Reviewer #3 (Public Review):

Summary:<br /> Birman and colleagues have introduced an invaluable tool designed specifically for electrophysiologists, simplifying the precise planning of trajectories for placing high-density probes within designated locations. Pinpoint offers users an interactive 3D environment within which they can explore electrophysiological trajectories within the anatomical context of the mouse brain. Within this environment, users can visualize the probe, target regions, and the constraints imposed by their experimental setup. Advanced users also have the flexibility to customize the entire Pinpoint scene to align with alternative coordinate systems and rig geometries. In cases involving multiple-probe recordings, Pinpoint shows 3D paths while issuing warnings about potential collisions. Additionally, Pinpoint can account for the individual variability in brain size among mice.

Strengths:<br /> Pinpoint provides real-time visualization of current brain region targets alongside neural data. Anatomical targeting information is accessible live during recordings. This is made possible through two sets of features: hardware that allows Pinpoint to communicate with micro-manipulators and software that broadcasts the current location of each recording channel to data acquisition software. Researchers can monitor the precise positioning of their probe during insertion and observe the anatomical locations of live electrophysiology data throughout an experiment, enabling them to make corrections if necessary.

Weaknesses:<br /> 1. Pinpoint's novelty lies in its ability to be linked to data acquisition programs and electronic micro-manipulators. However, a similar program, Neuropixels Trajectory Explorer, was released before Pinpoint with comparable features. Please refer to https://github.com/petersaj/neuropixels_trajectory_explorer. It would be beneficial to clarify the distinctions between these two applications and discuss on the necessity and advantages of creating Pinpoint.

2. Currently, in Pinpoint, users can only select one area of the mouse brain for probe placement and then use the controller to adjust the probe´s position if they wish to target multiple brain areas. This can complicate planning when inserting multiple probes. It would be advantageous to have the option to choose the specific areas the probes are to traverse, with Pinpoint automatically suggesting the most optimal trajectories while avoiding potential collisions. While this may require additional development, a comment on this possibility would be appreciated.

Reviewer #3 (Public Review):

Summary:<br /> Harada and colleagues describe an interesting set of experiments characterizing the relationship between dopamine cell activity in the ventral tegmental area (VTA) and orexin neuron activity in the lateral hypothalamus (LH). All experiments are conducted in the context of an opto-Pavlovian learning task, in which a cue predicts optogenetic stimulation of VTA dopamine neurons. With training, cues that predict DA stimulation come to elicit dopamine release in LH (a similar effect is seen in accumbens). After training, omission trials (cue followed by no laser) result in a dip (inhibition) of dopamine release in LH, characteristic of reward prediction error observed in the striatum. Across cue training, the activity pattern of orexin neurons in LH mirrors that of LH DA levels. However, unlike the DA signal, orexin neurons do not exhibit a decrease in activity in omission trials. Systemic blockade of D2 but not D1 receptors blocked DA release in LH following VTA DA cell stimulation.

Strengths:<br /> Although much work has been dedicated to examining projections from orexin cells to VTA, less has been done to characterize reciprocal projections and their function. In this way, this paper is a very important addition to the literature. The experiments are technically sound (with some limitations, below) and utilize sophisticated approaches, the manuscript is nicely written, and the conclusions are mostly reasonable based on the data collected.

Weaknesses:<br /> I believe the impact of the paper could be enhanced by considering and/or addressing the following:

Major:<br /> • I encourage the authors to discuss in the Introduction previous work on DA regulation of orexin neurons. In particular, the authors cite, but do not describe in any detail, the very relevant Linehan paper (2019; Am J Physiol Regul) which shows that DA differentially alters excitatory/inhibitory input onto orexin neurons and that these actions are reversed by D1 vs D2 receptor antagonists. Another paper (Bubser, 2005, EJN) showed that dopamine agonists increase the activity of orexin neurons and that these effects are blocked by D1/D2 antagonists. The current findings should be discussed in the context of these (and any other relevant) papers in the Discussion, too.<br /> • In the Discussion, the authors provide two (plausible) explanations for why they did not observe a dip in the calcium signal of orexin neurons during omission trials. Is it not possible that these cells do not encode for this type of RPE?<br /> • Related to the above - I am curious about the authors' thoughts on why there is such redundancy in the system. i.e. why is dopamine doing the same thing in NAC and LH in the context of cue-reward learning?<br /> • The data, as they stand, are largely correlative and do not indicate that DA recruitment of orexin neurons is necessary for learning to occur. It would be compelling if blocking the orexin cell recruitment affected some behavioral outcomes of learning. Similarly - does raclopride treatment across training prevent learning?<br /> • Only single doses of SCH23390 and raclopride were used. How were these selected? It would be nice to use more of a dose range to show that 1) and effect of D1R blockade was not missed, and 2) that the reduction in orexin signal with raclopride was dose-dependent.<br /> • Fig 1C, could the effect the authors observed be due to movement? Relatedly, what was the behavior like when the cue was on? Did mice orient/approach the cue? Also, when does the learning about the cue occur? Does it take all 10 days of learning or does this learning/cue-induced increase in dopamine signaling occur in less than 10 days?<br /> • Also related to the above, could the observed dopamine signal be a result of just the laser turning on? It would seem important to include mice with a control sensor.<br /> • Fig 1E, the effect seems to be driven by one mouse which looks like it could be a statistical outlier. The inclusion of additional animals would make these data more compelling.<br /> • For Fig 1C, 3D, 3F, and 4D, could the authors please show the traces for the entire length of laser onset? It would be helpful to see both the rise and the fall of dopamine signals.<br /> • Fig 2C, could the authors comment on how they compared the AUC to baseline? Was this comparison against zero? Because of natural hills and troughs during signals prior to cue (which may not equate to a zero), comparing the omission-induced dip to a zero may not be appropriate. A better baseline might be using the signals prior to the cue.<br /> • Could the authors comment on how they came up with the 4-5.3s window to observe the AUC in Fig 3H?

Reviewer #3 (Public Review):

Summary:<br /> The authors conducted a human fMRI study investigating the omission of expected electrical shocks with varying probabilities. Participants were informed of the probability of shock and shock intensity trial-by-trial. The time point corresponding to the absence of the expected shock (with varying probability) was framed as a prediction error producing the cognitive state of relief/pleasure for the participant. fMRI activity in the VTA/SN and ventral putamen corresponded to the surprising omission of a high probability shock. Participants' subjective relief at having not been shocked correlated with activity in brain regions typically associated with reward-prediction errors. The overall conclusion of the manuscript was that the absence of an expected aversive outcome in human fMRI looks like a reward-prediction error seen in other studies that use positive outcomes.

Strengths:<br /> Overall, I found this to be a well-written human neuroimaging study investigating an often overlooked question on the role of aversive prediction errors, and how they may differ from reward-related prediction errors. The paper is well-written and the fMRI methods seem mostly rigorous and solid.

Weaknesses:<br /> I did have some confusion over the use of the term "prediction-error" however as it is being used in this task. There is certainly an expectancy violation when participants are told there is a high probability of shock, and it doesn't occur. Yet, there is no relevant learning or updating, and participants are explicitly told that each trial is independent and the outcome (or lack thereof) does not affect the chances of getting the shock on another trial with the same instructed outcome probability. Prediction errors are primarily used in the context of a learning model (reinforcement learning, etc.), but without a need to learn, the utility of that signal is unclear.

An overarching question posed by the researchers is whether relief from not receiving a shock is a reward. They take as neural evidence activity in regions usually associated with reward prediction errors, like the VTA/SN. This seems to be a strong case of reverse inference. The evidence may have been stronger had the authors compared activity to a reward prediction error, for example using a similar task but with reward outcomes. As it stands, the neural evidence that the absence of shock is actually "pleasurable" is limited-albeit there is a subjective report asking subjects if they felt relief.

I have some other comments, and I elaborate on those above comments, below:

1. A major assumption in the paper is that the unexpected absence of danger constitutes a pleasurable event, as stated in the opening sentence of the abstract. This may sometimes be the case, but it is not universal across contexts or people. For instance, for pathological fears, any relief derived from exposure may be short-lived (the dog didn't bite me this time, but that doesn't mean it won't next time or that all dogs are safe). And even if the subjective feeling one gets is temporary relief at that moment when the expected aversive event is not delivered, I believe there is an overall conflation between the concepts of relief and pleasure throughout the manuscript. Overall, the manuscript seems to be framed on the assumption that "aversive expectations can transform neutral outcomes into pleasurable events," but this is situationally dependent and is not a common psychological construct as far as I am aware.

2. The authors allude to this limitation, but I think it is critical. Specifically, the study takes a rather simplistic approach to prediction errors. It treats the instructed probability as the subjects' expectancy level and treats the prediction error as omission related activity to this instructed probability. There is no modeling, and any dynamic parameters affected by learning are unaccounted for in this design. That is subjects are informed that each trial is independently determined and so there is no learning "the presence/absence of stimulations on previous trials could not predict the presence/absence of stimulation on future trials." Prediction errors are central to learning. It is unclear if the "relief" subjects feel on not getting a shock on a high-probability trial is in any way analogous to a prediction error, because there is no reason to update your representation on future trials if they are all truly independent. The construct validity of the design is in question.

3. Related to the above point, even if subjects veered away from learning by the instruction that each trial is independent, the fact remains that they do not get shocks outside of the 100% probability shock. So learning is occurring, at least for subjects who realize the probability cue is actually a ruse.

4. Bouton has described very well how the absence of expected threat during extinction can create a feeling of ambiguity and uncertainty regarding the signal value of the CS. This in large part explains the contextual dependence of extinction and the "return of fear" that is so prominent even in psychologically healthy participants. The relief people feel when not receiving an expected shock would seem to have little bearing on changing the long-term value of the CS. In any event, the authors do talk about conditioning (CS-US) in the paper, but this is not a typical conditioning study, as there is no learning.

5. In Figure 2 A-D, the omission responses are plotted on trials with varying levels of probability. However, it seems to be missing omission responses in 0% trials in these brain regions. As depicted, it is an incomplete view of activity across the different trial types of increasing threat probability.

6. If I understand Figure 2 panels E-H, these are plotting responses to the shock versus no-shock (when no-shock was expected). It is unclear why this would be especially informative, as it would just be showing activity associated with shocks versus no-shocks. If the goal was to use this as a way to compare positive and negative prediction errors, the shock would induce widespread activity that is not necessarily reflective of a prediction error. It is simply a response to a shock. Comparing activity to shocks delivered after varying levels of probability (e.g., a shock delivered at 25% expectancy, versus 75%, versus 100%) would seem to be a much better test of a prediction error signal than shock versus no-shock.

7. I was unclear what the results in Figure 3 E-H were showing that was unique from panels A-D, or where it was described. The images looked redundant from the images in A-D. I see that they come from different contrasts (non0% > 0%; 100% > 0%), but I was unclear why that was included.

8. As mentioned earlier, there is a tendency to imply that subjects felt relief because there was activity in "the reward pathway."

9. From the methods, it wasn't entirely clear where there is jitter in the course of a trial. This centers on the question of possible collinearity in the task design between the cue and the outcome. The authors note there is "no multicollinearity between anticipation and omission regressors in the first-level GLMs," but how was this quantified? The issue is of course that the activity coded as omission may be from the anticipation of the expected outcome.

10. I did not fully understand what the LASSO-PCR model using relief ratings added. This result was not discussed in much depth, and seems to show a host of clusters throughout the brain contributing positively or negatively to the model. Altogether, I would recommend highlighting what this analysis is uniquely contributing to the interpretation of the findings.

Reviewer #3 (Public Review):

Summary:<br /> In the 'bCFS' paradigm, a monocular target gradually increases in contrast until it breaks interocular suppression by a rich monocular suppressor in the other eye. The present authors extend the bCFS paradigm by allowing the target to reduce back down in contrast until it becomes suppressed again. The main variable of interest is the contrast difference between breaking suppression and (re) entering suppression. The authors find this difference to be constant across a range of target types, even ones that differ substantially in the contrast at which they break interocular suppression (the variable conventionally measured in bCFS). They also measure how the difference changes as a function of other manipulations. Interpretation in terms of the processing of unconscious visual content, as well as in terms of the mechanism of interocular suppression.

Strengths:<br /> Interpretation of bCFS findings is mired in controversy, and this is an ingenuous effort to move beyond the paradigm's exclusive focus on breaking suppression. The notion of using the contrast difference between breaking and entering suppression as an index of suppression depth is interesting, but I also feel like it can be misleading at times, as detailed below.

Weaknesses:<br /> Here's one doubt about the 'contrast difference' measure used by the authors. The authors seem confident that a simple subtraction is meaningful after the logarithmic transformation of contrast values, but doesn't this depend on exactly what shape the contrast-response function of the relevant neural process has? Does a logarithmic transformation linearize this function irrespective of, say, the level of processing or the aspect of processing that we're talking about? Given that stimuli differ in terms of the absolute levels at which they break (and re-enter) suppression, the linearity assumption needs to be well supported for the contrast difference measure to be comparable across stimuli.

Here's a more conceptual doubt. The authors introduce their work by discussing ambiguities in the interpretation of bCFS findings with regard to preferential processing, unconscious processing, etc. A large part of the manuscript doesn't really interpret the present 'suppression depth' findings in those terms, but at the start of the discussion section (lines 560-567) the authors do draw fairly strong conclusions along those lines: they seem to argue that the constant 'suppression depth' value observed across different stimuli argues against preferential processing of any of the stimuli, let alone under suppression. I'm not sure I understand this reasoning. Consider the scenario that the visual system does preferentially process, say, emotional face images, and that it does so under suppression as well as outside of suppression. In that scenario, one might expect the contrast at which such a face breaks suppression to be low (because the face is preferentially processed under suppression) and one might also expect the contrast at which the face enters suppression to be low (because the face is preferentially processed outside of suppression). So the difference between the two contrasts might not stand out: it might be the same as for a stimulus that is not preferentially processed at all. In sum, even though the author's label of 'suppression depth' on the contrast difference measure is reasonable from some perspectives, it also seems to be misleading when it comes to what the difference measure can actually tell us that bCFS cannot.

The authors acknowledge that non-zero reaction time inflates their 'suppression depth' measure, and acknowledge that this inflation is worse when contrast ramps more quickly. But they argue that these effects are too small to explain either the difference between breaking contrast and re-entering contrast to begin with, or the increase in this difference with the contrast ramping rate. I agree with the former: I have no doubt that stimuli break suppression (ramping up) at a higher contrast than the one at which they enter suppression (ramping down). But about the latter, I worry that the RT estimate of 200 ms may be on the low side. 200 ms may be reasonable for a prepared observer to give a speeded response to a clearly supra-threshold target, but that is not the type of task observers are performing here. One estimate of RT in a somewhat traditional perceptual bistability task is closer to 500 ms (Van Dam & Van Ee, Vis Res 45 2005), but I am uncertain what a good guess is here. Bottom line: can the effect of contrast ramping rate on 'suppression depth' be explained by RT if we use a longer but still reasonable estimated RT than 200 ms?

A second remark about the 'ramping rate' experiment: if we assume that perceptual switches occur with a certain non-zero probability per unit time (stochastically) at various contrasts along the ramp, then giving the percept more time to switch during the ramping process will lead to more switches happening at an earlier stage along the ramp. So: ramping contrast upward more slowly would lead to more switches at relatively low contrast, and ramping contrast downward more slowly would lead to more switches at relatively high contrasts. This assumption (that the probability of switching is non-zero at various contrasts along the ramp) seems entirely warranted. To what extent can that type of consideration explain the result of the 'ramping rate' experiment?

When tying the 'dampened harmonic oscillator' finding to dynamic systems, one potential concern is that the authors are seeing the dampened oscillating pattern when plotting a very specific thing: the amount of contrast change that happened between two consecutive perceptual switches, in a procedure where contrast change direction reversed after each switch. The pattern is not observed, for instance, in a plot of neural activity over time, threshold settings over time, etcetera. I find it hard to assess what the observation of this pattern when representing a rather unique aspect of the data in such a specific way, has to do with prior observations of such patterns in plots with completely different axes.

Reviewer #3 (Public Review):

This study aims to provide a generalizable definition of retinal amacrine cell function in visual processing. The authors used larval tiger salamander retinas and white noise stimulus to measure the retinal ganglion cell responses with multielectrode array recording, while either measuring individual amacrine cell membrane potential or stimulating the amacrine cell by injecting white noise currents using a sharp electrode. Modulatory effects of an amacrine cell on ganglion cells are analyzed by a computational framework that parses the signaling processing underlying ganglion cell responses into multiple conceptual pathways that are differentially subject to the amacrine cell signaling. The authors conclude that an individual amacrine cell can have diverse modulatory effects on ganglion cell responses. One class of effects modulates the sensitivity of the ganglion cell to specific visual features, while the other class of effects modulates the gain of responses to all features.

Amacrine cells are known for their remarkable cell type diversity and serve as key players underlying the complexity of computations performed by the vertebrate retina. However, their functions largely remain a mystery except for a few better-studied cell types. Therefore, the topic of this study is important. Furthermore, the study aims to extract general computational functions from these neurons, which will have broader applications to sensory processing beyond the retina. My main questions are centered around the interpretation of the computational analysis. First of all, the definition of a "visual feature" in this study using the white noise stimulus is different from that used in many other retinal studies using more structured stimuli than white noise. In this study, a major finding is that amacrine cells can control the sensitivity of specific visual features of the ganglion cell. However, it is difficult to gain intuition about how such feature specificity is related to the processing of other artificial and natural stimuli. More discussion along this line will help to clarify the significance of this result.

Another concern is the assumption that the somatic membrane potential of the amacrine cell represents its transmission property to ganglion cells. There are compelling examples that amacrine cells often exhibit local response properties that dramatically differ across the dendritic arbor and the soma (e.g. AIIs, Vlgut3+ ACs, starburst amacrine cells, A17s). This potential (and likely) complication should be addressed.

The dataset in this study is from 8 sustained and 3 transient amacrine cells. Immediate questions are: do all sustained or all transient cells belong to the same cell type in terms of functional properties or morphology? Is there any difference in the modulatory effects between the sustained and transient groups?

There is a rich body of literature on the functions of various amacrine cell types in the mammalian retina in shaping the receptive field properties, gain, and sensitivity of retinal ganglion cells. It would help the reader if the novelty of the current study is adequately discussed in the context of previous work.

Technical:<br /> One concern of sharp electrode recordings is the dialysis of intracellular solution into the cytoplasm, causing changes in membrane properties over time (e.g. Hooper et al., 2015). Have the authors examined the data obtained at the earlier and later phases of the recording to assess the potential effect of dialysis?

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, Salazar-Lázaro et al. presented interesting data that C-terminal half of the Syx1 SNARE domain is responsible for clamping of spontaneous release, stabilizing RRP, and also Ca2+-evoked release. The authors routinely utilized the chimeric approach to replace the SNARE domain of Syx1 with its paralogue Syx2 and analyzed the neuronal activity through electrophysiology. The data are straightforward and fruitful. The conclusions are partly reasonable.

Strengths:<br /> The electrophysiology data that illustrate the important functions of Syx1 in clamping of spontaneous release, stabilizing RRP, and Ca2+-evoked release were clear and convincing.

Weaknesses:<br /> One obvious weakness is that the authors did not explore the underlying mechanism. I think it is easy for the authors to carry out some simple assays to verify their hypothesis for the mechanism, instead of just talking about it in the discussion section.

Reviewer #3 (Public Review):

Summary:<br /> This is an interesting paper investigating fMRI changes during sensory (visual, tactile) stimulation and absence seizures in the GAERS model. The results are potentially important for the field and do suggest that sensory stimulation may not activate brain regions normally during absence seizures. However the findings are limited by substantial methodological issues that do not enable fMRI signals related to absence seizures to be fully disentangled from fMRI signals related to the sensory stimuli.

Strengths:<br /> Investigating fMRI brain responses to sensory stimuli during absence seizures in an animal model is a novel approach with the potential to yield important insights.

The use of an awake, habituated model is a valid and potentially powerful approach.

Weaknesses:<br /> The major difficulty with interpreting the results of this study is that the duration of the visual and auditory stimuli was 6 seconds, which is very close to the mean seizure duration per Table 1. Therefore the HRF model looking at fMRI responses to visual or auditory stimuli occurring during seizures was simultaneously weighting both seizure activity and the sensory (visual or auditory) stimuli over the same time intervals on average. The resulting maps and time courses claiming to show fMRI changes from visual or auditory stimulation during seizures will therefore in reality contain some mix of both sensory stimulation-related signals and seizure-related signals. The main claim that the sensory stimuli do not elicit the same activations during seizures as they do in the interictal period may still be true. However the attempts to localize these differences in space or time will be contaminated by the seizure-related signals.

The claims that differences were observed for example between visual cortex and superior colliculus signals with visual stim during seizures vs. interictal are unconvincing due to the above.

The maps shown in Figure 3 do not show clear changes in the areas claimed to be involved.

Reviewer #3 (Public Review):

Summary:<br /> The study uses structural MRI to identify how the number, degree of experience, and phonemic diversity of language(s) that a speaker knows can influence the thickness of different sub-segments of the auditory cortex. In both a primary and replication sample of adult speakers, the authors find key differences in cortical thickness within specific subregions of the cortex due to either the age at which languages are acquired (degree of experience), or the diversity of the phoneme inventories carried by that/those language(s) (breadth of experience).

Strengths:<br /> The results are first and foremost quite fascinating and I do think they make a compelling case for the different ways in which linguistic experience shapes the auditory cortex.

The study uses a number of different measures to quantify linguistic experience, related to how many languages a person knows (taking into account the age at which each was learned) as well as the diversity of the phoneme inventories contained within those languages. The primary sample is moderately large for a study that focuses on brain-behaviour relationships; a somewhat smaller replication sample is also deployed in order to test the generality of the effects.

Analytic approaches benefit from the careful use of brain segmentation techniques that nicely capture key landmarks and account for vagaries in the structure of STG that can vary across individuals (e.g., the number of transverse temporal gyri varies from 1-4 across individuals).

Weaknesses:<br /> The specificity of these effects is interesting; some effects really do appear to be localized to the left hemisphere and specific subregions of the auditory cortex e.g., TTG. However because analyses only focus on auditory regions along the STG and MTG, one could be led to the conclusion that these are the only brain regions for which such effects will occur. The hypothesis is that these are specifically auditory effects, but that does make a clear prediction that non-auditory regions should not show the same sort of variability. I recognize that expanding the search space will inflate type-1 errors to a point where maybe it's impossible to know what effects are genuine. And the fine-grained nature of the effects suggests a coarse analysis of other cortical regions is likely to fail. So I don't know the right answer here. Only that I tend to wonder if some control region(s) might have been useful for understanding whether such effects truly are limited to the auditory cortex. Otherwise one might argue these are epiphenomenal or some hidden factor unrelated to auditory experience predicting that we'd also see them in the non-auditory cortex as well, either within or outside the brain's speech network(s).

The reason(s) why we might find a link between cortical thickness and experience is not fully discussed. The introduction doesn't really mention why we'd expect cortical thickness to be correlated (positively or negatively) with speech experience. There is some discussion of it in the Discussion section as it relates to the Pliatsikas' Dynamic Restructuring Model, though I think that model only directly predicts thinning as a function of experience (here, negative correlations). It might have less to say about observed positive correlations e.g., HG in the right hemisphere. In any case, I do think that it's interesting to find some relationship between brain morphology and experience but clearer explanations for why these occur could help, and especially some mention of it in the intro so readers are clearer on why cortical thickness is a useful measure.

One pitfall of quantifying phoneme overlap across languages is that what we might call a single 'phoneme', shared across languages, will, in reality, be realized differently across them. For instance, English and French may be argued to both use the vowel /u/ although it's realized differently in English vs. French (it's often fronted and diphthongized in many English speaker groups). Maybe the phonetic dictionaries used in this study capture this using a close phonetic transcription, but it's hard to tell; I suspect they don't, and in that case, the diversity measures would be an underestimate of the actual number of unique phonemes that a listener needs to maintain.

Discussion of potential genetic differences underlying the findings is interesting. One additional data point here is a study finding a relationship between the number of repeats of the READ1 (a factor of the DCDC2 gene) in populations of speakers, and the phoneme inventory of language(s) predominant in that population (DeMille, M. M., Tang, K., Mehta, C. M., Geissler, C., Malins, J. G., Powers, N. R., ... & Gruen, J. R. (2018). Worldwide distribution of the DCDC2 READ1 regulatory element and its relationship with phoneme variation across languages. Proceedings of the National Academy of Sciences, 115(19), 4951-4956.) Admittedly, that paper makes no claim about the cortical expression of that regulatory factor under study, and so more work needs to be done on whether this has any bearing at all on the auditory cortex. But it does represent one alternative account that does not have to do with plasticity/experience.

The replication sample is useful and a great idea. It does however feature roughly half the number of participants meaning statistical power is weaker. Using information from the first sample, the authors might wish to do a post-hoc power analysis that shows the minimum sample size needed to replicate their effect; given small effects in some cases, we might not be surprised that the replication was only partial. I don't think this is a deal breaker as much as it's a way to better understand whether the failure to replicate is an issue of power versus fragile effects.

Reviewer #3 (Public Review):

Summary:<br /> The authors generate and characterize two phosphospecific antisera for FFA2 receptor and claim a "bar code" difference between white fat and Peyers patches.

Strengths:<br /> The question is interesting and the antibody characterization is convincing.

Weaknesses:<br /> The mass spectrometry analysis is not convincing because the method is not quantitative (no SILAC, TMT, internal standards etc). Figure 1 shows single tryptic peptides with one and two phosphorylation fragmentations as claimed, but there is no data testing the abundance of these so the differences claimed between cell treatment conditions are not established.

The blot analysis cannot distinguish 296/7 but it does convincingly show an agonist increase. Can the authors clarify why the amount of constitutive phosphorylation is much higher in the example blot in Figure 2 than in Figure 3? It would be helpful to quantify this across more than one example, like in Figures 4 and 5 for tissue.

Compound 101 is shown in Figure 2 to block barrestin recruitment. I agree this suggests phosphorylation mediated by GRK2/3 but this is not tested. The new antibodies should be good for this so I don't understand why the indirect approach.

The conditions used to inhibit dephosphorylation are not specified, the method only says "phosphatase inhibitors". How do the authors know that low P at 306/7 in white fat is not a result of dephosphorylation during sample preparation? If these sites are GRK2/3 dependent (see above) then does adipose tissue lack this GRK?

Reviewer #3 (Public Review):

Summary:

Despite being preventable and treatable, cervical cancer remains the second most common cause of cancer death globally. This cancer, and associated deaths, occur overwhelmingly in low- and middle-income countries (LMIC), reflecting a lack of access to vaccination, screening and treatment services. Cervical screening is the second pillar in the WHO strategy to eliminate cervical cancer as a public health problem and will be critical in delivering early gains in cervical cancer prevention as the impact of vaccination will not be realized for several decades. However, screening strategies implemented in high income countries are not feasible or affordable in LMICs. This ambitious multi-center study aims to address these issues by developing and systematically evaluating a novel approach to cervical screening. The approach, based on primary screening with self-collected specimens for HPV testing, is focused on optimizing triage of people in whom HPV is detected, so that sensitivity for the detection of pre-cancer and cancer is maximized while treatment of people without pre-cancer or cancer is minimized.

Strengths:

The triage proposed for this study builds on the authors' previously published work in designing the ScreenFire test to appropriately group the 13 detected genotypes into four channels and to develop automated visual evaluation (AVE) of images of the cervix, taken by health workers.

The move from mobile telephone devices to a dedicated device to acquire and evaluate images overcomes challenges previously encountered whereby updates of mobile phone models required retraining of the AVE algorithm.

The separation of the study into two phases, an efficacy phase in which screen positive people will be triaged and treated according to local standard of care and the performance of AVE will be evaluated against biopsy outcomes will be followed by the second phase in which the effectiveness, cost-effectiveness, feasibility and acceptability will be evaluated.

The setting in a range of low resource settings which are geographically well spread and reflective of where the global cancer burden is highest.

Reviewer #3 (Public Review):

This work contributes to the literature characterizing early and late waves of transcription and associated chromatin remodeling following neuronal depolarization, here in cultured embryonic striatum. While they find IEG transcription 1 h after depolarization, they find chromatin remodeling is slower (opening at the 4 h time point). While this is not the first paper to describe chromatin changes in response to neuronal activity, this paper ties previous findings all together in one place using novel sequencing analyses and visualizations. Previous work has found remodeling occurring at the 1 h time point, so the lack of differences at that early time point in the current study needs to be better understood and the "temporal decoupling" described by authors should be further explored. Differences may be due to chromatin at IEG regulatory regions already being open in embryonic tissue (here) vs generally more closed in adult tissue (previous), or due to previous studies using protocols to specifically silence neurons prior to activation. The authors next show that the chromatin remodeling that occurs at the late (4 h) stage is largely in putative regulatory regions of the genome (rather than gene bodies), and is dependent on translation, which validates and extends the prior literature. The authors then transition from genome-wide basic neuroscience to focus on a specific gene of interest, prodynorphin (Pdyn), and a putative enhancer they identify from their chromatin analysis. They target CRISPR-activating and -inhibiting complexes to the putative enhancer and demonstrate that accessibility of this locus is necessary and sufficient for Pdyn transcription. They then show that at least one PDYN enhancer is conserved from rodents to humans, and is only activity-regulated in human GABAergic but not glutamatergic neurons. Finally, the authors generate snATAC-seq and show Pdyn gene and enhancer activity is also cell-type-specific in rat striatum. The Pdyn work, in particular, is thorough and novel, and demonstrates a translational aspect of this work.

Reviewer #3 (Public Review):

Summary:<br /> In this work, the authors co-opt the RRM-binding protein Musashi-1 to act as a translational repressor. The novelty of the work is in the adoption of the allosteric RRM protein Musashi-1 into a translational reporter and the demonstration that RRM proteins, which are ubiquitous in eukaryotic systems, but rare in prokaryotic ones, may act effectively as post-translational regulators in E. coli. The extent of repression achieved by the best design presented in this work is not substantially improved compared to other synthetic regulatory schemes developed for E. coli, even those that similarly regulate translation (eg. native PP7 repression is approximately 10-fold, Lim et al. J. Biol. Chem. 2001 276:22507-22513). Furthermore, the mechanism of regulation is not established due to missing key experiments. The work would be of broader interest if the allosteric properties of Musashi-1 were more effective in the context of regulation. Unfortunately, the authors do not demonstrate that fatty acids can completely de-repress expression in the experimental system used for most of their assays, nor do they use this ability in their provided application (NIMPLY gate).

Strengths:<br /> The first major achievement of this work is the demonstration that a eukaryotic RRM protein may be used to post-transcriptionally regulate expression in bacteria. In my limited literature search, this appears to be the first engineering attempt to design an RBP to directly regulate translation in E. coli, although engineered control of translation via other approaches including alterations to RNA structure or via trans-acting sRNAs have been previously described (for review see Vigar and Wieden Biochim Biophys. Acta Gen. Subj. 2017, 1861:3060-3069). Additionally, several viral systems (e.g. MS2 and PP7) have been directly co-opted to work in a similar fashion in the past (utilized recently in Nguyen et al. ACS Synthetic Biol 2022, 11:1710-1718).

The second achievement of this work is the demonstration that the allosteric regulation of Musashi-1 binding can be utilized to modulate the regulatory activity. However, the liquid culture demonstration (Suppl. Fig 8) shows that this is not a very effective switch, with de-repressed reporter activity showing substantial change but not approaching un-repressed activity. This effect is stronger when colonies are grown on a solid medium (Fig. 5).

Weaknesses:<br /> In this work, the authors codon optimize the mouse Musashi-1 coding sequence for expression in E. coli and demonstrate using an sfGFP reporter that an engineered Musashi-1 binding site near the translational start site is sufficient to enable a modest reduction in reporter gene expression. The authors postulate that the reduction in expression due to inhibition of ribosome translocation along the transcript (lines 134/135), as an expression of a control transcript (mScarlet) driven by the same promoter (Plac) but without the Musashi-1 recognition site does not demonstrate the same repression. However, the situation could be more complex. Other possibilities include inhibition of translation initiation rather than elongation, as well as accelerated mRNA decay of transcripts that are not actively translated. The authors do not present any measurements of sfGFP mRNA levels.

