authors restricted the analysis to just those viruses with higher probability of being restricted to mosquitoes (e.g. based on phylogenetic placement) to see if the results remain the same.

You report an anthropogenic disturbance gradient from primary forest to village habitat but how was this quantified? How is a village more disturbed than an agricultural field (rice plantation?)? The method to rank these study sites, which becomes important for the analysis, was not described in the methods. Also, along this topic of study sites, it appears you really only had one replicate of each of the study site type. To test these hypotheses on how host communities influence viral communities it would seem prudent to have had multiple replicates of each study area.

Reviewer #1 (Public Review):

In this study, the authors study the effect of dynactin disruption on kinetochore fiber (k-fiber) length in spindles of dividing cultured mammalian cells. Dynactin disruption is known to interfere with dynein function and hence spindle pole formation. The main findings are that poles are not required for correct average k-fiber length and that severed k-fibers can regrow to their correct length both in the presence and absence of poles by modulating their dynamic properties at both k-fiber ends. In the presence of poles, regrowth is faster and the variation between k-fiber lengths is smaller. This is a very interesting study with high-quality quantitative imaging data that provides important new insight into potential mechanisms of spindle scaling, extending in an original manner previous work on this topic in cultured cells and in Xenopus egg extract. The Discussion is interesting to read as several possible mechanisms for k-fiber length control are discussed. The technical quality of the study is very high, the experiments are very original, and most conclusions are well supported by the data. Especially, the experiments observing the regrowth of k-fibers after severing and the study of the dynamic properties of these k-fibers provide very novel insight. Addressing the following concerns could potentially improve the manuscript:

(1) The phenotype generated here by disrupting dynactin via overexpressing p50 appears to be different from that caused by knocking down NuMA or dynein - as previously reported by the Dumont lab (Hueschen et al., 2019). In this study here, unfocused spindles are observed whereas earlier turbulent spindles were observed. This raises the question of whether dynein activity that contributes to pole focusing is really completely inhibited here. These discrepancies in phenotypes seem to deserve an explanation. Is k-fiber length in cultured mammalian cells only maintained in the case of this specific type of inhibition?

(2) p50 addition and also p150-cc1 addition was often used in Xenopus egg extract in order to inhibit dynein function. Considerably larger concentrations of p50 than p150-cc1 needed to be used. Can the authors estimate the level of overexpression of p50 in the cells they study? It seems that could be possible given that a mCherry fusion protein can be overexpressed. Was it necessary to select cells with a particular level of mCherry-p50 overexpression to observe the reported phenotypes?

(3) Some comparison to previous experiments using p50 and p150-cc1 addition to Xenopus egg extract spindles could put this study better into the context of the available literature. It seems from previous publications that the p50 addition produced short, unfocused, barrel-shaped spindles, indicating that spindle length is maintained without poles, whereas the p150-cc1 addition produced elongating spindles (e.g. Gaetz & Kapoor, 2004).

(4) In this context, it seems that some more explanation is required for the observations presented in Fig. 1D and 1E. It appears that spindle length and k-fiber length have been measured quite differently. Not much information is provided for how spindle length was defined and measured (please expand this part of the Methods). Could the two different methods of measurement be the reason for the mean k-fiber length remaining unaltered in dynactin-disrupted spindles, whereas the spindle length increases in these cells? If not, do non-k-fiber microtubules contribute to unfocused spindles being longer or are chromosomes not aligned in the metaphase plate causing the increase in spindle length by misalignment of k-fiber sister pairs?

(5) It seems that in the Discussion it is implied that k-fibers can respond to severing in both focused and unfocused spindles by modulating their dynamics at both ends of the k-fibers, but in the Results section the wording is more cautious because of the difference in 'flux' in severed and unsevered unfocused spindles is not significant (Fig. 4D, blue data). It appears indeed that there is also a difference in flux between severed and unsevered unfocused spindles, but the number of data points is too small. Depending on how difficult these experiments are, it could be worth increasing the size of the data set to come to a clear conclusion, given that the data shown in Figs. 3 and 4 are quite remarkable and form the core of the study.

(6) Can the authors exclude that the stopping of 'flux' at minus ends after severing is due to some sort of permanent damage induced by ablation? In other words, do severed spindles begin to flux again once they have regrown to their original length?

(7) To this reader, the conceptualization of distinguishing between 'global' and 'local' effects/behavior was a little confusing, both in the title and also later in the text. The concept of 'local' regulation of k-fiber length appears to contradict the observation that k-fiber length can be regained after severing by changes in the dynamics at both ends (so at two very different locations) which is a rather remarkable finding. Maybe distinguishing between 'individual' and 'collective' k-fiber behavior could be clearer.

(8) Can the authors exclude that some of the differences between unfocused and focused spindles could be due to altered dynein activity at kinetochores? Or due to the dynein-dependent accumulation of certain spindle proteins along microtubules towards the minus ends of k-fibers or other spindle microtubules, instead of being due to only the presence versus absence of poles? Could this be tested by ablating both poles? If this is too challenging, a discussion of these possibilities could be justified.

Reviewer #2 (Public Review):

The mitotic spindle of eukaryotic cells is a microtubule-based assembly responsible for chromosome segregation during cell division. For a given cell type, the steady-state size and shape of this structure are remarkably consistent. How this morphologic consistency is achieved, particularly when one considers the complex interplay between dynamic microtubules, spatial and temporal regulation of microtubule nucleation, and the activities of several microtubule-based motor proteins, remains a fundamental unanswered question in cell biology. In this work by Richter et al., the authors use biochemical and biophysical perturbations to explore the feedback between mitotic spindle shape and the dynamics of one of its main structural elements, kinetochore fibers (k-fibers) - bundles of microtubules that extend from kinetochores to spindle poles. Overexpression of the p50 dynactin subunit in mammalian tissue culture cells (Ptk2) was used to inhibit the microtubule motor cytoplasmic dynein resulting in misshapen spindles with unfocused poles. Measurements of k-fiber lengths in control and unfocused conditions showed that although mean k-fiber length was not statistically different, the variation of length was significantly higher in unfocused spindles, suggesting that k-fiber length is set locally, occurring in the absence of focused poles. With a clever combination of live-cell imaging with photoablation and/or photobleaching of fluorescently-labeled k-fibers, the authors went on to explore the mechanistic bases of this length regulation. K-fiber regrowth following ablation occurred in both conditions, albeit more slowly in unfocused spindles. Paired ablation and localized photobleaching on the same k-fiber revealed that microtubule dynamics, specifically those at the plus-end, can be tuned at the level of individual k-fiber. Lastly, the authors show that chromosome segregation is severely impaired when cells with unfocused spindles are forced to enter mitosis. The work's biggest strength is the application of an innovative experimental approach to address thoughtful and well-articulated hypotheses and predictions. Conclusions stemming from the experiments are generally well-supported, though the experiments addressing the "tuning" of k-fiber dynamics could be bolstered by additional data points and perhaps better presented. The manuscript would also benefit from the inclusion of some investigation of spatial differences in the observed effects as well as the molecular and biophysical basis of the observed feedback between k-fiber length and focused poles.

Comments/Concerns/Questions:

1) In the discussion, the authors acknowledge that the changes in spindle morphology resulting from p50 overexpression are likely also causing changes in the well-characterized RanGTP/SAF gradients that radiate from chromosome surfaces. Why did the authors did not include an analysis of k-fiber length as a function of positioning within the spindle? The inclusion of this data would not require more experimentation and could be added as a plot showing K-fiber length versus distance from the geometric center of the spindle (defined by the intersection of the major and minor axes perhaps?).<br /> 2) The authors also acknowledge the established relationship between MT length and MT end dynamics, yet in their ablation studies, the average initial k-fiber length at ablation in control spindles was higher than that for k-fibers in unfocused spindles. It seems that this difference makes the interpretation of the data, particularly the conclusion that fiber growth rates differ due to the absence of focused poles, a bit tenuous. To address this, the authors should consider including plots of grow-back rates versus k-fiber length (again, this should not require additional experiments, just more analysis).<br /> 3) As presented, the data shown in Figure 4 is confusing and does not seem very compelling. The relationship between the kymographs and time series is unclear as is the relationship between the dashed lines in the kymographs and the triangles and the plots in the 4B time series and 4C, respectively. Furthermore, it's not always clear what the triangles are pointing to (e.g. in the unfocused condition time series). The authors might want to consider reworking this figure and providing more measurements of flux following ablation in both the control and unfocused conditions. Lastly, the authors should clarify what negative displacement means.

Reviewer #1 (Public Review):

One aim of this paper was to study historical migration from Botswana during the time of the development of the HIV epidemic. The second aim was to test whether the migration networks impacted the development of the epidemic. The first aim was achieved: this paper used historical census data in a clear way, to describe the qualities of characteristics of migration in the country at four points in time, from 1981 to 2011. Very detailed data are presented in clear ways, using network chord diagrams, sharing age- and sex-specific migration rates, and urban-rural classifications. However, data was not presented to achieve the second aim. The authors reviewed some important literature about migration and HIV. They suggested that the migration patterns, such as from specific mining towns and mostly between districts, could have been important in supporting the generalized spread of HIV. But without evidence linking HIV prevalence over time in the linked districts in Botswana, this aim was not supported.

One other limitation of the paper was that very little context, outside of migration rates, was provided. Is there any additional information about economic growth, or political event for example, that could clarify or add context to these migration flows? As it stands now, these analyses are quite basic and don't take into account underlying demographic, economic, or political trends.

The data presented in this paper has potential impact. As the paper stands now, it could be quite useful for future work when linked to additional data sources on HIV prevalence over time (or other questions that could have been influenced by migration patterns).

Reviewer #2 (Public Review):

To provide context into the HIV epidemic in Botswana over the latter half of the 20th century and the beginning of the 21st, the authors have analyzed micro census data to examine patterns of migration. They use this dataset to show how patterns between urban and rural areas have changed over several decades, and the demographic characteristics of migrants. The dataset used for this study is a very reliable source, and the insights in terms of migration patterns are interesting. The primary weakness of the analyses regards the link to HIV transmission: micro-census data only examine mobility that leads to individuals changing residence for longer periods of time, without accounting for shorter-term trips that may also lead to HIV transmission, such as seasonal migration or short trips. This is likely less of an issue with HIV than other diseases, however, due to its transmission often involving new sexual partners, which will generally be less likely to occur during short trips. Broadly, however, this is an interesting report on the migration patterns during a critical period for HIV transmission nationwide.

Reviewer #1 (Public Review):

Studies of the p38g/d MAP kinase signaling pathways using loss-of-function approaches are compromised the finding that the expression of the ERK family MAP3K Tpl2 is down-regulated. Dissection of the specific roles of p38g/d is therefore difficult. Here the authors report that compound mutant mice with a kinase-inactive p38y MAPK mutation and p38d deficiency show no defects in Tpl2 expression. The importance of this study is therefore that they describe a mouse model that can be used to examine p38g/d MAP kinase function. The data presented are solid and convincing. The authors show that p38g/d MAP kinase signaling contributes to macrophage responses to endotoxin. Moreover, the authors identify Ser44 as an inhibitory site of MEF2D phosphorylation by p38d.

Reviewer #2 (Public Review):

This paper addresses the specific function of p38γ/p38δ isoforms in inflammation. This was achieved by developing a novel mouse model in which p38γ was replaced by a kinase-inactive mutant (D171A mutation in a p38δ knock out background (p38γ/δKIKO). The results demonstrate that the p38γ/p38δ MAPKs are required for regulating the expression of inflammatory mediators implicated in the innate immune response. The phosphorylation of the transcription factor MEF2D at Ser444 constitutes one potential mechanism by which p38γ/p38δ suppresses iNOS and IL-1β mRNA expression.

The strength of this paper resides in the novelty of the mouse model that permitted to assess the specific requirement of p38γ/p38δ isoforms independently of the loss of TPL2 expression caused by compound deletion of the p38γ/p38δ alleles. The finding that p38γ/p38δ MAPKs inhibit MEF2D activity by phosphorylation at Ser444 is also novel.

One weakness lies in the lack of consistency between the expression profiles performed by RNA-seq/qPCR/cytokine arrays to identify inflammatory mediators whose expression is dependent on p38γ/δ in the two in vivo models of septic shock (i.e. fungal infection and induced by LPS) and in LPS activated macrophages in vitro.

The other issue is that gene expression analyses are performed using bone marrow-derived macrophages (BMDM) (Figs. 3 and 5A), whereas the proteomic analysis employs peritoneal macrophages given that "p38γ and p38δ are expressed at much higher levels in these macrophages than in BMDM (p11)" (Fig. 4). Although the authors state on p11 "Additionally, the LPS-induced cytokine production in peritoneal macrophages was comparable to that of BMDM", only two cytokines were measured, i.e. IL1b and IFNg (SI Appendix Fig. S4B). This really emphasises the importance of verifying that i) MEF2D is indeed a substrate of p38δ in macrophages and ii) p38γ/δ-mediated phosphorylation of MEF2D at Ser444 negatively regulates the expression of iNOS and IL-1β transcripts in macrophages.

Finally, no experiment was performed to demonstrate that the lower fungal burden or increased survival rate following LPS-induced sepsis in p38γ/δKIKO mice (Fig. 1) is a consequence of impaired production of inflammatory mediators by p38γ/δKIKO macrophages. This important issue should be addressed.

Reviewer #3 (Public Review):

This paper investigates the role of the p38g and p38d kinases in the immune response using genetically modified mice that are deficient in p38d and express a kinase-inactive form of p38g. This model avoids the possible confounding effect of the downregulation of the ERK1/2 activator Tpl2, which is observed in mice that are deficient for both p38g and p38d, making it more straightforward to determine the contribution of p38g/p38d to specific phenotypes. The mice that express kinase-inactive p38g and lack p38d show reduced susceptibility to both C. albicans infection and LPS-induced septic shock. Macrophages derived from these mice show dysregulated expression of a number of genes involved in innate immunity. Phospho-proteomics analysis identifies the transcription factor MEF2D as one of the targets of p38g/p38d in macrophages, and in vitro assays show that p38d can phosphorylate several residues of MEF2D including Ser444. Reporter assays provide evidence that a MEF2D-S444A mutant has enhanced transcriptional activity compared with the WT MEF2D, and this is also supported by analyzing the mRNA levels of MEF2D targets in fibroblasts overexpressing both proteins. Taken together, these results support that S444A phosphorylation negatively regulates MEF2D activity.

The manuscript contains a number of interesting observations supporting a role for p38g/p38d in the control of the innate immune response independently of the regulation of the Tpl2-ERK1/2 pathway. It also provides evidence that p38d but not p38g can phosphorylate MEF2D, which inhibits its transcriptional activity, and it is therefore a candidate target for some of the gene expression changes observed. Altogether, the manuscript adds new and exciting information on the functions performed by p38 MAPKs in macrophages and introduces a new mouse model that will be useful for further studies.

Reviewer #1 (Public Review):

In this manuscript, the authors have demonstrated the direct effect of androgen receptor activation on B - cell frequencies. In the clinical part of the research, they have found increased frequencies of age-associated double-negative B memory cells and elevated levels of circulating immunoglobulin M (IgM) in women with hyperandrogenic phenotypes of PCOS. The major study strengths are driven by their experimental part. It was shown that the transfer of serum IgG from women with PCOS into wild-type female mice increases body weight, whereas RAG1 knock-out mice, which lack mature T- and B cells, do not demonstrate any signs of hyperandrogenism. Simultaneously, an androgen receptor antagonist prevents increased B cell numbers induced by androgens, whereas B cell-deficient mice are not protected from developing a PCOS-like phenotype when exposed to DHT. Generally, the author's conclusions are based on evidence, and this study opens up a new direction of research in this area.

Reviewer #2 (Public Review):

The significance of these findings is that the role of B cells in mediating cardiometabolic complications in PCOS is not completely understood. The approach taken by this research group is both innovative and translational. One of the clear strengths of this manuscript is that it combines basic research with clinical studies in PCOS women.

Reviewer #1 (Public Review):

This paper provides valuable (and impressive) data on the geometry of cerebellar foliation among 56 species of mammals and gives novel insights into the evolution of cerebellar foliation and its relationship with the anatomy of the cerebrum. Thus far, the majority of the research on brain folding focuses on the cerebral cortex with little research on the cerebellum. The results from Heuer et al confirm that the evolution of the cerebellum and cerebrum follows a concerted fashion across mammals. Moreover, they suggest that both the cerebrum and cerebellum folding are explained by a similar mechanistic process.

1. Although I found the introduction well written, I think it lacks some information or needs to develop more on some ideas (e.g., differences between the cerebellum and cerebral cortex, and folding patterns of both structures). For example, after stating that "Many aspects of the organization of the cerebellum and cerebrum are, however, very different" (1st paragraph), I think the authors need to develop more on what these differences are. Perhaps just rearranging some of the text/paragraphs will help make it better for a broad audience (e.g., authors could move the next paragraph up, i.e., "While the cx is unique to mammals (...)").

2. Given that the authors compare the folding patterns between the cerebrum and cerebellum, another point that could be mentioned in the introduction is the fact that the cerebellum is convoluted in every mammalian species (and non-mammalian spp as well) while the cerebrum tends to be convoluted in species with larger brains. Why is that so? Do we know about it (check Van Essen et al., 2018)? I think this is an important point to raise in the introduction and to bring it back into the discussion with the results.

3. In the results, first paragraph, what do the authors mean by the volume of the medial cerebellum? This needs clarification.

4. In the results: When the authors mention 'frequency of cerebellar folding', do they mean the degree of folding in the cerebellum? At least in non-mammalian species, many studies have tried to compare the 'degree or frequency of folding' in the cerebellum by different proxies/measurements (see Iwaniuk et al., 2006; Yopak et al., 2007; Lisney et al., 2007; Yopak et al., 2016; Cunha et al., 2022). Perhaps change the phrase in the second paragraph of the result to: "There are no comparative analyses of the frequency of cerebellar folding in mammals, to our knowledge".

5. Sultan and Braitenberg (1993) measured cerebella that were sagittally sectioned (instead of coronal), right? Do you think this difference in the plane of the section could be one of the reasons explaining different results on folial width between studies? Why does the foliation index calculated by Sultan and Braitenberg (1993) not provide information about folding frequency?

6. Another point that needs to be clarified is the log transformation of the data. Did the authors use log-transformed data for all types of analyses done in the study? Write this information in the material and methods.

7. The discussion needs to be expanded. The focus of the paper is on the folding pattern of the cerebellum (among different mammalian species) and its relationship with the anatomy of the cerebrum. Therefore, the discussion on this topic needs to be better developed, in my opinion (especially given the interesting results of this paper). For example, with the findings of this study, what can we say about how the folding of the cerebellum is determined across mammals? The authors found that the folial width, folial perimeter, and thickness of the molecular layer increase at a relatively slow rate across the species studied. Does this mean that these parameters have little influence on the cerebellar folding pattern? What mostly defines the folding patterns of the cerebellum given the results? Is it the interaction between section length and area? Can the authors explain why size does not seem to be a "limiting factor" for the folding of the cerebellum (for example, even relatively small cerebella are folded)? Is that because the 'white matter' core of the cerebellum is relatively small (thus more stress on it)?

8. One caveat or point to be raised is the fact that the authors use the median of the variables measured for the whole cerebellum (e.g., median width and median perimeter across all folia). Although the cerebellum is highly uniform in its gross internal morphology and circuitry's organization across most vertebrates, there is evidence showing that the cerebellum may be organized in different functional modules. In that way, different regions or folia of the cerebellum would have different olivo-cortico-nuclear circuitries, forming, each one, a single cerebellar zone. Although it is not completely clear how these modules/zones are organized within the cerebellum, I think the authors could acknowledge this at the end of their discussion, and raise potential ideas for future studies (e.g., analyse folding of the cerebellum within the brain structure - vermis vs lateral cerebellum, for example). I think this would be a good way to emphasize the importance of the results of this study and what are the main questions remaining to be answered. For example, the expansion of the lateral cerebellum in mammals is suggested to be linked with the evolution of vocal learning in different clades (see Smaers et al., 2018). An interesting question would be to understand how foliation within the lateral cerebellum varies across mammalian clades and whether this has something to do with the cellular composition or any other aspect of the microanatomy as well as the evolution of different cognitive skills in mammals.

Reviewer #2 (Public Review):

This study explores the variability of cerebellar anatomy in the mammal. By capturing a set of anatomical measures in the cerebellum and including previously reported cerebral and cerebellar metrics in a set of 58 different mammalian species, this study depicts both consistency and heterogeneity in the co-occurrence of different brain features, with a focus on cerebellar structures such as folial wavelength or median depth of the molecular layer. This is very informative as the cerebellum is currently under-explored and the phylogenetic aspect of this work gives insights into evolutionary processes linked to the morphology of the cerebellum.

Strengths:

- The methods used to capture the different brain features are relevant, and include the reuse of previously reported metrics, which makes sense and valorises the previous work of other teams.<br /> - One interesting novel method to detect the depth of the molecular layer is implemented.<br /> - A generous amount of results are reported (including correlations, phylogenetic principal component analyses, ancestor character state estimation, and allometries), with visually effective figures to support them.<br /> - A remarkable effort has been made to make data and code available, which will be of great use to the community.

Weaknesses:

- The methods section does not address all the numerical methods used to make sense of the different brain metrics. In the results section, it sometimes makes it difficult for the reader to understand the reason for a sub-analysis and the interpretation of the numerical findings.<br /> - The originality of the article is not sufficiently brought forward:<br /> a) the novel method to detect the depth of the molecular layer is not contextualized in order to understand the shortcomings of previously-established methods. This prevents the reader from understanding its added value and hinders its potential re-use in further studies.<br /> b) The numerous results reported are not sufficiently addressed in the discussion for the reader to get a full grasp of their implications, hindering the clarity of the overall conclusion of the article.

Reviewer #3 (Public Review):

This paper enhances our understanding of the evolution of cerebellar size and structure and is a potentially valuable addition to the recent literature on this. The examination of both the correlated evolution and divergent patterns of folding in the cerebellum and cortex may help us to understand what processes are involved and how these relate to the structural organisation at macro- and micro-levels. The study combines careful anatomical measurements based on a curated, publicly available mammalian brain collection, consideration of theoretical explanation of folding patterns, and for the most part a good comparative sample size. However, questions about the sample size arise in the authors' more complex statistical models (see below).

The main issues I have are with the statistical analyses. The authors use a standard phylogenetic approach - Phylogenetic Generalised Least Squares - which is adequate for these questions. I think the authors need to be a bit more cautious in interpreting their results in two respects.

1. The first problem relates to their use of the Ornstein-Uhlenbeck (OU) model: they try fitting three evolutionary models, and conclude that the Ornstein-Uhlenbeck model provides the best fit. However, it has been known for a while that OU models are prone to bias and that the apparent superiority of OU models over Brownian Motion is often an artefact, a problem that increases with smaller sample sizes. (Cooper et al (2016) Biological Journal of the Linnean Society, 2016, 118, 64-77,

2. Second, for the partial correlations (e.g. fig 7) and Principal Components (fig 8) there is a concern about over-fitting: there are 9 variables and only 56 data points (violating the minimal rule of thumb that there should be >10 0bservations per parameter). Added to this, the inclusion of variables lacks a clear theoretical rationale. The high correlations between most variables will be in part because they are to some extent measuring the same things, e.g. the five different measures of cerebellar anatomy which include two measures of folial size. This makes it difficult to separate their effects. I get that the authors are trying to tease apart different aspects of size, but in practice, I think these results (e.g. the presence of negative coefficients in Fig 7) are really hard or impossible to interpret. The partial correlation network looks like a "correlational salad" rather than a theoretically motivated hypothesis test. It isn't clear to me that the PC analyses solve this problem, but it partly depends on the aims of these analyses, which are not made very clear.

The claim of concerted evolution between cortical and cerebellar values (P 11-12) seems to be based on analyses that exclude body size and brain size. It, therefore, seems possible - or even likely - that all these analyses reveal overall size effects that similarly influence the cortex and cerebellum. When the authors state that they performed a second PC analysis with body and brain size removed "to better understand the patterns of neuroanatomical evolution" it isn't clear to me that is what this achieves. A test would be a model something like [cerebellar measure ~ cortical measure + rest of the brain measure], and this would deal with the problem of 'correlation salad' noted below.

It is not quite clear from fig 6a that the result does indeed support isometry between the data sets (predicted 2/3 slope), and no coefficient confidence intervals are provided.

Referencing/discussion/attribution of previous findings<br /> - With respect to the discussion of the relationship between cerebellar architecture and function, and given the emphasis here on correlated evolution with cortex, Ramnani's excellent review paper goes into the issues in considerable detail, which may also help the authors develop their own discussion: Ramnani (2006) The primate cortico-cerebellar system: anatomy and function. Nature Reviews Neuroscience 7, 511-522 (2006<br /> - The result that humans are outliers with a more folded cerebellum than expected is interesting and adds to recent findings highlighting evolutionary changes in the hominin human cerebellum, cerebellar genes, and epigenetics. Whilst Sereno et al (2020) are cited, it would be good to explain that they found that the human cerebellum has 80% of the surface area of the cortex. It would surely also be relevant to highlight some of the molecular work here, such as Harrison & Montgomery (2017). Genetics of Cerebellar and Neocortical Expansion in Anthropoid Primates: A Comparative Approach. Brain Behav Evol. 2017;89(4):274-285. doi: 10.1159/000477432. Epub 2017 (especially since this paper looks at both cerebellar and cortical genes); also Guevara et al (2021) Comparative analysis reveals distinctive epigenetic features of the human cerebellum. PLoS Genet 17(5): e1009506. https://doi.org/10.1371/journal. pgen.1009506. Also relevant here is the complex folding anatomy of the dentate nucleus, which is the largest structure linking cerebellum to cortex: see Sultan et al (2010) The human dentate nucleus: a complex shape untangled. Neuroscience. 2010 Jun 2;167(4):965-8. doi: 10.1016/j.neuroscience.2010.03.007.<br /> - The authors state that results confirm previous findings of a strong relationship between cerebellum and cortex (P 3 and p 16): the earliest reference given is Herculano-Houzel (2010), but this pattern was discovered ten years earlier (Barton & Harvey 2000 Nature 405, 1055-1058. https://doi.org/10.1038/35016580; Fig 1 in Barton 2002 Nature 415, 134-135 (2002). https://doi.org/10.1038/415134a) and elaborated by Whiting & Barton (2003) whose study explored in more detail the relationship between anatomical connections and correlated evolution within the cortico-cerebellar system (this paper is cited later, but only with reference to suggestions about the importance of functions of the cerebellum in the context of conservative structure, which is not its main point). In fact, Herculano-Houzel's analysis, whilst being the first to examine the question in terms of numbers of neurons, was inconclusive on that issue as it did not control for overall size or rest of the brain (A subsequent analysis using her data did, and confirmed the partially correlated evolution - Barton 2012, Philos Trans R Soc Lond B Biol Sci. 367:2097-107. doi: 10.1098/rstb.2012.0112.)

Reviewer #1 (Public Review):

This is a very elegant study of the dynamics of the longitudinal surface pH profile in growing Arabidopsis roots. The authors first present a new powerful method for the visualization of the surface pH profiles using the pH-sensitive fluorescent dye fluoresceine-5 (or 6)-sulfonic acid. This is an interesting new tool for studying surface pH in plants and perhaps other organisms. The main findings are that the presence of an alkaline band at the transition zone does not depend on AHA abundance (shown by immunolocalization) or activity shown by pharmacology (FC treatment) or by using plants expressing hyperactive, or PP2CD1- inhibited AHA2 or by using KO mutants aha2 or pp2c-d respectively. This band depends on auxin and AUX1-mediated auxin influx and rapid auxin response components AFB1 and CNGC14. The latter has a distribution along the root fitting the longitudinal surface pH zonation and are both required for it. Canonical auxin signaling (TIR) has more quantitative effects on the extent of the auxin-induced alkalinization. They also observe that the rapid auxin response module is constantly activated and inactivated as shown by the time-dependent variations in surface pH within the alkaline zone on both sides of the root and the rapid AUX1, AFB1, and CNGC14-dependent acidification of the upper surface and alkalinisation of the lower surface during gravitropic responses. Finally, they provide some evidence for the role of the rapid auxin responses in avoiding physical obstacles in the environment of the root.

