Reviewer #4 (Public Review):

Yao and Ochoa conducted a systematic examination of the association study using PCA and LMM with both simulated and empirical datasets. The overall finding is that LMM should be used and generally there is no need to include a few PCs in LMM. While similar studies have been conducted earlier with the comparison goal, the authors made additional effort to conduct this extensive study. Many scenarios were considered, and the results were clearly presented. This paper is interesting to researchers in statistical genetics.

Reviewer #1 (Public Review):

1. There was little comment on the strategy/mechanism that enabled subjects to readily attain Target I (MU 1 active alone), and then Target II (MU1 and MU2 active to the same relative degree). To accomplish this, it would seem that the peak firing rate of MU1 during pursuit of Target II could not exceed that during Target I despite an increased neural drive needed to recruit MU2. The most plausible explanation for this absence of additional rate coding in MU1 would be that associated with firing rate saturation (e.g., Fuglevand et al. (2015) Distinguishing intrinsic from extrinsic factors underlying firing rate saturation in human motor units. Journal of Neurophysiology 113, 1310-1322). It would be helpful if the authors might comment on whether firing rate saturation, or other mechanism, seemed to be at play that allowed subjects to attain both targets I and II.<br /> 2. Figure 4 (and associated Figure 6) is nice, and the discovery of the strategy used by subjects to attain Target III is very interesting. One mechanism that might partially account for this behavior that was not directly addressed is the role inhibition may have played. The size principle also operates for inhibitory inputs. As such, small, low threshold motor neurons will tend to respond to a given amount of inhibitory synaptic current with a greater hyperpolarization than high threshold units. Consequently, once both units were recruited, subsequent gradual augmentation of synaptic inhibition (concurrent with excitation and broadly distributed) could have led to the situation where the low threshold unit was deactivated (because of the higher magnitude hyperpolarization), leaving MU2 discharging in isolation. This possibility might be discussed.<br /> 3. In a similar vein as for point 2 (above), the argument that PICs may have been the key mechanism enabling the attainment of target III, while reasonable, also seems a little hand wavy. The problem with the argument is that it depends on differential influences of PICs on motor neurons that are 1) low threshold, and 2) have similar recruitment thresholds. This seems somewhat unlikely given the broad influence of neuromodulatory inputs across populations of motor neurons.

Reviewer #2 (Public Review):

The authors approach the question of whether humans can independently control the activation of multiple motor units (MUs) innervating a single muscle. They performed high-density surface electromyographic (EMG) recordings from the ankle dorsiflexor tibialis anterior, decomposed the signals into multiple single units online, and determined the recruitment order of these units using ramps of isometric force. Next, they selected a lower-threshold unit (MU1) and a higher-threshold unit (MU2) and used the discharge rates to control the position of a cursor in a two-dimensional visual workspace. Subjects were instructed to move this cursor to three targets requiring activation of either MU1 (TI), both units (TII), or MU2 only (TIII). With practice, subjects were able to achieve direct hits on TI by applying a low level of force, and on TII by activating both units at higher force. They were typically unable to move the cursor directly to TIII, however, and instead reached this target using a multi-stage approach by rapidly recruiting MU1 and MU2, lowering the force to inactivate MU1, and slowly increasing the force again to ramp up MU2. These results are broadly consistent with a one-dimensional drive to TA MUs, and suggest that, at least under the experimental conditions used here, humans cannot voluntarily override the size principle and independently control multiple MUs.

The experiments are well-designed, the data and analyses are convincing, the writing is clear, and the conclusions are novel and supported by the data. The manuscript will be of significant interest to researchers in neurophysiology, neuroengineering, and motor control. It has several broad implications. First, as the authors emphasize, EMG-based brain-computer interfaces (BCIs) will be strongly constrained by recruitment order, and the benefits of single-unit EMG for this purpose may be relatively limited. Second, the reachability of any point in a two-dimensional BCI workspace does not imply two controlled degrees of freedom, but may instead reflect hysteresis. Third, while much recent work on supraspinal neural dynamics and control (including intracortical BCIs) has modeled the state of a CNS region as a function of instantaneous firing rates, this study suggests that the neural state space might, in certain settings, need to be augmented with activation history or latent variables such as neuromodulation and persistent currents. I have several suggestions for improving the manuscript, described below.