In subsequent sections of the work, the authors create a series of point mutations to assess RNA-protein binding and assess these via both a sfGFP reporter and in vitro binding assays (switchSENSE). Ultimately, it is difficult to fully rationalize and interpret the behavior of these mutants in the context provided. The authors do identify a relationship between equilibrium constant (1/KD) and fold-repression. However, it is not clear from the narrative why this relationship should exist. Fold-repression is one measure of regulator efficacy, but it is an indirect measure determined from unrepressed and repressed expression. It is not clear why unrepressed expression (in the absence of the protein) is expected to be a function of the equilibrium constant.

Subsequent rational redesign of the Musashi-1 binding sequence to produce three alternative designs shows that fold-repression may be improved to approximately 8.6-fold. However, the rationalization of why the best design (red3) achieves this increase based on either the extensive modelling or in vitro measured binding constants is not well articulated. Furthermore, this extent of regulation is approximately that which can be achieved from the PP7 system with its native components (Lim et al. J. Biol. Chem. 2001 276:22507-22513).

The application provided for this regulator (NIMPLY gate), is not an inherently novel regulatory paradigm, and it does not capitalize on the allosteric properties of Musashi-1, but rather treats Musashi-1 as a non-allosteric component of a regulatory circuit.

Reviewer #3 (Public Review):

Summary:<br /> In the face of emerging antibiotic resistance and slow pace of drug discovery, strategies that can enhance the efficacy of existing clinically used antibiotics are highly sought after. In this manuscript, through genetic manipulation of a model bacterium (Escherichia coli) and clinically isolated and antibiotic resistant strains of concern (Pseudomonas, Burkholderia, Stenotrophomonas), an additional drug target to combat resistance and potentiate existing drugs is put forward. These observations were validated in both pure cultures, mixed bacterial cultures and in worm models. The drug target investigated in this study appears to be broadly relevant to the challenge posed by lactamases enzyme that render lactam antibiotics ineffective in the clinic. The compounds that target this enzyme are being developed already, some of which were tested in this study displaying promising results and potential for further optimization by medicinal chemists.

Strengths:<br /> The work is well designed and well executed and targets an urgent area of research with the unprecedented increase in antibiotic resistance.

Weaknesses:<br /> The impact of the work can be strengthened by demonstrating increased efficacy of antibiotics in mice models or wound models for Pseudomonas infections. Worm models are relevant, but still distant from investigations in animal models.

Reviewer #3 (Public Review):

Summary: The paper aims to investigate the relationship between anti-S protein antibody titers with the phenotypes&clonotypes of S-protein-specific T cells, in people who receive SARS-CoV2 mRNA vaccines. To do this, the paper recruited a cohort of Covid-19 naive individuals who received the SARS-CoV2 mRNA vaccines and collected sera and PBMCs samples at different timepoints. Then they mainly generate three sets of data: 1). Anti-S protein antibody titers on all timepoints. 2) Single-cell RNAseq/TCRseq dataset for divided T cells after stimulation by S-protein for 10 days. 3) Corresponding epitopes for each expanded TCR clones. After analyzing these results, the paper reports two major findings & claims: A) Individuals having sustained anti-S protein antibody response also have more so-called Tfh cells in their single-cell dataset, which suggests Tfh-polarization of S-specific T cells can be a marker to predict the longevity of anti-S antibody. B). S-reactive T cells do exist before the vaccination, but they seem to be unable to respond to Covid-19 vaccination properly.

The paper's strength is it uses a very systemic and thorough strategy trying to dissect the relationship between antibody titers, T cell phenotypes, TCR clonotypes and corresponding epitopes, and indeed it reports several interesting findings about the relationship of Tfh/sustained antibody and about the S-reactive clones that exist before the vaccination. However, the main weakness is these interesting claims are not sufficiently supported by the evidence presented in this paper. I have the following major concerns:

1) The biggest claim of the paper, which is the acquisition of S-specific Tfh clonotypes is associated with the longevity of anti-S antibodies, should be based on proper statistical analysis rather than just a UMAP as in Fig2 C, E, F. The paper only shows the pooled result, but it looks like most of the so-called Tfh cells come from a single donor #27. If separating each of the 4 decliners and sustainers and presenting their Tfh% in total CD4+ T cells respectively, will it statistically have a significant difference between those decliners and sustainers? I want to emphasize that solid scientific conclusions need to be drawn based on proper sample size and statistical analysis.

2) The paper does not provide any information to justify its cell annotation as presented in Fig 2B, 4A. Moreover, in my opinion, it is strange to see that there are two clusters of cells sit on both the left and right side of UMAP in Fig2B but both are annotated as CD4 Tcm and Tem. Also Tfh and Treg belong to a same cluster in Fig 2B but they should have very distinct transcriptomes and should be separated nicely. Therefore I believe the paper can be more convincing if it can present more information and discussion about the basis for its cell annotation.

3) Line 103-104, the paper claims that the Tfh cluster likely comes from cTfh cells. However considering the cells have been cultured/stimulated for 10 days, cTfh cells might lose all Tfh features after such culture. To my best knowledge there is no literature to support the notion that cTfh cells after stimulated in vitro for 10 days (also in the presence of IL2, IL7 and IL15), can still retain a Tfh phenotype after 10 days. It is possible that what actually happens is, instead of having more S-specific cTfh cells before the cell culture, the sustainers' PBMC can create an environment that favors the Tfh cell differentiation (such as express more pro-Tfh cytokines/co-stimulations). Thus after 10-days culture, there are more Tfh-like cells detected in the sustainers. The paper may need to include more evidence to support cTfh cells can retain Tfh features after 10-days' culture.

4) It is in my opinion inaccurate to use cell number in Fig4B to determine whether such clone expands or not, given that the cell number can be affected by many factors like the input number, the stimulation quality and the PBMC sample quality. A more proper analysis should be considered by calculating the relative abundance of each TCR clone in total CD4 T cells in each timepoint.

5) It is well-appreciated to express each TCR in cell line and to determine the epitopes. However, the author needs to make very sure that this analysis is performed correctly because a large body of conclusions of the paper are based on such epitope analysis. However, I notice something strange (maybe I am wrong) but for example, Table 4 donor #8 clonotype post_6 and _7, these two clonotypes have exactly the same TRAV5 and TRAJ5 usage. Because alpha chain don't have a D region, in theory these clonotypes, if have the same VJ usage, they should have the same alpha chain CDR3 sequences, however, in the table they have very different CDR3α aa sequences. I wish the author could double check their analysis and I apologize in advance if I raise such questions based on wrong knowledge.

Reviewer #3 (Public Review):

Summary:<br /> The authors aim to solve how landscape context impacts the community BEF relationship. They found habitat loss and fragmentation per se have inconsistent effects on biodiversity and ecosystem function. Habitat loss rather than fragmentation per se can weaken the positive BEF relationship by decreasing the degree of habitat specialization of the community.

Strengths:<br /> The authors provide a good background, and they have a good grasp of habitat fragmentation and BEF literature. A major strength of this study is separating the impacts of habitat loss and fragmentation per se using the convincing design selection of landscapes with different combinations of habitat amount and fragmentation per se. Another strength is considering the role of specialists and generalists in shaping the BEF relationship.

Weaknesses:<br /> 1. The authors used five fragmentation metrics in their study. However, the choice of these fragmentation metrics was not well justified. The ecological significance of each fragmentation metric needs to be differentiated clearly. Also, these fragmentation metrics may be highly correlated with each other and redundant. I suggest author test the collinearity of these fragmentation metrics for influencing biodiversity and ecosystem function.<br /> 2. I found the local environmental factors were not considered in the study. As the author mentioned in the manuscript, temperature and water also have important impacts on biodiversity and ecosystem function in the natural ecosystem. I suggest authors include the environmental factors in the data analysis to control their potential impact, especially the structural equation model.

Reviewer #3 (Public Review):

This study by Guan and co-workers focuses on a model neuronal lineage in the developing Drosophila nervous system, revealing interesting aspects about: a) the generation of supernumerary cells, later destined for apoptosis; and, b) new insights into the mechanisms that regulate this process. The two RNA-binding proteins, Imp and Syp, are shown to be expressed in temporally largely complementary patterns, their expression defining early vs later born neurons in this lineage, and thus also regulating the apoptotic elimination. Moreover, neuronal 'fate' transcription factors that are downstream of Imp and signatures of early-born neurons, can also be sufficient to convert later born cells to an earlier 'fate', including survival.

The authors provide solid evidence for most of their statements, including the temporal windows during which the early and the later-born motoneurons are generated by this model lineage, how this relates to patterns of cell death by apoptosis and that mis-expression of early-born transcription factors in later-born cells can be sufficient to block apoptosis (part of, and perhaps indicative of the late-born identity).

Other studies have previously outlined analogous, mutually antagonistic roles for Imp and Syp during nervous system development in Drosophila, in different parts and at different stages, with which the working model of this study aligns.

Overall, this study adds to and extends current working models and evidence on the developmental mechanisms that underlie temporal cell fate decisions.

Reviewer #3 (Public Review):

Yuan et al., set out to examine the role of functional and structural interaction between Slack and NaVs on the Slack sensitivity to quinidine. Through pharmacological and genetic means they identify NaV1.6 as the privileged NaV isoform in sensitizing Slack to quinidine. Through biochemical assays, they then determine that the C-terminus of Slack physically interacts with the N- and C-termini of NaV1.6. Using the information gleaned from the in vitro experiments the authors then show that virally-mediated transduction of Slack's C-terminus lessens the extent of SlackG269S-induced seizures. These data uncover a previously unrecognized interaction between a sodium and a potassium channel, which contributes to the latter's sensitivity to quinidine.

Reviewer #3 (Public Review):

In this manuscript, authors use the Drosophila wing as a model system and combine state-of-the-art genetic engineering to identify and validate the molecular players mediating the activity of one of the cis-regulatory enhancers of the apterous gene involved in the regulation of its expression domain in the dorsal compartment of the wing primordium during larval development.

(1) The authors raise two very important questions in the Introduction: (1) who is locating the relative position of the AP and DV boundaries in the developing wing, and (2) who is responsible for the maintenance of the apterous expression domain late in larval development. None of these two questions have been responded to and, indeed, the summary of the work (as stated in the conclusions of the last paragraph of the Introduction) does not resolve any of these questions.

(2) The authors have identified two different regions whose deletions give very interesting phenotypes in the adult wing (AP identify change & outgrowths, and loss of wing), and have bioinformatically identified and functionally verified 4 TFs that mediate the activity of these regions by their capacity to phenocopy the wing phenotype. While identification of the 2 TFs acting on the m1 is incremental with respect to previous work on the identification of the enhancer responsible for the early expression of Ap, identification of Antp and Grn does not explain the loss of function phenotype of the m3 enhancer. Does any of these results shed any light on the first two Qs? Do these results explain the compartment boundary position in the wing as stated in the title? Expression of lacZ reporter assays is fundamental to demonstrate their model of Figure 8. The reduction of the PD compartment is difficult to understand by the sole reduction in ap expression in this region (which has not been demonstrated).

(3) The authors state in one of the sections "Spatio-temporal analysis of apE via dCas9 ". No temporal manipulation of gene activity is shown. The authors should combine GAL4/UAs with the Gal80ts to demonstrate the temporal requirements of Antp/Grn and Pnt/Hth as depicted in their model of Figure 8.

(4) The authors have not managed to explain the AP phenotype. Thus, this work opens many unresolved questions and does not resolve the title, which is a big overstatement. Thus, strengths (technically excellent), weakness (there is not much to learn about wing development and apterous regulation from these results besides the incremental identification of 4 additional TFs mediating the regulation of ap expression by their ability to phenocopy regulatory mutations of the apterous gene).

Reviewer #3 (Public Review):

Summary: In this manuscript, Koh, Stratiievska, and their colleagues investigate the mechanism by which TRPV1 channels are delivered to the plasma membrane following the activation of receptor tyrosine kinases, specifically focusing on the NGF receptor. They demonstrate that the activation of the NGF receptor's PI3K pathway alone is sufficient to increase the levels of TRPV1 at the plasma membrane.

Strengths: The authors employ cutting-edge optogenetic, imaging, and chemical-biology techniques to achieve their research goals. They ingeniously use optogenetics to selectively activate the PI3K pathway without affecting other NGF pathways. Additionally, they develop a novel, membrane-impermeable fluorescent probe for labeling cell-surface proteins through click-chemistry.

Weaknesses: Previous research, including work by the authors themselves, has already established that PI3K activation is required for NGF-induced TRPV1 trafficking to the plasma membrane. Moreover, the paper suffers from issues such as subpar writing quality, a lack of statistical analysis, and insufficient control experiments, which dampen the reviewer's enthusiasm.

Reviewer #3 (Public Review):

Summary:<br /> The manuscript by Flaherty III S.E. et al identified SPAG7 gene in their forward mutagenetic screening and created the germline knockout and inducible knockout mice. The authors reported that the SPAG7 germline knockout mice had lower birth weight likely due to intrauterine growth restriction and placental insufficiency. The SPAG7 KO mice later developed obesity phenotype as a result of reduced energy expenditure. However, the inducible SPAG7 knockout mice had normal body weight and composition.

Strengths:<br /> In this reviewer's opinion, this study has high significance in the field of metabolic research for the following reasons.<br /> (1) The authors' findings are significant in the field of obesity research, especially from the perspective of maternal-fetal medicine. The authors created and analyzed the SPAG7 KO mice and found that the KO mice had a "thrifty phenotype" and developed obesity.<br /> (2) SPAG7 gene function hasn't been thoroughly studied. The reported phenotype will fill the gap of knowledge.<br /> Overall, the authors have presented their results in a clear and logically organized structure, clearly stated the key question to be addressed, used the appropriate methodology, produced significant and innovative main findings.

Weaknesses:<br /> The manuscript can be further strengthened with more clarification on the following points.<br /> 1. The germline whole-body KO mice were female mice (Line293), however the inducible knockout mice were male mice (Line549). Sexual dimorphism is often observed in metabolic studies, therefore the metabolic phenotype of both female and male mice needs to be reported for the germline and inducible knockouts in order to make the justified conclusion.<br /> 2. SPAG7 has an NLS. Does this protein function in gene expression? Whether the overall metabolic phenotype is the direct cause of SPAG7 ablation is unclear. For example, the Hsd17b10 gene was downregulated in all tissues in the KO mice. Could this have been coincidentally selected for and thus be the cause of the developmental issues and adulthood obesity? Do the iSpag7 mice demonstrate reduced expression of Hsd17b10?<br /> 3. Figure 2c should display the energy expenditure normalized to body weight (or lean body mass).<br /> 4. Please provide more information for the figure legend, including the statistical test that was conducted for each data set, animal numbers for each genotype and sexes.<br /> 5. The authors should report how long after treatment the data was collected for figures 4F-M.<br /> 6. The authors should justify ending the data collection after 8 weeks for the iSPAG7 mice in Figures 4C-E. In the WT vs germline KO mice, there was no clear difference in body weight or lean mass at 15 weeks of age.

Reviewer #3 (Public Review):

Summary:

Single-unit neural activity tuned to environmental or behavioral variables gradually changes over time. This phenomenon, called representational drift, occurs even when all external variables remain constant, and challenges the idea that stable neural activity supports the performance of well-learned behaviors. While a number of studies have described representational drift across multiple brain regions, our understanding of the underlying mechanism driving drift is limited. Ratzon et al. propose that implicit regularization - which occurs when machine learning networks continue to reconfigure after reaching an optimal solution - could provide insights into why and how drift occurs in neurons. To test this theory, Ratzon et al. trained a Feedforward Network to perform the oft-utilized linear track behavioral paradigm and compare the changes in hidden layer units to those observed in hippocampal place cells recorded in awake, behaving animals.

Ratzon et al. clearly demonstrate that hidden layer units in their model undergo consistent changes even after the task is well-learned, mirroring representational drift observed in real hippocampal neurons. They show that the drift occurs across three separate measures: the active proportion of units (referred to as sparsification), spatial information of units, and correlation of spatial activity. They continue to address the conditions and parameters under which drift occurs in their model to assess the generalizability of their findings. However, the generalizability results are presented primarily in written form: additional figures are warranted to aid in reproducibility. Last, they investigate the mechanism through which sparsification occurs, showing that the flatness of the manifold near the solution can influence how the network reconfigures. The authors suggest that their findings indicate a three-stage learning process: 1) fast initial learning followed by 2) directed motion along a manifold which transitions to 3) undirected motion along a manifold.

Overall, the authors' results support the main conclusion that implicit regularization in machine learning networks mirrors representational drift observed in hippocampal place cells. However, additional figures/analyses are needed to clearly demonstrate how different parameters used in their model qualitatively and quantitatively influence drift. Finally, the authors need to clearly identify how their data supports the three-stage learning model they suggest. Their findings promise to open new fields of inquiry into the connection between machine learning and representational drift and generate testable predictions for neural data.

Strengths:

1) Ratzon et al. make an insightful connection between well-known phenomena in two separate fields: implicit regularization in machine learning and representational drift in the brain. They demonstrate that changes in a Feedforward Network mirror those observed in the brain, which opens a number of interesting questions for future investigation.

2) The authors do an admirable job of writing to a large audience and make efforts to provide examples to make machine learning ideas accessible to a neuroscience audience and vice versa. This is no small feat and aids in broadening the impact of their work.

3) This paper promises to generate testable hypotheses to examine in real neural data, e.g., that drift rate should plateau over long timescales (now testable with the ability to track single-unit neural activity across long time scales with calcium imaging and flexible silicon probes). Additionally, it provides another set of tools for the neuroscience community at large to use when analyzing the increasingly high-dimensional data sets collected today.

Weaknesses:

1) Neural representational drift and directed/undirected random walks along a manifold in ML are well described. However, outside of the first section of the main text, the analysis focuses primarily on the connection between manifold exploration and sparsification without addressing the other two drift metrics: spatial information and place field correlations. It is therefore unclear if the results from Figures 3 and 4 are specific to sparseness or extend to the other two metrics. For example, are these other metrics of drift also insensitive to most of the parameters as shown in Figure 3 and the related text? These concerns could be addressed with panels analogous to Figures 3a-c and 4b for the other metrics and will increase the reproducibility of this work.

2) Many caveats/exceptions to the generality of findings are mentioned only in the main text without any supporting figures, e.g., "For label noise, the dynamics were qualitatively different, the fraction of active units did not reduce, but the activity of the units did sparsify" (lines 116-117). Supporting figures are warranted to illustrate which findings are "qualitatively different" from the main model, which are not different from the main model, and which of the many parameters mentioned are important for reproducing the findings.

3) Key details of the model used by the authors are not listed in the methods. While they are mentioned in reference 30 (Recanatesi et al., 2021), they need to be explicitly defined in the methods section to ensure future reproducibility.

4) How different states of drift correspond to the three learning stages outlined by the authors is unclear. Specifically, it is not clear where the second stage ends, and the third stage begins, either in real neural data or in the figures. This is compounded by the fact that the third stage - of undirected, random manifold exploration - is only discussed in relation to the introductory Figure 1 and is never connected to the neural network data or actual brain data presented by the authors. Are both stages meant to represent drift? Or is only the second stage meant to mirror drift, while undirected random motion along a manifold is a prediction that could be tested in real neural data? Identifying where each stage occurs in Figures 2C and E, for example, would clearly illustrate which attributes of drift in hidden layer neurons and real hippocampal neurons correspond to each stage.

Reviewer #3 (Public Review):

The authors claim that this dataset covers a timepoint of embryogenesis that is not well covered in the other published single cell datasets (Tintori et al 2016 and Packer et al 2019). The Tintori data indeed do not cover the 28-102-cell stages sufficiently, but it is unclear how the data presented here compare to the Packer et al data. It is true that the Packer et al data have fewer cells at earlier timepoints than at later ones, but given that they sequenced tens of thousands of cells, they report that they still have ~10,000 cells <210 min of embryogenesis. It seems that if the authors want to make any claims about how their data enables exploration of a stage that was previously not accessible, this would require a better comparison to the available data.

The authors provide thorough support for how they assigned cell identities in their data. It is surprising though that at the 102-cell stage they only identify 37 unique cell identities. They suggest that this is because there are many equivalence groups at this stage. However, I would strongly encourage the authors to perform a similar analysis or otherwise compare their obtained identities with the data from Packer et al. 2019. It seems possible that given the low number of cells in this dataset, the authors are missing certain identities and it would be important to know this.

The main analysis the authors perform is to look at expression patterns of various classes of TFs and ask whether they are enriched in particular lineages or at specific timepoints. This analysis is interesting but would be more informative if the authors provided in Figure 3d the numbers of each class of TFs. The authors then focus on the homeodomain class of TFs as they display interesting lineage-specific expression patterns, which when mapped on the embryo form stripes. The stripe pattern however is not that obvious, at least not as shown in Figure 4b. Perhaps separate embryo schematics showing the different TF expression patterns would show this more clearly. Moreover, given the relatively small number of cell identities found in this dataset (particularly at the 102-cell stage), a similar analysis using the Packer data would provide further support to these patterns. The localization of cells with shared expression patterns does show a stripe pattern at the 28-cell stage, but also not so clearly beyond this timepoint.

I am also unsure about the validity/value of the comparison of the stripes to Drosophila and the centrality of homeodomain TFs to anterior-posterior positional identity. First, it would be important to map other TFs, very likely there are several other TFs that correlate with positional identity. Also, even if the expression of the homeodomain TFs in C. elegans form stripes, there are still several cells within that stripe that do not express these TFs, it is thus unclear whether these TFs encode positional information or the identity of cells with different positions in the embryo.

Reviewer #3 (Public Review):

Summary:<br /> This paper uses indirect immunofluorescence, superresolution fluorescence microscopy, and X-ChIP to demonstrate radial distribution profiles of all histone H1 somatic variants with the exception of histone H1.1. The results support earlier work from chromatin immunoprecipitation experiments that revealed biases for active versus repressed states of chromatin. The previous studies provided some support for the subtle sequence variation found primarily within the C-terminus of histone H1 variants conferred preferences in the type of DNA (e.g. methylated DNA) or chromatin-bound. The current study significantly strengthens that argument. Importantly, this was shown across multiple cell lines and reveals conserved properties of localization of histone H1 variants.

Strengths:<br /> The strength of the manuscript is the combined use of quantitative analysis of indirect immunofluorescence and X-ChIP. The results generally support the polar organization of the genome and a corresponding distribution of histone H1 variants that reflect this polar organization. AT-rich chromatin is positioned near the lamina and is found to be enriched in H1.2, H1.3, and H1.5. H1.4 and H1.X were more biased towards the GC-rich intranuclear chromatin.

There is emerging functional evidence for variant-specific properties to histone H1 subtypes. This work provides an important building block in understanding how different histone H1 variants may have specific functional consequences. The histone H1 variant that is most abundant in most cell types, H1.2, was found to decrease the area of the immunofluorescent slice that was chromatin-free when depleted, suggesting a more important role in global chromatin organization.

Weaknesses:<br /> While histone H1 variants may show biases in their distributions, it is unlikely that these are more than biases. That is, it is unlikely that specific H1 variants are unable to bind to nucleosomes in regions where they are depleted. Fluorescence recovery after photobleaching experiments has demonstrated differences in binding affinity but the capacity to bind a range of chromatin structures, including highly acetylated chromatin, for histone H1 variants. Thus, it is critical in assessing this data to have accurate quantitative information on the relative abundance of the different histone variants amongst the cell lines tested here. The paper relies upon quantification by immunoblotting.

Another uncertainty in both the ChIP and immunofluorescence datasets is the accessibility of the epitope. This weakness is highlighted by the apparent loss of H1.2 and H1.4 in mitotic chromosomes which is revealed to be false by the detection of the phosphorylated species. The distributions relative to the surface of chromosomes in mitosis and the depletion of H1.2, H1.3, and H1.5 from the central regions of interphase nuclei reveal an unusual dissipation of the staining that is suggestive of antibody accessibility or potentially overstaining and quenching of the fluorescence in the center of highly stained structures. The overall image quality of the immunofluorescence images is poor.

Reviewer #3 (Public Review):

The manuscript by Sun et al. reveals several crystal structures that help underpin the offensive-defensive relationship between the sea slug Aplysia kurodai and algae. These centre on TNA (a algal glycosyl hydrolase inhibitor), EHEP (a slug protein that protects against TNA and like compounds) and BGL (a glycosyl hydrolase that helps digest algae). The hypotheses generated from the crystal structures herein are supported by biochemical assays.

The crystal structures of apo and TNA-bound EHEP reveals the binding (and thus protection) mechanism. The authors then demonstrate that the precipitated EHEP-TNA complex can be resolubilised at an alkaline pH, potentially highlighting a mechanism for EHEP recycling in the A. kurodai midgut. The authors also present the crystal structures of akuBGL, a beta-glucosidase utilised by Aplysia kurodai to digest laminarin in algae into glucose. The structure revealed that akuBGL is composed of two GH1 domains, with only one GH1 domain having the necessary residue arrangement for catalytic activity, which was confirmed via hydrolytic activity assays. Docking was used to assess binding of the substrate laminaritetraose and the inhibitors TNA, eckol and phloroglucinol to akuBGL. The docking studies revealed that the inhibitors bound akuBGL at the glycone-binding suggesting a competitive inhibition mechanism. Overall, most of the claims made in this work are supported by the data presented.

Reviewer #3 (Public Review):

In this manuscript, the authors report that human cortical radial glia asymmetrically segregates newly produced or old centrosomes after mitosis, depending on the fate of the daughter cell, similar to what was previously demonstrated for mouse neocortical radial glia (Wang et al. 2009). To do this, the authors develop a novel centrosome labelling strategy in human ESCs that allows recombination-dependent switching of tagged fluorescent reporters from old to newly produced centrosome protein, centriolin. The authors then generate human cortical organoids from these hESCs to show that radial glia in the ventricular zone retains older centrosomes whereas differentiated cells, i.e. neurons, inherit the newly produced centrosome after mitosis. The authors then knock down a critical regulator of asymmetric centrosome inheritance called Ninein, which leads to a randomization of this process, similar to what was observed in mouse cortical radial glia.

A major strength of the study is the combined use of the centrosome labelling strategy with human cortical organoids to address an important biological question in human tissue. This study is similarly presented as the one performed in mice (Wang et al. 2009) and the existence of the asymmetric inheritance mechanism of centrosomes in another species grants strength to the main claim proposed by the authors. It is a well-written, concise article, and the experiments are well-designed. The authors achieve the aims they set out in the beginning, and this is one of the perfect examples of the right use of human cortical organoids to study an important phenomenon. However, there are some key controls that would elevate the main conclusions considerably.

1) The lack of clonal resolution or timelapse imaging makes it hard to assess whether the inheritance of centrosomes occurs as the authors claim. The authors show that there is an increase in newly made non-ventricular centrosomes at a population level but without labelling clones and demonstrating that a new or old centrosome is inherited asymmetrically in a dividing radial glia would grant additional credence to the central conclusion of the paper. These experiments will put away any doubt about the existence of this mechanism in human radial glia, especially if it is demonstrated using timelapse imaging. Additionally, knowing the proportions of symmetric vs asymmetrically dividing cells generating old/new centrosomes will provide important insights pertinent to the conclusions of the paper. Alternatively, the authors could soften their conclusions, especially for Fig 2.<br /> 2) Some critical controls are missing. In Fig. 1B, there is a green dot that does not colocalize with Pericentrin. This is worrying and providing rigorous quantifications of the number of green and tdTom dots with Pericentrin would be very helpful to validate the labelling strategy. Quantifications would put these doubts to rest. Additionally, an example pericentrin staining with the GFP/TdTom signal in figure 4 would also give confidence to the reader. For figure 4, having a control for the retroviral infection is important. Although the authors show a convincing phenotype, the effect might be underestimated due to the incomplete infection of all the analyzed cells.<br /> 3) It would be helpful if the authors expand on the presence of old centrosomes in apical radial glia vs outer radial glia. Currently, in figure 3, the authors only focus on Sox2+ cells but this could be complemented with the inclusion of markers for outer radial glia and whether older centrosomes are also inherited by oRGCs. This would have important implications on whether symmetric/asymmetric division influences the segregation of new/old centrosomes.

Reviewer #3 (Public Review):

Myelodysplastic syndrome (MDS) is a heterogenous, clonal hematopoietic stem cell disorder characterized by morphological dysplasia in one or more hematopoietic lineages, cytopenias (most frequently anemia), and ineffective hematopoiesis. In patients with MDS, transfusion therapy treatment causes clinical iron overload; however it has been unclear if treatment with iron chelation yields clinical benefits. In the present study, the authors use a transgenic mouse model of MDS, NUP98-HOXD13 (referred to here as "MDS mice") to investigate this area. Starting at 5 months of age (before MDS mice progress to acute leukemia), the authors administered DFP in the drinking water for 4 weeks, and compared parameters to untreated MDS mice and WT controls.

The authors first show that MDS mice exhibit systemic iron overload and macrocytic anemia that is improved by treatment with the iron chelator deferiprone (DFP). They then perform a detailed characterization the effects of DFP treatment on erythroid differentiation and various parameters related to iron transport and trafficking in MDS erythroblasts. Strengths of the work are the use of a well-characterized mouse model of MDS with appropriate animal group sizes and detailed analyses of systemic iron parameters and erythroid subpopulations. A remediable weakness is that in certain areas of the Results and Discussion, the authors overinterpret their findings by inferring causation when they have only shown a correlation. Additionally, when drawing conclusions based on changes in erythroblast mRNA expression levels between groups, the authors should consider that translation efficiency may be altered in MDS and that the NUP98 fusion protein itself, by acting as a chimeric transcription factor, may also impact gene expression profiles. Given that the application of chelators for treatment of MDS remains controversial, this work will be of interest to scientists focused on erythroid maturation and iron dysregulation in MDS, as well as clinicians caring for patients with this disorder.

Major Comments

1. The authors define the stages of erythroblast differentiation using the CD44-FSC method, which assumes that CD44 expression levels during the stages of erythroid differentiation are not altered by MDS itself. Are morphologically abnormal erythroblasts, such as bi-nucleate forms, captured in this analysis, and if so, are they classified in the appropriate subset? The percentage of erythroblasts in the bone marrow of MDS mice in this current study is lower than that reported by Suragani et al (Nat Med 2014), who employed a different strategy to define erythroid precursors. While representative erythroblast gating is presented as Supplemental Figure 17, it would be important to present representative gating from all 3 animal groups: WT, MDS, and MDS+DFP mice.

2. Methods, "Statistical analysis." The authors state that all comparisons were done with 2-tailed student paired t test, which would not be appropriate for comparisons being made between independent animals groups (i.e. when groups are not "paired").

3. The Results (p.7) indicates that both sexes showed similar responses to DFP; however, the figure legends do not indicate sex. Given that systemic iron metabolism in mice shows sex-related differences, sex should be specified.

Reviewer #3 (Public Review):

Rhombomeres are key organizational structures for building cell type and even functional diversity in the brainstem. How these rhombmeres ultimately arise from a broad neuro-epithilium remains unclear. While genetic, cellular, tissue, and morphogen manipulations have revealed key processes in rhombomere development the hierarchical organization of neuron-epithelium into individual rhombomeres was less well understood. For example it is thought that rhombomeres are organized in an even odd fashion where two base identities i.e. even or odd where laminated with paired identifies i.e. rhombomeres 1 and 2 being paired and so on. However, there are many exceptions to these organizing constructs at the gene expression levels.

To further interrogate early development of the hindbrain neuro-epithelium and gain insight as to how rhombomere identities emerge at the earliest stages, Kim et al used ATACseq and RNAseq to query chromatin landscapes and gene expression for single nuclei at different developmental stages of zebrafish hindbrain development. The goal of the two pronged approach termed scMultiome analysis was to gain additional insight beyond either method individually for characterizing early events in rhombomere differentiation.

Using scMultiome, three stages of zebrafish hindbrain development were examined at 10hpf(whole embryos), 13hpf, and 16hpf. In the early hindbrain, the data shows that at 13hpf early rhombomere identities can be resolved but that the typical markers seen later are not fully expressed or resolved. At 10hpf clear rhombomere identities are not present. Rather at very early stages, the analysis suggests that three domains for pre-rhombomeres encompassing HB1 - r2+r3 (possibly r1, but this remains to be resolved); HB2 - r5+r6; and HB3 - 4 are present. These clusters or PHPDs are mixed populations that presumably resolve later as the embryo matures. They are shown to be responsive to developmental signals that pattern the neuroepithelium supporting the premise that these are rhombomeric organization structures.