The data look very sound. The originality of the approach used is the observation of dynamic responses at a second-to-minute time scale and to systematically correlate between the observed changes in the longitudinal surface pH profile with changes in growth rate. The manuscript is well-written with clear figures.

Reviewer #2 (Public Review):

Root growth is driven by cell elongation, and its local control allows roots to navigate the complex soil environment. Cell growth is driven by the relaxation of the cell wall, a process requiring a drop in pH. Auxin is a key regulator of root development that inhibits root growth. Auxin effects on proton dynamics are complex, it can promote both acidification and alkalinization of the extracellular space through different signaling modules, some only recently uncovered. Serre et al. report on using a new dye to monitor extracellular pH in the region surrounding the Arabidopsis thaliana root. Their manuscript aims to clarify the relationships between pH around the root, proton flux, auxin, cell elongation, and root growth with this tool. They show a typical zonation of pH values along the root: a more acidic domain corresponding to the transit-amplifying compartment, followed by a more alkaline one at the transition and early elongation zones and a more acidic one in the late elongation/root hair zone. This zonation is in agreement with previous reports obtained by other methods. A particularly puzzling aspect is the origin of the more alkaline domain. Serre et al. present evidence supporting the involvement of the AUX1-AFB1-CNGC14 module for the emergence of this more alkaline domain and how it can contribute to the ability of the root to navigate its environment.

Serre et al. show that the more alkaline domain in the transition zone is not directly determined by the activity or localization of the AHA proton pumps but rather by the auxin influx carrier AUX1. They show that the components of the rapid auxin response pathway, in particular, the auxin co-receptor AFB1 and the calcium channel CNGC14, contribute to the emergence of this more alkaline domain. Finally, they show that mutants in these two genes, impaired in the rapid auxin response pathway, show less efficient navigation of the root tip.

The manuscript is clear and well-written. The logic is sound, and the conclusions are supported by the data.

The new dye appears as a promising tool for monitoring the pH in the rhizosphere with advantages over the previous ones. Yet, as pointed out by the authors in the discussion, it reports on pH at the organ scale in the region around the root, not in the apoplast or the cell wall, which can eventually complexify the elaboration of a mechanistic model joining auxin, proton efflux, cell wall properties, cell elongation, and root growth. Although several of the findings confirm previous reports, the manuscript brings novelty by demonstrating the involvement of the rapid auxin response. I am overall supportive of the manuscript. Yet, several points should be addressed:

- The presentation of the more acidic and alkaline domains could be easier to visualize.<br /> - The authors refer to acidic and alkaline domains but do not report on absolute pH values; they monitor the emission ratio of the dye. They justify why to use relative pH value in the discussion and refer there to internal controls that are not clearly defined. In my opinion, the wording should be more consistent across the text and figures and refer to *more* acidic and *more* alkaline domains rather than acidic (pH<7) and alkaline (pH>7) domains.<br /> - The data related to the unaltered distribution of AHA using antibody staining should be backed up.<br /> - The way the pH profile and the statistical analyses should be improved.<br /> - The authors should test the effect of extracellular auxin perception (tmk, abp) mutants on pH zonation.<br /> - Conclusion could be strengthened by moving several pieces of data currently in supplemental material to the main text.

Reviewer #3 (Public Review):

This manuscript provides a high amount of data supporting the author's hypothesis. Serre et al aimed to address the root surface pH and the molecular factors regulating the establishment of the root surface pH pattern important for root growth and gravitropic response. The authors are able to provide solid data on the role of AUX1, AFB1, and CNGC14 in establishing an alkalic patch in the transition zone on the root surface. A weak point in the manuscript is the absence of cellular resolution. The authors mention the technical problems to assess apoplastic pH with previously published tools. They offer Fluorescein106 5-(and-6)-Sulfonic Acid, Trisodium Salt (FS) as an alternative. Even though they were able to generate valuable data with FS, bringing in cellular resolution would increase the quality of the paper even more. Overall, Serre et al provide a solid manuscript with novel data which is of high importance for the field of root and auxin biology.

Reviewer #1 (Public Review):

This paper studies color vision in anemonefish. The central conclusion of the paper is that anemonefish use signals from their UV cones to discriminate colors that would not otherwise be distinguishable; this differs from other fish in which UV cones extend the range of wavelengths of sensitivity but do not add a dimension to color vision. The work fits into a rich history of studies investigating how color vision fits into an animal's ecological niche. My primary concerns regard the microspectrophotometry data from single cones and some aspects of the presentation of the behavioral data.

Microspectrophotometry<br /> The spectral properties of the cone types are a key issue for interpreting the results. These were measured using MSP, and fits are shown in Figure 2. The raw data shown in Fig. S1 appears more complicated than indicated in the main text. The templates miss the measurements across broad wavelength bands in each cone type. Particularly concerning is the high UV absorbance across cone types and the long-wavelength absorbance in the UV cone. It is not clear how this picture supports the relatively simple description of cone types and spectral sensitivities given in the main text and which forms the basis of the modeling.

Presentation<br /> The results are not presented in a straightforward way - at least for this reviewer. What is missing for me is a clear link between the psychometric curves in Figure 3A and the discrimination thresholds indicated in Figure 3B and Figure 4. Figure 3A is only discussed in the text on line 289 - after Figure 4 has been introduced and discussed. It would have been very helpful for me if the psychometric curves were first introduced and described, then the relation to Figure 3B was clearly indicated (perhaps with a single psychometric curve as an example). Similarly for Figure 4 the relationship between specific psychometric curves and the threshold plotted would be quite helpful. Currently it takes a careful reading to understand why being below the dashed line in Figure 4 is important.

RNL model<br /> The data is fit and interpreted in the context of the receptor noise limited model. The paragraph in the discussion about complementary color pairs suggests that this model is incorrect (text around line 332). Consideration of how the results depend on the RNL model is important, especially given the interpretation here.

Figure 3B<br /> This is the key figure in the paper. But several issues make seeing the data in this figure difficult. First, the important part of the figure is buried near the origin and hard to see. Can you show a surface that connects the thresholds in the different chromatic directions, or otherwise highlight the regions of discriminable and not discriminable colors?

Reviewer #2 (Public Review):

Mitchell and colleagues examined the contribution of a UV-sensitive cone photoreceptor to chromatic detection in Amphiprion ocellaris, a type of anemonefish. First, they used biophysical measurements to characterize the response properties of the retinal receptors, which come in four spectrally-distinct subtypes: UV, M1, M2, and L. They then used these spectral sensitivities to construct a 4-dimensional (tetrahedral) color space in which stimuli with known spectral power distributions can be represented according to the responses they elicit in the four cone types. A novel five-LED display was used to test the fish's ability to detect "chromatic" modulations in this color space against a background of random-intensity, "achromatic" distractors that produce roughly equal relative responses in the four cone types. A subset of stimuli, defined by their high positive UV contrast, were more readily detected than other colors that contained less UV information. A well-established model was used to link calculated receptor responses to behavioral thresholds. This framework also enabled statistical comparisons between models with varying number of cone types contributing to discrimination performance, allowing inferences to be drawn about the dimensionality of color vision in anemonefish.

The authors make a compelling case for how UV light in the anemonefish habitat is likely an important ecological source of information for guiding their behavior. The authors are to be commended for developing an elegant behavioral paradigm to assess visual performance and for incorporating a novel display device especially suited to addressing hypotheses about the role of UV light in color perception. While the data are suggestive of behavioral tetrachromacy in anemonefish, there are some aspects of the study that warrant additional consideration:

1) One challenge faced by many biological imaging systems is longitudinal chromatic aberration (LCA) - that is, the focal power of the system depends on wavelength. In general, focal power increases with decreasing wavelength, such that shorter wavelengths tend to focus in front of longer wavelengths. In the human eye, at least, this focal power changes nonlinearly with wavelength, with the steepest changes occurring in the shorter part of the visible spectrum (Atchison & Smith, 2005). In the fish eye, where the visible spectrum extends to even shorter wavelengths, it seems plausible that a considerable amount of LCA may exist, which could in turn cause UV-enriched stimuli to be more salient (relative to the distractor pixels) due to differences in perceived focus rather than due solely to differences in their respective spectral compositions. Such a mechanism has been proposed by Stubbs & Stubbs (2016) as a means for supporting "color vision" in monochromatic cephalopods (but see Gagnon et al. 2016). It would be worth discussing what is known about the dispersive properties of the crystalline lens in A. ocellaris (or similar species), and whether optical factors could produce sufficient cues in the retinal image that might explain aspects of the behavioral data presented in the current study.

2) The authors provide a quantitative description of anemonefish visual performance within the context of a well-developed receptor-based framework. However, it was less clear to me what inferences (if any) can be drawn from these data about the post-receptoral mechanisms that support tetrachromatic color vision in these organisms. Would specific cone-opponent processes account for instances where behavioral data diverged from predictions generated with the "receptor noise limited" model described in the text? The general reader may benefit from more discussion centered on what is known (or unknown) about the organization of cone-opponent processing in anemonefish and related species.

Reviewer #3 (Public Review):

The comments below focus mainly on ways that the data and analysis as currently present do not to this reviewer compel the conclusions the authors wish to draw. It is possible that further analysis and/or clarification in the presentation would more persuasively bolster the authors' position. It also seems possible that a presentation with more limited conclusions but clarity on exactly what has been demonstrated and where additional future work is needed would make a strong contribution to the literature.

* Fig 3A. It might be worth emphasizing a bit more explicitly that the x-axis (delta S) is the result of a model fit to the data being shown, since this then means that if RNL model fit the data perfectly, all of the thresholds would fall at deltaS = 1. They don't, so I would like to see some evaluation from the authors' experience with this model as to whether they think the deviations (looks like the delta S range is ~0.4 to ~1.6 in Figure 4B) represent important deviations of the data from the model, the non-significant ANOVA notwithstanding. For example, Figure 4B suggests that the sign of the fit deviations is driven by the sign of the UV contrast and that this is systematic, something that would not be picked up by the ANOVA. Quite a bit is made of the deviations below, but that the model doesn't fully account for the data should be brought out here I think. As the authors note elsewhere, deviations of the data from the RNL model indicate that factors other than receptor noise are at play, and reminding the reader of this here at the first point it becomes clear would be helpful.

* Line 217 ff, Figure 4, Supplemental Figure 4). If I'm understanding what the ANOVA is telling us, it is that the deviations of the data across color directions and fish (I think these are the two factors based on line 649) is that the predictions deviate significantly from the data, relative to the inter-fish variability), for the trichromatic models but not the tetrachromatic model. If that's not correct, please interpret this comment to mean that more explanation of the logic of the test would be helpful.

Assuming that the above is right about the nature of the test, then I don't think the fact that the tetrachromatic model has an additional parameter (noise level for the added receptor type) is being taken into account in the model comparison. That is, the trichromatic models are all subsets of the tetrachromatic model, and must necessarily fit the data worse. What we want to know is whether the tetrachromatic model is fitting better because its extra parameter is allowing it to account for measurement noise (overfitting), or whether it is really doing a better job accounting for systematic features of the data. This comparison requires some method of taking the different number of parameters into account, and I don't think the ANOVA is doing that work. If the models being compared were nested linear models, than an F-ratio test could be deployed, but even this doesn't seem like what is being done. And the RNL model is not linear in its parameters, so I don't think that would be the right model comparison test in any case.

Typical model comparison approaches would include a likelihood ratio test, AIC/BIC sorts of comparisons, or a cross-validation approach.

If the authors feel their current method does persuasively handle the model comparison, how it does so needs to be brought out more carefully in the manuscript, since one of the central conclusions of the work hinges at least in part on the appropriateness of such a statistical comparison.

* Also on the general point on conclusions drawn from the model fits, it seems important to note that rejecting a trichromatic version of the RNL model is not the same as rejecting all trichromatic models. For example, a trichromatic model that postulates limiting noise added after a set of opponent transformations will make predictions that are not nested within those of RNL trichromatic models. This point seems particularly important given the systematic failures of even the tetrachromatic version of the RNL model.

* More generally, attempts to decide whether some human observers exhibit tetrachromacy have taught us how hard this is to do. Two issues, beyond the above, are the following. 1) If the properties of a trichromatic visual system vary across the retina, then by imaging stimuli on different parts of the visual field an observer can in principle make tetrachromatic discriminations even though visual system is locally trichromatic at each retinal location. 2) When trying to show that there is no direction in a tetrachromatic receptor space to which the observer is blind, a lot of color directions need to be sampled. Here, 9 directions are studied. Is that enough? How would we know? The following paper may be of interest in this regard: Horiguchi, Hiroshi, Jonathan Winawer, Robert F. Dougherty, and Brian A. Wandell. "Human trichromacy revisited." Proceedings of the National Academy of Sciences 110, no. 3 (2013): E260-E269. Although I'm not suggesting that the authors conduct additional experiments to try to address these points, I do think they need to be discussed.

* Line 277 ff. After reading through the paper several times, I remain unsure about what the authors regard as their compelling evidence that the UV cone has a higher sensitivity or makes an omnibus higher contribution to sensitivity than other cones (as stated in various forms in the title, Lines 37-41, 56-57, 125, 313, 352 and perhaps elsewhere).

At first, I thought they key point was that the receptor noise inferred via the RNL model as slightly lower (0.11) for the UV cone than for the double cones (0.14). And this is the argument made explicitly at line 326 of the discussion. But if this is the argument, what needs to be shown is that the data reject a tetrachromatic version of the RNL model where the noise value of all the cones is locked to be the same (or something similar), with the analysis taking into account the fewer parametric degrees of freedom where the noise parameters are so constrained. That is, a careful model comparison analysis would be needed. Such an analysis is not presented that I see, and I need more convincing that the difference between 0.11 and 0.14 is a real effect driven by the data. Also, I am not sanguine that the parameters of a model that in some systematic ways fails to fit the data should be taken as characterizing properties of the receptors themselves (as sometimes seems to be stated as the conclusion we should draw).

Then, I thought maybe the argument is not that the noise levels differ, but rather that the failures of the model are in the direction of thresholds being under predicted for discriminations that involve UV cone signals. That's what seems to be being argued here at lines 277 ff, and then again at lines 328 ff of the discussion. But then the argument as I read it more detail in both places switches from being about the UV cones per se to being about postive versus negative UV contrast. That's fine, but it's distinct from an argument that favors omnibus enhanced UV sensitivity, since both the UV increments and decrements are conveyed by the UV cone; it's an argument for differential sensitivity for increments versus decrements in UV mediated discriminations. The authors get to this on lines 334 of the discussion, but if the point is an increment/decrement asymmetry the title and many of the terser earlier assertions should be reworked to be consistent with what is shown.

Perhaps the argument with respect to model deviations and UV contrast independent of sign could be elaborated to show more systematically that the way the covariation with the contrasts of the other cone stimulations in the stimulus set goes, the data do favor deviations from the RNL in the direction of enhanced sensitivity to UV cone signals, but if this is the intent I think the authors need to think more about how to present the data in a manner that makes it more compelling than currently, and walk the reader carefully through the argument.

* On this point, if the authors decide to stick with the enhanced UV sensitivity argument in the revision, a bit more care about what is meant by "the UV cone has a comparatively high sensitivity (line 313 and throughout)" needs more unpacking. If it is that these cones have lower inferred noise (in the context of a model that doesn't account for at least some aspects of the data), is this because of properties of the UV cones, or the way that post-receptoral processing handles the signals from these cones mimicking a cone effect in the model. And if it is thought that it is because of properties of the cones, some discussion of what those properties might be would be helpful. As I understand the RNL model, relative numbers of cones of each type are taken into account, so it isn't that. But could it be something as simple as higher photopigment density or larger entrance aperture (thus more quantum catches and higher SNR)?

* Line 288 ff. The fact that the slopes of the psychometric functions differed across color directions is, I think, a failure of the RNL model to describe this aspect of the data, and tells us that a simple summary of what happens for thresholds at delta S = 1 does not generalize across color directions for other performance levels. Since one of the directions where the slope is shallower is the UV direction, this fact would seem to place serious limits on the claim that discrimination in the UV direction is enhanced relative to other directions, but it goes by here without comment along those lines. Some comment here, both about implications for fit of RNL model and about implications for generalizations about efficacy of UV receptor mediated discrimination and UV increment/decrement asymmetries, seems important.

* Line 357 ff. Up until this point, all of the discussion of differences in threshold across stimulus sets has been in terms of sensitivity. Here the authors (correctly) raise the possibility that a difference in "preference" across stimulus sets could drive the difference in thresholds as measured. Although the discussion is interesting and germaine, it does to some extent further undercut the security of conclusions about differential sensitivity across color directions relative to the RNL model predictions, and that should be brought out for the reader here. The authors might also discuss about how a future experiment might differentiate between a preference explanation and a sensitivity explanation of threshold differences.

* RNL model. The paper cites a lot of earlier work that used the RNL model, but I think many readers will not be familiar with it. A bit more descriptive prose would be helpful, and particularly noting that in the full dimensional receptor space, if the limiting noise at the photoreceptors is Gaussian, then the isothreshold contour will be a hyper-ellipsoid with its axes aligned with the receptor directions.

* Use of cone isolating stimuli? For showing that all four cone classes contribute to what the authors call color discrimination, a more direct approach would seem to be to use stimuli that target stimulation of only one class of cone at a time. This might require a modified design in which the distractors and target were shown against a uniform background and approximately matched in their estimated effect on a putative achromatic mechanism. Did the authors consider this approach, and more generally could they discuss what they see as its advantages and disadvantages for future work.

Reviewer #1 (Public Review):

The technical approach is novel, exciting, and very carefully calibrated, and can certainly lead to many interesting downstream applications, e.g. enhanced throughput and consistency for screening purposes. Compared to traditional single-assay designs, this solution eliminates some sources of human error associated with manual dilution of reagents and reproducibility and facilitates the study of a wide spectrum of concentrations particularly at the low-concentration (below nanomolar), high-sensitivity range.

However, the study itself does not generate any fundamentally novel insights or new understanding of the biology or biophysics of the chemotactic response. It mainly reproduces previously measured trends in a more efficient and controlled manner. The novelty of the paper is purely in the technology, whereas the major weakness is that this new technology was not used to demonstrate or discover some new biological phenomenon.

Reviewer #2 (Public Review):

This manuscript develops a new microfluidic platform to study how the chemotactic response of motile cells varies in relation to its strength. Typically, chemotaxis is assayed using one microfluidic channel at a time, which limits throughput when researchers want to how to resolve how chemotaxis varies with chemoeffector concentration/gradient strength. The authors have automated this process by designing a device that can logarithmically dilute a chemoaffector with a buffer "on chip", simultaneously generating five different chemical gradients in five different channels where the maximum concentration varies by five orders of magnitude (in addition to a control lacking a gradient).

Technically, this is a major feat, requiring the design of a two-layered device, the use of herringbone mixers, and the careful consideration of the hydraulic resistance of each section to ensure that flow splits at junctions in a defined way to achieve the desired dilutions. It is clear the authors had to overcome many challenges before obtaining the final design. The authors have achieved their intended aims and the results from the multiplexed device are consistent with that from lower throughput devices.

Strengths:

- The multiplexed device allows researchers to greatly increase their experimental throughput when mapping out how a microbe responds to chemicals at different concentrations. While such data might be useful in its own right, such a device might make it much easier to quantify how chemotaxis varies in a multidimensional parameter space using multiple runs of this device (e.g. in analyses of fold-change detection where both the background concentration and gradient strength are varied, or in analyses that compare how the sensitivity of a microbe's chemosensory system varies in response to different chemoaffectors). Currently, it is difficult to map out how multiple parameters affect chemotaxis by running only one microfluidic experiment at a time.

- The same exact cell culture can be used in simultaneous experiments. This could potentially dramatically reduce biological variability, as cells obtained from batch cultures often differ in their metabolic state and significant variability is often observed in cultures inoculated on different days. The reduction of such variability is expected to be particularly important for strains that are very difficult/slow to grow in the laboratory or when testing cells obtained directly from environmental/clinical samples.

Weaknesses:

- Given the complexity of the device, it appears difficult to validate that the concentrations within multiplexed are the ones that are expected. It is not clear whether these devices can be used directly "off the shelf" or whether each device would need to be calibrated individually with dye beforehand. In contrast, it is relatively straightforward to serially dilute chemoaffectors manually using pipettors and obtain accurate results. It is not clear whether the on-chip dilution is a distinct advantage or whether it might add additional uncertainty/complexity.

- It is not feasible to track swimming cells in six channels simultaneously, as one cannot automatically move the microscope stage from one channel to another rapidly enough (e.g. the data collected here have 8 seconds between subsequent frames). Thus, multiplexed devices are best suited to measuring independent snapshots of the distribution of track swimming cells, rather than resolving the cellular behaviours that generate chemotaxis. However, tracking the response of slower moving, surface attached cells (e.g. eukaryotes that use ameboid movement on surfaces or bacteria that chemotax using pili) might be feasible if the gradient is maintained with constant flow. This is not explored by the authors, but if feasible it would open up a completely new avenue. Surface-attached cells move ~1000 times slower than swimming cells and experiments last for ~10-15 hours. Thus, using these multiplexed devices with surface-attached cells might facilitate much larger time savings compared to swimming cell assays, which only last for several minutes.

Reviewer #1 (Public Review):

Croft, Pearce, and colleagues used a combination of spatial transcriptomics and analysis of publicly available scRNA-sequencing data of patients with PDAC to assess differences in the transcriptome of tumor proximal and distal stromal cells and associated these differences with survival data. Focusing on aSMA+ cancer-associated fibroblasts (CAFs), they find that tumor-proximal CAFs were defined by high expression of PDPN and Wnt ligands, and tumor-distal CAFs expressed inflammatory, complement, and Wnt inhibitor genes. While gene expression in relation to tumor distance did not per se correlate with clinical outcome, the authors find that individual genes within the tumor proximal and distal subsets were associated with increased or decreased survival. Based on this, the authors suggest a combination of targeting tumor-proximal CAFs defined by PDPN expression and inhibition of HIF-1a in the tumor distal stroma. Using an innovative approach to combine their spatial transcriptomics with single-cell transcriptomics data, the authors further identify an association between the expression of proximal CAF marker genes with elevated expression of complement and retinoic acid metabolism genes, and that complement genes were associated with increased survival.

While spatial differences in the abundance of different CAF subsets have been suggested before, the spatial transcriptomic data presented in this study provide a fresh look at CAF heterogeneity in PDAC and will be a useful resource for hypothesis generation and testing on the spatial regulation of CAF heterogeneity. However, there are major concerns associated with the interpretation of the data given the markers used to generate it, the association with single-cell data, and an overinterpretation of transcriptomic data, as detailed below.

1) The spatial transcriptomic data on fibroblasts relies on aSMA as a fibroblast marker. However, several studies in PDAC and other tumors have shown that there are subtypes of CAFs that do not (or only weakly) express aSMA, including inflammatory CAFs (iCAFs) and antigen-presenting CAFs (apCAFs) which therefore might have been missed by the authors. While there is no universal CAF marker, more recently the pancreatic cancer field has been using PDPN as a pan-CAF marker. aSMA is a classical myofibroblast marker across tissues and tumor types and the authors should state that they focused on myofibroblasts in their study.

2) While the association of the spatial data and single-cell data is innovative, it is flawed by the use of a single marker gene (DCN) to define the fibroblast population and the small number of cells (229) within this population. The authors need to corroborate their findings using a combination of fibroblast marker genes as well as other studies comprising a larger number of cells of fibroblast origin.

3) The authors find HIF1a expression to be associated with poor survival and enriched within the tumor-distal stroma. They interpret this as a reflection of the hypoxic environment of PDAC. However, the authors only ever look at HIF1a mRNA, but hypoxic regulation of HIF-1a occurs post-transcriptionally. Transcriptional regulation of HIF1a has been reported through pro-inflammatory cytokines and NFkB signaling. Therefore, the authors should either stain for HIF1a to confirm enrichment on the protein level or adjust their discussion to reflect the important biology behind HIF1a regulation.

4) The authors often misinterpret the correlations and associations they observe as causation and explanation - they should either adjust their language or perform experiments to show causation.

Reviewer #2 (Public Review):

In the current manuscript, the authors select 24 surgically resected pancreatic cancer samples from patients who had a poor outcome (survival of less than one year) or better outcome (survival of at least 3 years). They use a Nanostring Geomx Digital Spatial profiler using a panel of 94 probes. The authors identify a proximal fibroblast population that expresses high levels of PDPN, while a distal fibroblast population expresses high levels of inflammatory genes such as IL6 and IL11, as well as complement genes. Using single-cell RNA sequencing, the authors are able to identify fibroblast populations reflecting those identified in the spatial data and identify other pathways that distinguish the two populations, and that define better or poorer outcomes (for instance, Hif signaling is associated with a poorer prognosis while markers of T cell activation are associated with better prognosis).

The manuscript addresses an important topic, namely whether fibroblasts, a heterogenous and relatively poorly understood cell population within the pancreatic cancer microenvironment, predict poor response. Further, the manuscript integrates spatial and single-cell data, in the quest to identify how the tissue composition of a tumor affects the overall prognosis. Some weaknesses are also noted and should be addressed. Most notably, the prognostic predictions are based on a relatively small number of samples. Further, as spatial transcriptomics is not a single cell-level technology, the authors could use co-immunofluorescence to validate their cell populations and specifically prove that the signatures correspond to genes expressed by fibroblasts, rather than infiltrating immune cells. Finally, the author shows that my-CAF-like fibroblasts correlate with worse prognosis, while inflammatory CAFs predict better prognosis: this finding should be discussed in the context of other CAF literature, some indicating that iCAFs are a negative prognostic predictor.

Reviewer #1 (Public Review):

In this manuscript, the authors present a computational framework based on Bayesian observer models that parameterises several different sources of noise and bias in perceptual decision-making. The authors show that these sources of suboptimality cannot be dissociated in many typical decision-making tasks. They present an analysis of two previously published sets of experimental data, where the experimental design should allow them to dissociate suboptimalities parameterised in the model. They fit various versions of the model including different suboptimalities and in general show that including the suboptimalities improves the fit, depending on whether the data is aggregated across participants or not.

The major strengths of the methods and results include 1. The clear theoretical delineation of different forms of suboptimalities may help to guide research understanding them on the behavioural and neural levels. 2. The attention to scientific rigor in model fitting, including the use of power analysis and corrections for the number of model parameters. 3. Clear figures that are helpful in understanding the model.

The major weaknesses of the methods and results include 1. The lack of model/parameter recovery analysis shows the extent to which the model can separate sources of suboptimality against some ground truth. 2. The lack of generalisability, where the model parameters can only be dissociated using specific experimental manipulations, and a large number of trials. 3. It is unclear to what extent the assumptions of the model (and its parameterisation) limit the realisability of the proposed computational framework.

The authors achieve their aim of outlining a computational framework that accounts for various sources of suboptimalities and shows some evidence that this model may be useful for making inferences about these suboptimalities given careful experimental manipulation.

The work adds to the movement toward delineating the specific sources of suboptimalities as opposed to capturing 'noise' and 'bias' as overarching variables, and the model may prove useful for other researchers. However, given the model requires special experimental tasks to dissociate the parameters, it is unclear how this model improves upon the traditional approach of designing experiments to dissociate sources of suboptimalities directly.

Reviewer #2 (Public Review):

The study makes a useful contribution by showing that the classical binary discrimination task cannot distinguish different sources of suboptimality (perceptual vs. categorical bias; observation noise vs. approximate inference) in contrast to another task that is more complex (cue combination task). The paper provides the computational framework to define and quantify those sources of suboptimality and report the results of a task in which those different sources are disentangled indeed, in both model fitting and qualitative features of the data.