1. Some subjects seemed to hit TIII by repeatedly "pumping" the force up and down to increase the excitability of MU2 (this appears to happen in TIII trials 2-6 in Fig. 4 - c.f. p18 l30ff). It would be useful to see single-trial time series plots of MU1, MU2, and force for more example trials and sessions, to get a sense for the diversity of strategies subjects used. The authors might also consider providing additional analyses to test whether multiple "pumps" increased MU2 excitability, and if so, whether this increase was usually larger for MU2 than MU1. For example, they might plot the ratio of MU2 (and MU1) activation to force (or, better, the residual discharge rate after subtracting predicted discharge based on a nonlinear fit to the ramp data) over the course of the trial. Is there a reason to think, based on the data or previous work, that units with comparatively higher thresholds (out of a sample selected in the low range of <10% MVC) would have larger increases in excitability?
<br /> 2. I am somewhat surprised that subjects were able to reach TIII at all when the de-recruitment threshold for MU1 was lower than the de-recruitment threshold for MU2. It would be useful to see (A) performance data, as in Fig. 3D or 5A, conditioned on the difference in de-recruitment thresholds, rather than recruitment thresholds, and (B) a scatterplot of the difference in de-recruitment vs the difference in recruitment thresholds for all pairs.
<br /> 3. Using MU1 / MU2 rates to directly control cursor position makes sense for testing for independent control over the two MUs. However, one might imagine that there could exist a different decoding scheme (using more than two units, nonlinearities, delay coordinates, or control of velocity instead of position) that would allow subjects to generate smooth trajectories towards all three targets. Because the authors set their study in a BCI context, they may wish to comment on whether more complicated decoding schemes might be able to exploit single-unit EMG for BCI control or, alternatively, to argue that a single degree of freedom in input fundamentally limits the utility of such schemes.
<br /> 4. The conclusions of the present work contrast somewhat with those of Marshall et al. (ref. 24), who claim (for shoulder and proximal arm muscles in the macaque) that (A) violations of the "common drive" hypothesis were relatively common when force profiles of different frequencies were compared, and that (B) microstimulation of different M1 sites could independently activate either MU in a pair at rest. Here, the authors provide a useful discussion of (A) on p19 l11ff, emphasizing that independent inputs and changes in intrinsic excitability cannot be conclusively distinguished once the MU has been recruited. They may wish to provide additional context for synthesizing their results with Marshall et al., including possible differences between upper / lower limb and proximal / distal muscles, task structure, and species.

Reviewer #3 (Public Review):

This manuscript investigates whether humans can learn to independently control individual motor units (MUs) during voluntary movements. To do so, the authors devised a behavioral paradigm in which subjects received real-time feedback about the discharge rate of individual motor units, which are extracted via an online motor unit deconvolution algorithm. Subjects were tasked with activating an ankle muscle so that they hit experimenter-defined "targets" in the space of (simultaneously-recorded) motor unit activities. The authors argue that humans are able to successfully perform the task not by varying the order in which motor units are initially recruited, but rather in the order that units are de-recruited near the end of each trial. Based on these findings, the authors assert the performance of this task does NOT rely on independent control of the inputs to individual motor neurons, but rather on history-dependent changes in motor unit excitability that affect de-recruitment.

Major comments:

As outlined below I have significant concerns about multiple aspects of this study.

Even if the online decomposition of motor units were performed perfectly, the visual display provided to subject smooths the extracted motor unit discharge rates over a very wide time window: 1625 msec. This window is significantly larger than the differences in recruitment times in many of the motor unit pairs being used to control the interface. So while it's clear that the subjects are learning to perform the task successfully, it's not clear to me that subjects could have used the provided visual information to receive feedback about or learn to control motor unit recruitment, even if individuated control of motor unit recruitment by the nervous system is possible. I am therefore not convinced that these experiments were a fair test of subjects' ability to control the recruitment of individual motor units.