Altogether the use of two methods of transcriptional interrogation i.e. ATACseq and RNA seq are strengths for the presented work to offer increased resolution of cell type characterization. The data analysis is reasonably supported by expression studies using in situ Hybridization Chain Reaction (HCR) to show mixed markers in the early stages. the PHPDs are also responsive to perturbation in retinoid acid, supporting the overall premise.

Overall, the work is well executed and analyzed. The impact in the field largely resides in bringing increasing resolution to earlier stages of rhombomere development and re-examining long held paradigms about when and potentially how rhombomere periodicity and pairing are established at the earliest stages. The premise that pre-rhombomeres may first establish large domains that sort or otherwise resolve themselves into rhombomeres is the most notable outcome from the work and will be seen as impactful in the field.

Reviewer #3 (Public Review):

Proskurin and colleagues aim to test if neurons in rat medial prefrontal cortex encode strategy in a serial choice task. They recorded neural activity as rats performed a nose-poke task for reward. Rats were required to discover, without explicit instruction, which of the possible 3-action sequences were rewarded. One of several possible sequences remained the target (thereby triggering reward delivery) over a block of trials, before switching to an alternate sequence. The authors then used analysis of single neurons and ensembles of neural activity to determine if neural activity reflected whether a sequence was the dominant strategy in a block or an explorative test.

The strengths of the work include the timely and important hypothesis, and the use of appropriate methodologies to test it.

I commend the authors for endeavouring to tackle this challenging topic. The weaknesses of the work derive from the difficulties of studying such a challenging topic. It is extremely difficult to ascribe the variance of neural activity to a latent variable such as strategy, particularly in freely-moving animals motivated by reward. This is because of the plethora of potential confounders. For instance, the authors compare the encoding of one action (L) in two sequences (RLL and LLR). However, the analyzed action occurs in different local contexts. In the first, it is the middle action, and in the second it is the first action following a reward omission. Even though the reward is withheld, the rat presumably has some reward expectation. Because strategy is a latent variable, the evidentiary threshold is high, and alternate explanations of neural variance needed to be rejected. This is particularly important given the neural structures under investigation are involved in regulating motor output, suggesting that differences in response speed, body position, and related variables may explain considerable variance in neural activity. Other potential explanatory variables are rule certainty, position in the sequence, side chosen, preceding choice, and changes in firing rate as the session progresses due to changes in motivation, fatigue, or drift in the signal. The authors attempt to address some of these, but this is done in a very condensed presentation near the end of the results. This needs to be unpacked (and visualized) in order for readers to evaluate whether the strategy is the most likely explanation of neural variance, as proposed by the authors. The paper would benefit from analyses, such as multiple regression over all possible predictive variables, to evaluate the relative amount of neural signal variance attributable to strategy dominance compared to other information.

An additional weakness of the manuscript is the absence of some fundamental checks on data quality, particularly for bias in animal behavior, stability of neural activity during sessions, and bias in data sampling for classifier sampling.

In sum, the experimental methodology appears sufficient to address the authors' aim of evaluating the encoding of strategy by neurons in the medial prefrontal cortex. Alternate interpretations of the data, however, are not sufficiently ruled out by the analysis to strongly support the claim that the exploration of strategy is the primary driver of altered neural signalling. The data and methodologies are valuable to behavioral and systems neuroscientists. The task and the finding that rats appear to spontaneously explore alternate strategies are elegant, and a very nice paradigm for studying the neural mechanisms of strategy shifting. Moreover, the finding that many neurons in the medial prefrontal cortex change their firing rate during the task is an important new contribution. Future analysis and experiments will undoubtedly better resolve the information encoded by these changes in firing rate.

Reviewer #3 (Public Review):

Summary:<br /> In this article, Fox and colleagues describe the results of a novel and innovative task, coupled with a modified computational model, to explore pure directed exploration (not quite a pun, but intended nonetheless). In their task, participants make a series of discrete choices, importantly with no reward feedback, to navigate a nested series of rooms in a virtual environment. The initial 2-door choice is used as the primary probe and the complexity of the series of rooms behind each choice is used as the critical independent variable. The authors find that, as the number of follow-up options behind a door increases, "good" participants are more likely to choose the door that leads to the more complex choices. As the depth of the search increased (i.e. the room with the most doors was presented "farther" down the search), these same participants were less likely to choose the door leading to the more complex route. Finally, these same "good" participants showed an initial boost in preference towards the more complex exploration option after a few learning episodes that settled down after about 10 episodes, with a modest reliable preference towards the more complex route. This reflected the fact that information value decays over time in stable situations. Using an adaptation of standard Q-learning, with a proxy of information value being substituted for reward value, the authors show how their model can qualitatively capture most of the observed experimental effects, although with some critical differences in the temporal dynamics of learning, suggesting that the memory horizon for humans is longer than in the adapted Q-learning model.

Strengths:<br /> 1. Clever experimental design<br /> The novel task is really clever and gets around many of the limitations for understanding directed exploration that have plagued prior research (which typically involve some use of reward feedback). Finding a way to provide direct information that can be experimentally manipulated, without needing to provide any explicit reward feedback, makes this one of the few pure exploration tasks that I am aware of.

2. Compelling results<br /> The effect of manipulating choice complexity and depth on initial choice probability for "good" directed learners seems fairly strong, as do the learning dynamics. The heterogeneity in exploration style across participants is also interesting and brings up more questions that are useful for follow-up research.

3. Simple model<br /> The computational model used is a simple adaptation of standard reinforcement learning models, specifically Q-learning models. This is elegant as it doesn't require major changes in the dynamics of learning, simply a revision of the variables going into the update. The simplicity of this change, coupled with the ability to capture the results of the "good" directed explorers makes a strong case that information seeking and reward-seeking may share common underlying mechanisms (as shown previously by Kobayashi, K., & Hsu, M. (2019). Common neural code for reward and information value. Proceedings of the National Academy of Sciences, 116(26), 13061-13066.).

Weaknesses:

1. "Good" vs. "poor"<br /> There is an odd circularity, and implicit value judgment, in the classification of participants into "good" and "poor" directed explorers. The logic, based on the visit-counter model of directed exploration, is that the probability of repeating a choice (at the initial decision trial) should be low for directed explorers vs. random explorers. Doing the median split on repetition probability seems intuitively fine here, but it does bring up two issues. First, the labels "good" vs. "poor" seem arbitrarily judgemental, after all random exploration is a viable exploration strategy in many contexts. Would "directed" vs. "random" be more appropriate labels based on how the decision was made to categorize participants? Second, how much of the "good" participant performance is driven by the extreme non-repeaters? For example, if a tertiary split was performed instead of a binary median split, would the middle group show a weaker version of the effects seen in the "good" group or appear more like the "poor" group?

2. Characterization of information value<br /> The authors discuss primarily methods that can be summarized by visit counters as a description for all directed exploration models. However, that doesn't seem to be a good summary of the overall literature in this space. There are also entropy-based approaches, that quantify information value based on the statistics of the feedback. For example, in machine learning methods like the KL divergence are often used to represent the information value of a channel. A few such papers are highlighted below. Now it is entirely possible that these approaches can be extrapolated to simple visit-count approaches, but I am unaware of anything showing this. I think it would be good to broaden the discussion on directed exploration models beyond visit-counter methods like UCB, highlighting the other methods used to promote directed exploration.

Houthooft, R., Chen, X., Duan, Y., Schulman, J., De Turck, F., & Abbeel, P. (2016). Vime: Variational information maximizing exploration. Advances in neural information processing systems, 29.

Eysenbach, B., Gupta, A., Ibarz, J., & Levine, S. (2018). Diversity is all you need: Learning skills without a reward function. arXiv preprint arXiv:1802.06070.

Hazan, E., Kakade, S., Singh, K., & Van Soest, A. (2019, May). Provably efficient maximum entropy exploration. In International Conference on Machine Learning (pp. 2681-2691). PMLR.

3. Model vetting<br /> The model used to simulate the behavioral results is interesting and intuitive. However, there seem to be some things left on the table and unresolved. First, the definition of information value (E) that is maximized is assumed to satisfy the same constraints as typical reward does in the Bellman solution for reinforcement learning. This is the only way it can be substituted into the typical Q-learning method. Is that true here?

Second, the advantage of these simpler computational-level models is that they can be effectively fit to behavior. The model outlined in the paper has only a few free parameters (some of which can be fixed for convenience purposes). Was there an attempt to fit each participant's data into the model? This would be a powerful way of highlighting where exactly the differences between the "good" and "bad" participants arise.

Reviewer #3 (Public Review):

This study examines how the correlation structure of a perceptual decision-making task influences history biases in responding. By manipulating whether stimuli were more likely to be repetitive or alternating, they found evidence from both behavior and a neural signal of decision formation that history biases are flexibly adapted to the environment. On the whole, these findings are supported across an impressive range of detailed behavioral and neural analyses. The methods and data from this study will likely be of interest to cognitive neuroscience and psychology researchers. The results provide new insights into the mechanisms of perceptual decision-making.

The behavioral analyses are thorough and convincing, supported by a large number of experimental trials (~600 in each of 3 environmental contexts) in 38 participants. The psychometric curves provide clear evidence of adaptive history biases. The paper then goes on to model the effect of history biases at the single trial level, using an elegant cross-validation approach to perform model selection and fitting. The results support the idea that, with trial-by-trial accuracy feedback, the participants adjusted their history biases due to the previous stimulus category, depending on the task structure in a way that contributed to performance.

The paper then examines MEG signatures of decision formation, to try to identify neural signatures of these adaptive biases. Looking specifically at motor beta lateralization, they found no evidence that starting-level bias due to the previous trial differed depending on the task context. This suggests that the adaptive bias unfolds in the dynamic part of the decision process, rather than reflecting a starting level bias. This is supported by analysis of lateralization relative to the chosen hand as a proxy for a decision variable (DV), whose slope is shown to be influenced by these adaptive biases.

Reviewer #3 (Public Review):

In this improved version of the manuscript, Chang et al set out to find direct interactions with the Eph-B2 receptor, as our knowledge of its function/regulation is still incomplete. Using proteomic analysis of Hela cells expressing EPHB2, they identified MYCBP2 a potential binder, which they then confirm using extensive biochemical analyses, an interaction that seems to be negatively affected by binding of ephrin-B2 (but not B1). Furthermore, they find that FBXO45, a known MYCBP2 interaction, strongly facilitates its binding to EPHB2. Intriguingly, these interactions depend on the extracellular domains of EPHB2, suggesting the involvement of additional proteins as MYCBP2 is thought to be a cytoplasmic protein. Finally, they find that, in contrast to what could be expected given the known function of MYCBP2 as a ubiquitin E3 ligase, it actually positively regulates EPHB2 protein stability, and function.

The strength of this manuscript is the extensive biochemical analysis of the EPHB2/MYCBP2/FBXO43 interactions. The vast majority of the conclusions supported by the data.

The attempt to extend the study to an in vivo animal using the worm is important, however the additive insight is, unfortunately, minimal.

Reviewer #3 (Public Review):

Summary:<br /> This manuscript looks at the single-cell spike signatures taken from in vivo cerebellar nuclear neurons from awake mice suffering from 3 distinct diseases and uses a sophisticated classifier model to predict disease based on a number of different parameters about the spiking patterns, rather than just one or two. Single read-outs of spike firing patterns did not show significant differences between all 4 groups meaning that you need to analyze multiple parameters of the spike trains to get this information. The results are really satisfying and intriguing, with some diseases separating very well, and others having more overlap. It also represents a significant advancement for the rigor and creativity used for analyzing cerebellar output spike patterns. I really like this paper, it's a clever idea and has been done very well.

The authors examine multiple distinct forms of different diseases, including different types of ataxia, dystonia, and tremor. While some of the interpretation of this work remains unclear to this reviewer (in particular Figure 2, with ataxia models), I applaud the rigor and sharing of complex data that is not always straightforward to understand.

Strengths:<br /> The work is technically impressive and the analysis pushes the envelope of how cerebellar dysfunction is classified, which makes it an important paper for the field. It's well written. The approach it is taking is clever. The analysis is thorough, and the authors examine a wide array of different disease models, which is time-consuming, costly, and very challenging to do. It's a very strong manuscript.

Weaknesses:<br /> Weaknesses are few and quite minor. Some rewriting could be done to make certain sections clearer.

Reviewer #3 (Public Review):

Summary:<br /> This manuscript describes a study of the olfactory tubercle in the context of reward representation in the brain. The authors do so by studying the responses of OT neurons to odors with various reward contingencies and compare systematically to the ventral pallidum. Through careful tracing, they present convincing anatomical evidence that the projection from the olfactory tubercle is restricted to the lateral portion of the ventral pallidum.

Using a clever behavioral paradigm, the authors then investigate how D1 receptor- vs. D2 receptor-expressing neurons of the OT respond to odors as mice learn different contingencies. The authors find that, while the D1-expressing OT neurons are modulated marginally more by the rewarded odor than the D2-expressing OT neurons as mice learn the contingencies, this modulation is significantly less than is observed for the ventral pallidum. In addition, neither of the OT neuron classes shows significant modulation by the reward itself. In contrast, the OT neurons contained information that could distinguish odor identities. These observations have led the authors to conclude that the primary feature represented in the OT is not reward.

Strengths:<br /> The highly localized projection pattern from olfactory tubercle to ventral pallidum is a valuable finding and suggests that studying this connection may give unique insights into the transformation of odor by reward association.

Comparison of olfactory tubercle vs. ventral pallidum is a good strategy to further clarify the olfactory tubercle's position in value representation in the brain.

Weaknesses:

The authors' interpretation of the physiologic results - that a novel framework is needed to interpret the OT's role - requires more careful treatment.

Reviewer #3 (Public Review):

Summary: 72 subjects, and 144 hemispheres, from the Human Connectome Project had their parietal sulci manually traced. This identified the presence of previously undescribed shallow sulci. One of these sulci, the ventral supralateral occipital sulcus (slocs-v), was then demonstrated to have functional specificity in spatial orientation. The discussion furthermore provides an eloquent overview of our understanding of the anatomy of the parietal cortex, situating their new work into the broader field. Finally, this paper stimulates further debate about the relative value of detailed manual anatomy, inherently limited in participant numbers and areas of the brain covered, against fully automated processing that can cover thousands of participants but easily misses the kinds of anatomical details described here.

Strengths:<br /> - This is the first paper describing the tertiary sulci of the parietal cortex with this level of detail, identifying novel shallow sulci and mapping them to behaviour and function.<br /> - It is a very elegantly written paper, situating the current work into the broader field.<br /> - The combination of detailed anatomy and function and behaviour is superb.

Weaknesses:<br /> - the numbers of subjects are inherently limited both in number as well as in being typically developing young adults.<br /> - while the paper begins by describing four new sulci, only one is explored further in greater detail.<br /> - there is some tension between calling the discovered sulci new vs acknowledging they have already been reported, but not named.<br /> - the anatomy of the sulci, as opposed to their relation to other sulci, could be described in greater detail.

Overall, to summarize, I greatly enjoyed this paper and believe it to be a highly valued contribution to the field.

Reviewer #3 (Public Review):

Summary:<br /> Bzymek and Kloosterman carried out a complex experiment to determine the temporal spike dynamics of cells in the dorsal and intermediate lateral septum during the performance of a Y-maze spatial task. In this descriptive study, the authors aim to determine if inputting spatial and temporal dynamics of hippocampal cells carry over to the lateral septum, thereby presenting the possibility that this information could then be conveyed to other interconnected subcortical circuits. The authors are successful in these aims, demonstrating that the phenomenon of theta cycle skipping is present in cells of the lateral septum. This finding is a significant contribution to the field as it indicates the phenomenon is present in neocortex, hippocampus, and the subcortical hub of the lateral septal circuit. In effect, this discovery closes the circuit loop on theta cycle skipping between the interconnected regions of the entorhinal cortex, hippocampus, and lateral septum. Moreover, the authors make 2 additional findings: 1) There are differences in the degree of theta modulation and theta cycle skipping as a function of depth, between the dorsal and intermediate lateral septum; and 2) The significant proportion of lateral septum cells that exhibit theta cycle skipping, predominantly do so during 'non-local' spatial processing.

Strengths: The major strength of the study lies in its design, with 2 behavioral tasks within the Y-maze and a battery of established analyses drawn from prior studies that have established spatial and temporal firing patterns of entorhinal and hippocampal cells during these tasks. Primary among these analyses, is the ability to decode the animal's position relative to locations of increased spatial cognitive demand, such as the choice point before the goal arms. The presence of theta cycle skipping cells in the lateral septum is robust and has significant implications for the ability to dissect the generation and transfer of spatial routes to goals within and between the neocortex and subcortical neural circuits.

Weaknesses: There are no major discernable weaknesses in the study, yet the scope and mechanism of the theta cycle phenomenon remain to be placed in the context of other phenomena indicative of spatial processing independent of the animal's current position. An example of this would be the ensemble-level 'scan ahead' activity of hippocampal place cells (Gupta et al., 2012; Johnson & Redish, 2007). Given the extensive analytical demands of the study, it is understandable that the authors chose to limit the analyses to the spatial and burst firing dynamics of the septal cells rather than the phasic firing of septal action potentials relative to local theta oscillations or CA1 theta oscillations. Yet, one would ideally be able to link, rather than parse the phenomena of temporal dynamics. For example, Tingley et al recently showed that there was significant phase coding of action potentials in lateral septum cells relative to spatial location (Tingley & Buzsaki, 2018). This begs the question as to whether the non-uniform distribution of septal cell activity within the Y-maze may have a phasic firing component, as well as a theta cycle skipping component. If so, these phenomena could represent another means of information transfer within the spatial circuit during cognitive demands. Alternatively, these phenomena could be part of the same process, ultimately representing the coherent input of information from one region to another. Future experiments will therefore have to sort out whether theta cycle skipping, is a feature of either rate or phase coding, or perhaps both, depending on circuit and cognitive demands.

The authors have achieved their aims of describing the temporal dynamics of the lateral septum, at both the dorsal extreme and the intermediate region. All conclusions are warranted.

Reviewer #3 (Public Review):

Summary: This manuscript explores the development of a rodent voluntary oral THC consumption model. The authors use the model to demonstrate that similar effect levels of THC can be observed to what has previously been described for i.p. THC administration.

Strengths: Overall this is an interesting study with compelling data presented. There is a growing need within the field of cannabinoid research to explore more 'realistic' routes of cannabinoid administration, such as oral consumption or inhalation. The evidence presented here shows the utility of this oral administration model.

Weaknesses: The main weaknesses of the manuscript revolve around clarification of the Methods section. All of these weaknesses are described in the "Recommendations to authors" section. Revising the manuscript would account for many of these weaknesses.

Reviewer #3 (Public Review):

Summary:<br /> Davenport et al have investigated how the administration of a masculinizing dose of estrogen changes the transcriptomes of several key song nuclei song and adjacent brain areas in juvenile zebra finches of both sexes. Only male zebra finches sing, learn song, and normally have a fully developed song control circuitry, so the study was aimed at further understanding how genetic and hormonal factors contribute to the dimorphism in song behavior and related brain circuitry in this species. Using WGCNA and follow-up correlations to re-analyze published transcriptome datasets, the authors provide evidence that the main variance of several identified gene co-expression modules shows significant correlations with one or some of the factors examined, including sex, estrogen treatment, regional neuroanatomy, or occurrence of vocal learning.

Strengths:<br /> Among the main strengths are the thorough gene co-expression module and correlation analyses, and the inclusion of both song nuclei and adjacent areas, the latter serving as sort of controls for areas that are not dimorphic and likely broadly present in birds in general. The most relevant finding is arguably the identification of some modules where gene expression variation within song nuclei correlates with hormonal effects and/or gene location on sex chromosomes, which are present at different dosages between sexes. The study also shows how a published RNA-seq dataset can be reanalyzed in novel and informative ways.

Weaknesses:<br /> Among its main weaknesses, the study relies entirely on one set of transcriptomic data and lacks effort to validate the inferred direction of regulation in the identified co-expression modules using other molecular methods or approaches on independent samples. The study shows that some representative and/or highly significant genes in some of the main modules that correlate with anatomical, sex, or hormone treatment group comparisons indeed differ in expression when comparing song nuclei vs surroundings, male vs female, or E2- vs VEH-treated tissues in independent samples by qPCR or in situ hybridization would provide important validation and enhance experimental rigor for the analyses presented. In the absence of this further validation, the WGCNA data need to be interpreted with caution.

The findings related to ex-chromosome genes (i.e. module E) are a significant strength of the study. Two points, however, need to be taken into account more closely. First, sex differences in gene expression in areas that are not song nuclei are likely related to functions other than song behavior or vocal learning, thus not related to the main question posed by the study. Furthermore, an alternative interpretation with regard to sex chromosome gene expression is that the higher male expression for a large number of Z chromosome genes may not significantly or fundamentally affect brain cell function and can be tolerated, thus not requiring active compensation. This alternative interpretation (mentioned for song nucleus RA in Friedrich et al, Cell Reports, 2022) suggesting that the higher male dosage of many of these genes might not affect or contribute to sex differences in brain function, cannot at present be discarded, and should at least be acknowledged.

Friedrich et al, Cell Reports, 2022 (Table S3 ) presented an extensive manual curation of W chromosome genes in zebra finches. BLAST alignments showed that a large proportion of W chromosome genes are also on the Z, noting that only a small subset of these are annotated as Z:W pairs. The genes that are truly W-specific and present at a higher dosage in females are thus only a fraction of W-chromosome genes. This creates a complication when examining the mapping of RNA-seq reads to sex chromosomes: to conclude about higher expression of W genes in female tissue samples one needs to take into account reads that may also map to the homologous genes on the Z, if that gene is present as a W:Z pair. Because Friedrich et al mapped reads to a male genome assembly, W genes were not assessed, thus the present study provides novel info. However, the issues above need to be acknowledged and taken into account to accurately assess sex differences in W chromosome gene expression.

Reviewer #3 (Public Review):

Summary:

This manuscript develops a new method termed MINT for decoding of behavior. The method is essentially a table-lookup rather than a model. Within a given stereotyped task, MINT tabulates averaged firing rate trajectories of neurons (neural states) and corresponding averaged behavioral trajectories as stereotypes to construct a library. For a test trial with a realized neural trajectory, it then finds the closest neural trajectory to it in the table and declares the associated behavior trajectory in the table as the decoded behavior. The method can also interpolate between these tabulated trajectories. The authors mention that the method is based on three key assumptions: (1) Neural states may not be embedded in a low-dimensional subspace, but rather in a high-dimensional space. (2) Neural trajectories are sparsely distributed under different behavioral conditions. (3) These neural states traverse trajectories in a stereotyped order.

The authors conducted multiple analyses to validate MINT, demonstrating its decoding of behavioral trajectories in simulations and datasets (Figures 3, 4). The main behavior decoding comparison is shown in Figure 4. In stereotyped tasks, decoding performance is comparable (M_Cycle, MC_Maze) or better (Area 2_Bump) than other linear/nonlinear algorithms (Figure 4). However, MINT underperforms for the MC_RTT task, which is less stereotyped (Figure 4).

This paper is well-structured and its main idea is clear. The fact that performance on stereotyped tasks is high is interesting and informative, showing that these stereotyped tasks create stereotyped neural trajectories. The task-specific comparisons include various measures and a variety of common decoding approaches, which is a strength. However, I have several major concerns. I believe several of the conclusions in the paper, which are also emphasized in the abstract, are not accurate or supported, especially about generalization, computational scalability, and utility for BCIs. MINT is essentially a table-lookup algorithm based on stereotyped task-dependent trajectories and involves the tabulation of extensive data to build a vast library without modeling. These aspects will limit MINT's utility for real-world BCIs and tasks. These properties will also limit MINT's generalizability from task to task, which is important for BCIs and thus is commonly demonstrated in BCI experiments with other decoders without any retraining. Furthermore, MINT's computational and memory requirements can be prohibitive it seems. Finally, as MINT is based on tabulating data without learning models of data, I am unclear how it will be useful in basic investigations of neural computations. I expand on these concerns below.

Main comments:

1. MINT does not generalize to different tasks, which is a main limitation for BCI utility compared with prior BCI decoders that have shown this generalizability as I review below. Specifically, given that MINT tabulates task-specific trajectories, it will not generalize to tasks that are not seen in the training data even when these tasks cover the exact same space (e.g., the same 2D computer screen and associated neural space).

First, the authors provide a section on generalization, which is inaccurate because it mixes up two fundamentally different concepts: 1) collecting informative training data and 2) generalizing from task to task. The former is critical for any algorithm, but it does not imply the latter. For example, removing one direction of cycling from the training set as the authors do here is an example of generating poor training data because the two behavioral (and neural) directions are non-overlapping and/or orthogonal while being in the same space. As such, it is fully expected that all methods will fail. For proper training, the training data should explore the whole movement space and the associated neural space, but this does not mean all kinds of tasks performed in that space must be included in the training set (something MINT likely needs while modeling-based approaches do not). Many BCI studies have indeed shown this generalization ability using a model. For example, in Weiss et al. 2019, center-out reaching tasks are used for training and then the same trained decoder is used for typing on a keyboard or drawing on the 2D screen. In Gilja et al. 2012, training is on a center-out task but the same trained decoder generalizes to a completely different pinball task (hit four consecutive targets) and tasks requiring the avoidance of obstacles and curved movements. There are many more BCI studies, such as Jarosiewicz et al. 2015 that also show generalization to complex real-world tasks not included in the training set. Unlike MINT, these works can achieve generalization because they model the neural subspace and its association to movement. On the contrary, MINT models task-dependent neural trajectories, so the trained decoder is very task-dependent and cannot generalize to other tasks. So, unlike these prior BCIs methods, MINT will likely actually need to include every task in its library, which is not practical.

I suggest the authors remove claims of generalization and modify their arguments throughout the text and abstract. The generalization section needs to be substantially edited to clarify the above points. Please also provide the BCI citations and discuss the above limitation of MINT for BCIs.

2. MINT is shown to achieve competitive/high performance in highly stereotyped datasets with structured trials, but worse performance on MC_RTT, which is not based on repeated trials and is less stereotyped. This shows that MINT is valuable for decoding in repetitive stereotyped use-cases. However, it also highlights a limitation of MINT for BCIs, which is that MINT may not work well for real-world and/or less-constrained setups such as typing, moving a robotic arm in 3D space, etc. This is again due to MINT being a lookup table with a library of stereotyped trajectories rather than a model. Indeed, the authors acknowledge that the lower performance on MC_RTT (Figure 4) may be caused by the lack of repeated trials of the same type. However, real-world BCI decoding scenarios will also not have such stereotyped trial structure and will be less/un-constrained, in which MINT underperforms. Thus, the claim in the abstract or lines 480-481 that MINT is an "excellent" candidate for clinical BCI applications is not accurate and needs to be qualified. The authors should revise their statements according and discuss this issue. They should also make the use-case of MINT on BCI decoding clearer and more convincing.

3. Related to 2, it may also be that MINT achieves competitive performance in offline and trial-based stereotyped decoding by overfitting to the trial structure in a given task, and thus may not generalize well to online performance due to overfitting. For example, a recent work showed that offline decoding performance may be overfitted to the task structure and may not represent online performance (Deo et al. 2023). Please discuss.

4. Related to 2, since MINT requires firing rates to generate the library and simple averaging does not work for this purpose in the MC_RTT dataset (that does not have repeated trials), the authors needed to use AutoLFADS to infer the underlying firing rates. The fact that MINT requires the usage of another model to be constructed first and that this model can be computationally complex, will also be a limiting factor and should be clarified.

5. I also find the statement in the abstract and paper that "computations are simple, scalable" to be inaccurate. The authors state that MINT's computational cost is O(NC) only, but it seems this is achieved at a high memory cost as well as computational cost in training. The process is described in section "Lookup table of log-likelihoods" on line [978-990]. The idea is to precompute the log-likelihoods for any combination of all neurons with discretization x all delay/history segments x all conditions and to build a large lookup table for decoding. Basically, the computational cost of precomputing this table is O(V^{Nτ} x TC) and the table requires a memory of O(V^{Nτ}), where V is the number of discretization points for the neural firing rates, N is the number of neurons, τ is the history length, T is the trial length, and C is the number of conditions. This is a very large burden, especially the V^{Nτ} term. This cost is currently not mentioned in the manuscript and should be clarified in the main text. Accordingly, computation claims should be modified including in the abstract.

6. In addition to the above technical concerns, I also believe the authors should clarify the logic behind developing MINT better. From a scientific standpoint, we seek to gain insights into neural computations by making various assumptions and building models that parsimoniously describe the vast amount of neural data rather than simply tabulating the data. For instance, low-dimensional assumptions have led to the development of numerous dimensionality reduction algorithms and these models have led to important interpretations about the underlying dynamics (e.g., fixed points/limit cycles). While it is of course valid and even insightful to propose different assumptions from existing models as the authors do here, they do not actually translate these assumptions into a new model. Without a model and by just tabulating the data, I don't believe we can provide interpretation or advance the understanding of the fundamentals behind neural computations. As such, I am not clear as to how this library building approach can advance neuroscience or how these assumptions are useful. I think the authors should clarify and discuss this point.

7. Related to 6, there seems to be a logical inconsistency between the operations of MINT and one of its three assumptions, namely, sparsity. The authors state that neural states are sparsely distributed in some neural dimensions (Figure 1a, bottom). If this is the case, then why does MINT extend its decoding scope by interpolating known neural states (and behavior) in the training library? This interpolation suggests that the neural states are dense on the manifold rather than sparse, thus being contradictory to the assumption made. If interpolation-based dense meshes/manifolds underlie the data, then why not model the neural states through the subspace or manifold representations? I think the authors should address this logical inconsistency in MINT, especially since this sparsity assumption also questions the low-dimensional subspace/manifold assumption that is commonly made.

References

Weiss, Jeffrey M., Robert A. Gaunt, Robert Franklin, Michael L. Boninger, and Jennifer L. Collinger. 2019. "Demonstration of a Portable Intracortical Brain-Computer Interface." Brain-Computer Interfaces 6 (4): 106-17. https://doi.org/10.1080/2326263X.2019.1709260.

Gilja, Vikash, Paul Nuyujukian, Cindy A. Chestek, John P. Cunningham, Byron M. Yu, Joline M. Fan, Mark M. Churchland, et al. 2012. "A High-Performance Neural Prosthesis Enabled by Control Algorithm Design." Nature Neuroscience 15 (12): 1752-1757. https://doi.org/10.1038/nn.3265.

Jarosiewicz, Beata, Anish A. Sarma, Daniel Bacher, Nicolas Y. Masse, John D. Simeral, Brittany Sorice, Erin M. Oakley, et al. 2015. "Virtual Typing by People with Tetraplegia Using a Self-Calibrating Intracortical Brain-Computer Interface." Science Translational Medicine 7 (313): 313ra179-313ra179. https://doi.org/10.1126/scitranslmed.aac7328.

Darrel R. Deo, Francis R. Willett, Donald T. Avansino, Leigh R. Hochberg, Jaimie M. Henderson, and Krishna V. Shenoy. 2023. "Translating Deep Learning to Neuroprosthetic Control." BioRxiv, 2023.04.21.537581. https://doi.org/10.1101/2023.04.21.537581.

Reviewer #3 (Public Review):

This study explores how condensin and telomere proteins cooperate to facilitate sister chromatid disjunction at chromosome ends during anaphase. Building upon previous results published by the same group (Reyes et al. 2015, Berthezene et al. 2020), the authors demonstrate that condensin is essential for sister telomere disjunction in anaphase in fission yeast. The primary role of condensin appears to be counteracting cohesin, which holds sister telomeres together. Furthermore, condensin is found to be enriched at telomeres, and this enrichment partially relies on Taz1, the principal telomere factor in S. pombe. The loss of Taz1 does not cause an obvious defect in sister telomere disjunction, which prevents drawing strong conclusions about its role in this process.

Reviewer #3 (Public Review):

Summary:<br /> This paper by Portela Catani et al examines the antigenic relationships (measured using monotypic ferret and mouse sera) across a panel of N2 genes from the past 14 years, along with the underlying sequence differences and phylogenetic relationships. This is a highly significant topic given the recent increased appreciation of the importance of NA as a vaccine target, and the relative lack of information about NA antigenic evolution compared with what is known about HA. Thus, these data will be of interest to those studying the antigenic evolution of influenza viruses. The methods used are generally quite sound, though there are a few addressable concerns that limit the confidence with which conclusions can be drawn from the data/analyses.

Strengths:<br /> - The significance of the work, and the (general) soundness of the methods.<br /> - Explicit comparison of results obtained with mouse and ferret sera.