Strengths:<br /> - A very timely question: How to characterize the sources of suboptimality in (human) perceptual decisions?<br /> - The text is very clear and although the content is technical, the main ideas are conveyed in simple terms and figures, and the detail of mathematical derivations is restricted to the methods section.<br /> - The design of the cue-combination task is very interesting because the posterior distributions over categories predict no difference between the central and matched conditions in the case of perfect inference, but a difference whenever not too many samples are used in approximate inference, making it possible to disentangle different sources of suboptimality in the task.<br /> - The results from the first experiment are followed up by another experiment that includes manipulation of the stimulus duration, which should change the accuracy of approximate inference (and perceptual noise). The results are compatible with those predictions.<br /> - Effects are characterized by model fitting and model comparison, but different models also make qualitatively different predictions, making it possible to adjudicate between models simply by looking at the data (shape of the psychometric curves in different conditions).

Weaknesses:<br /> - There is no parameter recovery analysis based on the generative model in the multi-modal task.<br /> - Several results are not conclusive in most subjects. They are clearly visible only in a few participants and the aggregated data. It is not clear whether this is specific to this dataset (and task design) or whether it is a general conclusion.<br /> - The dataset is reused from a previous study and includes 20 participants. A replication of the result in an independent group of participants would make the result much more robust.<br /> - A replication attempt could use a different task (the current results are based on multi-modal sound localization), which would make the conclusion even more convincing.

Reviewer #1 (Public Review):

Precise regulation of gamete fusion ensures that offspring will have the same ploidy as the parents. However, breaking this regulation can be useful for plant breeding. Haploid induction followed by chemical-induced genome doubling can be used to fix desirable genotypes, while triparental hybrids where two sperm cells with two different genotypes fertilize an egg cell can be advantageous for bypassing hybridization barriers to create interspecies hybrids with increased fitness. This manuscript follows up on a previous study from the same research group that used a clever high throughput polyspermy detection assay (HIPOD) to show that wild-type Arabidopsis naturally forms triparental hybrids at very low frequencies (less than 0.05% of progeny) and that these triparental hybrids can bypass dosage barriers in the endosperm (Nakel, et al., 2017). Mao and co-authors hypothesized that mutants that conferred polytubey, the attraction of multiple pollen tubes by mutant female gametophytes, would also increase the rate of triparental hybrids. They used a double mutant in the endopeptidase genes ECS1 and ECS2 which had previously been reported to induce supernumerary pollen tube attraction to test this hypothesis with their two-component HIPOD system in which one pollen donor constitutively expresses the mGAL4-VP16 transcription factor while the second pollen donor carries an herbicide resistance gene regulated by the GAL4-responsive UAS promoter. Triparental hybrids are detected as herbicide-resistant progeny from wild-type Arabidopsis flowers that have been pollinated by the two paternal genotypes. The authors convincingly show that the ecs1 ecs2-1 double mutant more than doubled the frequency of triparental, triploid hybrids in HIPOD crosses. They next tested the hypothesis that this increase in triparental hybrids was due to a gametophytic effect by using an ecs1-/- ecs2-1/ECS2 maternal parent in the HIPOD assay and testing whether the ecs2-1 mutant allele was preferentially inherited in triparental hybrids. The mutant allele was inherited at a much higher rate than expected, confirming their hypothesis.

The triparental hybrid results with the ecs1 ecs2 mutant were not that surprising since the presence of extra sperm cells gives more opportunities for triparental hybrids to form, especially if gamete fusion is misregulated. However, an unexpected result came when the authors used aniline blue staining to analyze the ecs1 ecs2 polytubey phenotype. They confirmed that the double mutant had increased levels of polytubey compared to wild-type ovules, but they also noticed that 13% of seeds were not developing normally. This phenotype was confirmed with a second ecs2 allele and was complemented with both ECS1 and ECS2 transgenes under their native promoters. Microscopic analysis revealed normal gametophyte morphology before fertilization, but 8% of pollinated ovules failed to develop an embryo and 7% failed to develop endosperm, suggesting single fertilization events. In a logical set of experiments, they followed up on this result by crossing ecs1 ecs2 with pollen carrying a fluorescent reporter that would be expressed in developing embryos and endosperm. In this experiment, they were again surprised. Some of the wild-type-looking seeds lacked a paternal contribution (i.e. no fluorescent signal from the paternal reporter construct) in the embryo. This prompted them to look more closely at the progeny, upon which they detected small plants that were haploid. They confirmed the haploid nature by chromosome spreads. Finally, they used interaccession crosses between ecs1 ecs2 (Col-0) and Landsberg to verify that haploid progeny only carried maternal alleles of markers on all five chromosomes, indicating that the ecs1 ecs2 genotype can induce maternal haploids.

This interesting study highlights the importance of following up on unexpected results. The conclusions are well-supported by the data and quite exciting. Paternal haploid inducers have been discovered in several species, but this is one of only two examples of maternal haploid induction. While the percentage of maternal haploids is very low, this phenomenon could be useful for plant breeding.

Weaknesses<br /> The data in the manuscript is intriguing, but the question of how the same mutant combination promotes the formation of both triploid and haploid progeny remains unanswered and is not thoroughly discussed, nor is any model suggested for how the ECS1/2 peptidases could play a role in regulating gamete fusion and/or repressing parthenogenesis. A second unanswered question is whether the maternal haploids are a result of failed plasmogamy or karyogamy between the egg and sperm leading to parthenogenesis or a result of paternal genome elimination after plasmogamy. In figure 3B, the authors attempted to test whether plasmogamy occurs between the male and female gametes in ecs1 ecs2 ovules by crosses with pollen that expresses a mitochondrial marker under control of the pRPS5a promoter which is active in sperm cells as well as embryos and endosperm of fertilized ovules. This experiment allowed them to detect sperm cells that had not fused with the egg and central cell at 2 days after pollination. They also counted the percentage of seeds that expressed the mitochondrial marker in both embryo and endosperm at 2 DAP and found that ecs1 ecs2 mutants had a 20% reduction of visible mitochondria in embryo sacs compared to wildtype. They conclude that the result indicates a potential plasmogamy defect. However, the dependability of this marker is questionable since only ~55% of wild-type seeds had detectable signal in the embryo and endosperm. The authors imply that this experiment could be used to test plasmogamy, but it is not clear how any conclusions related to the abnormal seed phenotype could be drawn from examining the rate of signal in both the embryo and endosperm. Since the mitochondrial marker was not expressed from a sperm-specific promoter, the fluorescent signal at 2DAP is likely due to new gene expression from pRPS5a in the fertilized embryo and endosperm, not an indication of the presence of sperm-derived mitochondria. Perhaps an earlier timepoint could be used as well as a sperm-specific promoter instead of pRPS5a to answer the question of whether plasmogamy is happening in the ecs1 ecs2 ovules.

Reviewer #2 (Public Review):

The manuscript reports the triploid and haploid productions using an ecs1ecs2 mutant as the maternal donor, in addition to the evaluation of the sexual process observed in the mutant. The indicated data show exquisite quality. To improve the content, I recommend carefully reconsidering the descriptions because some of the insights would cause a stir in the controversy regarding EC1&2 functions in plant reproduction.

Strengths<br /> Triploid production by a combination of ecs1ecs2 mutant and HIPOD system has potential as a future plant breeding tool. Moreover, it's intriguing that both triploid and haploid productions were achieved using the same mutant as a maternal donor. I think authors can claim the value of their results more by adding descriptions about the usefulness of the aneuploid plants in plant breeding history.

The evidence of the persistent synergid nucleus (Figure 3A) is critical insight reported by this study. As Maruyama et al. (2013) reported by live cell imaging, synergid-endosperm fusion had occurred at the two endosperm nuclei stage. It would be valuable to claim the observed fact by citing Maruyama's previous observation.

Weakness<br /> As the authors suggested, the higher triploid frequency observed in ecs1ecs2 than WT was likely caused by the increased polyspermy. However, it also could be that reduction of normal seed number in ecs1ecs2 (whichever is due to failure of fertilization or embryo development arrest) accounts for the increased frequency of the triploid compared to WT.

The results in Figure 3C-E suggested the single fertilization for both egg and central cells at similar frequencies. This is an exciting result, but it is still possible that the fertilized egg or central cell degenerated after fertilization resulting in the disappearance of paternally inherited fluorescence. Evaluation of fertilization patterns at 7-10HAP in ecs1ecs2 mutant may provide more confident insight, although unfused sperm cell was evaluated at 1DAP (Figure 3-figure supplement 1B). The fertilization states can be distinguished depending on the HTR10RFP sperm nuclei morphology and their positions, as reported by Takahashi et al (2018).

Several recent studies have reported exciting insights on ECS1&2 functions; however, various results from different laboratories have raised controversy. Though, the commonly found feature is the repression of polytubey. For readers, it would be helpful to organize the explanation about which insights are concordant or different. In addition, a drawing that explains the time course in the process from pollination to seed development (up to 6DAP) based on WT would help to understand which point is evaluated in each data.

Reviewer #3 (Public Review):

In this manuscript, Mao et al. reported that the two proteases ECS1 and ECS2 participate in both polyspermy block and gamete fusion in Arabidopsis thaliana. The authors could observe polytubey phenotype which has been reported previously and obtain both triparental plants and haploids in ecs1 ecs2 mutants. Therefore they proposed that the triparental plants resulted from the polytubey block defect, whereas the haploids were caused by the gamete fusion defect. Together with two other previous reports, I think it is very interesting to see these two proteases participating in so many different but connected processes. Although they did not provide the molecular mechanism of how ECS participated in polyspermy block and gamete fusion, their findings provide more options for and thus promote plant breeding. The work may have a wide application in the future and will be of broad interest to cell biologists working on gamete fusion and plant breeders. Although most of the conclusions in this paper are well supported by the data, it could be improved with a minor revision including providing clearer data analysis and descriptions, images with higher resolution, and more discussions.

Reviewer #1 (Public Reviews):

The article describes the development of a highly concentrated antibody formulation for MS-Hu6, a first-in-class FSH-blocking humanized antibody proposed for clinical use in osteoporosis, obesity, and Alzheimer's disease. The authors utilized various techniques, including protein thermal shift, size exclusion chromatography, and dynamic light scattering, to examine the stability and physiochemical properties of the formulated MS-Hu6 at concentrations ranging from 1 to 100 mg/mL. They found that the thermal, monomeric, and colloidal stability of the formulated MS-Hu6 was maintained at a concentration of 100 mg/mL, and the addition of L-methionine and disodium EDTA improved its long-term colloidal and thermal stability. The authors further confirmed the thermal stability of the formulation through Nano differential scanning calorimetry (DSC). They demonstrated that MS-Hu6's structural integrity was maintained through Circular Dichroism (CD) and Fourier Transform Infrared (FTIR) spectroscopy. The formulated MS-Hu6 displayed excellent thermal and colloidal stability even after three rapid freeze-thaw cycles and storage for more than 90 days at 4{degree sign}C and 25{degree sign}C. The authors concluded that they had developed a stable, manufacturable, and transportable MS-Hu6 formulation at an ultra-high concentration, meeting acceptable industry standards. The study's findings may serve as a resource for developing biologics formulation in academic medical centers.

Reviewer #2 (Public Review):

The field of monoclonal antibody therapeutics for the treatment of clinical diseases is undergoing rapid growth in recent years and becoming a dominant force in the therapeutics market. In previous studies, Mone Zaidi's group has reported the development of a first-of-its-kind humanized FSH-blocking antibody, MS-Hu6, based on the established importance of FSH in bone loss, adiposity, and neurodegeneration. This study reports the creation of a unique formulation of highly concentrated MS-HU6 preparation and evaluates detailed physiochemical properties of formulated MS-Hu6 including viscosity, turbidity, and clarity. Furthermore, the structural integrity of the formulated MS-HU6 is confirmed through Circular Dichroism and Fourier Transform Infrared (FTIR). The manuscript is succinctly written, and the methods and results are well described. The authors' conclusions are largely supported by the experimental data. The methods described are highly relevant to the goal of future manufacturing of highly concentrated monoclonal antibody therapeutics for human trials, and, therefore, the study is significant.

Reviewer #1 (Public Review):

The authors solved cryoEM structural maps for the pLGIC homolog Alpo4 from an extreme thermophile worm in apo and CHAPS-bound conditions. The data appear to be of good quality and in addition to a first structural model of Alpo4 also provide several interesting observations. Notably, the detergent CHAPS was observed to occupy the orthosteric binding pocket where it induces a quaternary twist of extracellular relative to transmembrane domains opposite to that observed for activation in canonical pLGICs. Given recent advances in lipid modulation of pLGICs, this structural model of how a detergent can bind to the orthosteric site will be of interest. Additionally, a ring of 16' methionines forms a potential alternate pore gate to the 9' leucine ring in canonical pLGICs. Unfortunately, testing these hypotheses such as the alternate pore gate remains difficult due to the fact that the activating ligand for Alpo4 remains unknown. This also makes it hard to relate the observed changes upon CHAPS binding to the Alpo4 function. Nonetheless, the structures will aid in hypotheses for functional mechanisms of Alpo4 and pLGICs.

Reviewer #2 (Public Review):

De Gieter et al.'s structural report follows a previous screening effort, which identified pLGIC from Alvinella pompejana as suitable for structural studies.<br /> In the present manuscript, the authors report several structures of one homopentamer named Alpo4. The manuscript is organized around a thoughtful, convincing, description of the common points shared by Alpo4 with the mammalian homologues of known structures, and of its distinctive features. The most striking differences are 1. the unexpected presence of a CHAPS detergent molecule bound to the orthosteric site; 2. the unique rotamer switch of a conserved tryptophan in the apo binding pocket, creating what the authors call a 'self-liganded' state; 3. a tightly closed hydrophobic gate with a ring of methionine residues within the M2 helices. 4. A reversed ECD twist associated with the binding of CHAPS

The principal strength of the manuscript is to extend the structural knowledge of the pLGIC family beyond the mammalian receptors to invertebrates, for which structural information has remained scarce. In particular, the binding of CHAPS to an 'extended' binding site is shown. That site does not only comprise the place where neurotransmitter usually binds but is prolonged by a hydrophobic patch underneath loop C and in contact with loop F/beta 8.

In the discussion, the authors suggest that the binding of CHAPS could be an inspiration to develop compounds, targeting for instance mammalian receptors, that would bind to both the orthosteric site and a potential groove underneath loop C (where the sterol moiety of CHAPS binds in Alpo4). A figure (SI4) shows a few homologues in surface representation, giving an idea of whether this groove is generally present in the family. Seeing this figure, I wondered if it would be relevant to compare several conformations of one or a few chosen homologues. Given that gating always impacts the quaternary assembly, is this groove more pronounced in say the inhibited state of a given homologue than in its agonist-bound state?<br /> A related thought was that some of the protein binders affecting pLGIC function (toxins, VHH) contact two subunits and wrap around/below loop C. Do these have binding sites that overlap with the groove?

Very interestingly, the binding of CHAPS stabilizes a conformation that differs from the apo one. It includes a twist of the ECDs but does not lead to a significant opening of the M2 bundle. The authors note that the direction of the twist is reversed to that often associated with the binding of ligands in homologues. This reversion is quite a feature, which deserves to be shown in a supplementary movie (e.g overlay of the Alpo apo>CHAPs transition with the nico>apo transition of a7). My mental framework was that in this family 1. inhibitors do not trigger much of a quaternary conformational change 2. agonists trigger changes always in the same direction (even if the amplitude and exact rotation vary from receptor to receptor). So it's interesting to see a compound (of unknown functional effect) triggering a reversed change.

The principal weakness of the manuscript lies in the absence of a known agonist for Alpo4, The authors do a good job at explaining what they tried and why (and they did perform quite an array of unsuccessful functional experiments), yet it remains frustrating to be unable to link the observed structures to some function.

Reviewer #3 (Public Review):

Pentameric ligand-gated ion channels are a class of neurotransmitter receptors playing a key role in cellular communication. Besides their presence in mammalians, a multitude of receptors is found in lower organisms such as bacteria and invertebrates. They display a large diversity of molecular architectures and functions, as exemplified by atypical bacterial channels GLIC, ELIC, STELIC, or DeCLIC that have been characterized at the structural and functional levels. The study of unorthodox receptors, while challenging, is thus fascinating and is expected to give insights into the evolution, as well as the functional and structural divergence occurring in the superfamily.

In this work, authors solve the structure of the orphan receptor Alpo4 from an extreme thermophile worm Alvinella pompejana. Alpo4 is solved in two conformations, Apo and CHAPS-bound, both displaying a closed channel. The structures show several unusual features, in particular in the orthosteric site where, in the Apo, the tryptophan residues at the heart of the site lie in a place usually occupied by the neurotransmitter resembling a "self-liganded" conformation. In addition, the channel is bordered by unusual rings of hydrophobic residues in its upper part, and the protein shows substantial reorganization upon CHAPS binding. Alpo4 was previously investigated by electrophysiology but no agonist was found. Based on the structures, a number of gain-of-function mutants and chimeric constructs have been tested, but unfortunately, none are allowed to observe a ligand-gated ion channel function.

Overall, the paper is written in a very clear and fair manner, presenting the structural architecture and conformational reorganizations but also the limitation of the work concerning the lack of functional identification.

The paper constitutes a substantial amount of work (six cryo-EM structures in total). While it failed to identify an agonist and capture an open-channel conformation, the structure of a member of the family from an extreme thermophile species is novel and interesting for our fundamental knowledge of this important family of receptors.

Reviewer #1 (Public Review):

This manuscript examines how subjects change their decision strategy in a visual motion change detection task between blocks of trials where they sought to detect stronger versus weaker signals. The authors hypothesized that decision bounds would be reduced for the weaker signal condition. While behavioral changes were reasonably consistent with this hypothesis, it was challenged by EEG measures that have been previously found to relate to decision variables. In particular, a Beta-band EEG measure suggested decision bounds being reduced for the stronger signal condition, in distinct contrast to the initial hypothesis. Based on this, the authors developed an alternative behavioral model that could explain behavioral adjustments while having decision bounds that were constrained by the putative signature of the decision variable derived from the EEG Beta amplitude. This alternative model has two central features: 1) Sensory evidence is referenced to an adjustable criterion before accumulation such that evidence below the criterion provides negative input to the decision variable. 2) There is a lower reflecting bound on the decision variable such that it cannot go to negative values.

This experiment makes a strong case for the benefit of reconciling behavioral modeling with ideas in the literature about neural signatures of the corresponding decision processes. In this work, the standard behavioral modeling and the EEG-derived putative signatures of the decision process did not initially agree. This is an important finding. The authors also go further. Something must give. Either the standard modeling approach for this type of task provides an incorrect account or the putative mapping of EEG Beta amplitude to the decision process is incorrect. The authors argue for the former. They build from the starting point that the neural signatures are correct and develop alternative behavioral models that they argue to be consistent with these while also explaining behavior. One of these models (described above) fits the data as well as the standard model. I think this approach and the resulting model are interesting, but I have concerns.

1) I think the authors should give greater consideration to the possibility that the relationship between the decision process and EEG-derived neural signatures - despite having a basis in previous results - is either incorrect in some ways, not the full story, or might not fully apply to this paradigm. The authors include important analyses that already suggest that point to some degree, such as the analysis of the raw Beta amplitude in Figure 4. I give the authors credit for including that analysis and the critical discussion they have surrounding it. I suggest that the authors should include further discussion on this general point. What is the main evidence for the relationship of Beta amplitude to decision bound? Are there any differences from the previous studies that might break the relationship in this paradigm? With the approach taken here so heavily dependent on that relationship, discussing how it might not be correct seems of utmost importance.

2) My biggest concern revolves around where the Beta amplitude measurement fits into the model. Figure 3 (neurally informed modeling) shows it informing an evidence-independent component that adds to the decision variable, and it is described as such in the methods. However, Beta amplitude is clearly affected by the evidence (Figure 2A). And in the results, it is described as best corresponding to a decision variable, which would include the influence of the sensory evidence. If Beta amplitude depends on evidence, then an adjustment of the evidence criterion would influence Beta amplitude, even during the ITI. So, I don't see how it can be properly used as the constraining factor for the evidence-independent urgency in neurally-informed modeling.

To illustrate my concern, consider the evidence criterion adjustment model. In this model, the average DV value during the ITI will be closer to the decision bound in the weaker signal condition compared to the stronger signal condition - that is how the model fits the false alarm rate differences. This should be reflected in Beta amplitude if the latter reflects the DV. However, the Beta-derived urgency is highest for the condition with the lowest false alarm rate and lowest for the condition with the highest false alarm rate. It seems there is an inescapable conclusion that Beta amplitude does not fully reflect the main behavioral-driving features of the DV in this paradigm, even in the criterion-adjustment model. That said, I do think the development and consideration of the criterion-adjustment model is an important contribution, even with this shortcoming.

Additional comments:

1) The criterion-adjustment model appears equivalent to one with a constant negative drift added to the decision variable in addition to the contribution of evidence (and a lower reflecting bound). If true, it seems that there could be an alternative equivalent account that does not involve regulation of the transfer of incoming evidence.

2) I understand why one of the model parameters (e.g., noise or bound) must be fixed, but I don't understand why the authors didn't keep it the same parameter across all models. Couldn't they have fixed the noise parameters for all the models? Having it different makes it difficult to compare parameter values across the models, especially because the fitted noise term in the neurally-informed models appears dramatically reduced compared to the fixed value of noise used for the bound-adjustment model. On the topic of parameters, can the leak parameter be reported in more intuitive units? It seems to be parameterized as a fraction leak per time step, and I couldn't easily find the time step used. Reporting the leak as a time constant would be immediately understandable.

Reviewer #2 (Public Review):

Geuzebroek and colleagues use computational modeling and EEG to investigate how people adjust continuous decision-making across different contexts. By neurally informing computational models of decision-making, they reject models in which in contexts with weaker sensory evidence a lower decision threshold or greater leak is applied, in favor of a model implementing a novel control mechanism, in which an adjustable sensory criterion determines which samples are considered evidence to be accumulated. This work was rigorously performed and in a compelling manner teases apart competing mechanisms to reveal a significant novel one.

The contributions of this work are at least two-fold: First, the work outlines a novel mechanism by which decision-makers adjust to different environments by taking expectations about sensory evidence into account. Second, they demonstrate how behavior alone can be insufficient to tease apart competing models and lead to misattribution of observed behavioral differences and how neural measures can help arbitrate between models and avoid misattribution.

This work is of great relevance for the decision-neuroscience community, calls for a re-examination of previous findings, and opens exciting new avenues for future research.

Reviewer #1 (Public Review):

This paper provides compelling and clear analyses that show that the coding level (sparsity) of the granule-cell layer of a cerebellar-like network does not only change the dimensionality of the representation, but also the inductive bias of the network: Higher sparsity biases the network to learning more high-frequency representations. Depending on the dominant frequencies of the target function, different coding levels are therefore optimal. The results are important/fundamental to theories of cerebellar learning and speak to a relevant ongoing debate in cerebellar learning, but will be of interest to readers outside of cerebellar neurophysiology.

Reviewer #2 (Public Review):

Here, a simple model of cerebellar computation is used to study the dependence of task performance on input type: it is demonstrated that task performance and optimal representations are highly dependent on task and stimulus type. This challenges many standard models which use simple random stimuli and concludes that the granular layer is required to provide a sparse representation. This is a useful contribution to our understanding of cerebellar circuits, though, in common with many models of this type, the neural dynamics and circuit architecture are not very specific to the cerebellum, the model includes the feed-forward structure and the high dimension of the granule layer, but little else. This paper has the virtue of including tasks that are more realistic, but by the paper's own admission, the same model can be applied to the electrosensory lateral line lobe and it could, though it is not mentioned in the paper, be applied to the dentate gyrus and large pyramidal cells of CA3. The discussion does not include specific elements related to, for example, the dynamics of the Purkinje cells or the role of Golgi cells, and, in a way, the demonstration that the model can encompass different tasks and stimuli types is an indication of how abstract the model is. Nonetheless, it is useful and interesting to see a generalization of what has become a standard paradigm for discussing cerebellar function.

Reviewer #3 (Public Review):

The paper by Xie et al is a modelling study of the mossy fiber-to-granule cell-to-Purkinje cell network, reporting that the optimal type of representations in the cerebellar granule cell layer depends on the type task. The paper stresses that the findings indicate a higher overall bias towards dense representations than stated in the literature, but it appears the authors have missed parts of the literature that already reported on this. While the modelling and analysis appear mathematically solid, the model is lacking many known constraints of the cerebellar circuitry, which makes the applicability of the findings to the biological counterpart somewhat limited.

I have some concerns with the novelty of the main conclusion, here from the abstract:<br /> 'Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classic theories.'<br /> Stated like this, this has in principle already been shown, i.e. for example:<br /> Spanne and Jorntell (2013) Processing of multi-dimensional sensorimotor information in the spinal and cerebellar neuronal circuitry: a new hypothesis. PLoS Comput Biol. 9(3):e1002979.<br /> Indeed, even the 2 DoF arm movement control that is used in the present paper as an application, was used in this previous paper, with similar conclusions with respect to the advantage of continuous input-output transformations and dense coding. Thus, already from the beginning of this paper, the novelty aspect of this paper is questionable. Even the conclusion in the last paragraph of the Introduction: 'We show that, when learning input-output mappings for motor control tasks, the optimal granule cell representation is much denser than predicted by previous analyses.' was in principle already shown by this previous paper.

However, the present paper does add several more specific investigations/characterizations that were not previously explored. Many of the main figures report interesting new model results. However, the model is implemented in a highly generic fashion. Consequently, the model relates better to general neural network theory than to specific interpretations of the function of the cerebellar neuronal circuitry. One good example is the findings reported in Figure 2. These represent an interesting extension to the main conclusion, but they are also partly based on arbitrariness as the type of mossy fiber input described in the random categorization task has not been observed in the mammalian cerebellum under behavior in vivo, whereas in contrast, the type of input for the motor control task does resemble mossy fiber input recorded under behavior (van Kan et al 1993).

The overall conclusion states:<br /> 'Our results....suggest that optimal cerebellar representations are task-dependent.'<br /> This is not a particularly strong or specific conclusion. One could interpret this statement as simply saying: ' if I construct an arbitrary neural network, with arbitrary intrinsic properties in neurons and synapses, I can get outputs that depend on the intensity of the input that I provide to that network.'<br /> Further, the last sentence of the Introduction states: 'More broadly, we show that the sparsity of a neural code has a task-dependent influence on learning...' This is very general and unspecific, and would likely not come as a surprise to anyone interested in the analysis of neural networks. It doesn't pinpoint any specific biological problem but just says that if I change the density of the input to a [generic] network, then the learning will be impacted in one way or another.

The interpretation of the distribution of the mossy fiber inputs to the granule cells, which would have a crucial impact on the results of a study like this, is likely incorrect. First, unlike the papers that the authors cite, there are many studies indicating that there is a topographic organization in the mossy fiber termination, such that mossy fibers from the same inputs, representing similar types of information, are regionally co-localized in the granule cell layer. Hence, there is no support for the model assumption that there is a predominantly random termination of mossy fibers of different origins. This risks invalidating the comparisons that the authors are making, i.e. such as in Figure 3. This is a list of example papers, there are more:<br /> van Kan, Gibson and Houk (1993) Movement-related inputs to intermediate cerebellum of the monkey. Journal of Neurophysiology.<br /> Garwicz et al (1998) Cutaneous receptive fields and topography of mossy fibres and climbing fibres projecting to cat cerebellar C3 zone. The Journal of Physiology.<br /> Brown and Bower (2001) Congruence of mossy fiber and climbing fiber tactile projections in the lateral hemispheres of the rat cerebellum. The Journal of Comparative Neurology.<br /> Na, Sugihara, Shinoda (2019) The entire trajectories of single pontocerebellar axons and their lobular and longitudinal terminal distribution patterns in multiple aldolase C-positive compartments of the rat cerebellar cortex. The Journal of Comparative Neurology.

The nature of the mossy fiber-granule cell recording is also reviewed here:<br /> Gilbert and Miall (2022) How and Why the Cerebellum Recodes Input Signals: An Alternative to Machine Learning. The Neuroscientist<br /> Further, considering the recoding idea, the following paper shows that detailed information, as it is provided by mossy fibers, is transmitted through the granule cells without any evidence of recoding: Jorntell and Ekerot (2006) Journal of Neuroscience; and this paper shows that these granule inputs are powerfully transmitted to the molecular layer even in a decerebrated animal (i.e. where only the ascending sensory pathways remains) Jorntell and Ekerot 2002, Neuron.