Along similar lines, it seems likely to me that subjects are using some other strategy to learn the task, quite possibly one based on control of over overall force at the ankle and/or voluntary recruitment of other leg/foot muscles. Each of these variables will presumably be correlated with the activity of the recorded motor units and the movement of the cursor on the screen. Moreover, because these variables likely change on a similar (or slower) timescale than differences in motor units recruitment or derecruitment, it seems to me that using such strategies, which do not reflect or require individuated motor unit recruitment, is a highly effective way to successfully complete the task given the particular experimental setup.

To summarize my above two points, it seems like the author's argument is that absence of evidence (subjects do not perform individuated MU recruitment in this particular task) constitutes evidence of absence (i.e. is evidence that individuated recruitment is not possible for the nervous system or for the control of brain-machine interfaces). Therefore given the above-described issues regarding real-time feedback provided to subjects in the paper it is not clear to me that any strong conclusions can be drawn about the nervous system's ability or inability to achieve individuated motor unit recruitment.

Second, to support the claims based on their data the authors must explain their online spike-sorting method and provide evidence that it can successfully discriminate distinct motor unit onset/offset times at the low latency that would be required to test their claims. In the current manuscript, authors do not address this at all beyond referring to their recent IEEE paper (ref [25]). However, although that earlier paper is exciting and has many strengths (including simultaneous recordings from intramuscular and surface EMGs), the IEEE paper does not attempt to evaluate the performance metrics that are essential to the current project. For example, the key metric in ref 25 is "rate-of-agreement" (RoA), which measures differences in the total number of motor unit action potentials sorted from, for example, surface and intramuscular EMG. However, there is no evaluation of whether there is agreement in recruitment or de-recruitment times (the key variable in the present study) for motor units measured both from the surface and intramuscularly. This important technical point must be addressed if any conclusions are to be drawn from the present data.

My final concern is that the authors' key conclusion - that the nervous system cannot or does not control motor units in an individuated fashion - is based on the assumption that the robust differences in de-recruitment time that subjects display cannot be due to differences in descending control, and instead must be due to changes in intrinsic motor unit excitability within the spinal cord. The authors simply assert/assume that "[derecruitment] results from the relative intrinsic excitability of the motor neurons which override the sole impact of the receive synaptic input". This may well be true, but the authors do not provide any evidence for this in the present paper, and to me it seems equally plausible that the reverse is true - that de-recrutiment might influenced by descending control. This line of argumentation therefore seems somewhat circular.

For the above reasons, I am not convinced that this study provides significant insight into the important problems it investigates. However, as always I am a big fan of the eLife review/discussion process - I'm very interested to read and discuss the other Reviewers' assessments of this study's strengths and weaknesses.

Reviewer #4 (Public Review):

The authors used a smart, elegant paradigm to examine the question of the voluntary control in recruiting MUs. They used a state-of-the-art system to extract large numbers of MUs in a non-invasive manner. They show that recruiting MUs almost exclusively follows the classical recruitment order, so that the flexibility of voluntary control is limited. This is evident in the finding that subjects could not easily recruit a higher threshold of MU2 while suppressing the activity of MU1. Instead, subjects used an alternative strategy which relies on different thresholds for the de-recruitment of these units. However, the design of the study and the specific use of lower limb MUs may impede the comparison to other systems.

In my mind, the deviation of de-recruitment order from the expected order (as depicted in figure 2A) surprising, and requires a more thorough investigation. The authors suggest that this deviation is an outcome of persistent inward currents (PIC), but provide no direct evidence for this supposition. In addition, in this study the authors tested lower-limb muscles. It would be interesting to use the same paradigm on MUs of upper limb muscles, where the capacity for volitional control could be more extensive.