Weaknesses:<br /> - Approach for assessing the influence of individual polymorphisms on antigenicity does not account for the potential effects of epistasis.<br /> - Machine learning analyses were neither experimentally validated nor shown to be better than simple, phylogenetic-based inference.

Reviewer #3 (Public Review):

The dogma in the Trypanosome field is that transmission by Tsetse flies is ensured by stumpy forms. This has been recently challenged by the Engstler lab (Schuster et al. ), which showed that slender forms can also be transmitted by teneral flies. In this work, the authors aimed to test whether transmission by slender forms is possible and frequent.

For this, the authors repeated Tsetse transmission experiments but with some key critical differences relative to Schuster et al. First, they infected teneral and adult flies. Second, their infective meals lacked two components (N-acetylglucosamine and glutathione), which could have boosted the infection rates in the Schuster et al. work. In these conditions, the authors observed that most stumpy form infections with teneral and adult flies were successful while only 1 out of 24 slender-form infections was successful. Adult flies showed a lower infection rate, which is probably because their immune system is more developed.

Given that in Tsetse-infested areas most transmission is likely ensured by adult flies, the authors conclude that the parasite stage that will have a significant epidemiologic impact on transmission is the stumpy form.

Strengths:<br /> • This work tackles an important question in the field.<br /> • The Rotureau laboratory has well-known expertise in Tsetse fly transmission experiments.<br /> • Experimental setup is robust and data is solid.<br /> • The paper is concise and clearly written.

Weaknesses:<br /> • The reason(s) for why this work has lower infection rates with slender forms than Schuster et al. remain unknown. The authors suggested it could be because of the absence of N-acetylglucosamine and/or glutathione, but this was not formally tested. Could another source of variation be the clone of EATRO1125 AnTat1.1 (Paris versus Munich origin)? To reduce the workload, such additional experiments could be done with just one dose of parasites.<br /> • The characterization of what is slender and stumpy is critical. The authors used PAD1 protein expression as the sole reporter. While this is a robust assay to confirm stumpy, an analysis of the cell cycle would have been helpful to confirm that slender forms have not initiated differentiation (Larcombe S et al. 2023, preprint).<br /> • Statistical analysis is missing. Is the difference between adult and teneral infections statistically significant?

Reviewer #3 (Public Review):

Summary: In this paper, Ruan et al. studied the long-term impact of warming and altered precipitations on the composition and growth of the soil microbial community. The researchers adopted an experimental approach to assess the impact of climate change on microbial diversity and functionality. This study was carried out within a controlled environment, wherein two primary factors were assessed: temperature (in two distinct levels) and humidity (across three different levels). These factors were manipulated in a full factorial design, resulting in a total of six treatments. This experimental setup was maintained for ten years. To analyze the active microbial community, the researchers employed a technique involving the incorporation of radiolabeled water into biomolecules (particularly DNA) through quantitative stable isotope probing. This allowed for the tracking of the active fraction of microbes, accomplished via isopycnic centrifugation, followed by Illumina sequencing of the denser fraction. This study was followed by a series of statistical analysis to identify the impact of these two variables on the whole community and specific taxonomic groups. The full factorial design arrangement enabled the researchers to discern both individual contributions as well as potential interactions among the variables

Strengths: This work presents a timely study that assesses in a controlled fashion the potential impact of global warming and altered precipitations on microbial populations. The experimental setup, experimental approach and data analysis seem to be overall solid. I consider the paper of high interest for the whole community as it provides a baseline to the assessment of global warming on microbial diversity.

Weaknesses: While taxonomic information is interesting, it would have been highly valuable to include transcriptomics data as well. This would allow us to understand what active pathways become enriched under warming and altered precipitations. Non-metabolic OTUs hold significance as well. The authors could have potentially described these non-incorporators and derived hypotheses from the gathered information. The work would have benefited from using more biological replicates of each treatment.

Reviewer #3 (Public Review):

SUMMARY:

The manuscript by Bian et al. promotes the idea that creatine is a new neurotransmitter. The authors conduct an impressive combination of mass spectrometry (Fig. 1), genetics (Figs. 2, 3, 6), biochemistry (Figs. 2, 3, 8), immunostaining (Fig. 4), electrophysiology (Figs. 5, 6, 7), and EM (Fig. 8) in order to offer support for the hypothesis that creatine is a CNS neurotransmitter.

STRENGTHS:

There are many strengths to this study.<br /> • The combinatorial approach is a strength. There is no shortage of data in this study.<br /> • The careful consideration of specific criteria that creatine would need to meet in order to be considered a neurotransmitter is a strength.<br /> • The comparison studies that the authors have done in parallel with classical neurotransmitters is helpful.<br /> • Demonstration that creatine has inhibitory effects is another strength.<br /> • The new genetic mutations for Slc6a8 and AGAT are strengths and potentially incredibly helpful for downstream work.

WEAKNESSES:<br /> • Some data are indirect. Even though Slc6a8 and AGAT are helpful sentinels for the presence of creatine, they are not creatine themselves. Of note, these molecules themselves are not essential for making the case that creatine is a neurotransmitter.<br /> • Regarding Slc6a8, it seems to work only as a reuptake transporter - not as a transporter into SVs. Therefore, we do not know what the transporter into the TVs is.<br /> • Puzzlingly, Slc6a8 and AGAT are in different cells, setting up the complicated model that creatine is created in one cell type and then processed as a neurotransmitter in another. This matter will likely need to be resolved in future studies.<br /> • No candidate receptor for creatine has been identified postsynaptically. This will likely need to be resolved in future studies.<br /> • Because no candidate receptor has been identified, it is important to fully consider other possibilities for roles of creatine that would explain these observations other than it being a neurotransmitter? There is some attention to this in the Discussion.

There are several criteria that define a neurotransmitter. The authors nicely delineated many criteria in their discussion, but it is worth it for readers to do the same with their own understanding of the data.

By this reviewer's understanding (and combining some textbook definitions together) a neurotransmitter: 1) must be present within the presynaptic neuron and stored in vesicles; 2) must be released by depolarization of the presynaptic terminal; 3) must require Ca2+ influx upon depolarization prior to release; 4) must bind specific receptors present on the postsynaptic cell; 5) exogenous transmitter can mimic presynaptic release; 6) there exists a mechanism of removal of the neurotransmitter from the synaptic cleft.

For a paper to claim that the published work has identified a new neurotransmitter, several of these criteria would be met - and the paper would acknowledge in the discussion which ones have not been met. For this particular paper, this reviewer finds that condition 1 is clearly met.

Conditions 2 and 3 seem to be met by electrophysiology, but there are caveats here. High KCl stimulation is a blunt instrument that will depolarize absolutely everything in the prep all at once and could result in any number of non-specific biological reactions as a result of K+ rushing into all neurons in the prep. Moreover, the results in 0 Ca2+ are puzzling. For creatine (and for the other neurotransmitters), why is there such a massive uptick in release, even when the extracellular saline is devoid of calcium?

Condition 4 is not discussed in detail at all. In the discussion, the authors elide the criterion of receptors specified by Purves by inferring that the existence of postsynaptic responses implies the existence of receptors. True, but does it specifically imply the existence of creatinergic receptors? This reviewer does not think that is necessarily the case. The authors should be appropriately circumspect and consider other modes of inhibition that are induced by activation or potentiation of other receptors (e.g., GABAergic or glycinergic).

Condition 5 may be met, because authors applied exogenous creatine and observed inhibition. However, this is tough to know without understanding the effects of endogenous release of creatine. if they were to test if the absence of creatine caused excess excitation (at putative creatinergic synapses), then that would be supportive of the same. Nicely, Ghirardini et al., 2023 study cited by the reviewers does provide support for this exact notion in pyramidal neurons.

For condition 6, the authors made a great effort with Slc6a8. This is a very tough criterion to understand or prove for many synapses and neurotransmitters.

In terms of fundamental neuroscience, the story should be impactful. There are certainly more neurotransmitters out there than currently identified and by textbook criteria, creatine seems to be one of them taking all of the data in this study and others into account.

Reviewer #3 (Public Review):

Summary:

The authors, Y Chang and colleagues, have performed elegant studies in transgenic mouse models that were designed to examine glutamatergic transmission in noradrenergic neurons, with a focus on respiratory regulation. They generated 3 different transgenic lines, in which a red fluorophore was expressed in dopamine-B-hydroxylase (DBH; noradrenergic and adrenergic neurons) neurons that did not express a vesicular glutamate transporter (Vglut) and a green fluorophore in DBH neurons that did express one of either Vglut1, Vglut2 or Vglut3.

Further experiments generated a transgenic mouse with knockout of Vglut2 in DBH neurons. The authors used plethysmography to measure respiratory parameters in conscious, unrestrained mice in response to various challenges.

Strengths:

The distribution of the Vglut expression is broadly in agreement with other studies, but with the addition of some novel Vglut3 expression. Validation of the transgenic results, using in situ hybridization histochemistry to examine mRNA expression, revealed potential modulation of Vglut2 expression during phases of development. This dataset is comprehensive, well-presented and very useful.

In the physiological studies the authors observed that neither baseline respiratory parameters, nor respiratory responses to hypercapnea (5, 7, 10% CO2) or hypoxia (10% O2) were different between knockout mice and littermate controls. The studies are well-designed and comprehensive. They provide observations that are supportive of previous reports using similar methodology.

Weaknesses:

In relation to the expression of Vglut2, the authors conclude that modulation of expression occurs, such that in adulthood there are differences in expression patterns in some (nor)adrenergic cell groups. Altered sensitivity is provided as an explanation for different results between studies examining mRNA expression. These are likely explanations; however, the conclusion would really be definitive with inclusion of a conditional cre expressing mouse. Given the effort taken to generate this dataset, it seems to me that taking that extra step would be of value for the overall understanding of glutamatergic expression in these catecholaminergic neurons

The respiratory physiology is very convincing and provides clear support for the view that Vglut2 is not required for modulation of the respiratory parameters measured and the reflex responses tested. It is stated that this is surprising. However, comparison with the data from Abbott et al., Eur J Neurosci (2014) in which the same transgenic approach was used, shows that they also observed no change in baseline breathing frequency. Differences were observed with strong, coordinated optogenetic stimulation, but, as discussed in this manuscript, it is not clear what physiological function this is relevant to. It just shows that some C1 neurons can use glutamate as a signaling molecule. Further, Holloway et al., Eur J Neurosci (2015), using the same transgenic mouse approach, showed that the respiratory response to optogenetic activation of Phox2 expressing neurons is not altered in DBH-Vglut2 KO mice. The conclusion seems to be that some C1 neuron effects are reliant upon glutamatergic transmission (C1-DMV for example), and some not.

Further contrast is made in this manuscript to the work of Malheiros-Lima and colleagues (eLife 2020) who showed that the activation of abdominal expiratory nerve activity in response to peripheral chemoreceptor activation with cyanide was dependent upon C1 neurons and could be attenuated by blockade of glutamate receptors in the pFRG - i.e. the supposition that glutamate release from C1 neurons was responsible for the function. However, it is interesting to observe that diaphragm EMG responses to hypercapnia (10% CO2) or cyanide, and the expiratory activation to hypercapnia, were not affected by the glutamate receptor blockade. Thus, a very specific response is affected and one that was not measured in the current study.

These previous published observations are consistent with the current study which provides a more comprehensive analysis of the role of glutamatergic contributions respiratory physiology. A more nuanced discussion of the data and acknowledgement of the differences, which are not actually at odds, would improve the paper and place the information within a more comprehensive model.

Reviewer #3 (Public Review):

Summary:

This study aims to demonstrate that cortical feedback is not necessary to signal behavioral outcome to shell neurons of the inferior colliculus during a sound detection task. The demonstration is achieved by the observation of the activity of cortico-recipient neurons in animals which have received lesions of the auditory cortex. The experiment shows that neither behavior performance nor neuronal responses are significantly impacted by cortical lesions except for the case of partial lesions which seem to have a disruptive effect on behavioral outcome signaling.

Strengths:

The experimental procedure is based on state of the art methods. There is an in depth discussion of the different effects of auditory cortical lesions on sound detection behavior.

Weaknesses:

The analysis is not documented enough to be correctly evaluated. Have the authors pooled together trials with different sound levels for the key hit vs miss decoding/clustering analysis? If so, the conclusions are not well supported, as there are more misses for low sound levels, which would completely bias the outcome of the analysis. It would possible that the classification of hit versus misses actually only reflects a decoding of sound level based on sensory responses in the colliculus, and it would not be surprising then that in the presence or absence of cortical feedback, some neurons responds more to higher sound levels (hits) and less to lower sound levels (misses). It is important that the authors clarify and in any case perform an analysis in which the classification of hits vs misses is done only for the same sound levels. The description of feedback signals could be more detailed although it is difficult to achieve good temporal resolution with the calcium imaging technique necessary for targeting cortico-recipient neurons.

Reviewer #3 (Public Review):

ZMYM2 is a transcriptional repressor known to bind to the post-translational modification SUMO2/3. It has been implicated in the silencing of genes and transposons in a variety of contexts, but lacking sequence-specific DNA binding, little is known about how it is targeted to specific regions. At least two reports indicate association with TRIM28 targets (Tsusaka 2020 Epigenetics & Chromatin, Graham-Paquin 2023 NAR) but no physical association with TRIM28 targets had been demonstrated. Tsusaka 2020 theorizes an indirect, potentially SUMO-independent, interaction via ATF7IP and SETDB1.

Here, Owen and colleagues show that a subset of ZMYM2-binding sites in U2OS cells are clearly TRIM28 sites, and further find that hundreds of genes are silenced by both ZMYM2 and TRIM28. They next demonstrate that ZMYM2 homes to chromatin, and interacts with TRIM28, in a SUMOylation-dependent manner, suggesting that ZMYM2 is recognizing SUMOylation on TRIM28 or a protein associated with TRIM28. ZMYM2 separately homes to SINE elements bound by the ChAHP complex in an apparently SUMOylation independent manner. Although this is not the first report to show physical interaction between ZMYM2 and ChAHP, it is the first to show that ZMYM2 homes to ChAHP-binding sites and functions as a corepressor at these sites. Finally the authors demonstrate that ZMYM2 and TRIM28 coregulate genic targets by inducing repression at LTRs within the same TADs as the genes in question.

Overall, the manuscript is well-written, convincing, and fills a significant hole in our understanding of ZMYM2's mechanistic function. The revised version of this manuscript addresses all of my previous concerns well.

Reviewer #3 (Public Review):

Chen et al. investigated how intermittent fasting causes metabolic benefits in obese mice and found that intestinal ILC3 and IL-22-IL-22R signaling contribute to the beiging of white adipose tissue (WAT) and consequent metabolic benefits including improved glucose and lipid metabolism in diet-induced obese mice. They demonstrate that intermittent fasting causes increased IL22+ILC3 in small intestines of mice. Adoptive transfer of purified intestinal ILC3 or administration of exogenous IL-22 can lead to increases in UCP1 gene expression and energy expenditure as well as improved glucose metabolism. Importantly, the above metabolic benefits caused by intermittent fasting are abolished in IL-22R-/- mice. Using an in vitro experiment, the authors show that ILC3-derived IL-22 may directly act on adipocytes to promote SVF beige differentiation. Finally, by performing sc-RNA-seq analysis of intestinal immune cells from mice with different treatments, the authors indicate a possible way of intestinal ILC3 being activated by intermittent fasting. Overall, this study provides a new mechanistic explanation for the metabolic benefits of intermittent fasting and reveals the role of intestinal ILC3 in the enhancement of the whole-body energy expenditure and glucose metabolism likely via IL-22-induced beige adipogenesis.

Although this study presents some interesting findings, particularly IL-22 derived from intestinal ILC3 could induce beiging of WAT by directly acting on adipocytes, the experimental data are not sufficient to support the key claims in the manuscript.

Reviewer #3 (Public Review):

In animals, several recent studies have revealed a substantial role for non-replicative mutagenic processes such as DNA damage and repair rather than replicative error as was previously believed. Much less is known about how mutation operates in plants, with only a handful of studies devoted to the topic. Authors Satake et al. aimed to address this gap in our understanding by comparing the rates and patterns of somatic mutation in a pair of tropical tree species, slow-growing Shorea lavis and fast-growing S. leprosula. They find that the yearly somatic mutation rates in the two species is highly similar despite their difference in growth rates. The authors further find that the mutation spectrum is enriched for signatures of spontaneous mutation and that a model of mutation arising from different sources is consistent with a large input of mutation from sources uncorrelated with cell division. The authors conclude that somatic mutation rates in these plants appears to be dictated by time, not cell division numbers, a finding that is in line with other eukaryotes studied so far.

In general, this work shows careful consideration and study design, and the multiple lines of evidence presented provide good support for the authors' conclusions. In particular, they use a sound approach to identify rare somatic mutations in the sampled trees including biological replicates, multiple SNP-callers and thresholds, and without presumption of a branching pattern.

Inter-species comparisons of absolute mutation rates is challenging. This is largely due to differences in SNP-calling methods and reference genome quality leading to variable sensitivity and specificity in identifying mutations. By applying their pipeline consistently across both species, the authors provide confidence in the comparative mutation rate results. Moreover, the presented false negative and false positive rate estimates for each species would apparently have minimal impact on the overall findings.

Despite the overall elegance of the authors' experimental setup, one methodological wrinkle warrants consideration. The authors compare the mutation rate per meter of growth, demonstrating that the rate is higher in slow-growing S. laevis: a key piece of evidence in favor of the authors' conclusion that somatic mutations track absolute time rather than cell division. To estimate the mutation rate per unit distance, they regress the per base-pair rate of mutations found between all pairwise branch tips against the physical distance separating the tips (Fig. 2a). While a regression approach is appropriate, the narrowness of the confidence interval is overstated as the points are not statistically independent: internal branches are represented multiple times. (For example, all pairwise comparisons involving a cambium sample will include the mutations arising along the lower trunk.) Regressing rates and lengths of distinct branches might be more appropriate. Judging from the data presented, however, the point estimates seem unlikely to change much.

This work deepens our understanding of how mutation operates at the cellular level by adding plants to the list of eukaryotes in which many mutations appear to derive from non-replicative sources. Given these results, it is intriguing to consider whether there is a fundamental mechanism linking mutation across distantly related species. Plants, generally, present a unique opportunity in the study of mutation as the germline is not sequestered, as it is in animals, and thus the forces of both mutation and selection acting throughout an individual plant's life could in principle affect the mutations transmitted to seed. The authors touch on this aspect, finding no evidence for a reduction in non-synonymous somatic mutations relative to the background rate, but more work-both experimental and observational-is needed to understand the dynamics of mutation and cell-competition within an individual plant. Overall, these results open the door to several intriguing questions in plant mutation. For example, is somatic mutation age-dependent in other species, and do other tropical plants harbor a high mutation rate relative to temperate genera? Any future inquiries on this topic would benefit from modeling their approach for identifying somatic mutations on the methods laid out here.

Reviewer #3 (Public Review):

Overview<br /> The authors propose a personalized ventricular computational model (Geno-DT) that incorporates the patient's structural remodeling (fibrosis and scar locations based on LGE-CMR scans) as well as genotyping (cell membrane kinetics based on genetic testing results) to predict VT locations and morphologies in ARVC setting.<br /> To test the model, the authors conducted a retrospective study using 16 ARVC patient data with two genotypes (PKP2, GE) and reported high degree of sensitivity, specificity, and accuracy. In addition, the authors determined that in GE patients, VT was driven by fibrotic remodeling, whereas, in PKP2 patients, VT was associated with a combination of structural and electrical remodeling (slowed conduction and altered restitution).<br /> Based on the findings, the authors recommend using Geno-DT approach to augment therapeutic accuracy in treatment of ARVC patients.

Critiques<br /> 1. The small sample size is a limitation but has already been acknowledge and documented by the authors.<br /> 2. Another limitation is the consideration of only two of the possible genotypes in developing the cell membrane kinetics, but again has acknowledged by the authors.

Final Thoughts<br /> The authors have done a commendable job in targeting a disease phenotype that is relatively rare, which constrains the type of data that can be collected for research. Their personalized computational model approach makes a valuable contribution in furthering our understanding of ARVC mechanisms.

Reviewer #3 (Public Review):

Summary:<br /> During successive rounds of cell division in E. coli, a lineage of increasingly aging progeny arises whose members exhibit decreased elongation rates, increased accumulation of inclusion bodies, and reduced gene expression. These hallmarks of physiological aging point to an evolutionary antecedent to the better-studied phenomenon of biological aging in eukaryotic systems. In this work, the authors find an upstream phenotype attributable to this aging lineage of E. coli cells: a marked decrease in cellular ribosome levels. The authors conjecture that such an upstream effect may have cascading effects on cellular metabolism and reduced gene expression. This is a new hypothesis that challenges the more broadly held view that toxicity from protein aggregates asymmetrically retained by mother cells is the cause of asymmetric growth rates. The thesis and the broad scope that it entails offer a number of exciting directions to engage with in the future.

Strengths:<br /> The authors' single-cell analysis convincingly shows differential partitioning of ribosomes that correlates with growth (elongation) rates between daughter cells. This makes the authors' novel hypothesis that asymmetric ribosome partitioning determines asymmetric cell growth plausible.

Weaknesses:<br /> The authors' did not measure levels of misfolded proteins in mother and daughter cells to distinguish between a toxicity model (retained aggregates are toxic to older cells) and a protein synthesis disadvantage model (less ribosomes, slower growth in older cells) to explain slower growth in aged cells. Therefore, while the authors' hypothesis is plausible, it is not the sole potential mechanism that explains their observations.

Reviewer #3 (Public Review):

Summary:<br /> The authors employed a new strategy, covalent substrate-labeling, to address the open issue of the substrate transport mechanism by single particle cryogenic-EM. A cyclic peptide (QZ-Ala), which was already used in the past as a substrate for structural purposes, was modified and covalently attached to ABCB1 at strategic positions in the transmembrane domain via Cys-specific coupling chemistry. Overall, four mutations (two per TMD) were generated and functionally analyzed. These residues are in proximity to the QZ-Ala binding site and are labeled by verapamil. Interestingly, two mutants could only be labeled if ATP and Mg2+ were present.

Strengths:<br /> Three of the four mutants were structurally analyzed by single particle cryo-EM with structures in both, the inward- and outward-facing conformation and overall resolutions ranging from 2.6 to 4.3 Å. Applying multi-model analyses allowed for the extraction of additional structures from one data set. These structures formed the basis for a detailed analysis of the substrate translocation pathway. This enabled the researcher to compare the IF and OF states of the same mutant and same substrate, which is pivotal for their conclusions. The mutations 335/978 trap the substrate at different points of the translocation pathway, while 971 located two helical turns away from the first set, trapped the system at a later stage. The described strategy revealed a cascade of conformational changes during substrate transport which focus on TMH1, which is straight in the IF state, but swings out in the OF state. Pivotal for such a change is G72. This residue was mutated to Ala and also structurally analyzed. These structures were supported by MD simulations and functional data. Thus, the new prosed kinking and straightening mechanisms are different from the so far accepted wide-open OF state observed in bacterial transporters. This clearly suggests a different mechanism for ABCB1.

Weaknesses:<br /> I have a couple of minor issues that I have listed in the section recommendations for authors Overall, the manuscript is very well written, sheds new light on the molecular mechanism of substrate translocation by ABCB1, and might even provide a new starting point for inhibitor design. I know that it is unusual, but I like the manuscript in its current version and recommend acceptance in its current form.

Reviewer #3 (Public Review):

Summary:<br /> This work uses multiscale molecular dynamics simulations to demonstrate molecular mechanism(s) for phosphatidylinositol regulation of voltage gated sodium channel (Nav1.4) gating. Recent experimental work by Gada et al. JGP 2023 showed altered Nav1.4 gating when Nav1.4 current was recorded with simultaneous application of PI(4,5)P2 dephosphorylate. Here the authors revealed probable molecular mechanism that can explain PI(4,5)P2 modulation of Nav1.4 gating. They found PIP lipids interacting with the gating charges - potentially making it harder to move the voltage sensor domain and altering the channels voltage sensitivity. They also found a stable PIP binding site that reaches the D_IV S4-S5 linker, reducing the mobility of the linker and potentially competing with the C-terminal domain.

Strengths:<br /> Using multiscale simulations with course-grained simulations to capture lipid-protein interactions and the overall protein lipid fingerprint and then all-atom simulations to verify atomistic details for specific lipid-protein interactions is extremely appropriate for the question at hand. Overall, the types of simulation and their length are suitable for the questions the authors pose and a thorough set of analysis was done which illustrates the observed PIP-protein interactions.

Weaknesses:<br /> Although the set of current simulations and analysis supports the conclusions drawn nicely, there are some limitations imposed by the authors on the course-grained simulations. If those were not imposed, it would have allowed for an even richer set and more thorough exploration of the protein-lipid interactions. The Martini 2 force field indeed cannot change secondary structure but if run with a properly tuned elastic network instead of backbone restraints, the change in protein configuration can be sampled and/or some adaptation of the protein to the specific protein environment can be observed. Additionally, with the 4to1 heavy atoms to a bead mapping some detailed chemical specificity is averaged out but parameters for different PIP family members do exist - including specific PIP(4,5)P2 vs PIP(3,4)P2, and could have been explored.

Reviewer #3 (Public Review):

Summary:<br /> This study investigates subtelomeric repetitive sequences in the budding yeast Saccharomyces cerevisiae, known as Y' and X-elements. Taking advantage of yeast strain SY12 that contains only 3 chromosomes and six telomeres (normal yeast strains contain 32 telomeres) the authors are able to generate a strain completely devoid of Y'- and X-elements.

Strengths: They demonstrate that the SY12 delta XY strain displays normal growth, with stable telomeres of normal length that were transcriptionally silenced, a key finding with wide implications for telomere biology. Inactivation of telomerase in the SY12 and SY12 delta XY strains frequently resulted in survivors that had circularized all three chromosomes, hence bypassing the need for telomeres altogether. The SY12 and SY12 delta XY yeast strains can become a useful tool for future studies of telomere biology. The conclusions of this manuscript are mostly well supported by the data and are important for researchers studying telomeres.

Weaknesses: A weakness of the manuscript is the analysis of telomere transcriptional silencing. They state: "The results demonstrated a significant increase in the expression of the MPH3 and HSP32 upon Sir2 deletion, indicating that telomere silencing remains effective in the absence of X and Y'-elements". However, there are no statistical analyses performed as far as I can see. For some of the strains, the significance of the increased expression in sir2 (especially for MPH3) looks questionable. In addition, a striking observation is that the SY12 strain (with only three chromosomes) express much less of both MPH3 and HSP32 than the parental strain BY4742 (16 chromosomes), both in the presence and absence of Sir2. In fact, the expression of both MPH3 and HSP32 in the SY12 sir2 strain is lower than in the BY4742 SIR2+ strain. In addition, relating this work to previous studies of subtelomeric sequences in other organisms would make the discussion more interesting.

Reviewer #3 (Public Review):

Summary: in this manuscript, Hansen and co-authors investigated the role of R-coils in the multimerization and ice nucleation activity of PbINP, an ice nucleation protein identified in Pseudomonas borealis. The results of this work suggest that the length, localization, and amino acid composition of R-coils are crucial for the formation of PbINP multimers.

Strengths: The authors use a rational mutagenesis approach to identify the role of the length, localisation, and amino acid composition of R-coils in ice nucleation activity. Based on these results, the authors hypothesize a multimerization model. Overall, this is a multidisciplinary work that provides new insights into the molecular mechanisms underlying ice nucleation activity.

Weaknesses: Several parts of the work appear cryptic and unsuitable for non-expert readers. The results of this work should be better described and presented.

Reviewer #3 (Public Review):

The findings of Bo Yu and colleagues titled "Identification of fallopian tube microbiota and its association with ovarian cancer: a prospective study of intraoperative swab collections from 187 patients" describes the identification of the fallopian tube microbiome and relationship with ovarian cancer. The studies are highly rigorous obtaining specimens from the fallopian tube, ovarian surfaces, paracolic gutter of patients of known or suspected ovarian cancer or benign tumor patients. The investigators took great care to ensure there was no or limited contamination including test the surgical suite air, as the test locations are from low abundance microbiota. The findings provide evidence that the microbiota in the fallopian tube, especially in ovarian cancer has similarities to gut microbial communities. This is a potentially novel observation.

The studies investigate the microbiome of >1000 swabs from 81 ovarian cancer and 106 non-cancer patients. The sites collected are low biomass microbiota making the study particularly challenging. The studies provide descriptive evidence that the ovarian cancer fallopian tube microbiota contain species that are similar to the gut microbiota. In contrast the fallopian tube microbiota of non-cancer patients that exhibit more similarity to the uterine/cervical microbiota. This may be a relevant observation but is highly descriptive with limited insights on the functional relevance.

The data indicate the presence of low biomass FT microbiota. The findings support the existence of FT microbiota in ovarian cancer that appears to be related to gut microbial species. While interesting, there is no insights on how and why these microbial species are found in the FT. The studies only identify the species but there is no transcriptomic analysis to provide an indication on whether the bacteria are activating DNA damage pathways. This is an interesting observation that requires more insights to address how these bacteria reach the fallopian tube and a related question is whether these bacteria are found in the peritoneum.

An additional concern is whether these data can be used to develop biomarkers of disease and early detection of disease. can the investigators detect the ovarian cancer FT microbiota in cervical/vaginal secretions? That may yield more significant insights for the field.

David Lynch described the method from Frank Daniel (1926-1996) of the American Film Institute and Dean of advanced film studies who taught students to plot out their screenplays using 3 x 5" index cards. One would write out a total of 70 cards each with scene headings. Once fleshed out, one would have a complete screenplay.

via https://www.youtube.com/watch?v=yngWNmouhP0

Reviewer #3 (Public Review):

The presented structure of the ToxR and ToxS periplasmic domain complex reveals the formation of a bile binding pocket at the interface, stabilized in the heterodimer structure. In addition to the structural data, a series of biophysical interaction experiments were performed between sodium cholate and the ToxR periplasmic domain alone, as well as the ToxR-ToxS complex, to characterize the bile binding.

Reviewer #3 (Public Review):

In this study, the authors developed and tested a novel framework for extracting muscle synergies. The approach aims at removing some limitations and constraints typical of previous approaches used in the field. In particular, the authors propose a mathematical formulation that removes constraints of linearity and couples the synergies to their motor outcome, supporting the concept of functional synergies and distinguishing the task-related performance related to each synergy. While some concepts behind this work were already introduced in recent work in the field, the methodology provided here encapsulates all these features in an original formulation providing a step forward with respect to the currently available algorithms. The authors also successfully demonstrated the applicability of their method to previously available datasets of multi-joint movements.

Preliminary results positively support the scientific soundness of the presented approach and its potential. The added values of the method should be documented more in future work to understand how the presented formulation relates to previous approaches and what novel insights can be achieved in practical scenarios and confirm/exploit the potential of the theoretical findings.

In their revision, the authors have implemented major revisions and improved their paper. The work was already of good quality and now it has improved further. The authors were able to successfully:<br /> - improve the clarity of the writing (e.g.: better explaining the rationale and the aims of the paper);<br /> - extend the clarification of some of the key novel concepts introduced in their work, like the redundant synergies;<br /> - show a scenario in which their approach might be useful for increasing the understanding of motor control in patients with respect to traditional algorithms such as NMF. In particular, their example illustrates why considering the task space is a fundamental step forward when extracting muscle synergies, improving the practical and physiological interpretation of the results.

Reviewer #3 (Public Review):

In this manuscript, Picard et al. propose a Facial Expression Pain Signature (FEPS) as a distinctive marker of pain processing in the brain. Specifically, they attempt to use functional magnetic resonance imaging (fMRI) data to predict facial expressions associated with painful heat stimulation.

The main strengths of the manuscript are that it is built on an extensive foundation of work from the research group, and that experience can be observed in the analysis of fMRI data and the development of the machine learning model. Additionally, it provides a comparative account of the similarities of the FEPS with other proposed pain signatures. The main weaknesses of the manuscript are the absence of a proper control condition to assess the specificity of the facial pain expressions, a few relevant omissions in the methodology regarding the original analysis of the data and its purpose, and a biased interpretation of the results.

I believe that the authors partially succeed in their aims, as described in the introduction, which are to assess the association between pain facial expression and existing pain-relevant brain signatures, and to develop a predictive brain activation model of the facial responses to painful thermal stimulation. However, I believe that there is a clear difference between those aims and the claim of the title, and that the interpretation of the results needs to be more rigorous.