I could not find any description of the neuron model used in this paper, so I assume that the neurons are just modelled as linear summators with a threshold (in fact, Figure 5 mentions inhibition, but this appears to be just one big lump inhibition, which basically is an incorrect implementation). In reality, granule cells of course do have specific properties that can impact the input-output transformation, PARTICULARLY with respect to the comparison of sparse versus dense coding, because the low-pass filtering of input that occurs in granule cells (and other neurons) as well as their spike firing stochasticity (Saarinen et al (2008). Stochastic differential equation model for cerebellar granule cell excitability. PLoS Comput. Biol. 4:e1000004) will profoundly complicate these comparisons and make them less straight forward than what is portrayed in this paper. There are also several other factors that would be present in the biological setting but are lacking here, which makes it doubtful how much information in relation to the biological performance that this modelling study provides:<br /> What are the types of activity patterns of the inputs? What are the learning rules? What is the topography? What is the impact of Purkinje cell outputs downstream, as the Purkinje cell output does not have any direct action, it acts on the deep cerebellar nuclear neurons, which in turn act on a complex sensorimotor circuitry to exert their effect, hence predictive coding could only become interpretable after the PC output has been added to the activity in those circuits. Where is the differentiated Golgi cell inhibition?

The problem of these, in my impression, generic, arbitrary settings of the neurons and the network in the model becomes obvious here: 'In contrast to the dense activity in cerebellar granule cells, odor responses in Kenyon cells, the analogs of granule cells in the Drosophila mushroom body, are sparse...' How can this system be interpreted as an analogy to granule cells in the mammalian cerebellum when the model does not address the specifics lined up above? I.e. the 'inductive bias' that the authors speak of, defined as 'the tendency of a network toward learning particular types of input-output mappings', would be highly dependent on the specifics of the network model.

More detailed comments:<br /> Abstract:<br /> 'In these models [Marr-Albus], granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli.' Yes, I would agree with the first part, but I contest the second part of this statement. I think what is true for sparse coding is that the learning of random stimuli will be faster, as in a perceptron, but not necessarily better. As the sparsification essentially removes information, it could be argued that the quality of the learning is poorer. So from that perspective, it is not optimal. The authors need to specify from what perspective they consider sparse representations optimal for learning.

Introduction:<br /> 'Indeed, several recent studies have reported dense activity in cerebellar granule cells in response to sensory stimulation or during motor control tasks (Knogler et al., 2017; Wagner et al., 2017; Giovannucci et al., 2017; Badura and De Zeeuw, 2017; Wagner et al., 2019), at odds with classic theories (Marr, 1969; Albus, 1971).' In fact, this was precisely the issue that was addressed already by Jorntell and Ekerot (2006) Journal of Neuroscience. The conclusion was that these actual recordings of granule cells in vivo provided essentially no support for the assumptions in the Marr-Albus theories.

Results:<br /> 1st para: There is no information about how the granule cells are modelled.

2nd para: 'A typical assumption in computational theories of the cerebellar cortex is that inputs are randomly distributed in a high-dimensional space.' Yes, I agree, and this is in fact in conflict with the known topographical organization in the cerebellar cortex (see broader comment above). Mossy fiber inputs coding for closely related inputs are co-localized in the cerebellar cortex. I think for this model to be of interest from the point of view of the mammalian cerebellar cortex, it would need to pay more attention to this organizational feature.

Reviewer #1 (Public Review):

This study combines in vitro somatic and dendritic recordings and computational modeling to study how cholinergic agonists modulate the response of CA1 pyramidal neurons to triangular current injections. The authors have previously used a similar approach (Upchurch, 2022, JNeuroscience) to show that CA1 neurons exhibit asymmetric AP firing (more firing on the upward ramp) in response to such current injections and that this effect is due to Na channel inactivation. The present work builds on these results by showing that cholinergic modulation changes this response, i.e., there is more firing on the downward part of the ramp. This change appears to require an intracellular Ca2+ concentration increase (mediated via IP3 and voltage-gated Ca2+ channels), which activates TRPM4 channels. In this scheme, cholinergic activity increases IP3, and the depolarizing current injection opens voltage-gated Ca2+ channels. This study will be of some interest to cellular neurophysiology experts working on the hippocampus.

1) This study claims that the triangular current injections recapitulate hippocampal place cell activity. However, it has been shown recently that the asymmetric firing of CA1 place cells is due to synaptic weight changes resulting from synaptic plasticity (e.g., Bittner et al., 2017). This suggests that the asymmetric firing of place cells is primarily the result of asymmetric synaptic input. Therefore, the authors should test whether carbachol similarly affects a synaptically driven membrane potential ramp. If this is not the case, the strong claim that this work has implications for place cell firing is not justified, in my opinion.

2) Along the same lines, it has been shown before that the precision of spike timing depends on the stimulation pattern in vitro (Mainen and Sejnowski, 1995). Constant stimuli led to imprecise AP firing trains, whereas current injections that included fluctuations resembling synaptic input generated spike trains that were more reliable and reproducible in terms of timing. This study concluded that a low intrinsic noise level in spike generation was essential in generating informative spike sequences. Following this pivotal work, the authors could add noise to their current stimulus and observe the effect on the AP firing patterns. If this is not possible, the authors should at least report the sweep-to-sweep variability for the data shown, e.g., in panels 1A2, 1B2, 1D2, and 1E2.

3) In most of the data presented in this manuscript, Carbachol appears to induce a 3 mV hyperpolarization and increase input resistance. As a result, the amount of current injected during Carbachol is drastically lower than during the controls. This should be emphasized more, and the input resistance should be quantified for each experimental condition. It should also be discussed whether this change in input resistance can account for the changes in the firing pattern observed. Finally, it should be clearly stated how the amount of the current injected was chosen for each cell, and data from a range of injected current ramps should be shown for each cell.

4) It remains unclear how the current result that TRPM4 channels can mediate the firing pattern change relates to the previous finding that the current injection evoked CA1 neuronal firing pattern is due to long-term Na channel inactivation.

5) Figure 8: Panel C is supposed to confirm the prediction from the model that the carbachol-mediated change of firing activity is related to intracellular Ca2+ domains. However, the example cell shown is depolarized to -52 mV, and there is no hyperpolarization following Carbachol. Is this an effect of the high concentration of BAPTA? Again, what was the current injected under this experimental condition?

Reviewer #2 (Public Review):

The manuscript focuses on the cholinergic modulation of TRPM4 channels in the CA1 pyramidal neurons. The authors presented solid convincing evidence that TRPM4 but not TRPC channels are the Ca2+-activated nonselective cation channel in CA1 pyramidal neurons being modulated by activation of muscarinic receptors. Using bi-directional ramp protocol, the authors revealed that ACh modulation could lead to forward shifts in place field center of mass, whereas decreased ACh modulation could contribute to backward shifts. This represents a significant molecular/cellular finding that links neuromodulation of intrinsic properties to place field shifts, a phenomenon seen in vivo. The authors used a computational approach to model this CA1 neuron spiking to further reveal the mechanism.

To further improve the manuscript, I have the following suggestions/questions:<br /> 1. The triangular ramp stimulation (introduced by the same group; Upchurch et al., 2022) makes it possible to emulate the hill-shaped depolarization during place field firing. However, one concern is the time scale/duration of the ramp (2 sec) compared to the physiological pattern (100ms~200ms in the in vivo recording in freely moving rat, Epsztein et al., 2011). Using a longer ramp to generate more spikes for calculating the adaptation index is understandable. However, considering the Ca entry/accumulation during prolonged depolarization, repeating one set of experiments with a shorter ramp is crucial to verify the major findings.

2. Strictly speaking, the term "Ca2+-induced Ca2+ release (CICR)" is only used in ER Ca2+ release via ryanodine receptors (RyR) rather than IP3Rs. The author should be careful since it is used in the abstract (Line 36). In addition, pharmacology inhibition experiments should be incorporated to further dissect the role of RyR-induced CICR.

3. Applying strong buffering BAPTA not only removed the IP3R-TRPM nanodomain but also hindered Ca entry via VGCC. To validate the role of ER Ca2+ release in regulating TRPM, depletion of ER Ca2+ pool with SERCA inhibitor (e.g. thapsigargin) would be a more direct way to test the model (also make sure to add TRPC inhibitor to avoid the store-operated Ca2+ entry).

4. How does the TRPM current overcome the long-term inactivation of Nav? A channel state model should be added to the manuscript to make it easier to understand.

Reviewer #3 (Public Review):

Combining slice physiology and simulation, Combe and colleagues discovered that TRPM4 channels activated by Ca2+ in nanodomains mediate ICAN currents in CA1 pyramidal neurons that drive the cholinergic modulation of firing rate. The finding is novel and interesting.

Strengths:<br /> 1) Identification of TRPM4 channels as the carrier of ICAN currents with independent pharmacological inhibitors and other supporting evidence.<br /> 2) Physiological and simulational verification of physically closely located Ca2+ source and TRPM4 channels required for ICAN activation.

Weaknesses:<br /> 1) The conclusion of the cholinergic role in down-ramp or backward firing shifts is not convincing.

Reviewer #1 (Public Review):

The manuscript studies the spontaneous contractions (SC) of the Hydra body wall and presents a detailed simple mathematical model of nutrient transport to hypothesize the role of SC on maintaining the microbiota. This work provides valuable insights on the functional im- plications of the SC and the increased nutrient update obtained from mixing the local fluid environment through body wall contractions.

Reviewer #1 (Public Review):

Brunetti et al. investigated the mechanism used by the SARS CoV-2 virus to infect CD4 T cells and the potential impact on the immune system by viral infection. They find that SARS CoV-2 infects CD4 helper T cells and not CD8 T cells present in the blood and bronchoalveolar fluid of infected patients. The ACE-2 receptor expression on T lymphocytes is less compared to epithelial and endothelial cells, but still, the virus is able to establish a productive infection of T lymphocytes by some alternative mechanism. The group also demonstrated that interaction between CD4 and SARS virus spike protein further enhances viral entry and infectivity as CD4 is acting as an auxiliary molecule for viral entry.

By performing a technically impressive analysis of the infectivity of CD4 T cells isolated from healthy donors/controls invitro with the SARS CoV-2 virus and also by testing the infected population of CD4 cells invivo from COVID patients, the authors find that SARS CoV-2 infects CD4 T cells using Insitu Hybridization using probes against viral polymerase (RdRP), immunofluorescence and electron microscopy. Further, the authors also identified the region of SARS CoV-2 spike protein that interacts with CD4 by performing molecular docking analysis and found CD4 NTD interacts directly with the RBD region of the SARS Virus. The specific interaction between CD4 and SARS virus is further demonstrated by the use of anti-CD4 blocking antibodies and cell lines that over-express CD4 molecules. Interestingly by antibody inhibition of ACE-2 and camostat inhibition of TMPRSS2, the authors demonstrated that SARS CoV-2 infection of CD4 T cells requires ACE-2, TMPRSS2, and CD4. The data also show that viral infection of CD4 T cells leads to the expression of cytokines like 1L-10 that may impact cell viability and dampens immune response.

The experiments in the paper are well- performed and the conclusions of this paper are mostly well supported by data, but some aspects of the impact of viral infection of CD4 cells leading to lymphopenia need to be clarified and extended.<br /> A major weakness of the paper is the reference citations. Inconsistency in maintaining the citation style and numbering in the manuscript draft drastically impacts the readability. For example, the use of superscript format references in the introduction and results section and paraphrasing format in materials and methods could not make the readers identify the correct citations.

1. In this paper, the authors describe infection of CD4 T cells may lead to T cell death. Although the extended Fig. 9 suggests the expression of a multitude panel of gene expression, including apoptotic genes, the authors could not provide a piece of direct evidence to show how CD4 T cell death happens. What is the underlying mechanism of cell death? Is it by necrosis or apoptosis or pyroptosis? The exact mechanism of CD4 cell death needs to be discovered and adding control experiments to assess the exact mechanism of cell death would increase confidence in the presented results of the functional interaction of proteins (Ext Fig 9).

2. Some previous studies suggested that lymphocytopenia in COVID infection could be due to impaired T cell proliferation or extravasation of T cells into tissue. The exact mechanism for lymphocytopenia could be addressed by performing an animal experiment, but it would be interesting to see what are the author's opinion about other possibilities of lymphocytopenia.

3. The data on CREB-1 Ser 133 in Figure 4E is not sufficiently convincing. It is difficult to understand what is the difference between every three lanes within mock and SARS CoV-2 infection. There is a pCREB band in lane 5 (2nd lane of CoV-2), but not in the other two. Better data would have helped to substantiate the authors' conclusions.

Reviewer #2 (Public Review):

The manuscript by Brunetti et al. represents an important contribution where SARS-CoV-2 infection of T-helper cells is implicated and found to be mediated by CD4. Interestingly and appealingly, the work progressed through a computationally driven hypothesis, by analysing the interaction partners of SARS-CoV-2 spike glycoprotein (as initially modelled through similar SARS-CoV-1), followed by experimental validations, and further computational and experimental insights on the mechanism of binding. I find most of the computational outcomes well validated, and the results and claims well supported by the performed experiments. There are a few points where the manuscript will benefit from dedicated discussion and additional simulation/exploratory plots to establish and validate the adopted methodology for analogous future usage in protein binding characterisations by others.

Major comments:

1) The bioinformatics selection method to arrive at CD4 as the main interaction partner is interesting, and the zoomed-in finding is well justified by the whole body of the experimentation as brought in the manuscript. However, it is interesting from a computational biology perspective that were we to remove GO database (too unvalidated), and "Cell surface" component of the Jensen database (considering its more dedicated "Plasma membrane" and "External side of plasma membrane" components considered in the work) out of the Venn diagram (Extended Data Fig. 3), then we would be left with more interaction partners shared between the remaining 3 databases. Interestingly, these additional partners would include CD8A and CD8B. However, the authors show that the interaction was experimentally noted to happen with CD4+ T cells but not with CD8+ ones. This warrants some discussion on why this might be the case. I wonder what would be the computational docking/MD results were you to attempt modelling an interaction between the spike glycoprotein and CD8? Should you not arrive at stable complexes with your MD workflow and 4 Angstrom cutoff for temperature-induced stability scrutinization, that would be extra validation and weight on the adopted computational scheme for the discovery.

2) Looking at the last complex in Figure 2, where the full-length sCov2 is recovered on top of the modelled fragment, one can see some additional interaction points or potential clashes with CD4 NTD. Were some of the models discarded on the ground of the orientation between CD4 NTD and sCov2 RBD being incompatible with the full-length sCov2 due to possible steric clashes?

3) The 4 Angstrom cutoff for the temperature gradient-based structural stability check sounds reasonable, but would be more justifiable if the authors would also present a histogram of all RMSDs (of final aberrations) for all the tried models and show how outlying the 4 Angstrom is in the whole distribution, additionally attributing a p-value on the selected cutoff.

Reviewer #1 (Public Review):

Guo et al. demonstrate that Metformin, a first-line anti-diabetic drug, significantly improves bone healing in diabetic mice. Mechanistically, they demonstrate that Metformin improves BMSC differentiation in T2D type 2 diabetic mice, potentially through an indirect mechanism. Overall the study is comprehensive and the effects of Metformin on bone healing are demonstrated by overwhelming data. The study further offers important information for management of the complications associated with type-2 diabetes. The weakness of the study is the lack of in-depth understanding of the mechanism underlying Metformin's effects on healing. The writing of the manuscript could also be improved for clarity and accuracy.

Reviewer #2 (Public Review):

Diabetes mellitus is a worldwide public health menace, and the fracture healing is usually impaired in diabetic patients. Metformin is the first-line medicine for type-2 diabetes (T2D). However, its effects on bone in T2D patients remain unclear. To assess the impacts of metformin on fracture healing, the authors study the healing process after injuries caused by three different types of bone fractures in diabetic mouse models with or without metformin treatment. The authors studied three fracture models and looked at various aspects of the bone healing process and concluded that metformin rescues the delayed bone healing and remodeling in T2D mice. Moreover, the authors present novel information on the impact of metformin on the bone proliferation, bone formation, and cartilage formation in the bone marrow stromal cells (BMSCs) derived from T2D mice. Administration of metformin in T2D mice can rescue the impaired differentiation potential and lineage commitment of BMSCs both in vitro and in vivo, compromised by the hyperglycemic conditions. In addition, several key chondrocyte transcript factors such as SOX9 and PGC1α, are upregulated in callus tissue isolated at the fracture site of metformin-treated diabetic mice during the healing process after the fracture. In summary, the authors present convincing evidence that metformin facilitates bone healing, bone formation and chondrogenesis in diabetic mice. The prior literature has focused on the effects on mesenchymal stem cells (MSCs) and this paper's data is novel as it's using MKR models for studying Metformin 's role in bone formation under diabetes condition. The paper's conclusions and results are strong, but more attention needs to be paid to the introduction and description of the prior literature and understanding of the potential specific targets and signaling pathway of metformin in the MKR mouse model bone healing.

Reviewer #3 (Public Review):

The authors, in their research manuscript, dissected the role of Metformin in bone healing under type-2 diabetics conditions. The authors used three classic bone fracture models to assess the impacts of Metformin in bone healing under hyperglycemic conditions. In all three models, Metformin treatment showed bone formation. At the cellular level, the authors showed the effect of Metformin on promoting bone healing using BMSCs in vitro. The authors in the paper demonstrated that Metformin promotes bone growth only in hyperglycemic conditions. The experiments were appropriately well-defined and carried out to support the role of Metformin in bone healing. The use of three different bone-defective rat models to study the role of Metformin in skeletal tissues is convincing.

Reviewer #1 (Public Review):

In the manuscript the authors identify new players regulating cell state transitions. They show that heme biosynthesis is required for the naïve-to-primed pluripotency transition. In particular, they provide a link between heme biosynthesis inhibition and failure to activate TGFβ and MAPK pathways, two crucial regulators of the exit from the naïve pluripotent state. Heme biosynthesis inhibition increases the percentage of 2CLCs within the mESC population. The authors further show that this increased level of 2CLCs depends on the accumulation of succinate in non-mitochondrial cell compartments as a consequence of heme biosynthesis inhibition. Based on experiments using chemical inhibitors, the authors conclude that succinate acts in a paracrine and autocrine manner to enhance reprogramming of mESC into 2CLCs.

The observations provided by the authors are interesting and potentially relevant in the field of pluripotent cell state transitions. However, in the present state, neither the role of heme biosynthesis on FGF-ERK and TGF beta signalling and exit from naïve pluripotency nor the role of heme biosynthesis in controlling the 2CLC state are clear.

Below I list my main concerns:

1. The authors claim that the heme metabolic pathway is important for the naïve-to-primed transition. Interfering with its proper function appears to have developmental consequences. However, it is unclear how exactly this pathway is regulated during the exit from naïve pluripotency in WT cells. Hence the physiological relevance remains unclear. Are the levels of heme itself, or the mentioned 7 enzymes of the heme pathway regulated during differentiation?

2. The claim that "the roles of this [heme] biosynthetic pathway and this metabolite have never been studied in the context of pluripotency" is a bit misleading. It has been reported that HO1 is regulated by Oct4 (https://doi.org/10.1002/1873-3468.14138).

3. The link between heme biosynthesis and the TGFβ and MAPK pathways remains unclear. Is there any evidence for a direct link, or are these two observations simply linked through an altered cell state? Without further experiments it remains unclear whether the lack of proper Tgf beta and Fgf-ERK signalling activation are cause or consequence of the observed differentiation defects. Results must be discussed with this limitation in mind.

4. The fact that MEKi did not recapitulate the phenotype of SA treatment to prevent EpiLC differentiation should already be clarified in the results section. Moreover, the fact that SMAD inhibition seems to delay downregulation of naïve markers more than SA treatment, and the fact that SMAD inhibition combined with MEK inhibition seems weaker than SMAD inhibition alone seem counterintuitive and needs explanation. Can the authors attempt to titrate pathway inhibition to a similar level as observed in heme pathway deficient ESCs? Furthermore, can the differentiation defect be rescued upon overstimulation of Fgf-ERK and TGF beta to reach WT levels?

5. To better characterize the direct effect of heme biosynthesis inhibition it is necessary to in depth analyse any possible cell proliferation, viability of cell cycle defects after SA (or AA5) treatment. If there is an impact on cellular health, this needs to be reported and taken into careful consideration when interpreting results.

6. The authors claim that SA pre-treatment of mESC is able to enhance their differentiation ability into throphoblast like cells; however, they do not show statistically significant differences in terms of throphoblast expression markers between SA-treated and control cells (Supplemental Fig.2d). Furthermore, the variance of measurements in 2iL are not shown. Expression levels in TS cells or in trophoblast tissue must be used as control to judge the effect size. 2iL cells do also generate cells similar in morphology to 2iL+SA cells (Suppl Fig. 2b). Should these also be giant cells; this would be very surprising. Together, this makes drawing conclusions from these experiments impossible. If the statement that SA treatment expands the lineage potential of ESCs is made, it will need to be supported by appropriate and statistically strong data. A mere increase in some marker genes (which are not really specific for the TE lineage but also expressed in embryonic tissues) and statements based on morphology are no good support for this hypothesis.

7. What is the reason for calling the increase in 2C like cells 2C-like reprogramming? Is there any evidence that this is indeed a reprogramming event? There is no evidence for disruption of heme biosynthesis directly instructing cells to take on a 2CLC state, there is simply an increase in the Zscan4 expressing population, for a reason that remains unclear.

8. Addition of AA5 or SA results in absolutely striking changes in the epigenetic state of ESCs. H3K4me3, H3K27me3 and H3K9me3 together with 5mC levels appear drastically increased based on IF images in Supp. Fig. 5a. I wonder how physiological these levels are. How much data directly meaningful for 'normal' differentiation can be obtained from such a massively perturbed system? This needs to be appropriately acknowledged and discussed.

Reviewer #1 (Public Review):

In this manuscript, Dhekne et al. sought to identify modulators of LRRK2 activity. Thereto, the authors performed a FACS-based genome-wide pooled CRISPR/Cas9 screen in NIH-3T3 cells monitoring phosphorylation of the Rab GTPase Rab10 which is a well-characterized target of LRRK2. Besides Rab10 and LRRK2, this unbiased screen surprisingly uncovered another Rab GTPase, Rab12, as one of the most significant hits. Validation experiments with Rab12 knockout (KO) NIH-3T3 cells, the LRRK2 inhibitors MLi-2, and Rab12 KO mice unanimously confirmed the dependency of Rab10 phosphorylation on Rab12. Conversely, the authors used A549 LRKK2 and PPM1H KO cells to show that overexpression of Rab12 but not Rab29 which is another LRRK2 modulator leads to elevated phospho-Rab10 levels in a manner dependent on LRRK2 but insensitive to pathogenic mutations thereof. Moreover, the authors mapped E240 and S244 of LRRK2 as specific binding sites for Rab12 and showed that these residues are important to sustain full LRKK2 activity towards Rab10. Lastly, the authors showed that RAB12-dependent LRRK2 activation occurs under (patho)physiological stress conditions, namely endolysosomal membrane damage. Together, this comprehensive and elegant study of Dhekne and colleagues reveals exciting new mechanistic insights into the regulation of LRRK2 which have therapeutic potential for Parkinson's disease.

Reviewer #2 (Public Review):

Dhekne and colleagues present an unbiased genome-wide screen by systematic CRISPR-Cas9 gene knock-out in mouse NIH-3T3 fibroblasts to identify regulators of the LRRK2 pathway which is relevant for Parkinson's disease. The screen identified Rab12 as the most potent regulator of the LRRK2 activity. Phosphorylation of the well-established LRRK2 substrate Rab10 has been used as a read-out. To allow a large-scale screen, the authors established a flow cytometry-based assay using phospho-Rab10-specific antibodies. Subsequently, Rab12 has been confirmed as an upstream effector of LRRK2 acting in a similar way as Rab29. Using computational modelling by Alphafold in conjunction with Colabfold the authors could model the Rab12:LRRK2 complex and identify a third Rab binding site within the N-terminal Armadillo repeats which is distinct from the two sites, previously identified for Rab8a/Rab10 and Rab29. The predicted interaction epitope could be experimentally confirmed by systematic mutational analysis.

The experimental setting and the data presented are overall sound. It should however be considered that the selected cell model is most likely not covering the full set of LRRK2 pathway regulators as these are likely expressed in a tissue and cell-type-specific manner. It could therefore be interesting to also include more disease-relevant models, such as neuronal or immune cells. Nevertheless, Rab12 is an important effector, which is also expressed in cell types relevant to Parkinson's disease.<br /> To validate their computational model of the Rab12 binding epitope within the N-terminal Armadillo domain of LRRK2, the authors determined the binding affinity of Rab12 which is in the lower µM range and similar to the affinities of Rab10 and Rab29 to LRRK2. The authors conducted a mutational screen mutating surface exposed residues within the predicted Rab12 binding epitope in the N-terminus of LRRK2. The study could identify critical residues, which significantly contribute to the affinity of LRRK2 for Rab12. Corresponding alanine mutations could significantly reduce the enhanced LRRK2-mediated Rab10 phosphorylation observed upon Rab12 co-expression. The effect size is similar to the previously identified Rab29 effector. Furthermore, the authors could convincingly demonstrate that Rab12 and Rab29 bind to different LRRK2 epitopes.

Noteworthy, besides disrupting mutations targeting the predicted Rab12 binding epitope, the authors also found one mutation enhancing the cellular effect of Rab12 overexpression demonstrated by increased phospho-Rab10 levels. For a better evaluation of the presented computational model of the Rab12:LRRK2 complex, it would be interesting, if the authors could study the binding affinity of that mutant (F283A), as well.

Overall, the authors could convincingly demonstrate that Rab12, previously identified as LRRK2 substrate, acts upstream of LRRK2 similar to Rab29 but via a distinct binding site. The site located within the N-terminal Ankyrin domain has been predicted by a computational 3D model of the complex structure and experimentally validated. The interaction epitope might be an interesting target for the future development of allosteric modulators to treat LRRK2-mediated PD.

Reviewer #3 (Public Review):

Dhekne et al. set out to identify novel activators of the LRRK2 kinase. They developed a flow cytometry assay to separate pools of unmodified and phosphorylated Rab10 (pRab10) from mouse NIH-3T3 cells. They then used this methodology to perform a CRISPR-based genome-wide screen to identify genes responsible for increased pRab10 levels. Candidates were validated with knock-out experiments. As far as we know, LRRK2 is the only kinase that phosphorylates the Switch II motif in Rab10. Therefore, the genes affecting pRab10 levels were classified into positive and negative LRRK2 regulators. Knocking out a positive LRRK2 regulator led to a decrease in pRab10 while knocking out a negative regulator led to an increase in pRab10. The authors found several interesting, previously unknown modulators of LRRK2 activity, including SPTLC2 and CERT1, which are involved in ceramide synthesis.

The major finding of this work is the unexpected effect of Rab12 on pRab10 levels in cells. Knocking out Rab12 resulted in a five-fold decrease in pRab10 levels. This observation was validated in an animal model. Conversely, overexpression of Rab12 led to a ten-fold increase in pRab10 levels. To exclude the possibility that other kinases were responsible for modifying Rab10, the authors overexpressed Rab12 in A549 cells lacking LRRK2; no increase in pRab10 was observed in these cells.

Dhenke et al. then used AlphaFold to model possible interaction between LRRK2 and Rab12 and identified a putative binding site for Rab12 in its Armadillo domain. This is the third Rab binding site in the domain of LRRK2. To validate this interaction, they mutated E240 and S244, both of which are involved in the interface; they observed no changes in pRab10 levels in cells expressing LRRK2 carrying the E240R and S244R mutations. The previously reported site #1 and site #2, both of which also bind Rabs and are involved in feed-forward LRRK2 activation, seem to be unrelated to the binding of Rab12 to site #3. The authors propose that site #3 might open the kinase of LRRK2 to increase its activity.

Finally, the authors point out the important role of Rab12 in lysosomal damage by showing that LLOME- or Nigericin-induced cellular stress increases LRRK2 activity in a Rab12-dependent manner.