Specific comments

1. Figure 6a nicely demonstrates the strategy used by subjects to hit target TIII. In this example, MU2 was both recruited and de-recruited after MU1 (which is the opposite of what one would expect based on the standard textbook description). The authors state (page 17, line 15-17) that even in the reverse case (when MU2 is de-recruited before MU1) the strategy still leads to successful performance. I am not sure how this would be done. For clarity, the authors could add a panel similar to panel A to this figure but for the case where the MU pairs have the opposite order of de-recruitment.
2. The authors discuss a possible type of flexible control which is not evident in the recruitment order of MUs (page 19, line 27-28). This reasoning was not entirely clear to me. Specifically, I was not sure which of the results presented here needs to be explained by such mechanism.
3. The authors argue that using a well-controlled task is necessary for understanding the ability to control the descending input to MUs. They thus applied a dorsi-flexion paradigm and MU recordings from TA muscles. However, it is not clear to what extent the results obtained in this study can be extrapolated to the upper limb. Controlling the MUs of the upper limb could be more flexible and more accessible to voluntary control than the control of lower limb muscles. This point is crucial since the authors compare their results to other studies (Formento et al., bioRxiv 2021 and Marshall et al., bioRxiv 2021) which concluded in favor of the flexible control of MU recruitment. Since both studies used the MUs of upper limb muscles, a fair comparison would involve using a constrained task design but for upper limb muscles.
4. The authors devote a long paragraph in the discussion to account for the variability in the de-recruitment order. They mostly rely on PIC, but there is no clear evidence that this is indeed the case. Is it at all possible that the flexibility in control over MUs was over their recruitment threshold? Was there any change in de-recruitment of the MUs during learning (in a given recording session)?
5. The need for a complicated performance measure (define on page 5, line 3-6) is not entirely clear to me. What is the correlation between this parameter and other, more conventional measures such as total-movement time or maximal deviation from the straight trajectory? In addition, the normalization process is difficult to follow. The best performance was measured across subjects. Does this mean that single subject data could be either down or up-regulated based on the relative performance of the specific subject? Why not normalize the single-subject data and then compare these data across subjects?
6. Figure 3C appears to indicate that there was only moderate learning across days for target TI and TII. Even for target TIII there was some improvement but the peak performance in later days was quite poor. The fact that the MUs were different each day may have affected the subjects' ability to learn the task efficiently. It would be interesting to measure the learning obtained on single days.
7. On page 16 line 12-13, the authors describe the rare cases where subjects moved directly towards TIII. These cases apparently occurred when the recruitment threshold of MU2 was lower. What is the probable source of this lower recruitment level in these specific trials? Was this incidental (i.e., the trial was only successful when the MU threshold randomly decreased) or was there volitional control over the recruitment threshold? Did the authors test how the MU threshold changed (in percentages) over the course of the training day?

Reviewer #1 (Public Review):

In the manuscript by Weera et al., the authors describe a novel transgenic rat in which Cre/tdTomato is expressed under the Crhr1 promoter effectively fluorescently tagging CRF1 containing neurons in the rat brain and allowing for cre-dependent manipulations; they use cre-dependent chemogenetic manipulations in this paper. They validate this rat line by focusing on CRF1 expression in the CeA. Through use of ISH, IHC and electrophysiology they validate and characterize CRF1-expressing neurons (i.e. tdTomato+/Cre+) neurons. The authors go on to use chemogenetics to demonstrate that increased activity of CRF1 containing neurons is anxiogenic and potentiates mechanical nociception. Generally, they do not report sex differences, although there does seem to be a sex difference in the spontaneous firing rate of CRF1+ CeA neurons.

In my view, this paper appears to be two incomplete studies put into one manuscript. The manuscript seeks to characterize a novel tool, generated by this laboratory, that could be of great value to the CRF field. As is the case with other transgenic mouse and rat tools that have come before it, this requires a brain wide characterization that describes the specificity and penetrance of cre-recombinase under Crhr1 promoter in several brain regions, not just one. Take the case of the Pomerenze Crh-Cre rat. In that case, cre is only expressed in CRF+ neurons that are GABAergic, not glutamatergic, excluding cre expression in the PVN. If this new tool is going to be widely utilized, it is important we understand what exactly it is and it is not. It cannot be assumed that the specificity and penetrance are uniform across the brain. If you look at the commonly used Crh-cre mouse line, there are significant differences in penetrance across regions (see Walker et al., 2019, Neuropharmacology). Without this brain-wide information, the tool has reduced utility to CRF researchers.