Reviewer #3 (Public Review):

Summary:<br /> In Hunter, Coulson et al, the authors seek to expand our understanding of how neural activity during developmental critical periods might control the function of the nervous system later in life. To achieve increased excitation, the authors build on their previous results and apply picrotoxin 17-19 hours after egg-laying, which is a critical period of nervous system development. This early enhancement of excitation leads to multiple effects in third-instar larvae, including prolonged recovery from electroshock, increased synchronization of motor neuron networks, and increased AP firing frequency. Using optogenetics and whole-cell patch clamp electrophysiology, the authors elegantly show that picrotoxin-induced over-excitation leads to increased strength of excitatory inputs and not loss of inhibitory inputs. To enhance inhibition, the authors chose an approach that involved the stimulation of mechanosensory neurons; this counteracts picrotoxin-induced signs of increased excitation. This approach to enhancing inhibition requires further control experiments and validation.

Strengths:<br /> • The authors confirm their previous results and show that 17-19 hours after egg laying is a critical period of nervous system development.<br /> • Using Ca2+/Sr2+ substitutions, the authors demonstrate that synaptic connections between A18a  aCC show increased mEPSP amplitudes. The authors show that this aCC input is what is driving enhanced excitation.<br /> • The authors demonstrate that the effects of over-excitation attributed to picrotoxin exposure are generalizable and also occur in bss mutant flies.

Weaknesses:<br /> • The authors build on their previous work and argue that the critical period (17-19h after egg-laying) is a uniquely sensitive period of development. Have the authors already demonstrated that exposure to picrotoxin at L1 or L2 (and even early L3 if experimentally possible) does not lead to changes in induced seizure at L3? This would further the authors' hypothesis of the uniqueness of the 17-19h AEL period. If this has already been established in prior publications, then this needs to be further explained. I do note in Gaicehllo and Baines (2015) that Fig 2E shows the identification of the 17-19h window.<br /> • Regarding experiments in Fig 2, authors only report changes in AP firing frequency. Can the authors also report other metrics of excitability, including measures of intrinsic excitability with and without picrotoxin exposure (including RMP, Rm)? Was a different amount of current injection needed to evoke stable 5-10 Hz firing with and without picrotoxin? In the representative figure (Fig. 2A), it appears that the baseline firing frequencies are different prior to optogenetic stimulation.<br /> • The ch-related experiments require further controls and explanation. Regarding experiments in Fig 6, what is the effect of ch neuron stimulation alone on time lag and AP frequency? Can the authors further clarify what is known about connections between aCC and ch neurons? It is difficult for this reviewer to conceptualize how enhancing ch-mediated inhibition would worsen seizures. While the cited study (Carreira-Rosario et al 2021) convincingly shows that inhibition of mechanosensory input leads to excessive spontaneous network activity, has it been shown that the converse - stimulation of ch neurons - indeed enhances network inhibition?<br /> • The interpretation of ch-related experiments is further complicated by the explanation in the Discussion that ch neuron stimulation depolarizes aCC neurons; this seems to undercut the authors' previous explanation that the increased E:I ratio is corrected by enhanced inhibition from ch neurons. The idea that ch neurons are placing neurons in a depolarized refractory state is not substantiated by data in the paper or citations.<br /> • In the Discussion, the authors suggest that enhanced proprioception leading to seizures is reminiscent of neurological conditions. This seems to be an oversimplification. Connecting abnormal proprioception to seizures is quite different from connecting abnormal proprioception to disorders of coordination. This should be revised.

Reviewer #3 (Public Review):

Summary:<br /> I am pleased to have had the opportunity to review this manuscript, which investigated the role of statistical learning in the modulation of pain perception. In short, the study showed that statistical aspects of temperature sequences, with respect to specific manipulations of stochasticity (i.e., randomness of a sequence) and volatility (i.e., speed at which a sequence unfolded) influenced pain perception. Computational modelling of perceptual variables (i.e., multi-dimensional ratings of perceived or predicted stimuli) indicated that models of perception weighted by expectations were the best explanation for the data. My comments below are not intended to undermine or question the quality of this research. Rather, they are offered with the intention of enhancing what is already a significant contribution to the pain neuroscience field. Below, I highlight the strengths and weaknesses of the manuscript and offer suggestions for incorporating additional methodological details.

Strengths:<br /> - The manuscript is articulate, coherent, and skilfully written, making it accessible and engaging.

- The innovative stimulation paradigm enables the exploration of expectancy effects on perception without depending on external cues, lending a unique angle to the research.

- By including participants' ratings of both perceptual aspects and their confidence in what they perceived or predicted, the study provides an additional layer of information to the understanding of perceptual decision-making. This information was thoughtfully incorporated into the modelling, enabling the investigation of how confidence influences learning.

- The computational modelling techniques utilised here are methodologically robust. I commend the authors for their attention to model and parameter recovery, a facet often neglected in previous computational neuroscience studies.

- The well-chosen citations not only reflect a clear grasp of the current research landscape but also contribute thoughtfully to ongoing discussions within the field of pain neuroscience.

Weaknesses:<br /> - In Figure 1, panel C, the authors illustrate the stimulation intensity, perceived intensity, and prediction intensity on the same scale, facilitating a more direct comparison. It appears that the stimulation intensity has been mathematically transformed to fit a scale from 0 to 100, aligning it with the intensity ratings corresponding to either past or future stimuli. Given that the pain threshold is specifically marked at 50 on this scale, one could logically infer that all ratings falling below this value should be deemed non-painful. However, I find myself uncertain about this interpretation, especially in relation to the term "arbitrary units" used in the figure. I would greatly appreciate clarification on how to accurately interpret these units, as well as an explanation of the relationship between these values and the definition of pain threshold in this experiment.

- The method of generating fluctuations in stimulation temperatures, along with the handling of perceptual uncertainty in modelling, requires further elucidation. The current models appear to presume that participants perceive each stimulus accurately, introducing noise only at the response stage. This assumption may fail to capture the inherent uncertainty in the perception of each stimulus intensity, especially when differences in consecutive temperatures are as minimal as 1{degree sign}C.

- A key conclusion drawn is that eKF is a better model than eRL. However, a closer examination of the results reveals that the two models behave very similarly, and it is not clear that they can be readily distinguished based on model recovery and model comparison results.

Regarding model recovery, the distinction between the eKF and eRL models seems blurred. When the simulation is based on the eKF, there is no ability to distinguish whether either eKF or eRL is better. When the simulation is based on the eRL, the eRL appears to be the best model, but the difference with eKF is small. This raises a few more questions. What is the range of the parameters used for the simulations? Is it possible that either eRL or eKF are best when different parameters are simulated? Additionally, increasing the number of simulations to at least 100 could provide more convincing model recovery results.

Regarding model comparison, the authors reported that "the expectation-weighted KF model offered a better fit than the eRL, although in conditions of high stochasticity, this difference was short of significance against the eRL model." This interpretation is based on a significance test that hinges on the ratio between the ELPD and the surrounding standard error (SE). Unfortunately, there's no agreed-upon threshold of SEs that determines significance, but a general guideline is to consider "several SEs," with a higher number typically viewed as more robust. However, the text lacks clarity regarding the specific number of SEs applied in this test. At a cursory glance, it appears that the authors may have employed 2 SEs in their interpretation, while only depicting 1 SE in Figure 4.

- With respect to parameter recovery, a few additional details could be included for completeness. Specifically, while the range of the learning rate is understandably confined between 0 and 1, the range of other simulated parameters, particularly those without clear boundaries, remains ambiguous. Including scatter plots with the simulated parameters on the x-axis and the recovered parameters on the y-axis would effectively convey this missing information. Furthermore, it would be beneficial for the authors to clarify whether the same priors were used for both the modelling results presented in the main paper and the parameter recovery presented in the supplementary material.

- While the reliance on R-hat values for convergence in model fitting is standard, a more comprehensive assessment could include estimates of the effective sample size (bulk_ESS and/or tail_ESS) and the Estimated Bayesian Fraction of Missing Information (EBFMI), to show efficient sampling across the distribution. Consideration of divergences, if any, would further enhance the reliability of the results.

- The authors write: "Going beyond conditioning paradigms based in cuing of pain outcomes, our findings offer a more accurate description of endogenous pain regulation." Unfortunately, this statement isn't substantiated by the results. The authors did not engage in a direct comparison between conditioning and sequence-based paradigms. Moreover, even if such a comparison had been made, it remains unclear what would constitute the gold standard for quantifying "endogenous pain regulation."

Reviewer #3 (Public Review):

Summary:

Coping with stress by the animal in danger is essential for survival. The current study identified a novel population of neurons in the murine supramammillary nucleus (SuM) projecting to the POA as well as diverse brain regions relevant to the decision-making by combinatory labeling of the neurons with adeno-associated viruses (AAVs). Such a unique population of glutamatergic neurons was activated under a variety of acute stress, while the optogentic stimulation of them induced behaviors relevant to the active coping of the stress.

Strengths:

Discovery of the neural circuit converting the passive to the active stress coping strategy of the behavior in this study will provide deep insight into understanding how the animal survives with flexibility and must be informative for the neuroscience community.

Weaknesses:

Despite a large advance in understanding the role of this circuit in behavior in the study, I primarily have concerns about the interaction between SuM and other neural pathways.

Reviewer #3 (Public Review):

Flagella are crucial for bacterial motility and virulence of pathogens. They represent large molecular machines that require strict hierarchical expression control of their components. So far, mainly transcriptional control mechanisms have been described to control flagella biogenesis. While several sRNAs have been reported that are environmentally controlled and regulate motility mainly via control of flagella master regulators, less is known about sRNAs that are co-regulated with flagella genes and control later steps of flagella biogenesis.

In this carefully designed and well-written study, the authors explore the role of four E. coli σ28-dependent 3' or 5' UTR-derived sRNAs in regulating flagella biogenesis. UhpU and MotR sRNAs are generated from their own σ28(FliA)-dependent promoter, while FliX and FlgO sRNAs are processed from the 3'UTRs of flagella genes under control of FliA. The authors provide an impressive amount of data and different experiments, including phenotypic analyses, genomics approaches as well as in-vitro and in-vivo target identification and validation methods, to demonstrate varied effects of three of these sRNAs (UhpU, FliX and MotR) on flagella biogenesis and motility. For example, they show different and for some sRNAs opposing phenotypes upon overexpression: While UhpU sRNA slightly increases flagella number and motility, FliX has the opposite effect. MotR sRNA also increases the number of flagella, with minor effects on motility.

While the mechanisms and functions of the fourth sRNA, FlgO, remain elusive, the authors provide convincing experiments demonstrating that the three sRNAs directly act on different targets (identified through the analysis of previous RIL-seq datasets), with a variety of mechanisms. The authors demonstrate, UhpU sRNA, which derives from the 3´UTR of a metabolic gene, downregulates LrhA, a transcriptional repressor of the flhDC operon encoding the early genes that activate the flagellar cascade. According to their RIL-seq data analyses, UhpU has hundreds of additional potential targets, including multiple genes involved in carbon metabolism. Due to the focus on flagellar biogenesis, these are not further investigated in this study and the authors further characterize the two other flagella-associated sRNAs, FliX and MotR. Interestingly, they found that these sRNAs seem to target coding sequences rather than acting via canonical targeting of ribosome binding sites. The authors show FliX sRNA represses flagellin expression by interacting with the CDS of the fliC mRNA. Both FliX and MotR sRNA turn out to modulate the levels of ribosomal proteins of the S10 operon with opposite effects. MotR, which is expressed earlier, interacts with the leader and the CDS of rpsJ mRNA, leading to increased S10 protein levels and S10-NusB complex mediated anti-termination, promoting readthrough of long flagellar operons. FliX interacts with the CDSs of rplC, rpsQ, rpsS-rplV, repressing the production of the encoded ribosomal proteins. The authors also uncover MotR and FliX affect transcription selected representative flagellar genes, with an unknown mechanism.

Overall, this comprehensive study expands the repertoire of characterized UTR derived sRNAs and integrates new layers of post-transcriptional regulation into the highly complex flagellar regulatory cascade. Moreover, these new flagella regulators (MotR, FliX) act non-canonically, and impact protein expression of their target genes by base-pairing with the CDS of the transcripts. Their findings directly connect flagella biosynthesis and motility, highly energy consuming processes, to ribosome production (MotR and FliX) and possibly to carbon metabolism (UhpU). In their revised version, the authors have addressed many of the previously raised questions and comments. This made their manuscript easier to read and to follow.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, Lim and colleagues use an innovative CDRA chip platform to derive and mechanistically elucidate the molecular wiring of doxorubicin-resistant (DOXR) MDA-MB-231 cells. Given their enlarged morphology and polyploidy, they termed these cells as Large-DOXR (L-DORX). Through comparative functional omics, they deduce the NUPR1/HDAC11 axis to be essential in imparting doxorubicin resistance and, consequently, genetic or pharmacologic inhibition of the NUPR1 to restore sensitivity to the drug.

Strengths:<br /> The study focuses on a major clinical problem of the eventual onset of resistance to chemotherapeutics in patients with triple-negative breast cancer (TNBC). They use an innovative chip-based platform to establish as well as molecularly characterize TNBC cells showing resistance to doxorubicin and uncover NUPR1 as a novel targetable driver of the resistant phenotype.

Weaknesses:<br /> Critical weaknesses are the use of a single cell line model (i.e., MDA-MB-231) for all the phenotypic and functional experiments and absolutely no mechanistic insights into how NUPR1 functionally imparts resistance to doxorubicin. It is imperative that the authors demonstrate the broader relevance of NUPR1 in driving dox resistance using independent disease models.

Reviewer #3 (Public Review):

In the current study, Li et al. address the difficulty in early non-invasive cancer diagnosis due to the limitations of current diagnostic methods in terms of sensitivity and specificity. The study brings attention to exosomes - membrane-bound nanovesicles secreted by cells, containing DNA, RNA, and proteins reflective of their originating cells. Given the prevalence of exosomes in various biological fluids, they offer potential as reliable biomarkers. Notably, the manuscript introduces a new computational approach, rooted in machine learning, to differentiate cancers by analyzing a set of proteins associated with exosomes. Utilizing exosome protein datasets from diverse sources, including cell lines, tissues, and various biological fluids, the study spotlights five proteins as predominant universal exosome biomarkers. Furthermore, it delineates three distinct panels of proteins that can discern cancer exosomes from non-cancerous ones and assist in cancer subtype classification using random forest models. Impressively, the models based on proteins from plasma, serum, or urine exosomes achieve AUROC scores above 0.91, outperforming other algorithms such as Support Vector Machine, K Nearest Neighbor Classifier, and Gaussian Naive Bayes. Overall, the study presents a promising protein biomarker signature tied to cancer exosomes and proposes a machine learning-driven diagnostic method that could potentially revolutionize non-invasive cancer diagnosis.

Reviewer #3 (Public Review):

In this manuscript, Daniels et al explored the role of Cystatin F in an A-driven mouse model of Alzheimer's disease. By crossing a constitutive knockout mouse lacking the gene that encodes Cystatin F, Cst7, to the AppNL-G-F mouse line, the authors describe impairments in microglial gene expression and phagocytic function that emerge more prominently in females versus males lacking Cst7. A strength of the study is its focus: given mounting evidence that microglia are a hub of neurological dysfunction with particular potential to trigger or exacerbate neurodegenerative disorders, it is essential to determine the changes in microglia that occur pathologically to promote disease progression. Similarly, the wide-spread identification of the gene in question, Cst7, as upregulated in AD models makes this gene a good target for mechanistic studies.

The paper in its current form also has several weaknesses which limit the insights derived, weaknesses that are largely related to the experimental tools and approaches chosen by the authors to test their hypotheses. For example, the paper begins with a figure replotting data from previous studies showing that Cst7 is upregulated in mouse models of Alzheimer's disease. Though relevant to the current study, there are no new insights provided here. Next, the authors perform bulk RNA-sequencing on microglia isolated from male and female mice in the Cst7-/-; AppNL-G-F mouse line. In the methods, it is unclear whether the authors took precautions to preserve the endogenous transcriptional state of these cells given evidence that microglia can acquire a DAM-like signature simply due to the process of dissociation (Marsh et al, Nature Neuroscience, 2022). If the authors did not control for this, their results may not support the conclusions they draw from the data. Relatedly, it appears the authors pooled all microglia together here, instead of just isolating DAMs specifically or analyzing microglia at single-cell resolution, which could reveal the heterogeneous nature of the role of Cst7 in microglia. In addition to losing information about heterogeneity, another concern is that they could be diluting out the major effects of the model on microglial function by including all microglia. Overall, the biggest issue I have with the RNA-sequencing data is the lack of validation of the gene expression changes identified using a different method that does not require dissociation, like immunohistochemistry or fluorescence in situ hybridization. Especially given the limited number of genes they found to be mis-regulated (see Fig. 2 E and G), I worry that these changes might simply be noise, especially since the authors provide no further evidence of their mis-regulation. Without further validation, the data presented are not sufficient to support the authors' claims.

In assessing the changes in microglial function and A pathology that occur in males and females of the Cst7-/-; AppNL-G-F line, the authors identify some differences between how females and males are affected by the loss of Cst7. While the statistical analyses the authors perform as given in the figure legends appear to be correct, the plots do not show significant changes between males and females for a given parameter. Take for example Figure 3H. Loss of Cst7 decreases IBA+Lamp+ microglia in males but increases this parameter in females. However, it does not appear that there is a significant difference in IBA+Lamp+ microglia in male versus female mice lacking Cst7. If there is no absolute difference between males and females, can the differential effects of Cst7 knockout on the sexes really be so relevant to the sexual dimorphism observed in the disease? I question this connection, but perhaps a greater discussion of what the result might mean by the authors would be helpful for placing this into context.

Finally, the use of in vitro assays of microglial function can be helpful as secondary analyses when coupled with in vivo or ex vivo approaches, but are not on their own sufficient to support the authors' conclusions. Quantitative engulfment assays (see Schafer et al, Neuron, 2012) on brain tissue showing that male and female microglia lacking Cst7 engulf different amounts of material (e.g. plaques, synapses, myelin) in the intact brain would be more convincing.

In general, a major limitation to the insights that can be derived in the study is the decision of the authors to perform all experiments at a single late-stage time point of 12 months of age. As this is quite far into disease progression for many AD models, phenotypic changes identified by the authors could arise due to the downstream effects of plaque deposition and therefore may not implicate Cst7 as a mechanism driving neurodegeneration rather than one of many inflammatory changes that accompany AD mouse models nearing the one-year time point. A related problem is that the study uses a constitutive KO mouse that has lacked Cst7 expression throughout life, not just during disease processes that increase with aging. In summary, the topic of the article is important and timely, but the connection between the data and the authors' conclusions is not as strong as it could be.

Reviewer #3 (Public Review):

CFTR is an anion-selective channel that plays important roles in epithelial physiology. In this paper, Simon and colleagues focus on the step of the CFTR gating cycle that opens the pore. But the authors are particularly interested in the reversal of this opening step. Wild-type (WT) CFTR channels do not usually close by reversal of the opening step, as closure via this "non-hydrolytic" pathway is slow. Instead, hydrolysis of the ATP molecule bound at site 2 destabilizes the open (or bursting) channel and triggers rapid "hydrolytic" channel closure - before the open channel has time to overcome the energetic barrier on the non-hydrolytic pathway. While it is generally (but not universally) accepted that such a non-equilibrium kinetic scheme underlies CFTR gating, how tightly gating and ATPase cycles are coupled is still quite controversial.

Here, combining simple electrophysiology measurements on mutant channels with solid arguments, the authors provide an improved estimate for the backward rate on the opening transition (rate k-1) in WT-CFTR channels. It turns out that this rate is indeed slow, compared to the rate of the hydrolytic step (k1) allowing authors to conclude that WT CFTR channels close via reversal of the opening step only less than once every 100 gating cycles. In addition, results of thermodynamic mutant cycles and careful analysis of cryo-EM structures are used to support plausible molecular mechanisms that explain why different mutations in CFTR's catalytic site slow down, speed up or barely affect non-hydrolytic closure.

The strength of this study is twofold. First, the methods are sound, and the effects seen are clear-cut. Records are competently acquired, with a high number of repeats, are well analysed and very clearly presented. Second, the authors interpret their results with interdisciplinary competence, drawing on structural knowledge of ABC transporter catalytic mechanism, as well as on an in-depth understanding of studies investigating kinetics and thermodynamics of CFTR gating. This study, bringing together conclusions obtained in many previous studies, is a useful step forward towards a comprehensive description of the energetic landscape CFTR channel proteins wander through when gating. The Csanády lab has greatly contributed to developing this over the past years, and this paper reads as a "capstone".

However the reliance on previous conclusions is, in some ways, also a weakness. Many of the inferences made in interpreting the data depend on assumptions being met. There is evidence supporting the validity of these, but more clarity in stating implicit assumptions, and why the authors believe them to be valid, could improve the manuscript. The results fit well within the conceptual framework of CFTR's non-equilibrium gating. But some scientists, still sceptical of its basic premises, will not be convinced by these new results.

Within this context, the authors achieve their aim of estimating the microscopic rate constant for non-hydrolytic closure. The study will be of interest not only to scientists studying CFTR gating, but also to those wishing to understand how small-molecule drugs affect such gating. The mechanism of action of ivacaftor (currently taken by thousands of people for treatment of cystic fibrosis) is still not completely clear, and some evidence suggests that it stabilizes the pre-hydrolytic bursting state investigated here. Aspects of CFTR's conformational dynamics will probably also be true for some of its phylogenetic relatives. Thus, those studying other ABC transporters, many of which have medical relevance, will find it interesting to learn how CFTR couples its gating and hydrolytic cycles. This is especially true now, when cryogenic electron microscopy and other methods allow detailed structural comparisons between related ABC transporters, which can be correlated with differences in their function. Now more than ever CFTR could be a "model ABC protein".

Reviewer #3 (Public Review):

This paper examines the existence of a fear memory engram in acetylcholine neurons of the basal forebrain and seeks to link this to the modulation of the amygdala for fear expression. Using genetically encoded ACh sensors, they show that ACh is released in the basolateral amygdala (BLA) in response to cues that had been paired with aversive shock (CS+) and by shock itself. They then use a cfos activity capture specifically of ACh neurons approach to show that an overlapping population of basal forebrain ACh neurons are activated during learning and recall, that chemogenetically silencing them reduced aversive memory recall, and that these cells have enhanced excitability. Moving on to examining the role of basal forebrain ACh neurons in regulating BLA, the authors show that chemogenetically inhibiting BLA projecting ACh neurons reduces memory recall-induced Fos activity in BLA neurons. Finally, they demonstrate the importance of these cells in producing freezing responses to both learned and innate aversive stimuli, though from different ACh populations.

The identification of specific activity-defined acetylcholine neurons for aversive memory expression as well as the role of basal forebrain ACh neurons in regulating BLA to produce expression of defensive behaviors is important and interesting. However, the paper is missing important control groups and experiments that are necessary to adequately support the authors' claims.

Reviewer #3 (Public Review):

This work provides a new approach to simultaneously control elbow and wrist degrees of freedom using movement based inputs, and demonstrate performance in a virtual reality environment. The work is also demonstrated using a proof-of-concept physical system. This control algorithm is in contrast to prior approaches which electrophysiological signals, such as EMG, which do have limitations as described by the authors. In this work, the movements of proximal joints (eg shoulder), which generally remain under voluntary control after limb amputation, are used as input to neural networks to predict limb orientation. The results are tested by several participants within a virtual environment, and preliminary demonstrated using a physical device, albeit without it being physically attached to the user.

Strengths:<br /> Overall, the work has several interesting aspects. Perhaps the most interesting aspect of the work is that the approach worked well without requiring user calibration, meaning that users could use pre-trained networks to complete the tasks as requested. This could provide important benefits, and if successfully incorporated into a physical prosthesis allow the user to focus on completing functional tasks immediately. The work was also tested with a reasonable number of subjects, including those with limb-loss. Even with the limitations (see below) the approach could be used to help complete meaningful functional activities of daily living that require semi-consistent movements, such as feeding and grooming.

Weaknesses:<br /> While interesting, the work does have several limitations. In this reviewer's opinion, main limitations are: the number of 'movements' or tasks that would be required to train a controller that generalized across more tasks and limb-postures. The authors did a nice job spanning the workspace, but the unconstrained nature of reaches could make restoring additional activities problematic. This remains to be tested.

The weight of a device attached to a user will impact the shoulder movements that can be reliably generated. Testing with a physical prosthesis will need to ensure that the full desired workspace can be obtained when the limb is attached, and if not, then a procedure to scale inputs will need to be refined.

The reliance on target position is a complicating factor in deploying this technology. It would be interesting to see what performance may be achieved by simply using the input target positions to the controller and exclude the joint angles from the tracking devices (eg train with the target positions as input to the network to predict the desired angles).

Treating the humeral rotation degree of freedom is tricky, but for some subjects, such as those with OI, this would not be as large of an issue. Otherwise, the device would be constructed that allowed this movement.

Overall, this is an interesting preliminary study with some interesting aspects. Care must be taken to systematically evaluate the method to ensure clinical impact.

Reviewer #3 (Public Review):

The manuscript by Siachisumo et al builds upon a previous publication from the same group of collaborators that showed that depletion of mouse RBMXL2 leads to a block in spermatogenesis associated with mis-splicing, particularly of large exons in genes associated with genome stability (Ehrmann et al eLife 2019). RBMXL2 is an RNA-binding protein and an autosomal retrotransposed paralog of the X-chromosomally encoded RBMX. RBMXL2 is expressed during meiosis when RBMX and the more distantly related RBMY (on the Y chromosome) are silenced. It is therefore an appealing hypothesis that RBMXL2 might provide cover for RBMX function during meiosis. To address this hypothesis the authors analysed the transcriptomic consequences of RBMX depletion by RNA-Seq in human cells (MDA-MB-231 and existing RNA-Seq data from HEK293 cells), complemented by iCLIP to analyze the binding targets of FLAG-tagged RBMX in HEK293 cells. The findings convincingly demonstrate that - like RBMXL2 - RBMX mainly acts as a splicing repressor and that it particularly acts to protect the integrity of very long ("ultra-long") exons. Upon RBMX depletion, many of these exons are shortened due to the use of cryptic 5' and/or 3' splice sites. Moreover, affected genes are particularly enriched for functions associated with genome integrity - indeed "comet assays" show that RBMX depletion leads to DNA damage defects.

The manuscrupt therefore delivers a clear affirmative answer to the question of whether the two highly related proteins have similar molecular functions, particularly with respect to suppressing cryptic splicing that affects ultra-long exons. This conclusion is reinforced by the ability of induced expression of either RBMXL2 or RBMY to fully complement the effects of RBMX knockdown upon three target events in the ETAA1, REV3L, and ATRX genes.

The manuscript also includes some experiments that address more mechanistic questions, such as the potential for RBMX to block access of spliceosome components to splice site elements and structure-function analyses of RBMX. These areas have a distinctly "preliminary" feel to them. For example, for one target (ETAA1) it is shown that CLIP tags are close to mapped branchpoints. However, no attempt is made to integrate the RNA-Seq and iCLIP data-sets to look for more generalized relationships between binding and activity. Likewise, one experiment shows that the RRM domain of RBMXL2 is not necessary for activity. Given that the RRM domain represents only ~25% of the total RBMXL2 sequence, this is a somewhat preliminary, albeit interesting, observation. Another surprising omission was that there was no global comparison of the consequences of RBMX depletion and complementation by RBMXL2, despite the fact that the relevant RNA-Seq data-sets had been generated (Figure 4 supplement 1 shows RNA-Seq IGV tracks that confirm the effects on ETAA1, REV3L and ATRX shown by RT-PCR in Figure 4).

In summary, this manuscript provides clear evidence to support the role of RBMX as a repressor of cryptic splice sites in ultra-long exons, similar to RBMXL2.

Reviewer #3 (Public Review):

Summary:<br /> In this interesting study, the authors examine the function of a C. elegans neuroendocrine TGF-beta ligand DAF-7 in regulating foraging movement in response to signals of food and ingestion. Building on their previous findings that demonstrate the critical role of daf-7 in a sensory neuron ASJ in behavioral response to pathogenic P. aeruginosa PA14 bacteria and different foraging behavior between hermaphrodite and male worms, the authors show, here, that ingestion of E. coli OP50, a common food for the worms, suppresses ASJ expression of daf-7 and secreted water-soluble cues of OP50 increases it. They further showed that the level of daf-7 expression in ASJ is positively associated with a higher level of roaming/exploration movement. Furthermore, the authors identify that a C. elegans ortholog of Anaplastic Lymphoma Kinase, scd-2, functions in an interneuron AIA to regulate ASJ expression of daf-7 in response to food ingestion and related cues. These findings place the DAF-7 TGF-beta ligand in the intersection of environmental food conditions, food intake, and food-searching behavior to provide insights into how orchestrated neural functions and behaviors are generated under various internal and external conditions.

Strengths:<br /> The study addresses an important question that appeals to a wide readership. The findings are demonstrated by generally strong results from carefully designed experiments.

Weaknesses:<br /> However, a few questions remain to provide a complete picture of the regulatory pathways and some analyses need to be strengthened. Specifically,

1. The authors show that diffusible cues of bacteria OP50 increase daf-7 expression in ASJ which is suppressed by ingestible food. Their results on del-3 and del-7 suggest that NSM neuron suppresses daf-7 ASJ expression. What sensory neurons respond to bacterial diffusible cues to increase daf-7 expression of ASJ? Since ASJ is able to respond to some bacterial metabolites, does it directly regulate daf-7 expression in response to diffusible cues of OP50 or does it depend on neurotransmission for the regulation? Some level of exploration in this question would provide more insights into the regulatory network of daf-7.

2. The results including those in Figure 2 strongly support that daf-7 in ASJ is required for roaming. Meanwhile, authors also observe increased daf-7 expression in ASJ under several conditions, such as non-ingestible food. Does non-ingestible food induce more roaming? It would complete the regulatory loop by testing whether a higher (than wild type) level of daf-7 in ASJ could further increase roaming. The results in pdf-1 and scd-2 gain-of-function alleles support more ASJ leads to more roaming, but the effect of these gain-of-function alleles may not be ASJ-specific and it would be interesting to know whether ASJ-specific increase of daf-7 leads to a higher level of roaming. In my opinion, either outcome would be informative and strengthen our understanding of the critical function of daf-7 in ASJ demonstrated here.

3. The analyses in Figure 4 cannot fully support "We further observed that the magnitude of upregulation of daf-7 expression in the ASJ neurons when animals were moved from ingestible food to non-ingestible food was reduced in scd-2(syb2455) to levels only about one-fourth of those seen in wild-type animals (Figure 4D)...", because the authors tested and found the difference in daf-7 expression between ingestible and non-ingestible food conditions in both wild type and the mutant worms. The authors did not analyze whether the induction was different between wild type and mutant. Under the ingestible food condition, ASJ expression of daf-7 already looks different in scd-2(syb2455).

4. The authors used unpaired two-tailed t-tests for all the statistical analyses, including when there are multiple groups of data and more than one treatment. In their previous study Meisel et al 2014, the authors used one-way ANOVA, followed by Dunnett's or Tukey's multiple comparison test when they analyzed daf-7 expression or lawn leaving in different mutants or under different bacterial conditions. It is not clear why a two-tailed t-test was used in similar analyses in this study.

Reviewer #3 (Public Review):

Summary:<br /> Although the key role of estrogen receptor alpha (ERα ; encoded by ESR1 gene) in the control of reproduction has been known for more than two decades, the identity of the neuronal population(s) underlying the control of the negative and positive feedbacks exerted by estrogens on the hypothalamic pituitary ovary axis has been more complicated to pinpoint. Among the factors contributing to the difficulty in this endeavor are the cellular heterogeneity of the preoptic area and hypothalamus and the pulsatile activity of the axis. Several neuronal populations have been identified that control the activity of GnRH neurons, the hypothalamic headmasters of the axis. Among them, the kisspeptin neurons of the rostral periventricular aspect of the third ventricle (RP3V) have been considered the major candidates to convey preovulatory estrogen signals to GnRH neurons, which do not express ERα. Yet, the existence of other populations of kisspeptin neurons (notably in the arcuate nucleus) has made it difficult to selectively ablate ESR1 in one population. A first study (Wang et al., 2019) reported that knocking down ESR1 specifically in RP3V kisspeptin neurons led to decreased excitability of Kp neurons, blunted spontaneous as well estrogen-induced preovulatory LH surge, and reduced or absent corpora lutea indicative of impaired ovulation, but cyclicity was left unaltered.