Reviewer #1 (Public Review):

For many years it has been understood that transposable elements (TEs) are an important source of natural variation. This is because, in addition to simple knockouts of genes, TEs carry regulatory sequences that can, and sometimes do, affect the expression of genes near the TEs. However, because TEs can be difficult to map to reference genomes, they have generally not been used for trait mapping. Instead, single nucleotide polymorphisms are widely used because they are easy to detect when using short reads. However, improvements in sequencing technology, as well as an increased appreciation of the importance of TEs to both linked to favorable alleles and are more likely to be causing the changes that make those alleles beneficial in a given environment. Further, because TE activity can occur after bottlenecks, they can provide polymorphisms in the absence of variation in point mutations.

In this manuscript, the authors carefully examine insertion polymorphisms in rice and demonstrate linkage to differences in expression. To do this, they used expression quantitative trait locus (eQTL) GWAS using TIPs as genetic markers to examine variation in 208 rice accessions. Because they chose to focus on genes that were expressed in at least 10% of the accessions, presumably because more rare variants would end up lacking statistical power. This is an understandable decision, but it says that recent insertions, such as the MITE elements detailed in a previous paper, would not be included. Importantly, although TIPs associated with differentially expressed genes are far less common than SNPs' traditional eQTLs, there were a significant number of eQTLs that showed linkage to TIPs but not to QTL.

The authors then show that of the eQTLs associated with both TIPs and SNPs, TIPs are more tightly linked to the eQTL, and are more likely to be associated with a reduction in expression, with variation in the effects of various TEs families supporting that hypothesis. Here and throughout, however, the distance of the TEs could be an important variable. It is also worth noting the relative numbers in order to assess the claim in the title of the paper. The total number of eQTL-TIPs is ten-fold less than the number of eQTL-SNPs, and, of the eQTLs that have both, there are a significant number of eQTL-TIPs that are not more tightly linked to the expression differences than the eQTL.

The authors show that eQTL-TIPs are more likely to be in the promoter-proximal region, but this may be due to insertion bias, which is well documented in DNA-type elements. Here and throughout the authors are careful to state that the data is consistent with the hypothesis that TEs are the cause of the change, but do not claim that the data demonstrate that they are.

Throughout the rest of the manuscript, the authors systematically build the case for a causal role for TEs by showing, for instance, that eQTL-TIPs show much stronger evidence for selection, with increased expression being more likely to be selected than decreased expression. The authors provide examples of genes most likely to have been affected by TE insertions.

Overall, the authors build a convincing case for TEs being an important source of regulatory information. I don't have any issues with the analysis, but I am concerned about the sweeping claims made in the title. Once you get rid of eQTLs that could be altered by either SNPs or TIPs and include only those insertions that show strong evidence of selection, the number of genes is reduced to only 30. And even in those cases, the observed linkage is just that, not definitive evidence for the involvement of TEs. Although clearly beyond the scope of this analysis, transgenic constructs with the TEs present or removed, or even segregating families, would have been far more convincing.

The fact that many of the eQTL-TIPs were relatively old is interesting because it suggests that selection in domesticated rice was on pre-existing variation rather than new insertions. This may strengthen the argument because those older insertions are less likely to be purged due to negative effects on gene expression. Given that the sequence of these TEs is likely to have diverged from others in the same family, it would have been interesting to see if selection in favor of a regulatory function had caused these particular insertions to move away from more typical examples of the family.

Reviewer #2 (Public Review):

In this manuscript, Castanera et al. investigated how transposable elements (TEs) altered gene expression in rice and how these changes were selected during the domestication of rice. Using GWAS, the authors found many TE polymorphisms in the proximity of genes to be correlated to distinct gene expression patterns between O. sativa ssp. japonica and O. sativa ssp. indica and between two different growing conditions (wet and drought). Thereby, the authors found some evidence of positive selection on some TE polymorphisms that could have contributed to the evolution of the different rice subspecies. These findings are underlined by some examples, which illustrate how changes in the expression of some specific genes could have been advantageous under different conditions. In this work, the authors manage to show that TEs should not be ignored when investigating the domestication of rise as they could have played an important role in contributing to the genetic diversity that was selected. However, this study stops short of identifying causations as the used method, GWAS, can only identify promising correlations. Nevertheless, this study contributes interesting insights into the role TEs played during the evolution of rice and will be of interest to a broader audience interested in the role TEs played during the evolution of plants in general.

Reviewer #1 (Public Review):

In this manuscript the authors proposed a novel system by which they can suppress the expression of any gene of interest precisely and efficiently with a pre-validated, highly specific and efficient synthetic short-hairpin RNA. The idea of identifying potent artificial RNAi (ARTi) triggers is intriguing, and the authors successfully identify six ARTi with robust knockdown efficiency and limited to no off-target effects. As a proof-of-concept, the authors examined three oncology targets for validation, including EGFRdel19 (which already has a clinically approved drug for validation), KRASG12R (for which there are no in vivo compatible inhibitors yet) and STAG1 (which has a synthetic lethal interaction with recurrent loss-of-function mutations of STAG2). The authors demonstrated significant suppression of colony formation and in vivo tumor growth for all three oncology targets.

This novel system could serve as a powerful tool for loss-of-function experiments that are often used to validate a drug target. Not only this tool can be applied in exogenous systems (like EGFRdel19 and KRASG12R in this paper), the authors successfully demonstrated that ARTi can also be used in endogenous systems by CRISPR knocking in the ARTi target sites to the 3'UTR of the gene of interest (like STAG2 in this paper).

ARTi enables specific, efficient, and inducible suppression of these genes of interest, and can potentially improve therapeutic target validations. However, the system cannot be easily generalized as there are some limitations in this system:

• The authors claim in the introduction that CRISPR/Cas9-based methods are associated with off-target effects, however, the author's system requires the use CRISPR/Cas9 to knock out a given endogenous genes or to knock-in ARTi target sites to the 3' UTR of the gene of interest. Though the authors used a transient CRISPR/Cas9 system to minimize the potential off-target effects, the methods does not, as the authors acknowledge, eliminate the possibility of off-target effects.

• Instead of generating gene-specific loss-of-function triggers for every new candidate gene, the authors identified a universal and potent ARTi to ensure standardized and controllable knockdown efficiency. It seems this would save time and effort in validating each lost-of-function siRNAs/sgRNAs for each gene. However, users will still have to design and validate the best sgRNA to knock out endogenous genes or to knock in ARTi target sites by CRISPR/Cas9. The latter is by no-means trivial. Users will need to design and clone an expression construct for their cDNA replacement construct of interest, which will still be challenging for big proteins. This is not, as the authors point out, a replacement for other LOF methods, and there are other ways to achieve gene-specific regulation via, for example, degrons. However it is an effective orthogonal approach that many users may find compelling for their applications.

Reviewer #2 (Public Review):

In this manuscript, Hoffmann et al. introduce a novel and innovative method to validate and study the mechanism of action of essential genes and novel putative drug targets. In the wake of many functional genomics approaches geared towards identifying novel drug targets or synthetic lethal interactions, there is a dire need for methods that allow scientists to ablate a gene of interest and study its immediate effect in culture or in xenograft models. In general, these genes are lethal, rendering conventional genetic tools such as CRISPR or RNAi inept.

The ARTi system is based on expression of a transgene with an artificial RNAi target site in the 3'-UTR as well as a TET-inducible miR-E-based shRNAi. Using this system, the authors convincingly show that they can target strong oncogenes such as EGFRdel19 or KRasG12 as well as synthetic lethal interactions (STAG1/2) in various human cancer cell lines in vivo and in vitro.

The system is very innovative, likely easy to be established and used by the scientific community and thus very meaningful.

Reviewer #1 (Public Review):

This is a carefully performed and well documented study to indicate that the FUS protein interacts with the GGGGCC repeat sequence in Drosophila fly models, and the mechanism appears to include modulating the repeat structure and mitigating RAN translation. They suggest FUS, as well as a number of other G-quadruplex binding RNA proteins, are RNA chaperones, meaning they can alter the structure of the expanded repeat sequence to modulate its biological activities.Overall this is a nicely done study with nice quantitation.

Reviewer #2 (Public Review):

Fuijino et al provide interesting data describing the RNA-binding protein, FUS, for its ability to bind the RNA produced from the hexanucleotide repeat expansion of GGGGCC (G4C2). This binding correlates with reductions in RNA foci formation, the production of toxic dipeptides and concomitant reductions in toxic phenotypes seen in (G4C2)30+ expressing Drosophila. Both FUS and G4C2 repeats of >25 are associated with ALS/FTD spectrum disorders. Thus, these data are important for increasing our understanding of potential interactions between multiple disease genes.

Reviewer #3 (Public Review):

In this manuscript Fujino and colleagues used C9-ALS/FTD fly models to demonstrate that FUS modulates the structure of (G4C2) repeat RNA as an RNA chaperone, and regulates RAN translation, resulting in the suppression of neurodegeneration in C9-ALS/FTD. They also confirmed that FUS preferentially binds to and modulates the G-quadruplex structure of (G4C2) repeat RNA, followed by the suppression of RAN translation. The potential significance of these findings is high, since C9ORF72 repeat expansion is the most common genetic cause of ALS/FTD, especially in Caucasian populations and the DPR proteins have been considered the major cause of the neurodegenerations.

1) While the effect of RBP as an RNA chaperone on (G4C2) repeat expansion is supposed to be dose-dependent according to (G4C2)n RNA expression, the first experiment of the screening for RBPs in C9-ALS/FTD flies lacks this concept. It is uncertain if the RBPs of the groups "suppression (weak)" and "no effect" were less or no ability of RNA chaperone or if the expression of the RBP was not sufficient, and if the RBPs of the group "enhancement" exacerbated the toxicity derived from (G4C2)89 RNA or the expression of the RBP was excessive. The optimal dose of any RBPs that bind to (G4C2) repeats may be able to neutralize the toxicity without the reduction of (G4C2)n RNA.

2) In relation to issue 1, the rescue effect of FUS on the fly expressing (G4C2)89 (FUS-4) in Figure 4-figure supplement 1 seems weaker than the other flies expressing both FUS and (G4C2)89 in Figure 1 and Figure 1-figure supplement 2. The expression level of both FUS protein and (G4C2)89 RNA in each line is important from the viewpoint of therapeutic strategy for C9-ALS/FTD.

3) While hallmarks of C9ORF72 are the presence of DPRs and the repeat-containing RNA foci, the loss of function of C9ORF72 is also considered to somehow contribute to neurodegeneration. It is unclear if FUS reduces not only the DPRs but also the protein expression of C9ORF72 itself.

4) In Figure 5E-F, it cannot be distinguished whether FUS binds to GGGGCC repeats or 5' flanking region. Same experiment should be done by using FUS-RRMmut to elucidate whether FUS binding is the major mechanism for this translational control. Authors should show that FUS binding to long GGGGCC repeats is important for RAN translation.

5) It is not possible to conclude, as the authors have, that G-quadruplex-targeting RBPs are generally important for RAN translation (Figure 6), without showing whether RBPs which do not affect to (G4C2)89 RNA levels lead to decreased DPR protein level or RNA foci.

Reviewer #1 (Public Review):

Based on a recent report of spontaneous and reversible remapping of spatial representations in the enthorhinal cortex (Low et al 2021), this study sets out to examine possible mechanisms by which a network can simultaneously represent a positional variable and an uncorrelated binary internal state. To this end, the authors analyse the geometry of activity in recurrent neural networks trained to simultaneously encode an estimate of position in a one-dimensional track and a transiently-cued binary variable. They find that network activity is organised along two separate ring manifolds. The key result is that these two manifolds are significantly more aligned than expected by chance, as previously found in neural recordings. Importantly, the authors show that this is not a direct consequence of the design of the model, and clarify scenarios by which weaker alignment could be achieved. The model is then extended to a two-dimensional track, and to more than two internal variables. The latter case is compared with experimental data that had not been previously analysed.

Strengths:

• rigorous and careful analysis of activity in trained recurrent neural networks

• particular care is taken to show that the obtained results are not a necessary consequence of the design of the model

• the writing is very clear and pleasant to read

• close comparison with experimental data

• extensions beyond the situations studied in experiments (two-dimensional track, more than two internal states)

Weaknesses:

• no major weaknesses

• (minor) the comparison with previous models of remapping could be expanded

Altogether the conclusions claimed by the authors seem to be strongly supported and convincing.

Reviewer #2 (Public Review):

This important work presents an example of a contextual computation in a navigation task through a comparison of task driven RNNs and mouse neuronal data. Authors perform convincing state of the art analyses demonstrating compositional computation with valuable properties for shared and distinct readouts. This work will be of interest to those studying contextual computation and navigation in biological and artificial systems.

This work advances intuitions about recent remapping results. Authors trained RNNs to output spatial position and context given velocity and 1-bit flip-flops. Both of these tasks have been trained separately, but this is the first time to my knowledge that one network was trained to output both context and spatial position. This work is also somewhat similar to previous work where RNNs were trained to perform a contextual variation on the Ready-Set-Go with various input configurations (Remington et al. 2018). Additionally findings in the context of recent motor and brain machine interface tasks are consistent with these findings (Marino et al in prep). In all cases contextual input shifts neural dynamics linearly in state space. This shift results in a compositional organization where spatial position can be consistently decoded across contexts. This organization allows for generalization in new contexts. These findings in conjunction with the present study make a consistent argument that remapping events are the result of some input (contextual or otherwise) that moves the neural state along the remapping dimension.

The strength of this paper is that it tightly links theoretical insights with experimental data, demonstrating the value of running simulations in artificial systems for interpreting emergent properties of biological neuronal networks. For those familiar with RNNs and previous work in this area, these findings may not significantly advance intuitions beyond those developed in previous work. It's still valuable to see this implementation and satisfying demonstration of state of the art methods. The analysis of fixed points in these networks should provide a model for how to reverse engineer and mechanistically understand computation in RNNs.

I'm curious how the results might change or look the same if the network doesn't need to output context information. One prediction might be that the two rings would collapse resulting in completely overlapping maps in either context. I think this has interesting implications about the outputs of the biological system. What information should be maintained for potential readout and what information should be discarded? This is relevant for considering the number of maps in the network. Additionally, I could imagine the authors might reproduce their current findings in another interesting scenario: Train a network on the spatial navigation task without a context output. Fix the weights. Then provide a new contextual input for the network. I'm curious whether the geometric organization would be similar in this case. This would be an interesting scenario because it would show that any random input could translate the ring attractor that maintains spatial position information without degradation. It might not work, but it could be interesting to try!

I was curious and interested in the authors choice to not use activity or weight regularization in their networks. My expectation is that regularization might smooth the ring attractor to remove coding irrelevant fluctuations in neural activity. This might make Supplementary Figure 1 look more similar across model and biological remapping events (Line 74). I think this might also change the way authors describe potential complex and high dimensional remapping events described in Figure 2A.

Overall this is a nice demonstration of state-of-the-art methods to reverse engineer artificial systems to develop insights about biological systems. This work brings together concepts for various tasks and model organisms to provide a satisfying analysis of this remapping data.

Reviewer #3 (Public Review):

This important work provides convincing evidence that artificial recurrent neural networks can be used to model neural activity during remapping events while an animal is moving along a one-dimensional circular track. This will be of interest to neuroscientists studying the neural dynamics of navigation and memory, as well as the community of researchers seeking to make links between artificial neural networks and the brain.

Low et al. trained artificial recurrent neural networks (RNNs) to keep track of their location during a navigation task and then compared the activity of these model neurons to the firing rates of real neurons recorded while mice performed a similar task. This study shows that a simple set of ingredients, namely, keeping track of spatial location along a one-dimensional circular track, along with storing the memory of a binary variable (representing which of the two spatial maps are currently being used), are enough to obtain model firing rates that reproduce features of real neural recordings during remapping events. This offers both a normative explanation for these neural activity patterns as well as a potential biological implementation.

One advantage of this modeling approach using RNNs is that this gives the authors a complete set of firing rates that can be used to solve the task. This makes analyzing these RNNs easier, and opens the door for analyses that are not always practical with limited neural data. The authors leverage this to study the stable and unstable fixed points of the model. However, in this paper there appear to be a few places where analyses that were performed on the RNNs were not performed on the neural data, missing out on an opportunity to appreciate the similarity, or identify differences and pose challenges for future modeling efforts. For example, in the neural data, what is the distribution of the differences between the true remapping vectors for all position bins and the average remapping vector? What is the dimensionality of the remapping vectors? Do the remapping vectors vary smoothly over position? Do the results based on neural data look similar to the results shown for the RNN models (Figures 2C-E)?

There are many choices that must be made when simulating RNNs and there is a growing awareness that these choices can influence the kinds of solutions RNNs develop. For example, how are the parameters of the RNN initialized? How long is the RNN trained on the task? Are the firing rates encouraged to be small or smoothly varying during training? For the most part these choices are not explored in this paper so I would interpret the authors' results as highlighting a single slice of the solution space while keeping in mind that other potential RNN solutions may exist. For example, the authors note that the RNN and biological data do not appear to solve the 1D navigation and remapping task with the simplest 3-dimensional solution. However, it seems likely that an RNN could also be trained such that it only encodes the task relevant dynamics of this 3-dimensional solution, by training longer or with some regularization on the firing rates. Similarly, a higher-dimensional RNN solution may also be possible and this would likely be necessary to explain the more variable manifold misalignment reported in the experimental data of Low et al. 2021 as opposed to the more tightly aligned distribution for the RNNs in this paper. However, thanks to the modeling work done in this paper, the door has now been opened to these and many other interesting research directions.

Reviewer #2 (Public Review):

The ATPase protein machine cohesin shapes the genome by loop extrusion and holds sister chromatids together by topological entrapment. When executing these functions, cohesin is tightly regulated by multiple cofactors, such as Scc2/Nipbl, Pds5, Wapl, and Eco1/Esco1/2, and it undergoes dynamic conformational changes with ATP binding and hydrolysis. The mechanisms by which cohesin extrudes DNA loops and medicates siter-chromatid cohesion are still not understood. A major reason for the lack of understanding of cohesin dynamics and regulation is the failure to capture the structures of intact cohesin in different nucleotide-bound states and in complex with various regulators. So far only the ATP state cohesin bound to NIPBL and DNA have been experimentally determined.

In this manuscript, Nasmyth et al. made use of the powerful protein structure prediction tool, AlphaFold2 (AF), to predict the models of tens of cohesin subcomplexes from different species. The results provide important insight into how the Smc3-Scc1 DNA exiting gate is opened, how Pds5 and Wapl maintain the opened gate, how Pds5 and Scc3/SA recruit different cofactors, how Eco1 and Sororin antagonize Wapl, and how Scc2/Nipbl interacts with Scc3/SA. The models are for the most part consistent with published mutations in these proteins that affect cohesin's functions in vitro and in vivo and raise testable hypotheses of cohesin dynamics and regulation. This study also serves as an example of how to use AF to build models of protein complexes that involve the docking of flexible regions to globular domains.

Major points<br /> (1) As it stands, the manuscript is simply too long and not readable. The authors should streamline their presentations and remove excessive speculations and models of minor importance.

(2) AF has been accurate in predicting both the fold and sidechain conformations of globular domains. It is less accurate in predicting structural regions with conformational flexibility. Comparisons of predicted and determined structures of large protein complexes have shown considerable differences, particularly with respect to regions lacking tertiary fold. The authors should be more cautious in interpreting some of their models, particularly when the predicted models are inconsistent with determined structures and published biochemical data. For example, human WAPL-C in isolation does not interact with the SA-SCC1 complex while the N-terminal region of WAPL does.

(3) The predicted SA/Scc3-Pds5-Scc1-WaplC quaternary complex is fascinating. Can the authors provide some experimental evidence to support the formation of this quaternary complex or at least the formation of the SA/Scc3-Pds5-WaplC ternary complex? In vitro pulldown or gel filtration can be used to test their predictions.

Reviewer #1 (Public Review):

There are a number of outstanding questions concerning how cohesin turnover on DNA is controlled by various accessory factors and how such turnover is controlled by post-translational modification. In this paper, Nasmyth et al. perform a series of AlphaFold structure predictions that aim to address several of these outstanding questions. Their structure predictions suggest that the release factor WAPL forms a ternary complex with PDS5 and SA/SCC3. This ternary complex appears to be able to bind the N-terminal end of SCC1, suggesting how formation of such a complex could stabilize an open state of the cohesin ring. Additional calculations suggest how the Eco/ESCO acetyltransferases and Sororin engage the SMC3 head domain and thereby protect against WAPL-mediated release.

This work thus demonstrates the power of AF prediction methods and how they can lead to a number of interesting and testable hypotheses that can transform our understanding of cohesin regulation. These findings require orthogonal experimental validation, but the authors argue convincingly that such validation should not be a pre-requisite to publication.

As the authors did not systematically include model confidence scores it is difficult for the reader to evaluate the reliability of the models obtained. The caveat is that many readers will by default assume that the presented models are correct, when in fact, some of them may score poorly and require careful assessment. As numerous readers will not be very familiar with the AF confidence scoring mechanisms, it would be important to include such metrics and indicate what these scores mean for the different interfaces (Acceptable, Medium and High confidence?). pLDDT and PAE plots should be included. When they report on a key interaction (E.g. WAPL-SCC1) they should indicate the key region (SCC1 N-terminus) on the PAE plot. False positives are always possible even with good scores, especially when many protein pairs are tried. It would therefore be important to also include a table showing the global scores for pTM and ipTM to summarise the confidence scores of interfaces.

It is exciting to see AF-multimer predictions being applied to cohesin. As some of the reported interactions are not universally conserved and some involve relatively small interfaces the possibility arises that these interfaces show poor or borderline confidence scores. As some of these interfaces map to mutants that have previously been obtained by hypothesis-free genetic screens and mutational analyses they appear nevertheless valid. Thus, an important point to make is that even interfaces that show modest confidence scores may turn out to be valid while others may be not. The authors therefore should emphasize that the proposed models are just predictions and that additional orthogonal validations are required.

IAPWE Review: Should You Join?

Reviewer #1 (Public Review):

This work by Gonzalez-Segarra et al. greatly extends previous research from the same group that identified ISNs as a key player in balancing nutrition and water ingestion. Using well-balanced sets of exploratory anatomical analyses and rigorous functional experiments, the authors identify and compile various peptidergic circuits that modulate nutrient and/or water ingestion. The findings are convincing and the experiments rigorous.

Strengths:

- The authors complement anatomically-reconstructed and functionally-validated neuronal connectivity with extensive and intensive morphological and synaptic reconstruction.

- Neurons and genes involved in specific components of feeding control are undoubtedly challenging, because numerous neurons and circuits redundantly and reciprocally regulate the same components of feeding behavior. This work dissociates how multiple, parallel and interconnected, peptidergic circuits (dilp3, CCHa2, CCAP) modulate sucrose and water ingestion, in tandem and in parallel.

- The authors address some of the incongruencies / discrepancies in current literature (IPCs) and try to provide explanations, rather than ignoring inconsistent findings.

Weaknesses:

- Is "function" of the ISNs to balance "nutrient need" or osmolarity? Balancing hemolymph osmolarity for physiological homeostasis is conceptually different from balancing thirst and hunger.

- The final schematic nicely sums up how the different peptidergic pathways might work together, but it is unclear which connections are empirically-validated or speculative. It would be informative to show which parts of the model are speculative versus validated. For example, does FAFB volume synapse = functional connectivity and not just anatomical proximity? A bulk of the current manuscript relies on "synapses of relatively high confidence" (according to Materials and methods: line 522). I recommend distinguishing empirically tested & predicted connections in the final schematic, and maybe reword/clarify throughout the manuscript as "predicted synaptic partners"

Reviewer #2 (Public Review):

In this manuscript, González-Segarra et al. investigated how ISNs regulate sugar and water ingestion in Drosophila.

Strengths:

• In their previous paper, authors have shown that inhibiting neurotransmission in ISNs has opposite effects on sugar and water ingestion. In this new manuscript, they investigated the downstream neurons connected to ISNs.

• The authors first identified the effector molecules released by ISNs. Their RNAi screen found that, surprisingly, ISNs use ilp3 as a neuromodulator.

• Next, using light and electron microscopy, they investigated the downstream neural circuits ISNs connect with to regulate water or sugar ingestion. These analyses identified a new group of neurons named Bilateral T-shaped neurons (BiT) as the main output of ISNs, and several other peptidergic neurons as downstream effectors of ISNs. While BiT activity regulated both sugar and water ingestion, BiT downstream neurons, such as CCHa2R, only impacted water ingestion.

• These results suggested that ISNs might interact with distinct neural circuits to control sugar or water ingestion.

• The authors also investigated other ISN downstream neurons, such as ilp2 and CCAP, and revealed that their activity also contributes to ingestive behaviors in flies.

Areas for further development:

• Does BIT inhibit all of the IPCs or some of them? I think it is critical to indicate the ROIs used for each neuron in the methods. Which part of the neuron is used for imaging experiments? Dendrites, cell bodies, or synaptic terminals?

• The discussion section is not giving big picture explanation of how these neurons work together to regulate sugar and water ingestion. Silencing and activation experiments are good, but without showing the innate activity of these neural groups during ingestion, it is not clear what their functions are in terms of regulating fly behavior.

Reviewer #1 (Public Review):

This paper presents extensive numerical simulations using a model that incorporates up to second-order epistasis to study the joint effects of microscopic epistasis and clonal interference on the evolutionary dynamics of a microbial population. Previous works that explicitly modeled microscopic epistasis typically assumed strong selection & weak mutation (SSWM), a condition that is generally not met in real-life evolutionary processes. Alternatively, another class of models coarse-grained the effects of microscopic epistasis into a generic distribution of fitness effects. The framework introduced in this paper represents an important advance with respect to these previous approaches, allowing for the explicit modeling of microscopic epistasis in non-SSWM scenarios. The modeling framework presented promises to be a valuable tool to study microbial evolution in silico.

Reviewer #2 (Public Review):

This paper presents an extensive numerical study of microbial evolution using a model of fitness inspired by spin glass physics. It places special emphasis on elucidating the combined effects of microscopic epistasis, which dictates how the fitness effect of a mutation depends on the genetic background on which it occurs, and clonal interference, which describes the proliferation of and competition between multiple strains. Both microscopic epistasis and clonal interference have been observed in microbial evolution experiments, and are chief contributors to the complexity of evolutionary dynamics. Correlations between random mutations and nonlinearities associated with interactions between sub-populations consisting of competing strains make it extremely challenging to make quantitative theoretical predictions for evolutionary dynamics and associated observables such as the mean fitness. While the body of theoretical and computational research on modeling evolutionary dynamics is extensive, most theoretical efforts rely on making simplifications such as the strong selection weak mutation (SSWM) limit, which neglects clonal interference, or assumptions about the distribution of fitness effects that are not experimentally verifiable.

The authors have addressed this challenge by running a numerical microbial evolution experiment over realistic population sizes (~ 100 million cells) and timescales (~ 10,000 generations) using a spin glass model of fitness that considers pairwise interactions between mutations on distinct genetic loci. By independently tuning mutation rate as well as the strength of epistasis, the authors have shown that epistasis generically slows down the growth of fitness trajectories regardless of the amount of clonal interference. On the other hand, in the absence of epistasis, clonal interference speeds up the growth of fitness trajectories, but leaves the growth unchanged in the presence of epistasis. The authors quantitatively characterize these observations using asymptotic power law fits to the mean fitness trajectories. Further, the authors employ more simplified macroscopic models that are informed by their empirical findings, to reveal the mechanistic origins of the epistasis mediated slowing down of fitness growth. Specifically, they show that epistasis leads to a broadening of the distribution of fitness increments, leading to the fixation of a large number of mutations that confer small benefits. Effectively, this leads to an increase in the number of fixed mutations required to climb the fitness peak. This increased number of required beneficial mutations together with the decreasing availability of beneficial mutations at high fitness lead to the slowdown of fitness growth. The authors' data analysis is quite solid and their conclusions are well supported by quantitative macroscopic models. The paper can be strengthened further by conducting a deeper analysis of correlations between mutations, using tools for analyzing dynamical correlations developed in the spin glass literature.