In addition, the authors make the case for the necessity of this tool "given" that there are behavioral studies that can be done only in rats, but not mice. However, they do not really put this supposition to task. Can this tool give us new insight into the role of CRF1 in the CeA or any brain region that cannot be done in mice. This would be the kind of experiment that would be of broad interest to the stress neurobiology field.

The second study within this manuscript is utilizing this tool to gain insight into the role of CRF1 regulation of CeA neurons in males and females and how it impacts anxiety-like behavior and nociception. First, they uncover some interesting sex differences in the spontaneous firing of CRF1+ CeA neurons, yet they do not follow up that finding. The anxiety-like behavior replicates the consensus of findings in the literature. However, the role of CRF in the CeA in nociception is a bit controversial and I think delving into that controversy using this new tool would have made the manuscript more impactful and of interest to a broad readership. To summarize a collection of studies, there are some that indicate CRF/CRF1 in the CeA mediates stress-induced analgesia, others show that intra-CeA CRF increases paw withdrawal latency to both mechanical/thermal stimulation and others that show that stress causes hyperalgesia via CRF1 function in the CeA. It seems like a missed opportunity to offer new insights into this question.

Reviewer #2 (Public Review):

Major Comments:

1) The important contribution of this work is the introduction of a model organism that can improve on available mouse lines, in terms of what can be learned about CRFR1 neurons and behavior. The advantages of the rat line are many, including behavioral repertoire, anatomical targeting precision and body size/blood volume. The authors could do a better job of emphasizing this advantage throughout the manuscript, and provide concrete examples of how the rat line improves on murine models. Moreover, it will be important to note specific examples of how the 'behavioral repertoire' of the mouse is limited (page 4) relative to rat.

2) Given this is a new model, some additional mapping of iCRE/TDtomato is needed to assure that the fidelity of transgene expression viz. known CRFR1-expressing regions extends beyond the amygdala.

3) Some behavioral data in Figure 7 analyzed by t-test instead of ANOVA. The rationale for this analysis scheme is unclear. Authors explain that control and DREADD virus readouts were analyzed separately because control virus group was treated as a replication of a previous experiment. If the study was performed in the same cohort of rats, it would still seem appropriate to perform an ANOVA. If performed at another point in time or a separate experiment, breaking it out would be most appropriate.

4) Male and female data have been combined for behavioral readout of Figure 7. Given the low 'n's for each sex here (as low as 3/sex in some groups), the argument for the conclusion of 'no sex difference' in anxiety-like behavior is extremely weak.

Reviewer #3 (Public Review):

The present manuscript reports efforts to develop and validate a genetically-modified rat to allow visualization of and recombinatorial genetic access to cells that express the CRF1 receptor. Such a model is highly significant because of the inadequacy of current analogous genetic models in mice or other species for studying complex behaviors, especially those that involve small brain nuclei.

In addition to the highly significant and much needed research tool sought and provided evidence of, there are many other major strengths to this paper, including:

1. a well-reasoned bacterial artificial chromosome (BAC) design that drives both iCre recombinase and Tomato reporter via an optimized IRES element, with adequate pre-injection sequencing verification.

2. the high experimental rigor with consideration of batch, blind and counter-balanced analyses at all levels of analysis.

3. RNAScope analysis that clearly demonstrates strong (90-95%) cellular co-expression of Cre mRNA within CRF1 receptor mRNA-expressing amygdala cells and with the distinction seen between CeL vs. CeM populations recapitulating previously seen physiologic distributions.

4. Successful utilization of Tomato-fluorescence to perform a comprehensive electrophysiologic assessment of identified cells that included intrinsic membrane properties, I-V relations, burst classification, and pharmacologically-isolated GABAA-R-mediated sIPSCs.

5. Successful demonstration that fluorescently-identified cells are still predictably responsive to CRF stimulation effects on firing rate.

6. Successful ex vivo evidence of recombination, inferred from functionally-relevant expression of an excitatory DIO-DREADD, observed as CNO actuator-driven increases in c-Fos expression and firing rate of fluorescently-identified neurons.