As GABAergic afferences to GnRH neurons are also implicated in mediating the effects of estrogens on the HPG axis, the present study sought to investigate their role in the positive feedback using genetically driven Crispr-Cas9 mediated knockdown of ERα in VGAT expressing neurons in a specific subregion of the preoptic area. To this end, they stereotaxically delivered viral vectors expressing validated guide RNA into the preoptic area and evaluated their impact on estrus cyclicity and the ability of mice to mount an LH surge induced by estrogens and associated activation of GnRH neurons assessed by the co-expression of Fos. The results demonstrate that knocking down ESR1 in preoptic gabaergic neurons leads to an absence of LH surge and acyclicity when associated with severely reduced kisspeptin expression suggesting that a subpopulation of neurons co-expressing Kp and VGAT neurons are key for LH surge since total absence of Kp is associated with an absence of GnRH neuron activation and reduced LH surge (although this was not confirmed by the post-hoc). Although the implication of kisspeptin neurons was highly suspected already, the novelty of these results lies in the fact that estrogen signaling is necessary for only a selected fraction of them to maintain both regular cycles and LH surge capacity.

Strengths:<br /> Remarkable aspects of this study are, its dataset which allowed them to segregate animals based on distinct neuronal phenotypes matching specific physiological outcomes, the transparency in reporting the results (e.g. all statistical values being reported, all grouping variables being clearly defined, clarity about animals that were excluded and why) and the clarity of the writing. This allows the reader to understand clearly what has been done and how the analyses have been carried out. The same applies to the discussion which describes clearly possible interpretations as well as the limitations of this study based on a single in vivo experiment.

Another remarkable feature of this work lies in the analysis of the dataset. As opposed to the cre-lox approach which theoretically allows for the complete ablation of specific neuronal populations, but may lack specificity regarding timing of action and location, genetically driven in vivo Crispr-Cas9 editing offers both temporal and neuroanatomic selectivity but cannot achieve a complete knockdown. This approach based on stereotaxic delivery of the AAV-encoded guide RNAs comes with inevitable variability in the location where gene knockdown is achieved. By adjusting their original grouping of the animals based on the evaluation of the extent of kisspeptin expression in the target region, the authors obtained a much clearer and interpretable picture. Although only a few animals (n=4) displayed absent kisspeptin expression, the convergence of observations suggesting a central impairment of the reproductive axis is convincing.

In particular, the lack of activation of GnRH neurons in these mice despite a non-significant effect on the reduced LH levels in the post-hoc following a significant ANOVA (which is likely due to the limited number of concerned animals [n=4]), is convincing. Did the authors test whether LH concentrations correlated with the percentage of GnRH+ESR1 positive neurons? This could reinforce the conclusion.

Moreover, the apparent complete absence of kisspeptin expression in these 4 animals is compelling as it provides indirect confirmation of the key role of Kisspeptin neurons in this phenomenon using a different LH surge induction paradigm than Wang et al., 2019. Yet, the quality of the kisspeptin immunostaining in control animals does seem suboptimal and casts doubts about this conclusion (see the section on weaknesses for more details).

It is also interesting that a few animals with unilateral reduction in Kp expression also showed deficits in GnRH neuron activation suggesting that the impact of Kp may not be limited to the side of the brain where it is produced or the existence of a dose effect of Kp activation of GnRH neurons.

Finally, the observation that the pulsatile secretion of LH is maintained in the absence of Kp expression in the RP3V lends support to the notion that LH surge and pulsatility are regulated independently by distinct neuronal populations, a model put forward by the corresponding author a few years ago.

Weaknesses:<br /> One aspect for which I have ambiguous feelings is the minimal level of detail regarding the HPG axis and its regulation by estrogens. This limited amount of detail allows for an easy read with the well-articulated introduction quickly presenting the framework of the study. Although not presenting the axis itself nor mentioning the position of GnRH neurons in this axis or its lack of ERα expression is not detrimental to the understanding of the study, presenting at least the position of GnRH neurons in the axis and their critical role for fertility would likely broaden the impact of this work beyond a rather specialist audience.

The expression of kisspeptin constitutes a key element for the analysis and conclusion of the present work. However, the quality of the kisspeptin immunostaining seems suboptimal based on the representative images. The staining primarily consists of light punctuated structures and it is very difficult to delineate cytoplasmic immunoreactive material defining the shape of neurons in LacZ animals. For some of the cells marked by an arrow, it is also sometimes difficult to determine whether the staining for ESR1 and Kp are in the same focal plane and thus belong to the same neurons. Although this co-expression is not critical for the conclusions of the study, this begs the question of whether Kp expression was determined directly at the microscope (where the focal plan can be adjusted) or on the picture (without possible focal adjustment). Moreover, in the representative image of Kp loss, several nuclei stained for fos (black) show superimposed brown staining looking like a dense nucleus (but smaller than an actual nucleus). This suggests some sort of condensed accumulation of Kp immunoproduct in the nucleus which is not commented. Given the critical importance of this reported change in Kp expression for the interpretation of the present results, it is important to provide strong evidence of the quality/nature of this staining and its analysis which may help interpret the observed functional phenotype.

As acknowledged in the introduction, this study is not the first to use in vivo Crisp-Cas editing to demonstrate the role of kisspeptin neurons in the control of positive feedback. Although the present work achieved this indirectly by targeting VGAT neurons, I was surprised that the paper did not include more comparison of their results with those of Wang et al., 2019. In particular, why was the present approach more successful in achieving both lack of surge and complete acyclicity? Moreover, why is it that targeting ESR1 in a selected fraction of GABAergic neurons can lead to a near-complete absence of Kp expression in this region? This is briefly discussed in the penultimate paragraph but mostly focuses on the non-kisspeptinergic GABAneurons rather than those co-expressing the two markers.

Reviewer #3 (Public Review):

The current manuscript investigates the molecular basis of calcium-sensitive regulation of the guanine exchange factor Ric8A by the neuronal calcium sensor 1 (NCS-1). The authors provide insight into a number of aspects of this interaction, including high-resolution structures of the NCS1-Ric8A binding interface (using peptides based on Ric8A), low-resolution cryo-EM data that hints at a structural rearrangement, and a biochemical investigation of the influence of calcium binding, sodium binding, and phosphorylation on this interaction. Altogether, this manuscript provides a comprehensive set of experiments that provide insight into this important interaction. In particular, the identification of ions bound to NCS-1 using crystallography and binding assays is very nicely done and convincing. The cryo-EM data is at low resolution and provides only weak support for the proposed mechanism.

Reviewer #3 (Public Review):

Summary:<br /> The authors developed a lipid transfer protein knock-out library to identify lipid transfer proteins with roles in lipid homeostasis/metabolism. They investigated one of their hits, the ORP9/ORP11 dimer, which they found affects sphingomyelin synthesis. Further. they found that ORP9/11 localizes to ER-Golgi contact sites via interactions between a known FFAT motif in ORP9, which can interact with the ER protein VAP, and the PH domains of ORP9 and ORP11 that target PI4P at the Golgi. They showed defects in Golgi PI4P and PS levels when ORP9 or 11 were dysfunctional, supporting but not demonstrating that ORP9/11 might exchange PI4P and PS at these contact sites. Their in vitro data indicates that both ORP9 and 11 can transfer PS. They do not assess whether either protein can transfer PI4P (although there is a very nice recent paper by He et al et You, PMID 36853333, showing that ORP9 can transfer PI4P in vitro), and they do not assess the consequences of heterodimerization on either PS or PI4P transfer. The mechanisms by which PI4P/PS level perturbations affect sphingomyelin synthesis remain unclear.

Strengths:<br /> The authors have developed an LTP knock-out library that might generate hypotheses regarding lipid metabolism, although defects are not unexpected and mechanisms will be difficult to work out--as, in fact, evidenced by this manuscript.

The OPR9/11 localization data and imaging studies are well done; this is the first more comprehensive characterization of the ORP9/11 heterodimer, which was discovered in 2010.

Weaknesses:<br /> A major flaw is that the authors claim to but do not, in fact, provide evidence of PS/PI4P counter exchange in vitro. That the presence of PI4P on the acceptor liposomes accelerates PS transfer in the in vitro assays is not proof that there is a counter exchange. In fact, since the rate-determining step in the transfer reactions is lipid transfer between membrane and transfer protein and this depends on the association of the transfer protein with donor and/or acceptor liposomes, a more likely explanation for the more efficient transfer in the presence of PI4P is that PI4P allows for longer association of lipid transfer protein with acceptor liposomes. To show the plausibility of the counter-exchange idea as applied to the ORP9/ORP11 heterodimer, the authors would need to show that it can transfer PI4P. (The work by He et al et You, 2023, mentioned above, is a very nice study that the authors might use as a model.)

Mechanistic insights from the study are limited. How does a PI4P/PS imbalance affect sphingomyelin synthesis?

The ORP9/11 heterodimer seems to behave very much like ORP9/ORP10 heterodimer, including in its localization and dimerization mode. Is ORP9/11 just another version of 9/10? I wonder whether discussions of whether they are redundant or what their different roles are might be in order. There is little mechanistic or conceptual novelty arising from this study.

A minor point, but the statement (p2., lines 19-20) that "vesicular trafficking contributes only to a small portion of lipid trafficking" is not correct and raises eyebrows. What is more correct is that protein-mediated lipid transfer ALSO plays an important role in lipid transfer. It might even be said that LTP-mediated lipid transfer is critical in fine-tuning membrane lipid composition, including of phosphoinositides.

Reviewer #3 (Public Review):

Prior studies in humans and in chickens suggested that TMEM263 could play an important role in longitudinal bone growth, but a definitive assessment of the function and potential mechanism of action of this species-conserved plasma membrane protein has been lacking. Here, the authors create a TMEM263 null mouse model and convincingly show a dramatic cessation of post-natal growth, which becomes apparent by day PND21. They report proportional dwarfism, highly significant bone and related phenotypes, as well as notable alterations of hepatic GH signaling to IGF1. A large body of prior work has established an essential role for GH and its stimulation of IGF1 production in liver and other tissues in post-natal growth. On this basis, the authors conclude that the observed decrease in serum IGF1 seen in TMEM263-KO mice is causal for the growth phenotype, which seems likely. Moreover, they ascribe the low serum IGF1 to the observed decreases in hepatic GH receptor (GHR) expression and GHR/JAK2/STAT5 signaling to IGF1, which is plausible but not proven by the experiments presented.

The finding that TMEM263 is essential for normal hepatic GHR/IGF1 signaling is an important, and unexpected finding, one that is likely to stimulate further research into the underlying mechanisms of TMEM263 action, including the distinct possibility that these effects involve direct protein-protein interactions between GHR and TMEM263 on the plasma membrane of hepatocytes, and perhaps on other mouse cell types and tissues as well, where TMEM263 expression is up to 100-fold higher (Fig. 1C).

An intriguing finding of this study, which is under-emphasized and should be noted in the Abstract, is the apparent feminization of liver gene expression in male TMEM263-KO mice, where many male-biased genes are downregulated, and many female-biased genes are upregulated. Further investigation of these liver gene responses by comparison to public datasets could be very useful, as it could help determine: (1) whether the TMEM263 liver phenotype is similar to that of hypophysectomized male mouse liver, where GH and GHR/STAT5/IGF1 signaling are both totally ablated; or alternatively, (2) whether the phenotype is more similar to that of a male mouse given GH as a continuous infusion, which induces widespread feminization of gene expression in the liver, and is perhaps similar to the gene responses seen in the TMEM263-KO mice. Answering this question could provide critical insight into the mechanistic basis for the hepatic effects of TMEM263 gene KO.

One notable weakness of this study is the conclusion (in the Abstract, and elsewhere), that the low serum IGF-I "is due to a deficit in hepatic GH receptor (GHR) expression, and GH-induced JAK2/STAT5 signaling." This conclusion is speculative in the absence of any experimental assessment of the impact of TMEM 263-KO on GHR/IGF1 signaling in other tissues that contribute to systematic IGF1 production and which likely also impact bone growth. More direct evidence for the impact of hepatic IGF1 production per se in this mouse model could be obtained by liver-specific delivery into the TMEM263-KO mice of a constitutively active. STAT5 construct, which was recently reported to normalize hepatic and serum IGF1 levels in liver-specific GHR-KO mice (PMID: 35396838).

Another weakness is the experiment presented in Fig. 5E, which is presented as evidence for the proposed GH resistance of TMEM263-KO mice. This experiment has several design flaws: 1) It uses human GH, which unlike mouse GH, activates mouse prolactin receptor as well as GH receptor; 2) the dose of hGH used, 3µg GH/g BW, is 100 times higher than is required to activate liver STAT5; and 3) the experiment lacks a set of control livers, which are needed to establish the level of STAT5 tyrosine phosphorylation in the absence of exogenous GH treatment. Moreover, if the mice used in Fig. 5E are males (the sex was not specified), then high variability in the basal phospho-STAT5 levels of control livers is expected, in which case n=6 or more individual control male livers may be required.

Reviewer #3 (Public Review):

Summary:

Based on their observation that tumor has an iron-deficient microenvironment, and the assumption that nutritional immunity is important in bacteria-mediated tumor modulation, the authors postulate that manipulation of iron homeostasis can affect tumor growth. They show that iron chelation and engineered DGC-E. coli have synergistic effects on tumor growth suppression. Using engineered IroA-E. coli that presumably have more resistance to LCN2, they show improved tumor suppression and survival rate. They also conclude that the IroA-E. coli treated mice develop immunological memory, as they are resistant to repeat tumor injections, and these effects are mediated by CD8+ T cells. Finally, they show synergistic effects of IroA-E. coli and oxaliplatin in tumor suppression, which may have important clinical implications.

Strengths:

This paper uses straightforward in vitro and in vivo techniques to examine a specific and important question of nutritional immunity in bacteria-mediated tumor therapy. They are successful in showing that manipulation of iron regulation during nutritional immunity does affect the virulence of the bacteria, and in turn the tumor. These findings open future avenues of investigation, including the use of different bacteria, different delivery systems for therapeutics, and different tumor types.

Weaknesses:

-- There is no discussion of the cancer type and why this cancer type was chosen. Colon cancer is not one of the more prominently studied cancer types for LCN2 activity. While this is a proof-of-concept paper, there should be some recognition of the potential different effects on different tumor types. For example, this model is dependent on significant LCN production, and different tumors have variable levels of LCN expression. Would the response of the tumor depend on the role of iron in that cancer type? For example, breast cancer aggressiveness has been shown to be influenced by FPN levels and labile iron pools.<br /> -- Are the effects on tumor suppression assumed to be from E. coli virulence, i.e. Does the higher number of bacteria result in increased immune-mediated tumor suppression? Or are the effects partially from iron status in the tumor cells and the TME?<br /> -- If the effects are iron-related, could the authors provide some quantification of iron status in tumor cells and/or the TME? Could the proteomic data be queried for this data?

Reviewer #3 (Public Review):

Summary: This manuscript aims to unravel the mechanisms behind Aquaporin-0 (AQP0) tetramer array formation within lens membranes. The authors utilized electron crystallography and molecular dynamics (MD) simulations to shed light on the role of cholesterol in shaping the structural organization of AQP0. The evidence suggests that cholesterol not only defines the positions and orientations of associated molecules but also plays a crucial role in stabilizing AQP0 tetramer arrays. This study provides valuable insights into the potential principles driving protein clustering within lipid rafts, advancing our understanding of membrane biology.

In this review, I will focus on the MD simulations part, since this is my area of expertise. The authors conducted an impressive set of MD simulations aiming at understanding the role of cholesterol in structural organization of AQP0 arrays. These simulations clearly demonstrate the well-defined localization of cholesterol molecules around a single AQP0 tetramer, aligning with previous computational studies and the crystallographic structures presented in this manuscript. Interestingly, authors identified an unusual position for one cholesterol molecule, located near the center of the lipid bilayer, which was stabilized by the adjacent AQP0 tetramers. The authors showed that these adjacent tetramers can withstand a larger lateral detachment force when deep cholesterol molecules are present at the interface compared to scenarios with sphingomyelin (SM) molecules at the interface between two AQP0 tetramers. Authors interpret that result as evidence that deep cholesterol molecules mechanically stabilize the interface of the AQP0 tetramers. This conclusion has minor weaknesses, and the rigor of the lateral detachment simulations could be increased by establishing a reference point for the detachment force needed to separate AQP0 tetramers in a scenario without lipids at the interface between tetramers, and by increasing the number of repeats for the non-equilibrium steered MD simulations. Thermodynamic integration might be a better approach to compute the stabilization energy in the presence of cholesterol compared to the SM case.

Reviewer #3 (Public Review):

Summary:<br /> This paper by Sabelo et al. describes a new pathway by which lack of IgM in the mouse lowers bronchial hyperresponsiveness (BHR) in response to metacholine in several mouse models of allergic airway inflammation in Balb/c mice and C57/Bl6 mice. Strikingly, loss of IgM does not lead to less eosinophilic airway inflammation, Th2 cytokine production or mucus metaplasia, but to a selective loss of BHR. This occurs irrespective of the dose of allergen used. This was important to address since several prior models of HDM allergy have shown that the contribution of B cells to airway inflammation and BHR is dose dependent.

After a description of the phenotype, the authors try to elucidate the mechanisms. There is no loss of B cells in these mice. However, there is a lack of class switching to IgE and IgG1, with a concomitant increase in IgD. Restoring immunoglobulins with transfer of naïve serum in IgM deficient mice leads to restoration of allergen-specific IgE and IgG1 responses, which is not really explained in the paper how this might work. There is also no restoration of IgM responses, and concomitantly, the phenotype of reduced BHR still holds when serum is given, leading authors to conclude that the mechanism is IgE and IgG1 independent. Wild type B cell transfer also does not restore IgM responses, due to lack of engraftment of the B cells. Next authors do whole lung RNA sequencing and pinpoint reduced BAIAP2L1 mRNA as the culprit of the phenotype of IgM-/- mice. However, this cannot be validated fully on protein levels and immunohistology since differences between WT and IgM KO are not statistically significant, and B cell and IgM restoration are impossible. The histology and flow cytometry seems to suggest that expression is mainly found in alpha smooth muscle positive cells, which could still be smooth muscle cells or myofibroblasts. Next therefore, the authors move to CRISPR knock down of BAIAP2L1 in a human smooth muscle cell line, and show that loss leads to less contraction of these cells in vitro in a microscopic FLECS assay, in which smooth muscle cells bind to elastomeric contractible surfaces.

Strengths:<br /> 1. There is a strong reduction in BHR in IgM-deficient mice, without alterations in B cell number, disconnected from effects on eosinophilia or Th2 cytokine production<br /> 2. BAIAP2L1 has never been linked to asthma in mice or humans

Weaknesses:

1. While the observations of reduced BHR in IgM deficient mice are strong, there is insufficient mechanistic underpinning on how loss of IgM could lead to reduced expression of BAIAP2L1. Since it is impossible to restore IgM levels by either serum or B cell transfer and since protein levels of BAIAP2L1 are not significantly reduced, there is a lack of a causal relationship that this is the explanation for the lack of BHR in IgM-deficient mice. The reader is unclear if there is a fundamental (maybe developmental) difference in non-hematopoietic cells in these IgM-deficient mice (which might have accumulated another genetic mutation over the years). In this regard, it would be important to know if littermates were newly generated, or historically bred along with the KO line.<br /> 2. There is no mention of the potential role of complement in activation of AHR, which might be altered in IgM-deficient mice<br /> 3. What is the contribution of elevated IgD in the phenotype of the IgM-deficient mice. It has been described by this group that IgD levels are clearly elevated<br /> 4. How can transfer of naïve serum in class switching deficient IgM KO mice lead to restoration of allergen specific IgE and IgG1?<br /> 5. Alpha smooth muscle antigen is also expressed by myofibroblasts. This is insufficiently worked out. The histology mentions "expression in cells in close contact with smooth muscle". This needs more detail since it is a very vague term. Is it in smooth muscle or in myofibroblasts.<br /> 6. Have polymorphisms in BAIAP2L1 ever been linked to human asthma?<br /> 7. IgM deficient patients are at increased risk for asthma. This paper suggests the opposite. So the translational potential is unclear.

Reviewer #3 (Public Review):

The valuable work shows some unique characteristics of long-lived PCs in comparison with bulk PCs. In particular, the authors clearly indicated the dependency of CXCR4 in PC longevity and provided a deal of resource of PC transcriptomes. Though CD93 is known as a marker for long-lived PCs, the authors can provide more data related to CD93.

Summary: Long-lived PCs are maintained with low motility and in a CXCR4-dependent manner.

Strengths: The reporter mice for fate-mapping can clearly distinguish long-lived PCs from total PCs and greatly contribute to the identification of long-lived PCs.

Weaknesses: The authors are unable to find a unique marker for long-lived PCs.

Reviewer #3 (Public Review):

The authors successfully explain the sharp rise and subsequent saturation of the viscosity in dependence of cell packing fraction in zebrafish blastoderm with the help of a 2D model of soft deformable, polydisperse and self-propelled (active) disks. The main experimental observations can be reproduced and the unusual dependence of the viscosity on packing fraction can be explained by the available free area and the emergent motility of small sized cells facilitating multi-cell rearrangement in a highly jammed environment.

The paper is very well written, the results (experimental as well as theoretical) are original and scientifically valid. This is an important contribution to understanding the rheological properties of non-confluent tissues linking equilibrium and transport properties.

Reviewer #3 (Public Review):

Summary:<br /> In this study, the authors embarked on an exploration of how nifuroxazide could enhance the responsiveness to radiotherapy by employing both an in vitro cell culture system and an in vivo mouse tumor model.

Strengths:<br /> The researchers conducted an array of experiments aimed at revealing the function of nifuroxazide in aiding the radiotherapy-induced reduction of proliferation, migration, and invasion of HepG2 cells.

Weaknesses:<br /> The authors did not provide the molecular mechanism through which nifuroxazide collaborates with radiotherapy to effectively curtail the proliferation, migration, and invasion of HCC cells. Moreover, the evidence supporting the assertion that nifuroxazide contributes to the degradation of radiotherapy-induced upregulation of PD-L1 via the ubiquitin-proteasome pathway appears to be insufficient. Importantly, further validation of this discovery should involve the utilization of an additional syngeneic mouse HCC tumor model or an orthotopic HCC tumor model.

Reviewer #3 (Public Review):

Nakonechnaya et al present a valuable and comprehensive exploration of CD4+ T cell response in mice across stimuli and tissues through the analysis of their TCR-alpha repertoires.

The authors compare repertoires by looking at the relative overlap of shared clonotypes and observe that they sometimes cluster by tissue and sometimes by stimulus. They also compare different CD4+ subsets (conventional and Tregs) and find distinct yet convergent responses with occasional plasticity across subsets for some stimuli.

The observed lack of a general behaviour highlights the need for careful comparison of immune repertoires across cell subsets and tissues in order to better understand their role in the adaptive immune response.

In conclusion, this is an important paper to the community as it suggests several future directions of exploration.

Unfortunately, the lack of code and data availability does not allow the reproducibility of the results.

Reviewer #3 (Public Review):

Here the authors carried out a CRISPR/sgRNA screen with a DDR gene-targeted mini-library in HEK293A cells looking for genes whose loss increased sensitivity to treatment with the PARG inhibitor, PDD00017273 (PARGi). Surprisingly they found that PARG itself, which encodes the cellular poly(ADP-ribose) glycohydrolase (dePARylation) enzyme, was a major hit. Targeted PARG KO in 293A and HeLa cells also caused high sensitivity to PARGi. When PARG KO cells were reconstituted with catalytically-dead PARG, MMS treatment caused an increase in PARylation, not observed when cells were reconstituted with WT PARG or when the PARG KO was combined with PARP1/2 DKO, suggesting that loss of PARG leads to a strong PARP1/2-dependent increase in protein PARylation. The decrease in intracellular NADH+, the substrate for PARP-driven PARylation, observed in PARG KO cells was reversed by treatment with NMN or NAM, and this treatment partially rescued the PARG KO cell lethality. However, since NAD+ depletion with the FK868 nicotinamide phosphoribosyltransferase (NAMPT) inhibitor did not induce a similar lethality the authors concluded that NAD+ depletion/reduction was only partially responsible for the PARGi toxicity. Interestingly, PARylation was also observed in untreated PARG KO cells, specifically in S phase, without a significant rise in γH2AX signals. Using cells synchronized at G1/S by double thymidine blockade and release, they showed that entry into S phase was necessary for PARGi to induce PARylation in PARG KO cells. They found an increased association of PARP1 with a chromatin fraction in PARG KO cells independent of PARGi treatment, and suggested that PARP1 trapping on chromatin might account in part for the increased PARGi sensitivity. They also showed that prolonged PARGi treatment of PARG KO cells caused S phase accumulation of pADPr eventually leading to DNA damage, as evidenced by increased anti-γH2AX antibody signals and alkaline comet assays. Based on the use of emetine, they deduced that this response could be caused by unligated Okazaki fragments. Next, they carried out FACS-based CRISPR screens to identify genes that might be involved in cell lethality in WT and PARG KO cells, finding that loss of base excision repair (BER) and DNA repair genes led to increased PARylation and PARGi sensitivity, whereas loss of PARP1 had the opposite effects. They also found that BER pathway disruption exhibited synthetic lethality with PARGi treatment in both PARG KO cells and WT cells, and that loss of genes involved in Okazaki fragment ligation induced S phase pADPr signaling. In a panel of human ovarian cancer cell lines, PARGi sensitivity was found to correlate with low levels of PARG mRNA, and they showed that the PARGi sensitivity of cells could be reduced by PARPi treatment. Finally, they addressed the conundrum of why PARG KO cells should be sensitive to a specific PARG inhibitor if there is no PARG to inhibit and found that the PARG KO cells had significant residual PARG activity when measured in a lysate activity assay, which could be inhibited by PARGi, although the inhabited PARG activity levels remained higher than those of PARG cKO cells (see below). This led them to generate new, more complete PARG KO cells they called complete/conditional KO (cKO), whose survival required the inclusion of the olaparib PARPi in the growth medium. These PARG cKO cells exhibited extremely low levels of PARG activity in vitro, consistent with a true PARG KO phenotype.

The finding that human ovarian cancer cells with low levels of PARG are more sensitive to inhibition with a small molecule PARG inhibitor, presumably due to the accumulation of high levels of protein PARylation (pADPr) that are toxic to cells is quite interesting, and this could be useful in the future as a diagnostic marker for preselection of ovarian cancer patients for treatment with a PARG inhibitor drug. The finding that loss of base excision repair (BER) and DNA repair genes led to increased PARylation and PARGi sensitivity is in keeping with the conclusion that PARG activity is essential for cell fitness, because it prevents excessive protein PARylation. The observation that increased PARylation can be detected in an unperturbed S phase in PARG KO cells is also of interest. However, the functional importance of protein PARylation at the replication fork in the normal cell cycle was not fully investigated, and none of the key PARylation targets for PARG required for S phase progression were identified. Overall, there are some interesting findings in the paper, but their impact is significantly lessened by the confusing way in which the paper has been organized and written, and this needs to be rectified.

Reviewer #3 (Public Review):

Summary: Hariharan et al. establish an analysis pipeline using automated microscopy to detect features identified by morphological profiling from images of common dystrophin complex proteins present in differentiated diseased and unaffected human myoblasts. Ultimately, using a machine learning algorithm to generate high dimensional phenotypes, the authors can distinguish Duchenne patient myotubes from unaffected patient controls based on the morphological features of several Dystrophin complex proteins. Initially analyzed on their own or in pairs the authors identify an optimal combination of Utrophin and a-sarcoglycan and subsequently test their ability to distinguish perturbations of Dystrophin either by knock down (siRNA) in unaffected controls or following treatment with a splicing modifier, vivo-phosphorodiamidate morpholino oligonucleomer (vivo-PMO) to ameliorate the DMD phenotype. It is unclear whether this methodology will see widespread adoption due to the combination of unique methods (micro-patterned plates and machine learning based image analysis) combined with a lack of detail on the specific features responsible for supporting the high dimensional phenotypes generated using their machine learning algorithm.

Strengths: The overall concept of this paper is interesting in that subtle morphological phenotypes, not readily observable by the eye, exhibited by dystrophin complex associated proteins can distinguish DMD samples from unaffected controls. It is interesting In Fig. 3B to see Utrophin and a-Sarcoglycan distinguish DMD and non-DMD lines from each other. This finding is the core of the paper and yet little information on how or why this is detected by image analysis is presented. An argument could be made that Combinations 1-7 all "work" to a certain degree at segregating DMD from non-DMD lines. This finding is exciting and has broad applicability both within and beyond the muscle field.

Weaknesses: Significantly more detail on the 235 features that are identified would greatly benefit the paper. What are the most critical features that give rise to high F-Scores for Utrophin and a-Sarcoglycan? What do the image masks display for the top ~10 features (or 5)? In Fig. 3B what metric(s) is critical in this segregation? What is the effect on the dimensional display if PCA is conducted as opposed to a tSNE?

Biological replicates are lacking to draw conclusions upon. Non-DMD #4 is present in certain figures and absent from others. With 2 replicates (non-DMD) and 2 replicates (DMD) it is difficult to draw statistical conclusions on the data. Non-DMD #4 is identified as a poor line (37% Desmin compared to the other lines being >88%) in Table 1. If this line is a poor line please remove it from the data analysis.

It is not appropriate to calculate Euclidan distance based on tSNE plots. PCA, MDS or UMAP are the appropriate high dimensional visual representations that allow for Euclidian distance calculations. This brings into question the validity of Fig. 4D and Fig. 5D. The link below outlines the limitations of tSNE plots. https://distill.pub/2016/misread-tsne/

It is unclear why treatment (siRNA) results in a statistically significant F-score (>0.9) when comparing non-DMD samples treated with siRNA against Dystrophin with DMD samples. Given that the siRNA knock-down appears to be quite robust this was unexpected and brings into question whether Dystrophin protein is the primary driver for the high dimensional phenotypes observed.

Reviewer #3 (Public Review):

Summary:<br /> Zai et al. test whether birds can modify their vocal behavior in a manner consistent with planning. They point out that while some animals are known to be capable of volitional control of vocalizations, it has been unclear if animals are capable of planning vocalizations -that is, modifying vocalizations towards a desired target without the need to learn this modification by practicing and comparing sensory feedback of practiced behavior to the behavioral target. They study zebra finches that have been trained to shift the pitch of song syllables away from their baseline values. It is known that once this training ends, zebra finches have a drive to modify pitch so that it is restored back to its baseline value. They take advantage of this drive to ask whether birds can implement this targeted pitch modification in a manner that looks like planning, by comparing the time course and magnitude of pitch modification in separate groups of birds who have undergone different manipulations of sensory and motor capabilities. A key finding is that birds who are deafened immediately before the onset of this pitch restoration paradigm, but after they have been shifted away from baseline, are able to shift pitch partially back towards their baseline target. In other words, this targeted pitch shift occurs even when birds don't have access to auditory feedback, which argues that this shift is not due to reinforcement-learning-guided practice, but is instead planned based on the difference between an internal representation of the target (baseline pitch) and current behavior (pitch the bird was singing immediately before deafening).

The authors present additional behavioral studies arguing that this pitch shift requires auditory experience of the song in its state after it has been shifted away from baseline (birds deafened early on, before the initial pitch shift away from baseline, do not exhibit any shift back towards baseline), and that a full shift back to baseline requires auditory feedback. The authors synthesize these results to argue that different mechanisms operate for small shifts (planning, does not need auditory feedback) and large shifts (reinforcement learning, requires auditory feedback).

The authors also make a distinction between two kinds of planning: covert-not requiring any motor practice and overt-requiring motor practice but without access to auditory experience from which target mismatch could be computed. They argue that birds plan overtly, based on these deafening experiments as well as an analogous experiment involving temporary muting, which suggests that indeed motor practice is required for pitch shifts.

Strengths:<br /> The primary finding (that partially restorative pitch shift occurs even after deafening) rests on strong behavioral evidence. It is less clear to what extent this shift requires practice, since their analysis of pitch after deafening takes the average over within the first two hours of singing. If this shift is already evident in the first few renditions then this would be evidence for covert planning. This analysis might not be feasible without a larger dataset. Similarly, the authors could test whether the first few renditions after recovery from muting already exhibit a shift back toward baseline.