One of the highlights of this paper is the author's astute choice of model, which strikes an impressive balance between complexity, flexibility, and numerical accessibility. In particular, the authors were able to achieve results over realistic population sizes and timescales largely because of the amenability of the model to the implementation of an efficient simulation algorithm. At the same time, the strength of epistasis and clonal interference can be tuned in a facile manner, enabling the authors to map out a phase diagram spanning these two axes. One could argue that the numerical scheme employed here would only work for a specific class of models, and is therefore not generalizable to all models of evolutionary dynamics. While this is likely true, the model is capable of recapitulating several complex aspects of microbial evolution, and is therefore not unduly restrictive.

Spin glass physics has already provided significant insights into a wide range of topics in the life sciences including protein folding, neuroscience, ecology and evolution. The present work carries this approach forward, with immediate implications for microbial evolution, and potential implications in related areas of research such as microbial ecology. In addition to the theoretical value of spin glass physics, the high performance algorithm developed in this work lays the foundation for formulating data driven approaches aimed at understanding evolutionary dynamics. In the future, there is considerable scope for utilizing data generated by such models to train machine learning algorithms for quantifying parameters associated with epistasis, clonal interference, and the distribution of fitness effects in laboratory experiments.

Reviewer #1 (Public Review):

Ibar and colleagues investigate the function of spectrin in Drosophila wing imaginal discs and its effect on the Hippo pathway and myosin activity. The authors find that both βH-Spec and its canonical binding partner α-Spec reduce junctional localization of the protein Jub and thereby restrict Jub's inhibitory effect on Hippo signaling resulting in activation of the Hippo effector Yorkie regulating tissue shape and organ size. From genetic epistasis analysis and analysis of protein localization, the authors conclude that βH-Spec and α-Spec act independently in this regulation. The major point of this study is that the apical localization of βH-Spec and myosin is mutually exclusive and that the proteins antagonize each other's activity in wing discs. In vitro co-sedimentation assays and in silico structural modeling suggest that this antagonization is due to a competition of βH-Spec and myosin for F-actin binding.

The study's strengths are the genetic perturbation that is the basis for the epistasis analysis which includes specific knockdowns of the genes of interest as well as an elegant CRISPR-based overexpression system with great tissue specificity. The choice of the model for such an in-depth analysis of pathway dependencies in a well-characterized tissue makes it possible to identify and characterize quantitative differences between closely entangled and mutually dependent components. The method of quantifying protein localization and abundance is common for multiple figures which makes it easy to assess differences across experiments. The flow of experiments is logical and in general, the author's conclusions are supported by the presented data. The findings are very well embedded into the context of relevant literature and both confronting and confirming literature are discussed.

The study shows how components of the cytoskeleton are directly involved in the regulation of the mechanosensitive Hippo pathway in vivo and thus ultimately regulate organ size supporting previous data in other contexts. The molecular mechanism regulating myosin activity by out-competing it for F-actin binding has been observed for small actin-binding proteins such as cofilin but is a new mode for such a big, membrane-associated actin-binding protein. This may inspire future experiments in different morphogenetic contexts for the investigation of similar mechanisms. For example, the antagonistic activity of βH-Spec and myosin in this tissue context might help explain phenomena in other systems such as spectrin-dependent ratcheting of apical constriction during mesoderm invagination (as the authors discuss). Against the classical view, the work shows that βH-Spec can act independently of α-Spec. Together the results will be of interest to the cell biology community with a focus on the cytoskeleton and mechanotransduction.

Reviewer #2 (Public Review):

Ibar and colleagues address the role of the spectrin cytoskeleton in the regulation of tissue growth and Hippo signaling in an attempt to elucidate the underlying molecular mechanism(s) and reconcile existing data. Previous reports in the field have suggested three distinct mechanisms by which the Spectrin cytoskeleton regulates Hippo signaling and this is, at least in part, due to the fact that different groups have mainly focused on different spectrins (alpha, beta, or beta-heavy) in previous reports.

The authors start their investigation by trying to reconcile their previous data on the role of Ajuba in the regulation of Hippo signaling via mechanotransduction and previous observations suggesting that Spectrins affect Hippo signaling independently of any effect on myosin levels or Ajuba localization. Contrary to previous reports, the authors reveal that, indeed, depletion of alpha- and beta-heavy-spectrin leads to an increase in myosin levels at the apical membrane. Moreover, the authors also reveal that the depletion of spectrins leads to an increase in Ajuba levels.

Reviewer #1 (Public Review):

This manuscript by Mahlandt, et al. presents a significant advance in the manipulation of endothelial barriers with spatiotemporal precision, and in the use of optogenetics to manipulate cell signaling in vascular biology more generally. The authors establish the role of Rho-family GTPases in controlling the cytoskeletal-plasma membrane interface as it relates to endothelial barrier integrity and function, and adequately motivate the need for optogenetic tools for global and local signaling manipulation to study endothelial barriers.

Throughout the work, the optogenetic assays are conceptualized, described, and executed with exceptional attention to detail, particularly as it relates to potential confounding factors in data analysis and interpretation. Comparison across experimental setups in optogenetics is notoriously fraught, and the authors' control experiments and measurements to ensure equal light delivery and pathway activation levels across applications is very thorough. In demonstrating how these new opto-GEFs can be used to alter vascular barrier strength, the authors cleverly use fluorescent-labeled dextran polymers of different sizes and ECIS experiments to demonstrate the physiological relevance of BOEC monolayers to in vivo blood vessels. Of particular note, the resiliency of the system to multiple stimulation cycles and longer time course experiments is promising for use in vascular leakage studies.

Given that dozens of Rho GTPase-activating GEFs exist, expanded rationale for the selection of p63, ITSN1, and TIAM1 in the form of discussion and literature citations would be helpful to motivate their selection as protein effectors in the engineered tools. Extensive tool engineering studies demonstrate the superiority of iLID over optogenetic eMags or rapamycin-based chemogenetic tools for these purposes. However, as the utility of iLID and eMags has been demonstrated for manipulation of a variety of signaling pathways, the iSH-Akt demonstration does not seem necessary for these systems.

The demonstration of orthogonality in GTPase- and VE-cadherin-blocking antibody-mediated barrier function decreases and is compelling, even without full elucidation of the role of cell size or overlap in barrier strength. The discussion section presents a mature and thoughtful description of the limitations, remaining questions, and potential opportunities for the tools and technology developed in this work. Importantly, this manuscript demonstrates a commitment to scientific transparency in the ways in which the data are visualized, the methods descriptions, and the reagent and code sharing it presents, allowing others to utilize these tools to their full potential.

Reviewer #2 (Public Review):

This manuscript reports on the use of Optogenetics to influence endothelial barrier integrity by light. Light-induced membrane recruitment of GTPase GEFs is known to stimulate GTPases and modulate cell shape, and here this principle is used to modulate endothelial barrier function. It shows that Rac and CDc42 activating constructs enhance barrier function and do this even when a major junctional adhesion molecule, VE-cadherin, is blocked. Activation of Rac and Cdc42 enhanced lamellipodia formation and cellular overlaps, which could be the basis for the increase in barrier integrity.

The authors aimed at developing a light driven technique with which endothelial barrier integrity can be modulated on the basis of activating certain GTPases. They succeeded in using optogenetic tools that recruit GEF exchange domains to membranes upon light induction in endothelial cell monolayers. Similar tools were in principle known before to modulate cell shape/morphology upon light induction, but were used here for the first time as regulators of endothelial barrier integrity. In this way it was shown that the activation of Cdc42 and Rac can increase barrier integrity even if VE-cadherin, a major adhesion molecule of endothelial junctions, is blocked. Although it was shown before that stimulation of S1P1 receptor or of Tie-2 can enhance endothelial barrier integrity in dependence of Cdc42 or Rac1 and can do this independent of VE-cadherin, the current study shows this with tools directly targeting these GTPases.

Furthermore, this study presents very valuable tools. The immediate and repeatable responses of barrier integrity changes upon light-on and light-off switches are fascinating and impressive. It will be interesting to use these tools in the future in the context of analyzing other mechanisms which also affect endothelial barrier function and modulate the formation of endothelial adherens junctions.

Reviewer #3 (Public Review):

Mahlandt et al. report the design and proof of concept of Opto-RhoGEF, a new set of molecular tools to control the activation by light of the three best known members of the Rho GTPase family, RhoA, Rac1 and Cdc42.

The study is based on the optogenetically-controlled activation of chimeric proteins that target to the plasma membrane guanine nucleotide exchange factors (GEFs) domains, which are natural activators specific for each of these three Rho GTPases. Membrane-targeted GEFs encounter and activate endogenous Rho proteins. Further investigation on the effect of these tools on RhoGTPase signaling would have strengthened the report.

These three Opto-RhoGEFs are reversible and enable the precise spatio-temporal control of Rho-regulated processes, such as endothelial barrier function, cell contraction and plasma membrane extension. Hence, these molecular tools will be of broad interest for cell biologists interested in this family of GTPases.

Mahlandt et al. design and characterize three new optogenetic tools to artificially control the activation of the RhoA, Rac1 and Cdc42 by light. These three Rho GTPases are master regulators of the actin cytoskeleton, thereby regulating cell-cell contact stability or actin-mediated contraction and membrane protrusions.

The main strength of this new experimental resource lies in the fact that, to date, few tools controlling Rho activation by reversibly targeting Rho GEFs to the plasma membrane are available. In addition, a comparative analysis of the three Opto-RhoGEFs adds value and further strengthens the results, given the fact that each Opto-GEF produces different (and somehow expected) effects, which suggest specific GTPase activation. The design of the tools is correct, although the membrane targeting could be improved, since the Lck N-terminus used to construct the recombinant proteins contains myristoylation and palmitoylation sites, which has the potential to target the chimeric protein to lipid rafts. As a consequence, this may not evenly translocate these Rho-activating domains.

An additional technical feature that must be highlighted is an elegant method to activate Opto-RhoGEFs in cultured cells, independent of laser and microscopes, by using led strips, which notably expands the possibilities of this resource, potentially allowing biochemical analyses in large numbers of cells.

The experimental evidence clearly indicates that authors have achieved their aim and designed very useful tools. However, they should have taken more advantage of this remarkable technical advance and investigate in further detail the spatiotemporal dynamics of Rho-mediated signaling. Although the manuscript is a "tool and resource", readers may have better grasped the potential benefits of tuning GTPase activity with this tool by learning about some original and quantitative insights of RhoA, Rac1 and Cdc42 function.

One of such insights may have come from the set of data regarding the contribution of adherens junctions. The effect of other endothelial cell-cell junctions, such as tight junctions, may also contribute to barrier function, as well as junctional independent, cell-substratum adhesion. These optogenetic tools will undoubtedly impact on these future studies and help decipher whether these other adhesion events that are important for endothelial barrier integrity are also under control of these three GTPases. Overall, the manuscript is sound and presents new and convincing experimental strategies to apply optogenetics to the field of Rho GTPases.

Joint Public Review:

In this study, Anthoney and coworkers continue an important, unique, and technologically innovative line of inquiry from the van Swinderen lab aimed at furthering our understanding of the different sleep stages that may exist in Drosophila. Here, they compare the physiological and transcriptional hallmarks of sleep that have been induced by two distinct means, a pharmacological block of GABA signaling and optogenetic activation of dorsal fan-shaped-body neurons. They first employ an incredibly impressive fly-on-the-ball 2-photon functional imaging setup to monitor neural activity during these interventions, and then perform bulk RNA sequencing of fly brains at different stages. These transcriptomic analyses leads them to (a) knocking out nicotinic acetyl-choline receptor subunits and (b) knocking down AkhR throughout the fly brain testing the impact of these genetic interventions on sleep behaviors in flies. Based on this work, the authors present evidence that optogenetically and pharmacologically induced sleep produces highly distinct brain-wide effects on physiology and transcription.

The study is of significant interest, is easy to read, and the figures are mostly informative. However there are features of the experimental design and the interpretation of results that diminish enthusiasm.

a - Conditions under which sleep is induced for behavioral vs neural and transcriptional studies

1) There is a major conceptual concern regarding the relationships between the physiological and transcriptomic effects of optogenetic and pharmacological sleep promotion, and the effects that these manipulations have on sleep behavior. The authors show that these two means of sleep-induction produce remarkably distinct physiological and transcriptional responses, however, they also show that they produce highly similar effects on sleep behavior, causing an increase in sleep through increases in the duration of sleep bouts. If dFB neurons were promoting active sleep, the sleep it produces should be more fragmented than the sleep induced by the drug, because the latter is supposed to produce quiet sleep. Yet both manipulations seem to be biasing behavior toward quiet sleep.

2) The authors show that the pharmacological block of GABA signaling and the optogenetic activation of dorsal fan-shaped-body neurons cause different responses on brain activity. Based on these recordings and the behavioral and brain transcriptomic data they then claim that these responses correspond to different sleep states and are associated with the expression and repression of a different constellation of genes. Nevertheless, neural activity in animals was recorded following short stimulations whereas behavioral and transcriptomic data were obtained following chronic stimulation. In this regard, it would be interesting to determine how the 12-hour pharmacological intervention they employed for their transcriptomic analysis changes neural activity throughout the brain - 12 hours will likely be too long for the open-cuticle preps, but an in-between time-point (e.g. 1h) would probably be equally informative.

b - Efficiency of THIP treatment under different conditions

1) There are no data to quantify how THIP alters food consumption. It is evident that flies consume it otherwise they would not show increased sleep. However, they may consume different amounts of food overall than the minus THIP controls. This might have an influence on the animal's metabolism, which could at least explain the fact that metabolism-related genes are regulated (Figure 5). Therefore, in the current state, it is not possible to be certain that gene regulation events measured in this experiment are solely due to THIP effects on sleep.

2) A similar problem exists in the sleep deprivation experiments. If flies are snapped every 20 seconds, they may not have the freedom to consume appropriate amounts of food, and therefore their consumption of THIP or ATR may be smaller than in non-sleep deprived controls. Thus, it would be crucial to know whether the flies that are sleep-deprived (i.e. shaken every 20 seconds for 12 hours) actually consume comparable amounts of food (and therefore THIP) as those that are undisturbed. If not, then perhaps the transcriptional differences between the two groups are not sleep-specific, but instead reflect varying degrees of exposure to THIP.

3) The authors should further discuss the slow action of THIP perfusion vs dFB activation, especially as flies only seem to fall asleep several minutes after THIP is being washed away. Is it a technical artifact? If not, it may not be unreasonable to hypothesize that THIP, at the concentration used, could prevent flies from falling asleep, and that its removal may lower the concentration to a point that allows its sleep-promoting action. The authors could easily test this by extending THIP treatment for another 4-5 minutes.

c - Comments regarding the behavioral assays

1) L319-322: the authors conclude that dFB stimulation and THIP consumption have similar behavioral effects on sleep. However, this is inaccurate as in Figure S1 they explain that one increases bout number in both day and night and the other one only during the day.

2) The behavioral definitions used for active and quiet sleep do not fit well with strong evidence that deep sleep (defined by lowered metabolic rates) is probably most closely associated with bouts of inactivity that are much longer than the >5min duration used here, i.e., probably 30min and longer (Stahl et al. 2017 Sleep 40: zsx084). Given that the authors are providing evidence that quiet sleep is correlated with changes in the expression of metabolism related genes, they should at least discuss the fact that reductions in metabolism have been shown to occur after relatively long bouts of inactivity and might reconsider their behavioral sleep analysis (i.e., their criteria for sleep state) with this in mind.

d - Comments regarding the recordings of neuronal activity

1) There is an additional concern regarding the proposed active and quiet sleep states that rest at the heart of this study. Here these two states in the fly are compared to the REM and NREM sleep states observed in mammals and the parallels between active fly sleep and REM and quiet fly sleep and NREM provide the framework for the study. The establishment of such parallel sleep states in the fly is highly significant and identifying the physiological and molecular correlates of distinct sleep stages in the fly is of critical importance to the field. However, the proposal that the dorsal fan shaped body (dFB) neurons promote active sleep runs counter to the prevailing model that these neurons act as a major site of sleep homeostasis. If quiet sleep were akin to NREM, wouldn't we expect the major site of sleep homeostasis in the brain to promote it? Furthermore, the authors state that the effects of dFB neuron excitation on transcription have "almost no overlap" (line 500) with the transcriptomic effects of sleep deprivation (Supplementary Table 3), which is not what would be expected if dFB neurons are tracking sleep pressure and promoting sleep, as suggested by a growing body of convergent work summarized on page four of the manuscript. Wouldn't the 10h excitation of the dFB neurons be predicted to mimic the effects of sleep deprivation if these neurons "...serve as the discharge circuit for the insect's sleep homeostat..." (line 60)? Shouldn't their prolonged excitation produce an artificial increase in sleep drive (even during sleep) that would favor deep, restorative sleep? How do the authors interpret their results with regard to the current prevailing model that dFB neurons act as a major site of sleep homeostasis? This study could be seen as evidence against it, but the authors do not discuss this in their Discussion.

2) Regarding the physiological effects of Gaboxadol, to what extent is the quieting induced by this drug reminiscent of physiology of the brains of flies spontaneously meeting the behavioral criterion for quiet sleep? Given the relatively high dose of the drug being delivered to the de-sheathed brain in the imaging experiments (at least when compared to the dose used in the fly food), one worries that the authors may be inducing a highly abnormal brain state that might bear very little resemblance to the deeply sleeping brain under normal conditions. As the authors acknowledge, it is difficult to compare these two situations. Comparing the physiological state of brains put to sleep by Gaboxadol and brains that have spontaneously entered a deep sleep state therefore seems critical.

3) There are some issues with Figure 3, in particular 3C-D. It is not clear whether these panels show representative traces or an average, however both the baseline activity and fluorescence are different between C and D, in particular in their amplitude. Therefore, it is difficult to attribute the differences between C and D to the stimulation itself or to the previously different baseline. In addition, the fact that flies with dFB activation seem to keep a basal level of locomotor activity whereas THIP-treated ones don't is quite striking, however it is not being discussed. Finally, the authors claim that the flies eventually wake up from THIP-induced sleep (L360-361), however there are no data to support this statement.

4) In Figure 4C, it is strange that the SEM is always exactly the same across the whole experiment. Readers should be aware that there might have been an issue when plotting the figure.

e - Comments regarding the transcript analyses

1) General comment: the title of this manuscript is inaccurate - the "transcriptome" commonly refers to the entirety of all transcripts in a cell/tissue/organ/animal (including genes that are not differentially expressed following their interventions), and it is therefore impossible to "engage two non-overlapping transcriptomes" in the same tissue. Perhaps the word "transcriptional programs" or transcriptional profiles" would be more accurate here?

2) Given the sensitivity of transcriptomic methods, there is a significant concern that the optogenetic experiments are not as well controlled as they could be. Given the need for supplemental all-trans retinal (ATR) for functional light gating of channelrhodopsins in the fly, it is convenient to use flies with Gal4-driven opsin that have not been given supplemental ATR as a negative control, particularly as a control for the effects of light. However, there is another critical control to do here. Flies bearing the UAS-opsin responder element but lacking the GAL4 driver and that have been fed ATR are critical for confirming that the observed effects of optogenetic stimulation are indeed caused by the specific excitation of the targeted neurons and not due to leaky opsin expression, or the effect of ATR feeding under light stimulation or some combination of these factors. Given the sensitivity of transcriptomic methods, it would be good to see that the candidate transcripts identified by comparing ATR+ and ATR- R23E10GAL4/UAS-Chrimson flies are also apparent when comparing R23E10GAL4/UAS-Chrimson (ATR+) with UAS-Chrimson (ATR+) alone.

3) Figures about qPCR experiments (5G and 6G) are problematic. First, whereas the authors seem satisfied with the 'good correspondence' between their RNA-seq and qPCR results, this is true for only ~9/19 genes in 5G and 2/6 genes in 6G. Whereas discrepancies are not rare between RNA-seq and qPCR, the text in L460-461 and 540-541 is misleading. In addition, it is unclear whether the n=19 in L458 refers to the number of genes tested or the number of replicates. If the qPCR includes replicates, this should be more clearly mentioned, and error bars should be added to the corresponding figures.

4) There is a lack of error bars for all their RNAseq and qPCR comparisons, which is particularly surprising because the authors went to great lengths and analyzed an applaudably large amount of independent biological replicates, yet the variability observed in the corresponding molecular data is not reported.

Reviewer #1 (Public Review):

Ritvo and colleagues present an impressive suite of simulations that can account for three findings of differentiation in the literature. This is important because differentiation-in which items that have some features in common, or share a common associate are less similar to one another than are unrelated items-is difficult to explain with classic supervised learning models, as these predict the opposite (i.e., an increase in similarity). A few of their key findings are that differentiation requires a high learning rate and low inhibitory oscillations, and is virtually always asymmetric in nature.

This paper was very clear and thoughtful-an absolute joy to read. The model is simple and elegant, and powerful enough to re-create many aspects of existing differentiation findings. The interrogation of the model and presentation of the findings were both extremely thorough. The potential for this model to be used to drive future work is huge. I have only a few comments for the authors, all of which are relatively minor.

1. I was struck by the fact that the "zone" of repulsion is quite narrow, compared with the zone of attraction. This was most notable in the modeling of Chanales et al. (i.e., just one of the six similarity levels yielded differentiation). Do the authors think this is a generalizable property of the model or phenomenon, or something idiosyncratic to do with the current investigation? It seems curious that differentiation findings (e.g., in hippocampus) are so robustly observed in the literature despite the mechanism seemingly requiring a very particular set of circumstances. I wonder if the authors could speculate on this point a bit-for example, might the differentiation zone be wider when competitor "pop up" is low (i.e., low inhibitory oscillations), which could help explain why it's often observed in hippocampus? This seems related a bit to the question about what makes something "moderately" active, or how could one ensure "moderate" activation if they were, say, designing an experiment looking at differentiation.

2. With real fMRI data we know that the actual correlation value doesn't matter all that much, and anti-correlations can be induced by things like preprocessing decisions. I am wondering if the important criterion in the model is that the correlations (e.g., as shown in Figure 6) go down from pre to post, versus that they are negative in sign during the post learning period. I would think that here, similar to in neural data, a decrease in correlation would be sufficient to conclude differentiation, but would love the authors' thoughts on that.

3. For the modeling of the Favila et al. study, the authors state that a high learning rate is required for differentiation of the same-face pairs. This made me wonder what happens in the low learning rate simulations. Does integration occur? This paradigm has a lot of overlap with acquired equivalence, and so I am thinking about whether these are the sorts of small differences (e.g., same-category scenes and perhaps a high learning rate) that bias the system to differentiate instead of integrate.

4. For the simulations of the Schlichting et al. study, the A and B appear to have overlap in the hidden layer based on Figure 9, despite there being no similarity between the A and B items in the study (in contrast to Favila et al., in which they were similar kinds of scenes, and Chanales et al., in which they were similar colors). Why was this decision made? Do the effects depend on some overlap within the hidden layer? (This doesn't seem to be explained in the paper that I saw though, so maybe just it's a visualization error?)

5. It seems as though there were no conditions under which the simulations produced differentiation in both the blocked and intermixed conditions, which Schlichting et al. observed in many regions (as the present authors note). Is there any way to reconcile this difference?

6. A general question about differentiation/repulsion and how it affects the hidden layer representation in the model: Is it the case that the representation is actually "shifted" or repelled over so it is no longer overlapping? Or do the shared connections just get pruned, such that the item that has more "movement" in representational space is represented by fewer units on the hidden layer (i.e., is reduced in size)? I think, if I understand correctly, that whether it gets shifted vs. reduce would depend on the strength of connections along the hidden layer, which would in turn depend on whether it represents some meaningful continuous dimension (like color) or not. But, if the connections within the hidden layer are relatively weak and it is the case that representations become reduced in size, would there be any anticipated consequences of this (e.g., cognitively/behaviorally)?

Reviewer #2 (Public Review):

This paper addresses an important computational problem in learning and memory. Why do related memory representations sometimes become more similar to each other (integration) and sometimes more distinct (differentiation)? Classic supervised learning models predict that shared associations should cause memories to integrate, but these models have recently been challenged by empirical data showing that shared associations can sometimes cause differentiation. The authors have previously proposed that unsupervised learning may account for these unintuitive data. Here, they follow up on this idea by actually implementing an unsupervised neural network model that updates the connections between memories based on the amount of coactivity between them. The goal of the authors' paper is to assess whether such a model can account for recent empirical data at odds with supervised learning accounts. For each empirical finding they wish to explain, the authors built a neural network model with a very simple architecture (two inputs layers, one hidden layer, and one output layer) and with prewired stimulus representations and associations. On each trial, a stimulus is presented to the model, and inhibitory oscillations allow competing memories to pop up. Pre-specified u-shaped learning rules are used to update the weights in the model, such that low coactivity leaves model connections unchanged, moderate coactivity weakens connections, and high coactivity strengthens connections. In each of the three models, the authors manipulate stimulus similarity (following Chanales et al), shared vs distinct associations (following Favila et al), or learning strength (a stand in for blocked versus interleaved learning schedule; following Schlichting et al) and evaluate how the model representations evolve over trials.

As a proof of principle, the authors succeed in demonstrating that unsupervised learning with a simple u-shaped rule can produce qualitative results in line with the empirical reports. For instance, they show that pairing two stimuli with a common associate (as in Favila et al) can lead to *differentiation* of the model representations. Demonstrating these effects isn't trivial and a formal modeling framework for doing so is a valuable contribution. Overall, the authors do a good job of both formally describing their model and giving readers a high level sense of how their critical model components work, though there are some places where the robustness of the model to different parameter choices is unclear. In some cases, the authors are very clear about this (e.g. the fast learning rate required to observe differentiation). However, in other instances, the paper would be strengthened by a clearer reporting of the critical parameter ranges. For instance, it's clear from the manipulation of oscillation strength in the model of Schlichting et al that this parameter can dramatically change the direction of the results. The authors do report the oscillation strength parameter values that they used in the other two models, but it is not clear how sensitive these models are to small changes in this value. Similarly, it's not clear whether the 2/6 hidden layer overlap (only explicitly manipulated in the model of Chanales et al) is required for the other two models to work. Finally, though the u-shaped learning rule is essential to this framework, the paper does little formal investigation of this learning rule. It seems obvious that allowing the u-shape to collapse too much toward a horizontal line would reduce the model's ability to account for empirical results, but there may be other more interesting features of the learning rule parameterization that are essential for the model to function properly.

There are a few other points that may limit the model's ability to clearly map onto or make predictions about empirical data. The model(s) seems very keen to integrate and do so more completely than the available empirical data suggest. For instance, there is a complete collapse of representations in half of the simulations in the Chanales et al model and the blocked simulation in the Schlichting et al model also seems to produce nearly complete integration. Even if the Chanales et al paper had observed some modest behavioral attraction effects, this model would seem to over-predict integration. The author's somewhat implicitly acknowledge this when they discuss the difficulty of producing differentiation ("Practical Advice for Getting the Model to Show Differentiation") and not of producing integration, but don't address it head on. Second, the authors choice of strongly prewiring associations in the Chanales and Favila models makes it difficult to think about how their model maps onto experimental contexts where competition is presumably occurring while associations are only weakly learned. In the Chanales et al paper, for example, the object-face associations are not well learned in initial rounds of the color memory test. While the authors do justify their modeling choice and their reasons have merit, the manipulation of AX association strength in the Schlichting et al model also makes it clear that the association strength has a substantial effect on the model output. Given the effect of this manipulation, more clarity around this assumption for the other two models is needed.

Overall, this is strong and clearly described work that is likely to have a positive impact on computational and empirical work in learning and memory. While the authors have written about some of the ideas discussed in this paper previously, a fully implemented and openly available model is a clear advance that will benefit the field. It is not easy to translate a high-level description of a learning rule into a model that actually runs and behaves as expected. The fact that the authors have made all their code available makes it likely that other researchers will extend the model in numerous interesting ways, many of which the authors have discussed and highlighted in their paper.