This work will be a valuable addition to others studying birdsong learning and its neural mechanisms. It documents features of birdsong plasticity that are unexpected in standard models of birdsong learning based on reinforcement and are consistent with an additional, perhaps more cognitive, mechanism involving planning. As the authors point out, perhaps this framework offers a reinterpretation of the neural mechanisms underlying a prior finding of covert pitch learning in songbirds (Charlesworth et al., 2012).

A strength of this work is the variety and detail in its behavioral studies, combined with sensory and motor manipulations, which on their own form a rich set of observations that are useful behavioral constraints on future studies.

Weaknesses:<br /> The argument that pitch modification in deafened birds requires some experience hearing their song in its shifted state prior to deafening (Fig. 4) is solid but has an important caveat. Their argument rests on comparing two experimental conditions: one with and one without auditory experience of shifted pitch. However, these conditions also differ in the pitch training paradigm: the "with experience" condition was performed using white noise training, while the "without experience" condition used "lights off" training (Fig. 4A). It is possible that the differences in the ability for these two groups to restore pitch to baseline reflect the training paradigm, not whether subjects had auditory experience of the pitch shift. Ideally, a control study would use one of the training paradigms for both conditions, which would be "lights off" or electrical stimulation (McGregor et al. 2022), since WN training cannot be performed in deafened birds. This is difficult, in part because the authors previously showed that "lights off" training has different valences for deafened vs. hearing birds (Zai et al. 2020). Realistically, this would be a point to add to in discussion rather than a new experiment.

A minor caveat, perhaps worth noting in the discussion, is that this partial pitch shift after deafening could potentially be attributed to the birds "gaining access to some pitch information via somatosensory stretch and vibration receptors and/or air pressure sensing", as the authors acknowledge earlier in the paper. This does not strongly detract from their findings as it does not explain why they found a difference between the "mismatch experience" and "no mismatch experience groups" (Fig. 4).

More broadly, it is not clear to me what kind of planning these birds are doing, or even whether the "overt planning" here is consistent with "planning" as usually implied in the literature, which in many cases really means covert planning. The idea of using internal models to compute motor output indeed is planning, but why would this not occur immediately (or in a few renditions), instead of taking tens to hundreds of renditions? To resolve confusion, it would be useful to discuss and add references relating "overt" planning to the broader literature on planning, including in the introduction when the concept is introduced. Indeed, muddying the interpretation of this behavior as planning is that there are other explanations for the findings, such as use-dependent forgetting, which the authors acknowledge in the introduction, but don't clearly revisit as a possible explanation of their results. Perhaps this is because the authors equate use-dependent forgetting and overt planning, in which case this could be stated more clearly in the introduction or discussion.

Reviewer #3 (Public Review):

Summary:<br /> The neural retina is one of the most energetically active tissues in the body and research into retinal metabolism has a rich history. Prevailing dogma in the field is that the photoreceptors of the neural retina (rods and cones) are heavily reliant on glycolysis, and as oxygen tension at the level of photoreceptors is very low, these specialized sensory neurons carry out aerobic glycolysis, akin to the Warburg effect in cancer cells. It has been found that this unique metabolism changes in many retinal diseases, and targeting retinal metabolism may be a viable treatment strategy. The neural retina is composed of 11 different cell types, and many research groups over the past century have contributed to our current understanding of cell-specific metabolism of retinal cells. More recently, it has been shown in mouse models and co-culture of the mouse neural retina with human RPE cultures that photoreceptors are reliant on the underlying retinal pigment epithelium for supplying nutrients. Chen and colleagues add to this body of work by studying an ex vivo culture of the developing mouse retina that maintained contact with the retinal pigment epithelium. They exposed such ex vivo cultures to small molecule inhibitors of specific metabolic pathways, performing targeted metabolomics on the tissue and staining the tissue with key metabolic enzymes to lay the groundwork for what metabolic pathways may be active in particular cell types of the retina. The authors conclude that rod and cone photoreceptors are reliant on different metabolic pathways to maintain their cell viability - in particular, that rods rely on oxidative phosphorylation and cones rely on glycolysis. Further, their data support multiple mechanisms whereby glycolysis may occur simultaneously with anapleurosis to provide abundant energy to photoreceptors. The data from metabolomics revealed several novel findings in retinal metabolism, including the use of glutamine to fuel the mini-Krebs cycle, the utilization of the Cahill cycle in photoreceptors, and a taurine/hypotaurine shuttle between the underlying retinal pigment epithelium and photoreceptors to transfer reducing equivalents from the RPE to photoreceptors. In addition, this study provides robust quantitative metabolomics datasets that can be compared across experiments and groups. The use of this platform will allow for rapid testing of novel hypotheses regarding the metabolic ecosystem in the neural retina.

Strengths:<br /> The data on differences in the susceptibility of rods and cones to mitochondrial dysfunction versus glycolysis provides novel hypothesis-generating conjectures that can be tested in animal models. The multiple mechanisms that allow anapleurosis and glycolysis to run side-by-side add significant novelty to the field of retinal metabolism, setting the stage for further testing of these hypotheses as well.

Weaknesses:<br /> Almost all of the conclusions from the paper are preliminary, based on data showing enzymes necessary for a metabolic process are present and the metabolites for that process are also present. However, to truly prove whether these processes are happening, C13 labeling or knock-out or over-expression experiments are necessary. Further, while there is good data that RPE cultures in vitro strongly recapitulate RPE phenotypes in vivo, ex vivo neural retina cultures undergo rapid death. Thus, conclusions about metabolism from explants should either be well correlated with existing literature or lead to targeted in vivo studies. This paper currently lacks both.

Reviewer #3 (Public Review):

Summary:<br /> Anderson et al utilize an array of orthogonal techniques to highlight the importance of protein dynamics for the function and inhibition of the kinase ERK2. ERK2 is important for a large variety of biological functions.

Strengths:<br /> This is a thorough and detailed study that uses a variety of techniques to identify critical molecular/chemical parameters that drive ERK2 in specific states.

Weaknesses:<br /> No details rules were identified so that novel inhibitors could be designed. Nevertheless, the mode of action of these existing inhibitors is much better defined.

Reviewer #3 (Public Review):

Summary:

The authors image dopamine axons in medial prefrontal cortex (mPFC) using microprism-mediated two-photon calcium imaging. They image these axons as mice learn that two auditory cues predict two distinct outcomes, tailshock or water delivery. They find that some axons show a preference for encoding of the shock and some show a preference for encoding of water. The authors report a greater number of dopamine axons in mPFC that respond to shock. Across time, the shock-preferring axons begin to respond preferentially to the cue predicting shock, while there is a less pronounced increase in the water-responsive axons that acquire a response to the water-predictive cue (these axons also increase non-significantly to the shock-predictive cue). These data lead the authors to argue that dopamine axons in mPFC preferentially encode aversive stimuli.

Strengths:

The experiments are beautifully executed and the authors have mastered an impressively complex technique. Specifically, they are able to image and track individual dopamine axons in mPFC across days of learning. This technique is used the way it should be: the authors isolate distinct dopamine axons in mPFC and characterize their encoding preferences and how this evolves across learning of cue-shock and cue-water contingencies. Thus, these experiments are revealing novel information about how aversive and rewarding stimuli is encoded at the level of individual axons, in a way that has not been done before. This is timely and important.

Weaknesses:

The overarching conclusion of the paper is that dopamine axons preferentially encode aversive stimuli. This is prevalent in the title, abstract, and throughout the manuscript. This is fundamentally confounded. As the authors point out themselves, the axonal response to stimuli is sensitive to outcome magnitude (Supp Fig 3). That is, if you increase the magnitude of water or shock that is delivered, you increase the change in fluorescence that is seen in the axons. Unsurprisingly, the change in fluorescence that is seen to shock is considerably higher than water reward. Further, when the mice are first given unexpected water delivery and have not yet experienced the aversive stimuli, over 40% of the axons respond [yet just a few lines below the authors write: "Previous studies have demonstrated that the overall dopamine release at the mPFC or the summed activity of mPFC dopamine axons exhibits a strong response to aversive stimuli (e.g., tail shock), but little to rewards", which seems inconsistent with their own data]. Given these aspects of the data, it could be the case that the dopamine axons in mPFC encodes different types of information and delegates preferential processing to the most salient outcome across time. The use of two similar sounding tones (9Khz and 12KHz) for the reward and aversive predicting cues are likely to enhance this as it requires a fine-grained distinction between the two cues in order to learn effectively.

There is considerable literature on mPFC function across species that would support such a view. Specifically, theories of mPFC function (in particular prelimbic cortex, which is where the axon images are mostly taken) generally center around resolution of conflict in what to respond, learn about, and attend to. That is, mPFC is important for devoting the most resources (learning, behavior) to the most relevant outcomes in the environment. This data then, provides a mechanism for this to occur in mPFC. That is, dopamine axons signal to the mPFC the most salient aspects of the environment, which should be preferentially learned about and responded towards. This is also consistent with the absence of a negative prediction error during omission: the dopamine axons show increases in responses during receipt of unexpected outcomes, but do not encode negative errors. This supports a role for this projection in helping to allocate resources to the most salient outcomes and their predictors, and not learning per se. Below are a just few references from the rich literature on mPFC function (some consider rodent mPFC analogous to DLPFC, some mPFC), which advocate for a role in this region in allocating attention and cognitive resources to most relevant stimuli, and do not indicate preferential processing of aversive stimuli.

References:<br /> 1. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual review of neuroscience, 24(1), 167-202.<br /> 2. Bissonette, G. B., Powell, E. M., & Roesch, M. R. (2013). Neural structures underlying set-shifting: roles of medial prefrontal cortex and anterior cingulate cortex. Behavioural brain research, 250, 91-101.<br /> 3. Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual review of neuroscience, 18(1), 193-222.<br /> 4. Sharpe, M. J., Stalnaker, T., Schuck, N. W., Killcross, S., Schoenbaum, G., & Niv, Y. (2019). An integrated model of action selection: distinct modes of cortical control of striatal decision making. Annual review of psychology, 70, 53-76.<br /> 5. Ridderinkhof, K. R., Ullsperger, M., Crone, E. A., & Nieuwenhuis, S. (2004). The role of the medial frontal cortex in cognitive control. science, 306(5695), 443-447.<br /> 6. Nee, D. E., Kastner, S., & Brown, J. W. (2011). Functional heterogeneity of conflict, error, task-switching, and unexpectedness effects within medial prefrontal cortex. Neuroimage, 54(1), 528-540.<br /> 7. Isoda, M., & Hikosaka, O. (2007). Switching from automatic to controlled action by monkey medial frontal cortex. Nature neuroscience, 10(2), 240-248.

Reviewer #3 (Public Review):

Summary:

In their manuscript titled "Exposure to false cardiac feedback alters pain perception and anticipatory cardiac frequency", Parrotta and colleagues describe an experimental study on the interplay between false heart rate feedback and pain experience in healthy, adult humans. The experimental design is derived from Bayesian perspectives on interoceptive inference. In Experiment 1 (N=34), participants rated the intensity and unpleasantness of an electrical pulse presented to their middle fingers. Participants received auditory cardiac feedback prior to the electrical pulse. This feedback was congruent with the participant's heart rate or manipulated to have a higher or lower frequency than the participant's true heart rate (incongruent high/ low feedback). The authors find heightened ratings of pain intensity and unpleasantness as well as a decreased heart rate in participants who were exposed to the incongruent-high cardiac feedback. Experiment 2 (N=29) is equivalent to Experiment 1 with the exception that non-interoceptive auditory feedback was presented. Here, mean pain intensity and unpleasantness ratings were unaffected by feedback frequency.

Strengths:

The authors present interesting experimental data that was derived from modern theoretical accounts of interoceptive inference and pain processing.

1. The motivation for the study is well-explained and rooted within the current literature, whereas pain is the result of a multimodal, inferential process. The separation of nociceptive stimulation and pain experience is explained clearly and stringently throughout the text.

2. The idea of manipulating pain-related expectations via an internal, instead of an external cue, is very innovative.

3. An appropriate control experiment was implemented, where an external (non-physiological) auditory cue with parallel frequency to the cardiac cue was presented.

4. The chosen statistical methods are appropriate, albeit averaging may limit the opportunity for mechanistic insight, see weaknesses section.

5. The behavioral data, showing increased unpleasantness and intensity ratings after exposure to incongruent-high cardiac feedback, but not exteroceptive high-frequency auditory feedback, is backed up by ECG data. Here, the decrease in heart rate during the incongruent-high condition speaks towards a specific, expectation-induced physiological effect that can be seen as resulting from interoceptive inference.

Weaknesses:

Additional analyses and/ or more extensive discussion are needed to address these limitations:

1. I would like to know more about potential learning effects during the study. Is there a significant change in ∆ intensity and ∆ unpleasantness over time; e.g. in early trials compared to later trials? It would be helpful to exclude the alternative explanation that over time, participants learned to interpret the exteroceptive cue more in line with the cardiac cue, and the effect is driven by a lack of learning about the slightly less familiar cue (the exteroceptive cue) in early trials. In other words, the heartbeat-like auditory feedback might be "overlearned", compared to the less naturalistic tone, and more exposure to the less naturalistic cue might rule out any differences between them w.r.t. pain unpleasantness ratings.

2. The origin of the difference in Cohen's d (Exp. 1: .57, Exp. 2: .62) and subsequently sample size in the sensitivity analyses remains unclear, it would be helpful to clarify where these values are coming from (are they related to the effects reported in the results? If so, they should be marked as post-hoc analyses).

3. As an alternative explanation, it is conceivable that the cardiac cue may have just increased unspecific arousal or attention to a larger extent than the exteroceptive cue. It would be helpful to discuss the role of these rather unspecific mechanisms, and how it may have differed between experiments.

4. The hypothesis (increased pain intensity with incongruent-high cardiac feedback) should be motivated by some additional literature.

5. The discussion section does not address the study's limitations in a sufficient manner. For example, I would expect a more thorough discussion on the lack of correlation between participant ratings and self-reported bodily awareness and reactivity, as assessed with the BPQ.<br /> a. Some short, additional information on why the authors chose to focus on body awareness and supradiaphragmatic reactivity subscales would be helpful.

6. The analyses presented in this version of the manuscript allow only limited mechanistic conclusions - a computational model of participant's behavior would be a very strong addition to the paper. While this may be out of the scope of the article, it would be helpful for the reader to discuss the limitations of the presented analyses and outline avenues towards a more mechanistic understanding and analysis of the data. The computational model in [7] might contain some starting ideas.

Some additional topics were not considered in the first version of the manuscript:<br /> 1. The possible advantages of a computational model of task behavior should be discussed.<br /> 2. Across both experiments, there was a slightly larger number of female participants. Research suggests significant sex-related differences in pain processing [1,2]. It would be interesting to see what role this may have played in this data.<br /> 3. There are a few very relevant papers that come to mind which may be of interest. These sources might be particularly useful when discussing the roadmap towards a mechanistic understanding of the inferential processes underlying the task responses [3,4] and their clinical implications.<br /> 4. In this version of the paper, we only see plots that illustrate ∆ scores, averaged across pain intensities - to better understand participant responses and the relationship with stimulus intensity, it would be helpful to see a more descriptive plot of task behavior (e.g. stimulus intensity and raw pain ratings)

[1] Mogil, J. S. (2020). Qualitative sex differences in pain processing: emerging evidence of a biased literature. Nature Reviews Neuroscience, 21(7), 353-365. https://www.nature.com/articles/s41583-020-0310-6<br /> [2] Sorge, R. E., & Strath, L. J. (2018). Sex differences in pain responses. Current Opinion in Physiology, 6, 75-81. https://www.sciencedirect.com/science/article/abs/pii/S2468867318300786?via%3Dihub<br /> [3] Unal, O., Eren, O. C., Alkan, G., Petzschner, F. H., Yao, Y., & Stephan, K. E. (2021). Inference on homeostatic belief precision. Biological Psychology, 165, 108190.<br /> [4] Allen, M., Levy, A., Parr, T., & Friston, K. J. (2022). In the body's eye: the computational anatomy of interoceptive inference. PLoS Computational Biology, 18(9), e1010490.<br /> [5] Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A., Paliwal, S., Gard, T., ... & Petzschner, F. H. (2016). Allostatic self-efficacy: A metacognitive theory of dyshomeostasis-induced fatigue and depression. Frontiers in human neuroscience, 10, 550.<br /> [6] Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: the brain as a phantastic organ. The Lancet Psychiatry, 1(2), 148-158.<br /> [7] Eckert, A. L., Pabst, K., & Endres, D. M. (2022). A Bayesian model for chronic pain. Frontiers in Pain Research, 3, 966034.

Reviewer #3 (Public Review):

Summary:<br /> Previous studies suggest that humans may infer objects' stability through a world model that performs mental simulations with a priori knowledge of gravity acting upon objects. In this study, the authors test two alternative hypotheses about the nature of this a priori knowledge. According to the Natural Gravity assumption, the direction of gravity encoded in this world model is straight downwards as in the physical world. According to the alternative Mental Gravity assumption, that gravity direction is encoded in a Gaussian distribution, with the vertical direction as the maximum likelihood. They present two experiments and computer simulations as evidence in support of the Mental Gravity assumption. Their conclusion is that when the brain is tasked to determine the stability of a given structure it runs a mental simulation, termed Mental Gravity Simulation, which averages the estimated temporal evolutions of that structure arising from different gravity directions sampled from a Gaussian distribution.

Weaknesses:<br /> In spite of the fact that the Mental Gravity Simulation (MGS) seems to predict the data of the two experiments, it is an untenable hypothesis. I give the main reason for this conclusion by illustrating a simple thought experiment. Suppose you ask subjects to determine whether a single block (like those used in the simulations) is about to fall. We can think of blocks of varying heights. No matter how tall a block is, if it is standing on a horizontal surface it will not fall until some external perturbation disturbs its equilibrium. I am confident that most human observers would predict this outcome as well. However, the MSG simulation would not produce this outcome. Instead, it would predict a non-zero probability of the block to tip over. A gravitational field that is not perpendicular to the base has the equivalent effect of a horizontal force applied on the block at the height corresponding to the vertical position of the center of gravity. Depending on the friction determined by the contact between the base of the block and the surface where it stands there is a critical height where any horizontal force being applied would cause the block to fall while pivoting about one of the edges at the base (the one opposite to where the force has been applied). This critical height depends on both the size of the base and the friction coefficient. For short objects this critical height is larger than the height of the object, so that object would not fall. But for taller blocks, this is not the case. Indeed, the taller the block the smaller the deviation from a vertical gravitational field is needed for a fall to be expected. The discrepancy between this prediction and the most likely outcome of the simple experiment I have just outlined makes the MSG model implausible. Note also that a gravitational field that is not perpendicular to the ground surface is equivalent to the force field experienced by the block while standing on an inclined plane. For small friction values, the block is expected to slide down the incline, therefore another prediction of this MSG model is that when we observe an object on a surface exerting negligible friction (think of a puck on ice) we should expect that object to spontaneously move. But of course, we don't, as we do not expect tall objects that are standing to suddenly fall if left unperturbed. In summary, a stochastic world model cannot explain these simple observations.

The question remains as to how we can interpret the empirical data from the two experiments and their agreement with the predictions of the stochastic world model if we assume that the brain has internalized a vertical gravitational field. First, we need to look more closely at the questions posed to the subjects in the two experiments. In the first experiment, subjects are asked about how "normal" a fall of a block construction looks. Subjects seem to accept 50% of the time a fall is normal when the gravitational field is about 20 deg away from the vertical direction. The authors conclude that according to the brain, such an unusual gravitational field is possible. However, there are alternative explanations for these findings that do not require a perceptual error in the estimation of the direction of gravity. There are several aspects of the scene that may be misjudged by the observer. First, the 3D interpretation of the scene and the 3D motion of the objects can be inaccurate. Indeed, the simulation of a normal fall uploaded by the authors seems to show objects falling in a much weaker gravitational field than the one on Earth since the blocks seem to fall in "slow motion". This is probably because the perceived height of the structure is much smaller than the simulated height. In general, there are even more severe biases affecting the perception of 3D structures that depend on many factors, for instance, the viewpoint. Second, the distribution of weight among the objects and the friction coefficients acting between the surfaces are also unknown parameters. In other words, there are several parameters that depend on the viewing conditions and material composition of the blocks that are unknown and need to be estimated. The authors assume that these parameters are derived accurately and only that assumption allows them to attribute the observed biases to an error in the estimate of the gravitational field. Of course, if the direction of gravity is the only parameter allowed to vary freely then it is no surprise that it explains the results. Instead, a simulation with a titled angle of gravity may give rise to a display that is interpreted as rendering a vertical gravitational field while other parameters are misperceived. Moreover, there is an additional factor that is intentionally dismissed by the authors that is a possible cause of the fall of a stack of cubes: an external force. Stacks that are initially standing should not fall all of a sudden unless some unwanted force is applied to the construction. For instance, a sudden gust of wind would create a force field on a stack that is equivalent to that produced by a tilted gravitational field. Such an explanation would easily apply to the findings of the second experiment. In that experiment subjects are explicitly asked if a stack of blocks looks "stable". This is an ambiguous question because the stability of a structure is always judged by imagining what would happen to the structure if an external perturbation is applied. The right question should be: "do you think this structure would fall if unperturbed". However, if stability is judged in the face of possible external perturbations then a tall structure would certainly be judged as less stable than a short structure occupying the same ground area. This is what the authors find. What they consider as a bias (tall structures are perceived as less stable than short structures) is instead a wrong interpretation of the mental process that determines stability. If subjects are asked the question "Is it going to fall?" then tall stacks of sound structure would be judged as stable as short stacks, just more precarious.

The RL model used as a proof of concept for how the brain may build a stochastic prior for the direction of gravity is based on very strong and unverified assumptions. The first assumption is that the brain already knows about the force of gravity, but it lacks knowledge of the direction of this force of gravity. The second assumption is that before learning the brain knows the effect of a gravitational field on a stack of blocks. How can the brain simulate the effect of a non-vertical gravitational field on a structure if it has never observed such an event? The third assumption is that from the visual input, the brain is able to figure out the exact 3D coordinates of the blocks. This has been proven to be untrue in a large number of studies. Given these assumptions and the fact that the only parameters the RL model modifies through learning specify the direction of gravity, I am not surprised that the model produces the desired results.

Finally, the argument that the MGS is more efficient than the NGS model is based on an incorrect analysis of the results of the simulation. It is true that 80% accuracy is reached faster by the MGS model than the 95% accuracy level is reached by the NGS model. But the question is: how fast does the NGS model reach 80% accuracy (before reaching the plateau)?

Reviewer #3 (Public Review):

Summary:<br /> The authors aim to provide a multidisciplinary resource on the structural and physiological organization of the hippocampal system and make the available experimental data available for further theoretical work, providing tools to do so in a very flexible and user-friendly way. Since this is a new version of an already existing data-resource, the authors certainly reach their aim and fulfil expectations that the reader might have. The content of the database is as good as the original data, collected from the published knowledge-database, sometimes with the help of the original authors, and the overall quality depends further on how the data are curated by the team of authors and many others who helped them. That process is briefly described and more details are available in descriptions of previous versions and on the website. The data extraction, examples of how data can be used, and the part on attempts to model the hippocampus are exciting and open doors to new and exciting research opportunities.

Strengths:<br /> Excellent description with many outlined opportunities. Nicely illustrated and inviting to explore the online database.

Weaknesses:<br /> The figures are complex, containing a heavy information load with many abbreviations. You need some general knowledge of the system in order to grasp the enormous potential of what is provided.

Reviewer #3 (Public Review):

Seba et al. investigate whether chromosomal recruitment of the E. coli SMC complex MukBEF is initiated at a single site, how MukBEF activity is excluded from the replication terminus region, and whether its recruitment and activity depend on DNA replication. Upon induction of MukBEF, the authors find that chromosomal long-range contacts increase globally rather than from a single site. Using large-scale chromosome rearrangements, they show that matS sites can insulate separate areas of high MukBEF activity from each other. This suggests that MukBEF loads at multiple sites in the genome. Finally, the authors propose that MukBEF associates preferentially with newly replicated DNA, based on ChIP-seq experiments after DNA replication arrest.

The conclusions of the paper are mostly well supported by the data. The ratiometric contact analyses and range-of-contact analyses are compelling and nicely show the interplay between MukBEF and its proposed unloader MatP/matS. I particularly enjoyed the chromosome re-arrangement experiments, which lend strong support to the idea that MukBEF activity is independent of a centralized loading site.

The enrichment of MukBEF in newly replicated regions is somewhat less convincing, as the effect sizes are rather small and the background signal is unknown. The conclusion that matS density controls MukBEF activity is appealing, but would likely need additional support from more systematic studies. It is based on a comparison of only two strains (looking at different combinations of three matS sites), and the effect size is small. As it is, differences in matS sequence composition and genomic context cannot be factored out.

Overall, the work is an important advance in our understanding of bacterial chromosome organization. It will be of broad interest to chromosome biologists and bacterial cell biologists.

Reviewer #3 (Public Review):

Summary:<br /> This important paper provides the best-to-date characterization of chirping in weakly electric fish using a large number of variables. These include environment (free vs divided fish, with or without clutter), breeding state, gender, intruder vs resident, social status, locomotion state and social and environmental experience, as well as with playback experiments. It applies state-of-the-art methods for reducing dimensionality and finding patterns of correlation between different kinds of variables (factor analysis, K-means). The exceptional strength of the evidence, collated from a large number of trials with many controls, leads to the conclusion that a number of commonly accepted truths about which variable affects chirping must be carefully rewritten or nuanced. Based on their extensive analyses, the authors suggest that chirps are mainly used as probes that help detect beats and objects.

Strengths:<br /> The work is based on completely novel recordings using interaction chambers. The amount of new data and associated analyses is simply staggering, and yet, well organized in presentation. The study further evaluates the electric field strength around a fish (via modelling with the boundary element method) and how its decay parallels the chirp rate, thereby relating the above variables to electric field geometry.

The main conclusions are that the lack of any significant behavioural correlates for chirping, and the lack of temporal patterning in chirp time series, cast doubt on a communication goal for most chirps. Rather, the key determinants of chirping are the difference frequency between two interacting conspecifics as well as individual subjects' environmental and social experience. These conclusions by themselves will be hugely useful to the field. They will also allow scientists working on other "communication" systems to at least reconsider, and perhaps expand the precise goal of the probes used in those senses. There are a lot of data summarized in this paper, and thorough referencing to past work. For example, the paper concludes that there is a lack of evidence for stereotyped temporal patterning of chirp time series, as well as of sender-received chirp transitions beyond the known increase in chirp frequency during an interaction.

The alternative hypotheses that arise from the work are that chirps are mainly used as environmental probes for better beat detection and processing and object localization.

The authors also advance the interesting idea that the sinusoidal frequency modulations caused by chirps are the electric fish's solution to the minute (and undetectable by neural wetware) echo-delays available to it, due to the propagation of electric fields at the speed of light in water.

Weaknesses:<br /> My main criticism is that the alternative putative role for chirps as probe signals that optimize beat detection could be better developed. The paper could be clearer as to what that means precisely. And there is an egg-and-chicken type issue as well, namely, that one needs a beat in order to "chirp" the beating pattern, but then how does chirping optimize the detection of the said beat? Perhaps the authors mean (as they wrote elsewhere in the paper) that the chirps could enhance electrosensory responses to the beat.

A second criticism is that the study links the beat detection to underwater object localization. I did not see a sufficiently developed argument in this direction, nor how the data provided support for this argument. It is certainly possible that the image on the fish's body of an object in the environment will be slightly modified by introducing a chirp on the waveform, as this may enhance certain heterogeneities of the object in relation to its environment. The thrust of this argument seems to derive more from the notion of Fourier analysis with pulse type fish (and radar theory more generally) that the higher temporal frequencies in the beat waveform induced by the chirp will enable a better spatial resolution of objects. It remains to be seen whether this is significant.

I would also have liked to see a proposal for new experiments that could test these possible new roles.

The authors should recall for the readers the gist of Bastian's 2001 argument that the chirp "can adjust the beat frequency to levels that are better detectable" in the light of their current. Further, at the beginning of the "Probing with chirps" section, the 3rd way in which chirps could improve conspecific localization mentions the phase-shifting of the EOD. The authors should clarify whether they mean that the tuberous receptors and associated ELL/toral circuitry could deal with that cue, or that the T_unit pathway would be needed?

On p.17 I don't understand what is meant by most chirps being produced possibly aligned with the field lines, since field lines are everywhere. And what is one to conclude from the comparison of Fig.6D and 7A? Likewise it was not clear what is meant by chirps having a detectable effect on randomly generated beats.

In the section on Inconsistencies between behaviour and hypothesized signal meaning, the authors could perhaps nuance the interpretation of the results further in the context of the unrealistic copy of natural stimuli using EOD mimics. In particular, Kelly et al. 2008 argued that electrode placement mattered in terms of representation of a mimic fish onto the body of a real fish, and thus, if I properly understand the set up here, the movement would cause the mimic to vary in quality. This may nevertheless be a small confounding issue.

Reviewer #3 (Public Review):

Summary:<br /> The present manuscript by Reham Abdelaziz and colleagues addresses the gating of Kv10.1, which belongs to the KCNH gene family and contains other subfamilies such as Kv11 (ERG) and Kv12 (ELK). They all have fundamental physiological roles, from cardiac repolarization to modulation of neuronal excitability and cancer physiology. They have a non-domain swapped architecture at the molecular level; both voltage and Ca-CaM modulate the channel function. They contain an intracellular gating ring formed by a PAS domain (in the N-term) that interacts intimately with the cNBHD (C-term) of the neighbor subunit but also with the cytosolic part of the voltage sensor domain and the C-linker. Mutations in the N- or C- terminus modify the gating dramatically. This complex network of interactions makes the cytosolic section and the PAS domain in particular, an alluring part of the channel to study as responsible for the coupling between the movements of the voltage sensor and the gating ring.

In this paper, Reham Abdelaziz and colleagues address a fundamental question of how in the Kv10.1 channels, the movement of the voltage sensor is coupled to the intracellular gating ring rotation to make the channel conduct ions. The authors perform a series of deletions and mutations in the N-terminal section of the channel (PAS domain) and in the C-terminus (cNHBD) and observe a biphasic behavior on the modified EAG channels that they interpret as two populations of open states, one of them not visible in the WT and only available because of the mutations introduced. While this is a fascinating hypothesis and it fits with the depolarizing range of potentials of the WT channels, there are some issues that, if addressed, will make this work very valuable for biophysicists and molecular physiologists interested in voltage-gated ion channels.

Strengths:<br /> The work presented addresses one of this channel's most fascinating and challenging features in the KCNH family. The authors use adequate mutations and electrophysiological techniques to address the questions they are trying to answer. They help them explore the behavior of the channels and build a Markov model to understand the underlying mechanism.

Weaknesses:<br /> Although very well established, the experimental conditions used in the present manuscript introduce uncertainties, weakening their conclusions and complicating the interpretation of the results. The authors performed most of their functional studies in Cl-based solutions that can become a non-trivial issue when the range of voltages explored extends to very depolarizing potentials such as +120mV. Oocytes endogenously express Ca2+-activated Cl- channels that will rectify Cl- at very depolarizing potentials -due to an increase in the driving force- and contribute dramatically to the current's amplitude observed at the test pulse in the voltage ranges where the authors identify the second open state.

The authors propose a two-layer Markov model with two open states approximating their results. However, the results obtained with the mutants suggest an inactivated state accessible from closed states and a change in the equilibrium between the close/inactivated/open states that could also explain the observed results; therefore, other models could approximate their data.

That said, if the results obtained by the authors get confirmed under different experimental conditions in the absence of Cl-, this present work could be instrumental in understanding the gating mechanisms of the KCNH family.

Reviewer #3 (Public Review):

The manuscript describes new ligand-bound structures within the larger bile acid sodium symporter family (BASS). This is the primary advance in the manuscript, together with molecular simulations describing how sodium and the bile acids sit in the structure when thermalized. What I think is fairly clear is that the ligands are more stable when the sodiums are present, with a marked reduction in RMSD over the course of repeated trajectories. This would be consistent with a transport model where sodium ions bind first, and then the bile acid binds, followed by a conformational change to another state where the ligands unbind.

While the authors mention that BASS transporters are thought to undergo an elevator transport mechanisms, this is not tested here. In my reading, all the crystal structures belong to the same conformational state in the overall transport cycle, and the simulations do not make an attempt to induce a transition on accessible simulation timescales. Instead, there is a morph between two inward facing states.