Reviewer #3 (Public Review):

This paper proposes a computational account for the phenomenon of pattern differentiation (i.e., items having distinct neural representations when they are similar). The computational model relies on a learning mechanism of the nonmonotonic plasticity hypothesis, fast learning rate and inhibitory oscillations. The relatively simple architecture of the model makes its dynamics accessible to the human mind. Furthermore, using similar model parameters, this model produces simulated data consistent with empirical data of pattern differentiation. The authors also provide insightful discussion on the factors contributing to differentiation as opposed to integration. The authors may consider the following to further strengthen this paper:

The model compares different levels of overlap at the hidden layer and reveals that partial overlap seems necessary to lead to differentiation. While I understand this approach from the perspective of modeling, I have concerns about whether this is how the human brain achieves differentiation. Specifically, if we view the hidden layer activation as a conjunctive representation of a pair that is the outcome of encoding, differentiation should precede the formation of the hidden layer activation pattern of the second pair. Instead, the model assumes such pattern already exists before differentiation. Maybe the authors indeed argue that mechanistically differentiation follows initial encoding that does not consider similarity with other memory traces?

Related to the point above, because the simulation setup is different from how differentiation actually occurs, I wonder how valid the prediction of asymmetric reconfiguration of hidden layer connectivity pattern is.

Although as the authors mentioned, there haven't been formal empirical tests of the relationship between learning speed and differentiation/integration, I am also wondering to what degree the prediction of fast learning being necessary for differentiation is consistent with current data. According to Figure 6, the learning rates lead to differentiation in the 2/6 condition achieved differentiation after just one-shot most of the time. On the other hand, For example, Guo et al (2021) showed that humans may need a few blocks of training and test to start showing differentiation.

Related to the point above, the high learning rate prediction also seems to be at odds with the finding that the cortex, which has slow learning (according to the theory of complementary learning systems), also shows differentiation in Wammes et al (2022).

More details about the learning dynamics would be helpful. For example, equation(s) showing how activation, learning rate and the NMPH function work together to change the weight of connections may be added. Without the information, it is unclear how each connection changes its value after each time point.

In the simulation, the NMPH function has two turning points. I wonder if that is necessary. On the right side of the function, strong activation leads to strengthening of the connectivity, which I assume will lead to stronger activation on the next time point. The model has an upper limit of connection strength to prevent connection from strengthening too much. The same idea can be applied to the left side of the function: instead of having two turning points, it can be a linear function such that low activation keeps weakening connection until the lower limit is reached. This way the NMPH function can take a simpler form (e.g., two line-segments if you think the weakening and strengthening take different rates) and may still simulate the data.

Reviewer #1 (Public Review):

The authors present a carefully controlled set of experiments that demonstrate an additional complexity for GPCR signalling in that endosomal signalling make be different when beta-arrestin is or isn't associated with a G protein-bound V2 vasopressin receptor. It uses state of the art biosensor-based approaches and beta-arrestin KO lines to assess this. It adds to a growing body of evidence that G proteins and beta-arresting can associate with GPCR complexes simultaneously. They also demonstrate the possibility that Gq might also be activated by the V2 receptor. My sense is one thing they may need to be considered is the possibility of such "megacomplexes" might actually involve receptor dimers or oligomers.

Reviewer #2 (Public Review):

This manuscript by Daly et al., probes the emerging paradigm of GPCR signaling from endosomes using the V2R as a model system with an emphasis on Gq/11 and β-arrestins. The study employs cellular imaging, enzyme complementation assays and energy transfer-based sensors to probe the potential formation of GPCR-G-protein-β-arrestin megaplexes. While the study is certainly very interesting, it appears to be very preliminary at many levels, and clearly requires further development in order to make robust conclusions.

1. The use of mini-G-proteins in these experiments is a major concern as these are highly engineered and may not represent the true features of G-proteins. While these have been used as a readout in other publications, their use in demonstrating megaplex formation is sub-optimal, and native, full-length G-proteins should be used.<br /> 2. The interpretation of complementation (NanoLuc) or proximity (BRET) as evidence of signaling not appropriate, especially when overexpression system and engineered constructs are being used.<br /> 3. After the original work from the same corresponding authors on megaplex formation, the major challenge in the field is to demonstrate the existence and relevance of megaplex formation at endogenous levels of components, and the current study focuses solely on showing the proximity of Gq and β-arrestins.<br /> 4. The study lacks a coherent approach, and the assays are often shifted back and forth between the two β-arrestin isoforms (1 and 2), for example, confocal vs. complementation etc.<br /> 5. In every assay, only the G-proteins and β-arrestins are monitored without a direct assessment of the presence of receptor, and absent that data, it is difficult to justify calling these entities megaplexes.

In conclusion, the authors should consider expanding on this work further to make the points more convincingly to make the work solid and impactful. The two corresponding authors are among the leaders in the field having demonstrated the existence of megaplexes, and building on the work in a systematic fashion should certainly move the paradigm forward. As the work presented in the current manuscript is already pre-printed, the authors should take this opportunity to present a completer and more comprehensive story to the field.

Reviewer #3 (Public Review):

The manuscript by Daly et al examines endosomal signaling of the vasopressin type 2 receptors using engineered mini G protein (mG proteins) and a number of novel techniques to address if sustained G protein signaling in the endosomal compartment is enhanced by β arrestin. Employing these interesting techniques they have how V2R could activates Gαs and Gα in the endosomal compartments and how this modulation could occur in arrestin dependent and independent manner. Although the phenomenon of endosomal signaling is complex to address the authors have tried their best to examine these using a number of well controlled set of experiments.

Reviewer #2 (Public Review):

This study highlights the importance of including not only spatio-termporal scales to biodiversity assessments, but also to include some of the possible drivers of biodiversity loss and to study their joint contribution as environmental stressors.

Introduction - Well written and placed within the current trends of unprecedented biodiversity loss, with an emphasis on freshwater ecosystems. The authors identify three important points as to why biodiversity action plans have failed. Namely, community changes occur over large spatio-temporal scales and monitoring programs capture a fraction of these long-term dynamics (e.g. few decades) which although good at capturing trends in biodiversity change, they often fail at identifying the drivers of these changes. Additionally, most of these rely on manual sorting of samples, overlooking cryptic diversity, or state-of-the-art techniques such as sedimentary DNA (sedaDNA) which allow studying decade-long dynamics, usually focus on specific taxonomic groups unable to represent community-level changes. Secondly, the authors identify that biodiversity is threatened by multiple factors and are rarely studied in tandem. Finally, the authors stress the need for high-throughput approaches to study biodiversity changes since historically, most conservation efforts rely on highly specialized skills for biodiversity monitoring, and even well-studied species have relatively short time series data. The authors identify a model freshwater lake (Lake Ring, Denmark) - suitable due to its well-documented history over the last 100 years - to present a comprehensive framework using metabarcoding, chemical analysis and climatic records for identifying past and current impacts on this ecosystem arising from multiple abiotic environmental stressors.

Results - They are brief and should expand some more. Particularly, there are no results regarding metabarcoding data (number of reads, filtering etc.). These details are important to know the quality of the data which represents the bulk of the analyses. Even the supplementary material gives little information on the metabarcoding results (e.g. number of ASVs - whether every ASV of each family were pooled etc.). The drivers of biodiversity change section could be restructured and include main text tables showing the families positively or negatively correlated with the different variables (akin to table S2 but simplified).

Discussion<br /> The discussion is well written, identifying first some of the possible caveats of this study, particularly regarding the classification of metabarcoding data, its biases and the possible DNA degradation of ancient sediment DNA. The authors discuss how their results fit to general trends showing how agricultural runoff and temperature drive changes in freshwater functional biodiversity primarily due to their synergistic effects on bioavailability, adsorption, etc. The authors highlight the advantage of using a system-level approach rather than focusing on taxa-specific studies due to their indicator status. Similarly, the authors justify the importance of studying community composition as far back as possible since it reveals unexpected patterns of ecosystem resilience. Lake Ring, despite its partially recovered status, has not returned to its semi-pristine levels of biodiversity and community assemblage. Additionally, including enzyme activity allows to assess the functional diversity of the studied environment, although reference databases of these pathways are still lacking. Finally, the authors discuss the implications of their findings under a conservation and land management framework suggesting that by combining these different approaches, drivers of biodiversity stressors can be derived with high accuracy allowing for better-informed mitigation and conservation efforts.

Reviewer #1 (Public Review):

This study presents a conceptual and analytical framework for tracking the impacts of human activities on freshwater ecosystems over time. It demonstrates the application of the framework to a 100-year record of community-level biodiversity, climate change, and chemical pollution from sediments cores of Lake Ring, Denmark. By reconstructing biodiversity using environmental DNA (eDNA) and pollutant inputs using mass spectrometry, the authors identify the taxonomic groups responding positively and negatively in different phases of the lake's environmental history. Furthermore, they identify the independent and additive effects of climate variables and pollutants on biodiversity throughout the 100-year record.

Strengths:

The advances in paired molecular and machine learning analyses are an important step towards a better understanding of 20th/21st century trajectories of biodiversity and pollution.

The finding that taxonomic groups so central to ecosystem assessment in Europe (i.e., diatoms) do not appear to respond to degradation or amelioration - providing at least a partial explanation as to why "ecological status" (as defined under the EU Water Framework Directive) has proved so difficult to improve.

The framework shows how both taxonomic and functional indicators can be used to better understand ecological degradation and recovery.

The identification of individual biocides and climate variables driving observed changes is a particular strength.

Limitations:

The analytical framework is not sufficiently explained in the main text.

The significance of findings in relation to functional changes is not clear. What are the consequences of enrichment of RNA transport or ribosome biogenesis pathways between pesticides and recovery stages, for example?

The impact of individual biocides and climate variables, and their additive effects, are assessed but there is no information offered on non-additive interactions (e.g., synergistic, antagonistic).

The level of confidence associated with results is not made explicit. The reader is given no information on the amount of variability involved in the observations, or the level of uncertainty associated with model estimates.

The major implications of the findings for regulatory ecological assessment are missed. Regulators may not be primarily interested in identifying past "ecosystem shifts". What they need are approaches which give greater confidence in monitoring outcomes by better reflecting the ecological impact of contemporary environmental change and ecosystem management. The real value of the work in this regard is that: (1) it shows that current approaches are inappropriate due to the relatively stable nature of the indicators used by regulators, despite large changes in pollutant inputs; (2) it presents some better alternatives, including both taxonomic and functional indicators; and (3) it provides a new reference (or baseline) for regulators by characterizing "semi-pristine" conditions.

Reviewer #2 (Public Review):

Chen et. al investigated the effects of natural tannins, proanthocyanidins, and punicalagin, against infection by the SARS-CoV-2 virus and its variants. The authors found that these two compounds affect different parts of the SARS-CoV-2 viral infection mechanisms, namely that punicalagin may act ACE2-spike protein interaction and repress Main protease activity, whereas tannic acid and OPC inhibits TMPRSS2 activity. Additionally, the authors show that these tannic compounds can act upon multiple variants of the virus, which suggests a pan-inhibitory effect on SARS-CoV-2 viruses. The studies performed herein present a novel alternative to inhibiting viral infection by SARS-CoV-2 which may be of interest to patients with concerns about reinfection.

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

1) All compounds should be tested in vivo to test not only safety but efficacy and whether these compounds elicit any acute liver toxicity when administered in proposed doses.

2) Efficacy in vaccinated patients would be of great interest, especially since many reinfections occur in the vaccinated population (especially by variants such as Delta).

Reviewer #2 (Public Review):

The molecular mechanisms by which monoaminergic antidepressants exert their therapeutic effects are unknown. An emerging hypothesis in this regard is that these antidepressants work by modulating the glutamatergic system, yet the precise links remain unclear. In this manuscript, Lin et al. describe one such link. First, they observe that the small nucleolar RNA (snoRNA), SNORD90 is consistently elevated following antidepressant treatments in peripheral blood samples, in postmortem brain samples of individuals that received antidepressant treatments, mouse models of depression, and in induced neurons treated with antidepressants in culture. To test whether the elevation of SNORD90 could be significant for antidepressive-like behaviors, the authors perform bilateral injections of viral vectors carrying either SNORD90 or scrambled controls into the mouse cg1/2 and show that overexpression of SNORD90 reduces anxiety and depressive-like behaviors. Using in-silico analysis of base complementarity, the authors predict that the growth factor, neuregulin 3 (NRG3), could be a potential target of SNORD90, and they then validate this prediction by directly showing that SNORD90 overexpression results in the reduction of NRG3 in human neural progenitor cells, whereas knockdown of SNORD90 upregulates NRG3. The authors then show that the binding of SNORD90 to NRG3 pre-mRNA and mature mRNA results in their methylation and subsequent decay. Finally, they show that SNORD90 overexpression in the mouse anterior cingulate cortex is sufficient to increase the levels of glutamatergic neurotransmission.

Overall, the experiments described in the manuscript are well executed and their conclusions are fairly drawn. The observations that SNORD90 overexpression is sufficient to reduce anxiety and depression-like behaviors are indeed exciting, as are the links between SNORD90, and m6A methylation of NRG3, and glutamatergic neurotransmission. There are a few weaknesses in the data and the text, but these should be addressable by the authors.

Reviewer #1 (Public Review):

In their research article, Sapiro et al. overcome the technical burden of low B. burgdorferi numbers during infection by physically enriching for spirochetes prior to RNA-sequencing/mass spectrometry. This technology, which has potential broad applications, was applied to B. burgdorferi-infected ticks, generating datasets for future studies.

Sapiro et al. addressed many of the reviewers' comments including the addition of experimental details, comparisons to other studies and some caveats to their approach. The manuscript has been significantly improved and I appreciate the efforts to address our critiques. There are a few remaining comments that the authors should consider before creating the final Version of Record.

The authors sought to develop technology for a transcriptomic analysis of B. burgdorferi directly from infected ticks. The methodology has exciting implications to better understand pathogen RNA profiles during specific infection timepoints, even beyond the Lyme spirochete. The authors demonstrate successful sequencing of the B. burgdorferi transcriptome from ticks and perform mass spectrometry to identify possible tick proteins that interact with B. burgdorferi. This technology and first dataset will be useful for the field. The study is limited in that no transcripts/proteins are followed-up by additional experiments and no biological interactions/infectious-processes are investigated.

Remaining critiques:

Experimental data regarding the sensitivity of this approach are missing. What is the limit of detection for this protocol? While the authors have stated that they were unable to sequence B. burgdorferi from unfed nymphs, the number of bacteria needed for antibody enrichment are not tested. The starting CFU in their infected nymphal ticks was also not reported (the authors only report reisolation data from 12 ticks). Page 18, line 458 the authors claim their approach "captured the vast majority" of Bb inside of the tick. Data are missing to demonstrate this. Understanding the limits of this approach will be critical for future applications, especially when using B. burgdorferi infected material with low bacterial burden.

The authors should clarify the term "genes" in the abstract and throughout the manuscript. I think they actually mean "open reading frames" or "annotated mRNAs".

More information regarding the efficacy of RNA-seq coverage is still warranted and lacking from the results, especially on page 6. The authors skip right to differential expression analysis without fully examining sequencing effectiveness. This is especially important given their development of a new technique. What was the numbers of detected genes for each sample? How is this affected by bacterial burden of the sample? What is the distribution of reads among tRNAs, mRNAs, UTRs, and sRNAs? How reproducible is the coverage for one gene across replicates? A few browser images of RNA-seq data (ex. of BAM files) across different genes would be useful to visualize the read coverage per gene.

Downregulated genes are largely ignored and should be commented on further.

Page 11, line 258-260: authors state Rpos, Rrp1, and RelBbu are the "three main Bb regulatory programs active in the tick." Yes, these three regulons have been well studied but there could be other uncharacterized regulatory programs. Please consider changing the language.

Reviewer #2 (Public Review):

This work is significant as it provides insights into the global transcriptomic changes of Borrelia burgdorferi during tick feeding. The manuscript also provides methodological advances for the study of the transcriptome of Borrelia burgdorferi in the tick host.

This manuscript documents the study of the transcriptome of Borrelia burgdorferi at 1, 2, 3 and 4 days post-feeding in nymphs of Ixodes scapularis. The authors use antibody-based pull-downs to separate bacteria from tick and mouse cells to perform an enrichment. The data presented support that the transcriptome of B. burgdorferi changes over time in the tick. This work is important as, until now, only limited information on specific genes had been collected. The methodological advances described in this study are valuable for the field.

Reviewer #2 (Public Review):

This paper explores the mechanisms by which cells in tissues use the extracellular matrix (ECM) to reinforce and establish connections. This is a mechanistic and quantitative paper that uses imaging and genetics to establish that the Type IV collagen, DDR-2/collagen receptor discoidin domain receptor 2, signaling through Ras to strengthen an adhesion between two cell types in C. elegans. This connection needs to be strong and robust to withstand the pressure of the numerous eggs that pass through the uterus. The major strengths of this paper are in crisply designed and clear genetic experiments, beautiful imaging, and well supported conclusions. I find very few weaknesses, although, perhaps the evidence that DDR-2 promotes utse-seam linkage through regulation of MMPs could be stronger. This work is impactful because it shows how cells in vivo make and strengthen a connection between tissues through ECM interactions involving collaboration between discoidin and integrin.

Reviewer #2 (Public Review):

Numerous neurodegenerative diseases are thought to be driven by the aggregation of proteins into insoluble filaments known as "amyloids". Despite decades of research, the mechanism by which proteins convert from the soluble to insoluble state is poorly understood. In particular, the initial nucleation step is has proven especially elusive to both experiments and simulation. This is because the critical nucleus is thermodynamically unstable, and therefore, occurs too infrequently to directly observe. Furthermore, after nucleation much faster processes like growth and secondary nucleation dominate the kinetics, which makes it difficult to isolate the effects of the initial nucleation event. In this work Kandola et al. attempt to surmount these obstacles using individual yeast cells as microscopic reaction vessels. The large number of cells, and their small size, provides the statistics to separate the cells into pre- and post-nucleation populations, allowing them to obtain nucleation rates under physiological conditions. By systematically introducing mutations into the amyloid-forming polyglutamine core of huntingtin protein, they deduce the probable structure of the amyloid nucleus. This work shows that, despite the complexity of the cellular environment, the seemingly random effects of mutations can be understood with a relatively simple physical model. Furthermore, their model shows how amyloid nucleation and growth differ in significant ways, which provides testable hypotheses for probing how different steps in the aggregation pathway may lead to neurotoxicity.

In this study Kandola et al. probe the nucleation barrier by observing a bimodal distribution of cells that contain aggregates; the cells containing aggregates have had a stochastic fluctuation allowing the proteins to surmount the barrier, while those without aggregates have yet to have a fluctuation of suitable size. The authors confirm this interpretation with the selective manipulation of the PIN gene, which provides an amyloid template that allows the system to skip the nucleation event.

In simple systems lacking internal degrees of freedom (i.e., colloids or rigid molecules) the nucleation barrier comes from a significant entropic cost that comes from bringing molecules together. In large aggregates this entropic cost is balanced by attractive interactions between the particles, but small clusters are unable to form the extensive network of stabilizing contacts present in the larger aggregates. Therefore, the initial steps in nucleation incur an entropic cost without compensating attractive interactions (this imbalance can be described as a surface tension). When internal degrees of freedom are present, such as the conformational states of a polypeptide chain, there is an additional contribution to the barrier coming from the loss of conformational entropy required to the adopt aggregation-prone state(s). In such systems the clustering and conformational processes do not necessarily coincide, and a major challenge studying nucleation is to separate out these two contributions to the free energy barrier. Surprisingly, Kandola et al. find that the critical nucleus occurs within a single molecule. This means that the largest contribution to the barrier comes from the conformational entropy cost of adopting the beta-sheet state. Once this state is attained, additional molecules can be recruited with a much lower free energy barrier.

There are several caveats that come with this result. First, the height of the nucleation barrier(s) comes from the relative strength of the entropic costs compared to the binding affinities. This balance determines how large a nascent nucleus must grow before it can form interactions comparable to a mature aggregate. In amyloid nuclei the first three beta strands form immature contacts consisting of either side chain or backbone contacts, whereas the fourth strand is the first that is able to form both kinds of contacts (as in a mature fibril). This study used relatively long polypeptides of 60 amino acids. This is greater than the 20-40 amino acids found in amyloid-forming molecules like ABeta or IAPP. As a result, Kandola et al.'s molecules are able to fold enough times to create four beta strands and generate mature contacts intramolecularly. The authors make the plausible claim that these intramolecular folds explain the well-known length threshold (L~35) observed in polyQ diseases. The intramolecular folds reduce the importance of clustering multiple molecules together and increase the importance of the conformational states. Similarly, manipulating the sequence or molecular concentrations will be expected to manipulate the relative magnitude of the binding affinities and the clustering entropy, which will shift the relative heights of the entropic barriers.

The authors make an important point that the structure of the nucleus does not necessarily resemble that of the mature fibril. They find that the critical nucleus has a serpentine structure that is required by the need to form four beta strands to get the first mature contacts. However, this structure comes at a cost because residues in the hairpins cannot form strong backbone or zipper interactions. Mature fibrils offer a beta sheet template that allows incoming molecules to form mature contacts immediately. Thus, it is expected that the role of the serpentine nucleus is to template a more extended beta sheet structure that is found in mature fibrils.

A second caveat of this work is the striking homogeneity of the nucleus structure they describe. This homogeneity is likely to be somewhat illusory. Homopolymers, like polyglutamine, have a discrete translational symmetry, which implies that the hairpins needed to form multiple beta sheets can occur at many places along the sequence. The asparagine residues introduced by the authors place limitations on where the hairpins can occur, and should be expected to increase structural homogeneity. Furthermore, the authors demonstrate that polyglutamine chains close to the minimum length of ~35 will have strict limitations on where the folds must occur in order to attain the required four beta strands.

A novel result of this work is the observation of multiple concentration regimes in the nucleation rate. Specifically, they report a plateau-like regime at intermediate regimes in which the nucleation rate is insensitive to protein concentration. The authors attribute this effect to the "self-poisoning" phenomenon observed in growth of some crystals. This is a valid comparison because the homogeneity observed in NMR and crystallography structures of mature fibrils resemble a one-dimensional crystal. Furthermore, the typical elongation rate of amyloid fibrils (on the order of one molecule per second) is many orders of magnitude slower than the molecular collision rate (by factors of 10^6 or more), implying that the search for the beta-sheet state is very slow. This slow conformational search implies the presence of deep kinetic traps that would be prone to poisoning phenomena. However, the observation of poisoning in nucleation during nucleation is striking, particularly in consideration of the expected disorder and concentration sensitivity of the nucleus. Kandola et al.'s structural model of an ordered, intramolecular nucleus explains why the internal states responsible for poisoning are relevant in nucleation.

To achieve these results the authors used a novel approach involving a systematic series of simple sequences. This is significant because, while individual experiments showed seemingly random behavior, the randomness resolved into clear trends with the systematic approach. These trends provided clues to build a model and guide further experiments.

Reviewer #1 (Public Review):

The authors take on the challenge of defining the core nucleus for amyloid formation by polyglutamine tracts. This rests on the assertion that polyQ forms amyloid structures to the exclusion of all other forms of solids. Using their unique assay, deployed in yeast, the authors attempt to infer the size of the nucleus that templates amyloid formation by polyQ. Further, through a series of sequence titrations, all studied using a single type of assay, the authors converge on an assertion stating that a single polyQ molecule is the nucleus for amyloid formation, that 12-residues make up the core of the nucleus, that it takes ca. 60 Qs in a row to unmask this nucleation potential, and that polyQ amyloid formation belongs to the same universality class as self-poisoned crystallization, which is the hallmark of crystallization from polymer melts formed by large, high molecular weight synthetic polymers. Unfortunately, the authors have decided to lean in hard on their assertions without a critical assessment of whether their findings stand up to scrutiny. If their findings are truly an intrinsic property of polyQ molecules, then their findings should be reconstituted in vitro. Unfortunately, careful and rigorous experiments in vitro show that there is a threshold concentration for forming fibrillar solids. This threshold concentration depends on the flanking sequence context on temperature and on solution conditions. The existence of a threshold concentration defies the expectation of a monomer nucleus. The findings disagree with in vitro data presented by Crick et al., and ignored by the authors. Please see: https://doi.org/10.1073/pnas.1320626110. These reports present data from very different assays, the importance of which was underscored first by Regina Murphy and colleagues. The work of Crick et al., provides a detailed thermodynamic framework - see the SI Appendix. This framework dove tails with theory and simulations of Zhang and Muthukumar, which explains exactly how a system like polyQ might work (https://doi.org/10.1063/1.3050295). The picture one paints is radically different from what the authors converge upon. One is inclined to lean toward data that are gleaned using multiple methods in vitro because the test tube does not have all the confounding effects of a cellular milieu, especially when it comes to focusing on sequence-intrinsic conformational transitions of a protein. In addition to concerns about the limitations of the DAmFRET method, which based on the work of the authors in their collaborative paper by Posey et al., are being stretched to the limit, there is the real possibility that the cellular milieu, unique to the system being studied, is enabling transitions that are not necessarily intrinsic to the sequence alone. A nod in this direction is the work of Marc Diamond, which showed that having stabilized the amyloid form of Tau through coacervation, there is a large barrier that limits the loss of amyloid-like structure for Tau. There may well be something similar going on with the polyQ system. If the authors could show that their data are achievable in vitro without anything but physiological buffers one would have more confidence in a model that appears to contradict basic physical principles of how homopolymers self-assemble. Absent such additional evidence, numerous statements seem to be too strong. There are also several claims that are difficult to understand or appreciate.

Reviewer #3 (Public Review):

Kandola et al. explore the important and difficult question regarding the initiating event that triggers (nucleates) amyloid fibril growth in glutamine-rich domains. The researchers use a fluorescence technique that they developed, dAMFRET, in a yeast system where they can manipulate the expression level over several orders of magnitude, and they can control the length of the polyglutamine domain as well as the insertion of interfering non-glutamine residues. Using flow cytometry, they can interrogate each of these yeast 'reactors' to test for self-assembly, as detected by FRET.

In the introduction, the authors provide a fairly thorough yet succinct review of the relevant literature into the mechanisms of polyglutamine-mediated aggregation over the last two decades. The presentation as well as the illustrations in Figure 1A and 1B are difficult to understand, and unfortunately, there is no clear description of the experimental technique that would allow the reader to connect the hypothetical illustrations to the measurement outcomes. The authors do not explain what the FRET signal specifically indicates or what its intensity is correlated to. FRET measures distance between donor and acceptor, but can it be reliably taken as an indicator of a specific beta-sheet conformation and of amyloid? Does the signal increase with both nucleation and with elongation, and is the signal intensity the same if, e.g., there were 5 aggregates of 10 monomers each versus 50 monomeric nuclei? Is there a reason why the AmFRET signal intensity decreases at longer Q even though the number of cells with positive signal increases? Does the number of positive cells increase with time? The authors state later that 'non-amyloid containing cells lacked AmFRET altogether', but this seems to be a tautology - isn't the lack of AmFRET taken as a proof of lack of amyloid? Overall, a clearer description of the experimental method and what is actually measured (and validation of the quantitative interpretation of the FRET signal) would greatly assist the reader in understanding and interpreting the data.

The authors demonstrate that their assay shows that the fraction of cells with AmFRET signal increases strongly with an increase in polyQ length, with a 'threshold around 50-60 glutamines. This roughly correlates with the Q-length dependence of disease. The experiments in which asparagine or other amino acids are inserted at variable positions in the glutamine repeat are creative and thorough, and the data along with the simulations provide compelling support for the proposed Q zipper model. The experiments shown in Figure 5 are strongly supportive of a model where formation of the beta-sheet nucleus is within a monomer. This is a potentially important result, as there are conflicting data in the literature as to whether the nucleus in polyQ is monomer.

I did not find the argument, that their data shows the Q zipper grows in two dimensions, compelling; there are more direct experimental methods to answer this question. I was also confused by the section that Q zippers poison themselves. It would be easier for the reader to follow if the authors first presented their results without interpretation. The data seem more consistent with an argument that, at high concentrations, non-structured polyQ oligomers form which interfere with elongation into structured amyloid assemblies - but such oligomers would not be zippers.