The focus is on what kinds of substrates bind to this transporter, interrogating this with isothermal calorimetry together with mutations. With a Kd in the micromolar range, even the best binder, pantoate, actually isn't a particularly tight binder in the pharmaceutical sense. For a transporter, tight binding is not actually desirable, since the substrate needs to be able to leave after conformational change places it in a position accessible to the other side.

The structure and simulation analysis falls into the mainstream of modern structural biology work.

Reviewer #3 (Public Review):

Summary:

The unconventional myosin Myo10 (aka myosin X) is essential for filopodia formation in a number of mammalian cells. There is a good deal of interest in its role in filopodia formation and function. The manuscript describes a careful, quantitative analysis of Myo10 molecules in U2OS cells, a widely used model for studying filopodia, how many are present in the cytosol versus filopodia and the distribution of filopodia and molecules along the cell edge. Rigorous quantification of Myo10 protein amounts in a cell and cellular compartment are critical for ultimately deciphering the cellular mechanism of Myo10 action as well as understand the molecular composition of a Myo10-generated filopodium.<br /> Consistent with what is seen in images of Myo10 localization in many papers, the vast majority of Myo10 is in the cell body with only a small percentage (appr 5%) present in filopodia puncta. Interestingly, Myo10 is not uniformly distributed along the cell edge, but rather it is unevenly localized along the cell edge with one region preferentially extending filopodia, presumably via localized activation of Myo10 motors. Calculation of total molecules present in puncta based on measurement of puncta size and measured Halo-Myo10 signal intensity shows that the concentration of motor present can vary from 3 - 225 uM. Based on an estimation of available actin binding sites, it is possible that Myo10 can be present in excess over these binding sites.

Strengths:

The work represents an important first step towards defining the molecular stoichiometry of filopodial tip proteins. The observed range of Myo10 molecules at the tip suggests that it can accommodate a fairly wide range of Myo10 motors. There is great value in studies such as this and the approach taken by the authors gives one good confidence that the numbers obtained are in the right range.

Weaknesses:

One caveat (see below) is that these numbers are obtained for overexpressing cells and the relevance to native levels of Myo10 in a cell is unclear.<br /> An interesting aspect of the work is quantification of the fraction of Myo10 molecules in the cytosol versus in filopodia tips showing that the vast majority of motors are inactive in the cytosol, as is seen in images of cells. This has implications for thinking about how cells maintain this large population in the off-state and what is the mechanism of motor activation. One question raised by this work is the distinction between cytosolic Myo10 and the population found at the 'cell edge' and the filopodia tip. The cortical population of Myo10 is partially activated, so to speak, as it is targeted to the cortex/membrane and presumably ready to go. Providing quantification of this population of motors, that one might think of as being in a waiting room, could provide additional insight into a potential step-by-step pathway where recruitment or binding to the cortical region/plasma membrane is not by itself sufficient for activation.

Specific comments -

1) It is not obvious whether the analysis of numbers of Myo10 molecules in a cell that is ectopically overexpressing Myo10 is relevant for wild type cells. It would appear to be a significant excess based on the total protein stained blot shown in Fig S1E where a prominent band the size of tagged Myo10 seen in the transfected sample is almost absent in the WT control lane. Ideally, and ultimately an important approach, would be to work with a cell line expressing endogenously tagged Myo10 via genome engineering. This can be complicated in transformed cells that often have chromosomal duplications.

However, even though there is an excess of Myo10 it would appear that activation is still under some type of control as the cytosolic pool is quite large and its localization to the cell edge is not uniform. But it is difficult to gauge whether the number of molecules in the filopodium is the same as would be seen in untransfected cells. Myo10 can readily walk up a filopodium and if excess numbers of this motor are activated they would accumulate in the tip in large numbers, possibly creating a bulge as and indeed it does appear that some tips are unusually large. Then how would that relate to the normal condition?

2) Measurements of the localization of Myo10 focuses in large part on 'Myo10 punctae'. While it seems reasonable to presume that these are filopodia tips, the authors should provide readers with a clear definition of a puncta. Is it only filopodia tips, which seems to be the case? Does it include initiation sites at the cell membrane that often appear as punctae?<br /> Along those lines, the position of dim punctae along the length of a filopodium is measured (Fig 3D). The findings suggest that a given filopodium can have more than one puncta which seems at odds if a puncta is a filopodia tip. How frequently is a filopodium with two puncta seen? It would be helpful if the authors provided an example image showing the dim puncta that are not present at the tip.

3) The concentration of actin available to Myo10 is calculated based on the deduction from Nagy et al (2010) that only 4/13 of the actin monomers in a helical turn are accessible to the Myo10 motor (discussion on pg 9; Fig S4). Subsequent work (Ropars et al, 2016) has shown that the heads of the antiparallel Myo10 dimer are flattened, but the neck is rather flexible, meaning that the motor can a variable reach (36 - 52 nm). Wouldn't this mean that more actin could be accessible to the Myo10 motor than is calculated here?

4) Quantification of numbers of Myo10 molecules in filopodial puncta (Fig 3C) leads the authors to conclude that 'only ten or fewer Myo10 molecules are necessary for filopodia initiation' (pg 7, top). While this is a reasonable based on the assumption that the formation of a puncta ultimately results from an initiation event, little is known about initiation events and without direct observation of coalescence of Myo10 at the cell edge that leads to formation of a filopodium, this seems rather speculative.

Reviewer #3 (Public Review):

Summary:<br /> As clearly highlighted by the authors, a key plank in the ability of trypanosomes to evade the mammalian host's immune system is its high rate of endocytosis. This rapid turnover of its surface enables the trypanosome to 'clean' its surface removing antibodies and other immune effectors that are subsequently degraded. The high rate of endocytosis is likely reflected in the organisation and layout of the endosomal system in these parasites. Here, Link et al., sought to address this question using a range of light and three-dimensional electron microscopy approaches to define the endosomal organisation in this parasite.

Before this study, the vast majority of our information about the make-up of the trypanosome endosomal system was from thin-section electron microscopy and immunofluorescence studies, which did not provide the necessary resolution and 3D information to address this issue. Therefore, it was not known how the different structures observed by EM were related. Link et al., have taken advantage of the advances in technology and used an impressive combination of approaches at the LM and EM level to study the endosomal system in these parasites. This innovative combination has now shown the interconnected-ness of this network and demonstrated that there are no 'classical' compartments within the endosomal system, with instead different regions of the network enriched in different protein markers (Rab5a, Rab7, Rab11).

Strengths:<br /> This is a generally well-written and clear manuscript, with the data well-presented supporting the majority of the conclusions of the authors. The authors use an impressive range of approaches to address the organisation of the endosomal system and the development of these methods for use in trypanosomes will be of use to the wider parasitology community.

I appreciate their inclusion of how they used a range of different light microscopy approaches even though for instance the dSTORM approach did not turn out to be as effective as hoped. The authors have clearly demonstrated that trypanosomes have a large interconnected endosomal network, without defined compartments and instead show enrichment for specific Rabs within this network.

Weaknesses:<br /> My concerns are:

i) there is no evidence for functional compartmentalisation. The classical markers of different endosomal compartments do not fully overlap but there is no evidence to show a region enriched in one or other of these proteins has that specific function. The authors should temper their conclusions about this point.

ii) the quality of the electron microscopy work is very high but there is a general lack of numbers. For example, how many tomograms were examined? How often were fenestrated sheets seen? Can the authors provide more information about how frequent these observations were?

iii) the EM work always focussed on cells which had been processed before fixing. Now, I understand this was important to enable tracers to be used. However, given the dynamic nature of the system these processing steps and feeding experiments may have affected the endosomal organisation. Given their knowledge of the system now, the authors should fix some cells directly in culture to observe whether the organisation of the endosome aligns with their conclusions here.

iv) the discussion needs to be revamped. At the moment it is just another run through of the results and does not take an overview of the results presenting an integrated view. Moreover, it contains reference to data that was not presented in the results.

Reviewer #3 (Public Review):

In this study, Weiting Zhang et al., improved the editing efficiency of prime editor by reducing misfolded pegRNA interactions, and the improvement of efficiency for prime editor helped to expand its application range. It is a research paper on technology improvement. This study is somewhat innovative.

Reviewer #3 (Public Review):

Summary:<br /> In this paper presented by Liu et al, native MS on the lipid A transporter MsbA was used to obtain thermodynamic insight into protein-lipid interactions. By performing the analyses at different lipid A concentrations and temperatures, dissociation constants for 2-3 lipid A binding sites were determined, as well as enthalpies were calculated using non-linear van't Hoff fitting. Changes in free Gibb's energies were then calculated based on the determined dissociation constants, and together with the enthalpy values obtained via van' t Hoff analysis, the entropic contribution to lipid binding (DeltaS*T) was indirectly determined.

Strengths:<br /> This is an extensive high quality native MS dataset that provides unique opportunities to gain insights into the thermodynamic parameters underlying lipid A binding. In addition, it provides coupling energies between mutations introduced into MsbA, that are implicated in lipid A binding.

Weaknesses:<br /> The data all rely on the accuracy of determining KD values for lipid binding to MsbA. For the weaker binding sites, the range of lipid concentrations probed were in fact too low to generate highly accurate data. Another weakness is a lack of clear evidence, which KD values belong to which of the possible lipid A binding sites.

Reviewer #3 (Public Review):

Summary and Strengths:

The manuscript by Li et al. presents an elegant application of sterile insect technology (pgSIT) utilizing a CRISPR-Cas9 system to suppress mosquito vector populations. The pgSIT technique outlined in this paper employs a binary system where Cas9 and gRNA are conjoined in experimental crosses to yield sterile male mosquitoes. Employing a multiplexed strategy, the authors combine multiple gRNA to concurrently target various genes within a single locus. This approach successfully showcases the disruption of three distinct genes at different genomic positions, resulting in the creation of highly effective sterile mosquitoes for population control. The pioneering work of the Akbari lab has been instrumental in developing this technology, previously demonstrating its efficacy in Drosophila and Aedes aegypti.

By targeting the female-specific splice isoform (exon-5) of doublesex in conjunction with intersex and β-tubulin, the researchers induce female lethality, leading to a predominance of sterile male mosquitoes. This innovation is particularly noteworthy as the deployment of sterile mosquitoes on a large scale typically requires substantial investment in sex sorting. However, this study circumvents this challenge through genetic manipulation.

Weaknesses:

One notable concern arising from this manuscript pertains to the absence of data regarding the potential off-target effects of the gRNA. Given the utilization of multiple gRNA, the risk of unintended mutations in non-target areas of the genome increases. With around 1% of males still capable of producing fertile offspring, understanding the frequency of unintended genome targeting becomes crucial. Such mutations could potentially become fixed within the natural population.<br /> The experiments are well-conceived, featuring suitable controls and repeated trials to yield statistically significant data. However, a primary issue with the manuscript lies in its data presentation. The authors' graphical representations are intricate and demand considerable attention to discern the nuances, especially due to the striking similarity between the symbols representing different genotypes. As it stands, the manuscript primarily caters to experts within the field, thereby warranting improvements in data visualization for broader comprehension.

https://notsubrand.com/

Notsu has a variety of 3 x 5" index card products for productivity and planning.

Reviewer #3 (Public Review):

This manuscript describes CK21, a modified version of Triptolide, a natural compound with ant-cancer activities, to improve its bioavailability. The authors tested the compound in two human pancreatic cancer cell lines, in vitro and in vivo. The authors also use two human organoid lines derived from pancreatic cancer, and mouse KC and KPC cell lines. In all models, CK21 treatment induces dose-dependent cytotoxicity. In vivo, CK21 causes tumor regression. The authors perform gene expression analysis and show that treated organoids have generally lower transcription, consistent with cytotoxicity, and a reduction in the KFkB pathway activation.

Key experiments that would strengthen the current manuscript are: the inclusion of normal cell lines and organoids, too, presumably, show no cytotoxic effect. If that is the case, the authors would have the opportunity to compare responses and determine whether a tumor-specific mechanism can be defined.

The authors observe that few gene changes - besides from overall lowering in transcription, occur upon treatment with CK21. They suggest that the drug acts through inhibition of the NFkB pathway and an increase in reactive oxygen species (ROS). However, no experiments to test whether either/both of these findings explain the cytotoxic effect (rescue experiments would be particularly valuable).

In the last figure, the authors text whether CK21 is immunosuppressive by testing immunity against a mis-matched tumor cell line (using KPC tumors, mixed strain, in mixed strain mice). The immunity against HLA mis-matched cells is a very strong immune reaction, and mild immune suppression might be missed, which diminishes the value of these findings.

Reviewer #3 (Public Review):

Dysbiosis has a substantial impact on host physiology. Using the nematode C. elegans and E.coli as a model of host-microbe interactions, Yang et al. defined a mechanism by which the host deals with gut dysbiosis to maintain fitness. They found that accumulation of E. coli in the intestine secreted indole, a tryptophan metabolite, and activated the transcription factor DAF-16. DAF-16 induced the expression of lys-7 and lys-8, which in turn limited E. coli proliferation in the gut of worms and maintained the longevity of worms. Finally, these authors demonstrated that indole-activated DAF-16 via TRPA-1 in neurons of worms.

This study revealed a new mechanism of host-microbe interaction. The concept of their work is of broad interest and the results they present are convincing. However, there are some issues that need to be addressed to support the conclusions.

Major issues<br /> 1. The authors isolated the crude extract from a high-performance liquid chromatograph (HPLC). A candidate compound was detected by activity-guided isolation and further identified as indole with mass spectrometry and NMR data.<br /> The HPLC fractionations and activity-guided isolation experiments should be described in more detail with a schematic figure to reveal how these experiments were performed and how indole was identified. Showing a chemical characterization of indole in Figure 2A is not sufficient for the evaluation of the results. Rather, a figure comparing the fraction 26th with standard indole by MS and NMR is more appealing.

2. DAF-16::GFP was mainly located in the cytoplasm of the intestine in worms expressing daf-16p::daf-16::gfp fed live E. coli OP50 on Day 1 (Figure 1A and 1B). The nuclear translocation of DAF-16 in the intestine was increased in worms fed live E. coli OP50 on Days 4 and 7, but not in age-matched WT worms fed heat-killed (HK)E. coli OP50 (Figure 1A and 1B).<br /> Since DAF-16 functions downstream of DAF-2, have the levels of DAF-2 been tested during aging on OP50 and (HK)OP50, or with and without indole supplementation?

3. In lines 155-157, the author argued that the increase in the levels of indole in worms results from the intestinal accumulation of live E. coli OP50, rather than exogenous indole produced by E. coli OP50 on the NGM plates.<br /> However, the work also showed that supplementation with indole (50-200 μM) could significantly increase the indole levels in young adult worms on Day 1 (Figure 2-figure supplement 3B), which could induce nuclear translocation of DAF-16 in worms (Figure 2B).<br /> This result suggested that worms could take in indole from outside culturing environment. The concentration of indole in OP50 and (HK)OP50 could be measured.

4. Recent work showed that the multicopy DAF-16 transgene acts differently from the single copy GFP knockin DAF-16 transgene. Which DAF-16 transgene was used in this work?

5. In lines 190-193, the author argued that the supplementation with indole (100 M) inhibited the CFU of E. coli K-12 in WT worms, but not daf-16(mu86) mutants, on Days 4 and 7 (Figure 3H and 3I). These results suggest that endogenous indole is involved in maintaining a normal lifespan in worms.<br /> This is overstating. The data here more likely suggest that indole could inhibit the proliferation of E.coli through DAF-16.

6. Sonowal (2017) reported that AHR mediates indole-promoted lifespan extension at 16oC. Yet this work argued that RNAi knockdown of ahr-1 did not affect the nuclear translocation of DAF-16 in worms fed E. coli K12 strain on Day 7 (Figure 4-figure supplement 1A) or young adult worms treated with indole (100 M) for 24 h.<br /> The difference between these two works should be discussed.

7. Sonowal (2017) conducted mRNA profiling for worms growing on K12 and K12△tnaA. Is TRPA1 in their de-regulated gene list? Have other de-regulated genes been tested in this work?

8. How does indole activate TRPA1? In the absence of trpa1, what is the concentration of indole in worms? Since TRPA1 is a channel, is there any possibility that TRPA1 is involved in the transport of indole? It is really interesting and surprising that neuronal TRPA-1, but not intestinal TRPA-1, mediates the beneficial effect of indole. How does indole specifically activate TRPA-1 in neurons to preserve the longevity of worms?

9. How neuronal- and intestinal-specific knockdown of trpa-1 by RNAi was conducted? And what is the tissue-specific expression pattern of trap-1? Speculating how indole was transported to neuron cells is pretty appealing.

10. Supplementation with indole only up-regulated the expression of lys-7 and lys-8 in worms subjected to intestinal-specific (Figure 7-figure supplement 2C), but not neuronal-specific, RNAi of trpa-1 (Figure 7-figure supplement 2D).<br /> If this is the case, should the addition of indole specifically induce the expression of lys-7p::gfp or lys-8p::gfp in neurons?

11. The authors demonstrated that K-12△tnaA strain had undetectable tnaA mRNA or indole levels. Furthermore, the deletion of tnaA significantly inhibited the nuclear translocation of DAF-16 in worms. However, mutations in E. coli still have non-specific effects as there are several transposon insertions or polar mutations influencing downstream genes. The authors should demonstrate that only disruption of TnaA causes the failure of nuclear translocation of DAF-16.

Reviewer #3 (Public Review):

The authors are designing a novel continuous evidence accumulation task to look at neural and behavioral adaptations of continuously changing evidence. They particularly focus on centroparietal EEG potential that has been previously linked with evidence accumulation. This paper provides a novel method and analysis to investigate evidence accumulation in a continuous task set-up.

I am not familiar with either the EEG or evidence accumulation literature, therefore cannot comment on the strength of the findings related to centroparietal EEG in evidence accumulation. I have therefore commented only on the coherence and details of the method and clarity of the argumentation and results.

The main strength is in the task design which is novel and provides an interesting approach to studying continuous evidence accumulation. Because of the continuous nature of the task, the authors design new ways to look at behavioral and neural traces of evidence. The reverse-correlation method looking at the average of past coherence signals enables us to characterize the changes in signal leading to a decision bound and its neural correlate.<br /> By varying the frequency and length of the so-called response period, that the participants have to identify, the method potentially offers rich opportunities to the wider community to look at various aspects of decision-making under sensory uncertainty.

The main weaknesses that I see lie within the description and rigor of the method. The authors refer multiple times to the time constant of the exponential fit to the signal before the decision but do not provide a rigorous method for its calculation and neither a description of the goodness of the fit. The variable names seem to change throughout the text which makes the argumentation confusing to the reader. The figure captions are incomplete and lack clarity.<br /> The authors claim that the method enables continuous analysis of decision-making and evidence accumulation which is true. The analysis of the signals that come prior to the decision provides a rich opportunity to characterize decision bound in this task. The behavioral and neural analyses globally lack clarity and description and thus do not strongly support the claims of the paper. The interpretation of the figures within the figure caption and the lack of a neutral and exhaustive description of what is being shown prevent the claims to be strongly supported.

The continuous nature of the task and the computation of those evidence kernels are valuable methods to look at evidence accumulation that could be of use within the community. However, due to the lack of rigor in the analysis and description of the method, it is hard to know if the current dataset is under-exploited or whether the choice of the parameters for this set of experiment does not enable stronger claims.

Reviewer #3 (Public Review):

Akter et al. investigated how the astroglial Gi signaling pathway in the rat anterior cingulate cortex (ACC) affects cognitive functions, in particular schema memory formation. Using a stereotactic approach they intracranially introduced AAV8 vectors carrying mCherry-tagged hM4Di DREADD (Designer Receptor Exclusively Activated by Designer Drugs) under astrocyte selective GFAP promotor (AAV8-GFAP-hM4Di-mCherry) into the AAC region of the rat brain. hM4Di DREADD is a genetically modified form of the human M4 muscarinic (hM4) receptor insensitive to endogenous acetylcholine but is activated by the inert clozapine metabolite clozapine-N-oxide (CNO), triggering the Gi signaling pathway. The authors confirmed that hM4Di DREADD is selectively expressed in astrocytes after the application of the AAV8 vector by analysing the mCherry signals and immunolabeling of astrocytes and neurons in the ACC region of the rat brain. They activated hM4Di DREADD (Gi signalling) in astrocytes by intraperitoneal administration of CNO and measured cognitive functions in animals after CNO administration. Activation of Gi signaling in astrocytes by CNO application decreased paired-associate (PA) learning, schema formation, and memory retrieval in tested animals. This was associated with a decrease in cAMP in astrocytes and L-lactate in extracellular fluid as measured by immunohistochemistry in situ and in awake rats by microdialysis, respectively. Administration of exogenous L-lactate rescued the astroglial Gi-mediated deficits in PA learning, memory retrieval, and schema formation, suggesting that activation of astroglial Gi signalling downregulates L-lactate production in astrocytes and its transport to neurons affecting memory formation. Authors also show that expression level of proteins involved in mitochondrial biogenesis, which is associated with cognitive functions, is decreased in neurons, when Gi signalling is activated in astrocytes, and rescued when exogenous L-lactate is applied, suggesting the implication of astrocyte-derived L-lactate in the maintenance of mitochondrial biogenesis in neurons. The latter depended on lactate MCT2 transporter activity and glutamate NMDA receptor activity.

The paper is very well written and discussed. The conclusions of this paper are well supported by the data. Although this is a study that uses established and previously published methodologies, it provides new insights into L-lactate signalling in the brain, particularly in AAC, and further confirms the role of astroglial L-lactate in learning and memory formation. It also raises new questions about the molecular mechanisms underlying astrocyte-derived L-lactate-mediated mitochondrial biogenesis in neurons and its contribution to schema memory formation.

• The authors discuss astrocytic L-lactate signalling without considering the recently discovered L-lactate-sensitive Gs and Gi protein-coupled receptors in the brain, which are present in both astrocytes and neurons. The use of nonendogenous L-lactate receptor agonists (Compound 2, 3-chloro-5-hydroxybenzoic acid) would clarify the implication of L-lactate receptor signalling in schema memory formation.

• The use of control animals transduced with an "empty" AAV9 vector (AAV8-GFAP-mCherry) compared with animals transduced with AAV8-GFAP-hM4Di-mCherry throughout the study would strengthen the results of this study, since transfection itself, as well as overexpression of the mCherry protein, may affect cell function.

Reviewer #3 (Public Review):

Seeking a selective inhibitor that precisely inhibits on-target activities and avoids side effects is a major challenge in the field of drug discovery and therapeutics. The authors proposed an alternative method that combines multiple inhibitors to maximize on-target inhibition and minimize off-target inhibition. Focusing on the kinase-inhibitor interaction dataset, the authors developed a quantitative way to measure the selectivity for mixtures of inhibitors by using the Jenson-Sahannon distance metric. The method sounds technical.

From their computation and assays, the multi-compound-multitarget scoring (MMS) method framework was validated to be able to select a combination of inhibitors that is more selective than a single highly selective inhibitor for one kinase target, or for multiple targets. The MMS method is a promising solution to reduce off-target effects and could be applicable to other inhibitor-target interactions. My suggestion is that a comparative analysis of MMS with other similar methods can be conducted to highlight the advantage of MMS over others.

The paper is not well organized and not easily readable. For example, first, the captions of the figures are two long; some of these texts could be moved to methods or results sections. Second, the concept of "penalty distribution" or "penalty prior" is vital to understand the MMS method, thus, at least a brief definition and introduction should be put in the main text rather than supporting method, as well as the rationale to use it. Third, the method section can be divided into several subsections with clear organizations and connections. Fourth, what is the difference between "a less selective inhibitor profile" and "an even less selective inhibitor profile" in Figure 3? Overall, the details of the paper are difficult to understand in the current version. I suggest rewriting<br /> the paper in a more concise and logical style.

Reviewer #3 (Public Review):

This study investigated cognitive mechanisms underlying approach-avoidance behavior using a novel reinforcement learning task and computational modelling. Participants could select a risky "conflict" option (latent, fluctuating probabilities of monetary reward and/or unpleasant sound [punishment]) or a safe option (separate, generally lower probability of reward). Overall, participant choices were skewed towards more rewarded options, but were also repelled by increasing probability of punishment. Individual patterns of behavior were well-captured by a reinforcement learning model that included parameters for reward and punishment sensitivity, and learning rates for reward and punishment. This is a nice replication of existing findings suggesting reward and punishment have opposing effects on behavior through dissociated sensitivity to reward versus punishment.

Interestingly, avoidance of the conflict option was predicted by self-reported task-induced anxiety. Importantly, when a subset of participants were retested over 1 week later, most behavioral tendencies and model parameters were recapitulated, suggesting the task may capture stable traits relevant to approach-avoidance decision-making.

The revised paper commendably adds important additional information and analyses to support these claims. The initial concern that not accounting for participant control over punisher intensity confounded interpretation of effects has been largely addressed in follow-up analyses and discussion.

This study complements and sits within a broad translational literature investigating interactions between reward/punishers and psychological processes in approach-avoidance decisions.

Reviewer #3 (Public Review):

This study focused on collecting and analyzing odour samples from a wide range of vertebrate species to understand the composition and characteristics of vertebrate body odours. The researchers used dynamic headspace sampling to collect odour samples from 120 individual animals representing 64 vertebrate species. They collected odour from both live animals and hair samples, with hair being a reasonable proxy for mammalian body odour.

The odour samples were analyzed using thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) to identify compounds and estimate their abundance. They identified a total of 116 compounds in the vertebrate odour extracts, including aldehydes, ketones, alcohols, aromatics, terpenes, and hydrocarbons. The compounds varied in prevalence across species, but a large number of compounds were found in at least 15 samples, indicating a broad overlap in odour composition among vertebrates.

The study compared the vertebrate odour space to floral odour space and found that vertebrate odours shared more compounds compared to floral odours. Floral odours tended to be less complex and more likely to contain unique compounds found only in a single species. The analysis also revealed that odour profiles did not show strong phylogenetic signals, indicating that closely related species did not necessarily have similar odour profiles. However, within-species clustering was observed, suggesting that body odour composition may be species-specific.

The researchers also investigated specific compounds that could serve as host-seeking cues for animals. They compared the odour of live vertebrate hosts to non-host stimuli and identified straight-chain aldehydes as abundant compounds in vertebrate odours. These aldehydes were found at substantially lower levels in non-host stimuli. Additionally, when comparing human odour to other vertebrate species and non-host stimuli, several compounds, including decanal, sulcatone, geranylacetone, and undecanal, emerged as strong predictors of human hosts.

Three shortcomings of the study can be highlighted:<br /> 1. Undersampling of certain compound classes: The study acknowledged that they undersampled carboxylic acids, which are generally too polar or non-volatile to be analyzed without a special derivatization step. This limitation could have resulted in an incomplete understanding of the full range of compounds present in vertebrate odours.<br /> 2. Missing highly volatile compounds: The study mentioned the difficulty of capturing and quantifying highly volatile compounds reliably. This limitation suggests that certain compounds with high volatility may not have been adequately represented in the analysis, potentially impacting the comprehensiveness of the odour space.<br /> 3. Lack of controlled experiment for species replicates: Although the study observed strong within-species clustering for some species in their dataset, they cautioned that many of the species replicates came from the same farm or zoo, which could confound the results with sample origin. The lack of a well-controlled experiment limits the generalizability of the findings regarding consistent and characteristic odour profiles across animals.

These shortcomings should be considered when interpreting the results of the study and could be addressed in future research to further advance our understanding of vertebrate body odours.

The manuscript highlights three open questions. First, the authors discuss the implications of the differences between vertebrate and floral odors for olfactory coding in blood feeders and floral visitors. Specialist mosquitoes require odor blends to detect hosts, while honeybees can generalize from attractive mixtures to individual components. The authors suggest that these differences may be influenced by the different odor spaces mosquitoes and bees inhabit.

Second, the authors note that although compounds in vertebrate odor are shared broadly across species, they are also common in other natural odors. This poses a challenge for generalist blood feeders, but the study suggests that straight-chain, saturated aldehydes, which are highly abundant in vertebrate odors, may still serve as useful indicators. These aldehydes have been shown to enhance host-seeking in mosquitoes and are even used by malaria parasites and orchids to attract mosquitoes. However, the study did not capture highly volatile or polar compounds that may also indicate the presence of a vertebrate host.

Third, the manuscript discusses the lack of phylogenetic signal in the odors of mammals, which make up the majority of the sampled species. This may explain why few mosquitoes exhibit preferences for taxonomic groups at the family or order level. The study suggests that within a species, there is high consistency in odor-blend composition, which may mediate species-specific host preference through olfactory cues.

The authors also focus on odor features that may serve as valuable cues for human specialists. They find that certain components of human odor, such as sulcatone, geranylacetone, decanal, and undecanal, are distinctive and enriched in human odor. Undecanal, despite being less common across non-human animals and in nature overall, is a more reliable indicator of human odor than decanal. The two ketones are even more reliable indicators. The authors speculate that the reliance on aldehydes by human-specialist mosquitoes may be due to the evolutionary history of these mosquitoes, which arose from an ancestral generalist subspecies.

In conclusion, this manuscript presents a quantitative study of vertebrate animal odors, highlighting the differences between vertebrate and floral odors. It raises questions about olfactory coding in blood feeders and floral visitors, the challenges faced by generalist blood feeders, and the lack of phylogenetic signal in mammalian odors. The study also explores odor features that may be valuable cues for human specialists and discusses the evolutionary implications of these findings.

Reviewer #3 (Public Review):

This manuscript deal with the sex-related gene, DMRT1, showing that is has a testis-promoting function in the rabbit. Loss-of-function studies the mouse and human, DMRT1 has a role in testis maintenance after birth, although forced expression in mouse can induce testis formation.

The authors used CRISPR/Cas9 genome editing to generate DMRT1-/- rabbit embryos. The gonads of these embryos developed as ovaries. Interestingly, in addition Y-linked SRY, DMRT1 is required for timely up-regulation of SOX9 during Sertoli cell differentiation in the male gonad. This is quite different to the situation in mouse, where Dmrt1 is not required in the testis until after birth (and Sry induced up-regulation of Sox9 hence does not require Dmrt1).

The work adds to the field of sex determination by further broadening our understanding of the DMRT1 gene and the evolution of gonadal sex determination.

In the Discussion section, it is suggested that DMRT1 could act as a pioneering factor to allow SRY action upon Sox9 in the rabbit model. The data show that DMRT1 may be more central to testis formation in mammals than previously considered. The work supports the notion that our understanding that the genetics of gonadal development (and indeed development more generally) should not rest solely on findings in the mouse.

Reviewer #3 (Public Review):

It is well established that there is extensive post-transcriptional gene regulation in nervous systems, including the fly brain. For example, dynamic regulation of hundreds of genes during photoreceptor development could only be observed at the level of translated mRNAs, but not the entire transcriptomes. The present study instead addresses the role of differential translational regulation between cell types (or rather classes: neurons and glia, as both are still highly heterogenous groups) in the adult fly brain. By performing bulk RNA-seq and Ribo-seq on the same lysates, the authors are able to compare the translation efficiency (TE) of all transcripts between neurons and glia. Many genes display differential TE, but interestingly, they tend to be the genes that already show strong differences at their mRNA level. The most striking observation is the finding that neuronal transcripts in glia display increased ribosome stalling at their 5' UTR, and in particular at the start codons of short "upstream ORFs". This could suggest that glia specifically employ a mechanism to upregulate upstream ORF translation, enabling them to better suppress the expression of the genes that have them. And neuronal genes tend to have longer 5' UTRs, perhaps to facilitate this type of regulation.

However, it is difficult to evaluate the functional significance of these differences because the authors provide only one follow-up experiment to their RNA-seq analysis. Venus expressed with the Rh1 UTR sequences may be displaying differential levels between glia and neurons, but I find this image (Fig. 5C) rather unconvincing to support that conclusion. There are no quantifications of colocalization or even sample size information provided for this experiment. And if there is indeed a difference, it would still be difficult to argue this is because of the 5' stalling phenomenon authors observe with Rh1, because they switched both the 5' and 3' UTRs.

I also find it puzzling that the TE differences between the groups are mostly among the transcripts that are already strongly differentially expressed at the transcriptional level. The authors would like to frame this as a mechanism of 'contrast sharpening'; but it is unclear why that would be needed. Rh1, for instance, is not just differentially expressed between neurons and glia, but it is actually only expressed by a very specific neuronal type (photoreceptors). Thus it's not clear to me why the glia would need this 5' stalling mechanism to fully suppress Rh1 expression, while all the other neurons can apparently do so without it.