Although some speculation or hypothesizing is perfectly appropriate in the discussion, overall the authors stretch this beyond what can be supported by the results. A couple of examples: The conclusion that toxicity arises from 'self-poisoned polymer crystals' is not warranted, as there is no relevant data presented in this manuscript. The authors refer to findings 'that kinetically arrested aggregates emerge from the same nucleating event responsible for amyloid formation', but I cannot recall any evidence for this statement in the results section.

Reviewer #1 (Public Review):

This paper looks at nutrient-responsive Ca++ flux in islet cells of eight genetically diverse mouse strains. The investigators correlate Ca++ flux with insulin secretory capacity, demonstrating that calcium parameters in response to different nutrients are a better predictor of insulin secretory capacity than average calcium. They also correlate Ca++ flux with previously collected islet protein abundance followed by integration with human genome-wide association studies. This integration allows them to identify a sub-set of proteins that are both relevant to human islet function and that may play a causal role in regulating islet Ca++ oscillations. All data have been deposited in a searchable public database. There are many strengths to this paper. To my knowledge, this is the first work to assess the genetics of nutrient-responsive Ca++ flux in islets. Given the importance of Ca++ for beta cell insulin secretion, this work is of high importance. Investigators also use the founders of two powerful genetic mouse models: the diversity outbred and collaborative, opening up several avenues of future research into the genetics of Ca++ flux. By looking at multiple parameters of Ca++ flux, investigators are able to start to understand which parameters may be driving low or high insulin secretion. Integration with protein abundance and human GWAS has allowed identification of proteins with known roles in insulin secretory capacity, as well as several novel regulators, again opening up several avenues of future research. Finally, the public database is likely to be useful to multiple investigators interested in following up specific protein targets or in conducting future genetic studies. I found only minor weaknesses in this paper, mainly regarding clarity in certain areas. One specific area to be improved is Figure 4A, B where in addition to the heat maps, it would be useful to see regression plots that show the differences per sex and strain for the insulin secretion vs Ca++ parameters.

Reviewer #2 (Public Review):

This is an interesting paper from a reputable group in the field of islet physiology. The authors have provided the results from extensive studies, which will contribute to the knowledge of islet dysfunction and diabetes pathophysiology. One major critique is that the authors studied "the human orthologues of the correlated mouse proteins that are proximal to the glycemia-associated SNPs in human GWAS". This implies two assumptions - (1) human and mouse proteins do not differ in terms of islet physiology and calcium signaling; (2) the proteins proximal to the SNPs are the causal factors for functional differences, though the SNPs could affect protein/gene function distant from the SNPs.

Reviewer #1 (Public Review):

Secondary cell walls support vascular plants and conduct water throughout the plant body, but are also important resources for lignocellulosic feedstocks. Secondary cell wall synthesis is under complex transcriptional control, presumably because it must only be initiated after cell growth is complete. Here, the authors found that two Musashi-type RNA-binding proteins, MSIL2 and MSIL4 are redundantly required for secondary cell wall development in Arabidopsis. The plant phenotypes could be complemented by the wild-type version of either protein, but not by a MSIL4 version that carries mutations in the conserved RNA-binding domains, and the authors localized MSIL2 & 4 to stress granules, implicating the RNA-binding function of MSIL4 in the cell wall phenotype. Upon closer inspection, the secondary cell wall phenotypes included changes in vasculature morphology, and minor changes to lignin and hemicellulose (glucuronoxylan). While there were no changes to likely cell wall target genes in the transcriptome of msil2msil4 plants, proteomics experiments found glucuronoxylan biosynthesis components were upregulated in the mutants, and they detected an increase in substituted xylan via several methods. Finally, they documented MSIL4 binding to RNA encoding one of these targets, suggesting that MSIL2 and MSIL4 act to post-transcriptionally regulate glucuronoxylan modification. Altogether, this is a new mechanism by which cell wall composition could be regulated.

Overall, the manuscript is well-written, the data are generally high-quality, and the authors typically use several independent methods to support each claim. However, several important questions remain unanswered by this work in its current state and the model presented in Figure 7 is quite speculative. For example, the link between the striking plant phenotype and GXM misregulation is unclear since GXM overexpression doesn't alter plant phenotypes or lignin content (Yuan et al 2014 Plant Science), so misregulation of GXMs in msil2msil4 mutants clearly is not the whole story. It also remains to be determined why one particular secondary cell wall synthesis enzyme is regulated likely post-transcriptionally, while so much of the pathway is regulated at the transcriptional level. There are likely other targets for MSIL2- and MSIL4-mediated regulation since it seems that MSIL2 and MSIL4 are expressed in tissues that are not synthesizing secondary cell walls.

Reviewer #2 (Public Review):

This work explored the biological functions of a small family of RNA-binding proteins that was previously studied in animals, but was uncharacterized in plants. Combinatorial T-DNA insertional mutants disrupting the expression of the four Mushashi-like (MSIL) genes in Arabidopsis revealed that only the msil2 msil4 double mutant visibly alters plant development. The msil2/4 plants produced stems that could not stand upright. Transgene complementation, site-directed mutagenesis of MSIL4 conserved RNA-binding motifs, and in vitro RNA binding assays support the conclusion that the loss of MSIL2 and MISL4 function is responsible for the observed morphological defects. MSIL2/4 interact with proteins associated with mRNA 3'UTR binding and translational regulation.

The authors present compelling biochemical evidence that Mushashi-like2 (MSIL2) and MSIL4 jointly regulate secondary cell wall biosynthesis in the Arabidopsis stem. Quantitative analyses of proteins and transcripts in msil2/4 stems uncovered upregulation of several xylan-related enzymes (despite WT-like RNA levels). Consistent with MALDI-TOF data for released xylan oligosaccharides, the authors propose a model in which MSIL2/4 negatively regulate the translation of GXM (glucuronoxylan methyltransferase), a presumed rate-limiting step. The molecular links between overmethylated xylans and the observed stem defects (which include subtle reductions in lignin and increases beta-glucan polymer distribution) warrants further investigation in future studies. Similarly, as the authors point out, it is intriguing that the loss of the broadly expressed MSIL2/4 genes only significantly affects specific cell types in the stem.

Reviewer #3 (Public Review):

The manuscript by Kairouani et al. investigates the function of a small family of plant RNA binding proteins with similarity to the well-studied Musashi protein in animals, and, therefore, called MUSASHI-LIKE1-4 (MSL1-4). Studies on the biological importance of post-transcriptional control of gene expression via RNA-binding proteins in plants are not numerous, and advances in this important field are much needed. The thorough work presented in this manuscript is such an advance.

The central observations of the paper are:

- Knockout of any MSL gene alone does not produce a phenotype.<br /> It is of note that basic characterization of knockout mutations is really well done - for example, the authors have taken care to raise specific antibodies to each of the MSL proteins and use them to demonstrate that each of the T-DNA insertion mutants used actually does knock out protein production from the corresponding gene.

- Knockout of MSL2/4 (but no other double mutant) produces a clear leaf phenotype, and a remarkable stem phenotype in which the mutants collapse as they are unable to support upright growth

- The phenotypes of knockout mutants persist in point mutants defective in RNA-binding, indicating that RNA-binding is required for biological activity. Consistent with this, and associate physically with other RNA-binding proteins and translation factors.

- MSL proteins are cytoplasmic

- The msl2/4 mutants present multiple defects in secondary cell wall composition and structure, probably explaining their inability to grow upright. I did not examine the cell wall analyses in detail as I am no specialist in this field.

- Msl2/4 mutants show transcriptomic changes with at large two big categories of differentially expressed genes compared to wild type.<br /> (1) Genes related to cell wall metabolism<br /> (2) Genes associated with defense against herbivores and pathogens

- Two of the mRNAs encoding cell wall factors with significant upregulation in msl2/4 mutants compared to wild type also associate physically with MSL4 as judged by RNA-immunoprecipitation-RT-PCR assays, and this physical association is abrogated in the RNA-binding deficient MSL4 mutant.

Altogether, the study shows clear biological relevance of the MSL family of RNA-binding proteins, and provides good arguments that the underlying mechanism is control of mRNAs encoding enzymes involved in secondary cell wall metabolism (although concluding on translational control in the abstract is perhaps saying too much - post-transcriptional control will do given the evidence presented). One observation reported in the study makes it vulnerable to alternative interpretation, however, and I think this should be explicitly treated in the discussion:

The fact that immune responses are switched on in msl2/4 mutants could also mean that MSL2/4 have biological functions unrelated to cell wall metabolism in wild type plants, and that cell wall defects arise solely as an indirect effect of immune activation (that is known to involve changes in expression of many cell wall-modifying enzymes and components such as pectin methylesterases, xyloglucan endotransglycosylases, arabinogalactan proteins etc. Indeed, the literature is rich in examples of gene functions that have been misinterpreted on the basis of knockout studies because constitutive defense activation mediated by immune receptors was not taken into account (see for example Lolle et al., 2017, Cell Host & Microbe 21, 518-529).

With the evidence presented here, I am actually close to being convinced that the primary defect of msl2/msl4 mutants is directly related to altered cell wall metabolism, and that defense responses arise as a consequence of that, not the other way round. But I do not think that the reverse scenario can be formally excluded with the evidence at hand, and a discussion listing arguments in favor of the direct effect proposed here would be appropriate. Elements that the authors could consider to include would be the isolation of a cellulose synthase mutant as a constitutive expressor of jasmonic acid responses (cev1) as a clear example that a primary defect in cell wall metabolism can produce defense activation as secondary effect. The interaction of MSL4 with GXM1/3 mRNAs is also helpful to argue for a direct effect, and it would strengthen the argument if more examples of this kind could be included.

Reviewer #1 (Public Review):

In this study, the authors use prospective sorting and microarray analyses, extended by single-cell RNA sequencing, in the neural stem cell niche of the subventricular zone (SVZ) to identify and refine a series of states along the continuum from quiescent neural stem cells to mature progeny. Of note, changes in the levels and subgroups of RNA splicing regulators are detailed across this continuum. Using in vitro proliferation and differentiation assays, coupled with in vivo engraftment of some prospectively sorted subsets, the authors argue that a stage they define as immature neuroblasts (iNBs) retain proliferative and multilineage differentiation capacity that is not seen in the mature neuroblast population, and is unexpected based on prior models for lineage progression in this system. This iNB stage is accompanied by a change in RNA splicing regulator expression, which is of interest due to the emerging roles for RNA processing and preferential translation within this niche.

These data complement several additional sc-RNAseq studies of this stem cell niche, and use a different, but similar, sorting strategy to isolate and profile subpopulations of stem/progenitor cells and neuroblast progeny. The claim that immature neuroblasts retain multipotency - the ability to generate glia and neurons - is surprising and somewhat controversial given that this has largely not been reported before under homeostatic conditions. Some factors to consider when interpreting these data are that the "immature neuroblast" populations are studied in some experiments using a transcriptional signature and a functional assay, namely the timing of reappearance of these cells after use of agents that kill rapidly dividing cells (in this case, radiation), leading to reconstitution of the lineage by previously quiescent stem cells. In a separate set of experiments, a tamoxifen-inducible labeling system is used in combination with cell-surface markers to prospectively isolate and study the differentiation potential of neuroblast populations that are assumed to be equivalent to those found in transcriptional experiments. It would be of interest in future to confirm that the exact sorted populations (using CD24/EGFR/DCX-CreERT2::CAG) have the same transcriptional profile as those studied in earlier experiments within the paper, and to confirm the purity of the sorted populations. Finally, while elegant use is made of engraftment of the sorted populations to study the differentiation and lineage potential of these immature neuroblasts, a remaining question is the relative abundance of each lineage (neurons/astrocytes/oligodendrocytes) produced by the engrafted cells - is production of glia rare, or common? Could this be due to factors such as alteration of lineage potential due to culture conditions, a disconnect between transcript expression and protein expression, or an incompletely purified starter population?

Overall, this manuscript presents an intriguing possible refinement of models for SVZ neurogenesis, and highlights the role of RNA splicing at specific stages in the lineage. It will be of interest to see if additional groups confirm these findings and whether multiplexed immunostaining, highly multiplexed flow cytometry, or other approaches focused at the proteomic level confirm and extend these findings, particularly given recent data in the developing brain that suggest transcript and protein levels are relatively poorly correlated in stem/progenitor populations.

A final point on terminology: "iNB", "A cells", and "D1/D2 cells" are all used in the manuscript to denote different stages along the continuum from TAP/C cells to mature neuroblasts; however, historically "D cells" refers to neuroblasts in the dentate gyrus, not those derived from the SVZ. In this case, the authors are exclusively studying SVZ-derived neuroblasts.

Reviewer #2 (Public Review):

Bernou et al use a FACS-based method to sort different cells along the neurogenesis trajectory. They identify cells that are LeX+EGFR+CD24+ which they call i-NBs. The authors suggest these cells proliferate performing neurosphere assays, and that they can make all NSC-derived differentiated cell types through transplantation into mice. They performed microarrays on the different cell subtypes, which led them to their interest in RNA splicing proteins. They additionally performed single-cell analyses to try to identify the cluster of i-NBs compared to other cell types. Further, they performed an irradiation experiment to initiate quiescence exit and depletion of the dividing cell types to create a directionality in the progression through cell types. Comparison with other published sequencing datasets of the same cell type revealed that the i-NBs were most similar to Mitotic TAPs. The authors use their single cell sequencing data to observe expression changes of the RNA splicing factors in different clusters. They also suggest that the i-NB population is heterogeneous in their DCX mRNA levels, with a high group and a low group that have different characteristics. They erroneously use a DCX-Cre-ERT2 line to identify GFP+ or GFP- cells to transplant, and find no GFP+ cells at the end of 5 weeks after transplantation, and draw the conclusion that the high DCX cells don't have the same NSC potential. The authors propose they have identified a new cell type, and that there should be a rewrite of the SVZ neurogenesis cascade to include this population.

Summary of response<br /> This manuscript postulates the identification of a new cell type in the adult neurogenesis cascade. However, all of the author's analyses point to this population of sorted cells being the late mitotic TAPs on their way to becoming neuroblasts. This would suggest that these cells are in the trajectory between TAPs and NBs, so a pivot point, but not a unique cell type in its own. In their sequencing analyses, cell cycle becomes the defining factor of the clustering. Indeed, their cell type as compared to other datasets suggests this population is a mitotic TAP, which is supported by their own transcriptome data (Fig S2) showing that i-NBs are just further in mitosis than the TAPs.

Reviewer #1 (Public Review):

This study by Park et al. describes an interesting approach to disentangle gene-environment pathways to cognitive development and psychotic-like experiences in children. They have used data from the ABCD study and have included PGS of EA and cognition, environmental exposure data, cognitive performance data and self-reported PLEs. Although the study has several strengths, including its large sample size, interesting approach and comprehensive statistical model, I have several concerns:

- The authors have included follow-up data from the ABCD Study. However, it is not very clear from the beginning that longitudinal paths are being explored. It would be very helpful if the authors would make their (analysis) approach clearer from the introduction. Now, they describe many different things, which makes the paper more difficult to read. It would be of great help to see the proposed path model in a Figure and refer to that in the Method.

- There is quite a lot of causal language in the paper, particularly in the Discussion. My advice would be to tone this down.

- It's a bit unclear to me why the authors chose the PEs phenotype as their outcome of interest. They mainly speak about child development and mental health in general in the Introduction, so why focus on PLE? Aren't genes and environments also relevant for other types of mental health problems? Relatedly, in the Discussion the authors seem to conflate PLEs with psychosis. There is a large body of literature highlighting the differences between PLEs and psychosis, and this should - in my opinion - be adjusted throughout the introduction and discussion.

- I feel that the limitation section is a bit brief, and can be developed further.

- I like that the assessment of CP and self-reports PEs is of good quality. However, I was wondering which 4 items from the parent-reported CBCL were used and how did they correlate with the child-reported PEs? And how was distress taken into account in the child self-reported PEs measurement? Which PEs measures were used?

- What was the correlation between CP and EA PGSs?

- Regarding the PGS: why focus on cognitive performance and EA? It should be made clearer from the introduction that EA is not only measuring cognitive ability, but is also a (genetic) marker of social factors/inequalities. I'm guessing this is one of the reasons why the EA PGS was so much more strongly correlated with PEs than the CP PGS. See the work bij Abdellaoui and the work by Nivard.

- Considering previous work on this topic, including analyses in the ABCD Study, I'm not surprised that the correlation was not very high. Therefore, I don't think it makes a whole of sense to adjust for the schizophrenia PGS in the sensitivity analyses, in other words, it's not really 'a more direct genetic predictor of PLEs'.

- How did the FDR correction for multiple testing affect the results?

Overall, I feel that this paper has the potential to present some very interesting findings. However, at the moment the paper misses direction and a clear focus. It would be a great improvement if the readers would be guided through the steps and approach, as I think the authors have undertaken important work and conducted relevant analyses.

Reviewer #2 (Public Review):

This paper tried to assess the link between genetic and environmental factors on psychotic-like experiences, and the potential mediation through cognitive ability. This study was based on data from the ABCD cohort, including 6,602 children aged 9-10y. The authors report a mediating effect, suggesting that cognitive ability is a key mediating pathway in the link between several genetic and environmental (risk and protective) factors on psychotic-like experiences.

While these findings could be potentially significant, a range of methodological unclarities and ambiguities make it difficult to assess the strength of evidence provided.

Strengths of the methods:

The authors use a wide range of validated (genetic, self- and parent-reported, as well as cognitive) measures in a large dataset with a 2-year follow-up period. The statistical methods have the potential to address key limitations of previous research.

Weaknesses of the methods:

The rationale for the study is not completely clear. Cognitive ability is probably a more likely mediator of traits related to negative symptoms in schizophrenia, rather than positive symptoms (e.g., psychosis, psychotic-like symptom). The suggestion that cognitive ability might lead to psychotic-like symptoms in the general population needs further justification.

Terms are used inconsistently throughout (e.g., cognitive development, cognitive capacity, cognitive intelligence, intelligence, educational attainment...). It is overall not clear what construct exactly the authors investigated.

Not the largest or most recent GWASes were used to generate PGSes.<br /> It is not fully clear how neighbourhood SES was coded (higher or lower values = risk?). The rationale, strengths, and assumptions of the applied methods are not fully clear. It is also not clear how/if variables were combined into latent factors or summed (weighted by what). It is not always clear when genetic and when self-reported ethnicity was used. Some statements might be overly optimistic (e.g., providing unbiased estimates, free even of unmeasured confounding; use of representative data).

It appears that citations and references are not always used correctly.

Strengths of the results:

The authors included a comprehensive array of analyses.

Weaknesses of the results:

Many results, which are presented in the supplemental materials, are not referenced in the main text and are so comprehensive that it can be difficult to match tables to results. Some of the methodological questions make it challenging to assess the strength of the evidence provided in the results.

Appraisal:

The authors suggest that their findings provide evidence for policy reforms (e.g., targeting residential environment, family SES, parenting, and schooling). While this is probably correct, a range of methodological unclarities and ambiguities make it difficult to assess whether the current study provides evidence for that claim.

Impact:

The immediate impact is limited given the short follow-up period (2y), possibly concerns for selection bias and attrition in the data, and some methodological concerns.

Reviewer #2 (Public Review):

The manuscript by Petroccione et al., examines the modulatory role of the neuronal glutamate transporter EAAC1 on glutamatergic and GABAergic synaptic strength at D1- and D2-containing medium spiny neurons within the dorsolateral striatum. They find that pharmacological and genetic disruption of EAAC1 function increases glutamatergic synaptic strength specifically at D1-MSNs. They show that this is due to a structural change in release sites, not release probability. They also show that EAAC1 is critical in maintaining lateral inhibition specifically between D1-MSNs. Taken together, the authors conclude that EAAC1 functions to constrain D1-MSN excitation. Using a computational modeling technique, they posit that EAAC1's modulatory role at glutamatergic and GABAergic inputs onto D1-MSNs ultimately manifests as a reduction of gain of the input-output firing relationship and increases the offset. They go on to show that EAAC1 deletion leads to enhanced switching behavior in a probabilistic operant task. They speculate that this is due to a dysregulated E/I balance at D1-MSNs in the DLS.

Overall, this is a very interesting study focused on an understudied glutamate transporter. Generally, the study is done in a very thorough and methodical manner and the manuscript is well written.

Major Comments/Concerns:<br /> 1. Regional/Local manipulations in behavior study: The manuscript would be greatly improved if they provided data linking the ex vivo electrophysiological findings within the DLS with the behavior. Although they are using a DLS-dependent task, they are nonetheless, using a constitutive EAAC1 KO mouse. Thus, they cannot make a strong conclusion that the behavioral deficits are due to the EAAC1 dysfunction in the DLS (despite the strong expression levels in the DLS).

2. Statistics used in the study: There are some missing details regarding the precise stats using for the different comparisons. I am particularly concerned that the electrophysiology studies that were a priori designed as a 2-factor analysis did not have 2-way ANOVAs performed, but rather a series of t-tests. For example, in Figure 3b, the two factors are 1) cell type and 2) genotype. Was a 2-way ANOVA performed? It is hard for me to tell from the text.

Moderate Concerns:<br /> 3. Control mice: I am moderately concerned that littermates were not used for controls for the EAAC1 KO, but rather C57Bl/6NJ presumably ordered from a vendor. It has been shown that issues like transit and rearing conditions can have long term affects on behavior. Were the control mice reared in house? How long was the acclimation time before use?

4. OCD framework: I generally find the OCD framework unnecessary, particularly in the introduction. Compulsive behaviors are not restricted to OCD. Indeed, the link between the behavioral observations and OCD phenotype seems a bit tenuous. In addition, studying the mechanisms of behavioral flexibility in and of itself is interesting. I don't think such a strong link needs to be made to OCD throughout the entirety of the paper. The authors should consider tempering this language or restricting it to the discussion and end of the abstract.

Reviewer #1 (Public Review):

Park et al demonstrate that cells on either side of a BM-BM linkage strengthen their adhesion to that matrix using a positive feedback mechanism involving a discoidin domain receptor (DDR-2) and integrin (INA-1 + PAT-3). In response to its extracellular ligand (Collagen IV/EMB-9), DDR-2 is endocytosed and initiates signaling that in turn stabilizes integrin at the membrane. DDR-2 signaling operates via Ras/LET-60. This work's strength lies in its excellent in vivo imaging, especially of endogenously tagged proteins. For example, tagged DDR-2:mNG could be seen relocating from seam cell membranes to endosomes. I also think a second strength of this system is the ability to chart the development of BM-BM linkage over time based on the stages of worm larval development. This allows the authors to show DDR signaling is needed to establish linkage, rather than maintain it. It likely is relevant to many types of cells that use integrin to adhere to BM and left me pondering a number of interesting questions. For example: (1) Does DDR-2 activation require integrin? Perhaps integrin gets the process started and DDR-2 positively reinforces that (conversely is DDR-2 at the top of a linear pathway)? (2) In ddr-2(qy64) mutants, projections seem to form from the central portion of the utse cell. Does this reveal a second function for DDR-2, regulating perhaps the cytoskeleton? And (3) can you use the forward genetic tools available in C. elegans to find new genes connecting DDR-2 and integrin? The authors discuss these ideas in their response to the reviews, and I look forward to hearing about their future work on these questions.

I do see two areas where the manuscript could be improved. First, the authors rely on imprecise genetic methods to reach their conclusions (i.e. systemic RNAi, or expression of dominant negative constructs.) I think their conclusion would be stronger if they used tissue specific degradation to block ddr-2 function specifically in the utse or seam cells. Methods to do this are now regularly used in C. elegans and the authors have already developed the necessary tissue-specific promoters. Second, the manuscript is presented in the introduction as a study on formation and function of BM-BM linkage. However, their results actually demonstrate a mechanism by which cells adhere to BM. Since ddr-2 appears to function equally in both utse + seam cells (based on their dominant negative data), there are likely three layers of adhesion (utse-BM, BM-BM, BM-seam) and if any of those break down, you get a partially penetrant rupture phenotype. I pointed this out in my initial review, and after reading the revised manuscript, I do still feel the authors' introduction presents the paper as dealing with how basement membranes link together. But, I wonder if this might this be a question of terminology/language use? Maybe I am operating on a strict definition of linkage, and the authors use it more inclusively. What term(s) should we use to differentiate two basement membranes that are linked together, versus tissues that are connected through a basement membrane linkage? This is something that could be clarified in future publications.

These concerns do not undercut the significance of this work, which identifies an interesting mechanism cells use to strengthen adhesion during BM linkage formation. In fact, I am excited to read future papers detailing the connection between DDR-2 and integrin. But before undertaking those experiments the authors should be certain which cells require DDR-2 activity, and that should not be determined based solely on mis expression of a dominant negative.

Reviewer #2 (Public Review):

This paper explores the mechanisms by which cells in tissues use the extracellular matrix (ECM) to reinforce and establish connections. This is a mechanistic and quantitative paper that uses imaging and genetics to establish that the Type IV collagen, DDR-2/collagen receptor discoidin domain receptor 2, signaling through Ras to strengthen an adhesion between two cell types in C. elegans. This connection needs to be strong and robust to withstand the pressure of the numerous eggs that pass through the uterus. The major strengths of this paper are in crisply designed and clear genetic experiments, beautiful imaging, and well supported conclusions. I find very few weaknesses, although, perhaps the evidence that DDR-2 promotes utse-seam linkage through regulation of MMPs could be stronger. This work is impactful because it shows how cells in vivo make and strengthen a connection between tissues through ECM interactions involving collaboration between discoidin and integrin.

Reviewer #1 (Public Review):

The authors examine signaling factors that differentiate parallel routes to activating phosphoinositide 3-kinase gamma (PI3Kγ). Dissecting the convergent pathways that control PI3Kγ activity is critical because PI3Kγ is a therapeutic target for treating inflammatory disease and cancer. Here, the authors employ a multipronged approach to reveal new aspects for how p84 and p101 pair with p110γ to activate the PI3Kγ heterodimer. The key instigator to this study is a previously reported inhibitory Nanobody, NB7. The hypothesized mechanism for NB7 allosteric inhibition of p84- p110γ was previously proposed to involve blockage of the Ras-binding domain. The authors revise the allosteric inhibition model based on meticulous profiling of various PI3Kγ complex interactions with NB7. In parallel, a cryo-EM-derived model of NB7 bound to the p110γ subunit convincingly reveals a Nanobody interaction pocket involving the helical domain and regulatory motifs of the kinase domain. This revelation shifts the focus to the helical domain, a known target of PKC phosphorylation. While the connections between NB7 interactions and the effects of PKC phosphorylation are sometimes tenuous, it could be argued that the Nanobody served as a tool to reveal the importance of the helical domain to p110γ regulation.

The sites of PKC-mediated p110γ helical domain phosphorylation were unexpectedly inaccessible in the available structural models. Nevertheless, mass spectrometry (MS)-based phosphorylation profiling indicates that PKC can phosphorylate the helical domain of p110γ and p84/p110γ (but not p101/p110γ) in vitro. The authors hypothesize that helical domain dynamics dictate susceptibility to PKC phosphorylation. To explore this notion, carefully executed, rigorous H/D exchange MS (HDX-MS) experiments were performed comparing phosphorylated vs. unphosphorylated p110γ. Notably, this design reveals more about the consequences of p110γ phosphorylation, rather than the mechanisms of p84/p101 promoting/resisting phosphorylation. Nevertheless, HDX-MS is very well suited to exploring secondary structure dynamics, and helical domain phosphorylation strikingly increases dynamics consistent with increased regional accessibility. The increased dynamics also nicely map to the pocket enveloped by the inhibitory NB7 Nanobody.

Ultimately, this study reveals an unexpected p110γ pocket that allows an engineered Nanobody to allosterically inhibit PI3Kγ complexes. The cryo-EM characterization of the interaction inspired an HDX-MS investigation of known sites of phosphorylation in the region. These insights could be linked to differences/convergences of p84 and p101 complex formation and activation of PI3Kγ, and future work may clarify these mechanisms further. The data presented herein will also be useful for broadening the target surface for future therapeutic developments. New allosteric connections between effector binding sites and post-translational modifications are always welcome.

Reviewer #2 (Public Review):