7. Successful in vivo evidence of recombination, inferred from CNO actuator-driven increases in expected anxiogenic-like (plus-maze, open field) and mechanical nociceptive (von Frey) behavioral phenotypes.

8. Demonstration that these CNO phenotypes differed from those seen under vehicle administration as well as from CNO-treated rats that received non-DREADD control AAVs, supporting the proposed genetic recombination with the DIO-DREADD construct.

9. Clearly described, appropriate statistical analysis with clear results.

10. Consideration of sex differences in both the genetic model and the central amygdala CRF1 system.

Lesser, moderate weaknesses of the present manuscript include:

1. The model's potential utility for studying brain regions other than the central amygdala is unknown because histochemical and functional validation were only performed for the central amygdala, In the model's favor, inspection of figures shows cortical and stria terminalis Tomato expression where qualitatively expected based on known physiologic CRF1 expression in rat. Estimates of CRF1-Cre concordance in these other brain regions was not provided however. I appreciate that functional validation of all brain regions was beyond the scope of the present work.

2. The location, copy #, fidelity or potential mosaicism of BAC transgene insertions after germline transmission was not mentioned.

3. Some aspects of histochemical concordance were not provided or explored. For example, the proportion of Cre-expressing neurons that expressed CRF1 mRNA was not reported to confirm specificity (it appears to be even higher which would be good]? Cellular co-expression of Tomato with CRF1 mRNA also was not explicitly quantified.

Further, the measures of co-expression were based on a binary distinction between those with 3 or more (positive) vs fewer than 3 puncta (negative), and the basis for this threshold was not justified (e.g., PMID: 31451604). Because only a single binary threshold was used, it also is not known whether there is a continuous (vs. binary) relationship between CRFR1 and Cre expression on the other. The former could mean that different levels of expression may be associated with different degrees of labeling or genetic access to CRF1-positive cells.

Finally, there might be slight sex differences in co-expression in the model. Using the binary distinction, there appeared to be greater co-expression of Cre mRNA within CRF1-positive cells in 1 sex vs. the other (95.8%+1.3 vs. 90.5%+2.6). It would have been useful to rule out statistically significant differences in semi-quantitative expression or semi-quantitative (rather than only binary) co-expression of CRF1 and iCre puncta.

4. It is not clear whether the iCre expression obtained is sufficient to silence these populations via Cre-dependent constructs (e.g., for studies to reverse physiologic effect).

5. It is unknown whether the model recapitulates physiologic patterns of CRF1 expression across cell types (e.g., neuron and glia in brain, corticotrophs in pituitary, etc.).

6. The manuscript does not discuss possible genetic strain of origin issues. The rat was generated using a BAC that involves DNA from a different strain (F344) than that bred into (Wistar), so potential strain of origin differences in regulatory or enhancer elements for the Crhr1 gene might co-segregate in early generations with the transgene. Similar considerations are true with respect to linked alleles if the strain of the gamete donors, which was not specified, differed from Wistar. Such issues would become of lesser concern with continued Wistar breeding, but are not mentioned.

7. The nomenclature does not follow IUPHAR and Rat Genome Database guidelines. While the the protein is appropriately referred to as the CRF1(subscript) receptor, the preferred IUPHAR and RGD nomenclature for the rat gene is Crhr1(italicized) (see https://www.guidetopharmacology.org/GRAC/FamilyDisplayForward?familyId=19 and https://rgd.mcw.edu/rgdweb/report/gene/main.html?id=61276).

Overall, the authors appear to have developed and provided initial validation of a BAC transgenic Cre-Tomato reporter rat that is anticipated to be invaluable in dissecting the functional significance of amygdala CRF1 receptor systems in complex behaviors.

Reviewer #4 (Public Review):

In this study, Weera and colleagues generated a new transgenic rat that allows for the visualization and manipulation of neurons expressing the corticotropin-releasing factor type-1 reeptors (CRFR1) in a cre-dependent manner. The description and validation of this transgenic rat line was rigorous, the paper is well written and the presentation of results is clear. More importantly, this new tool is important and useful for the neuroscience community as it will facilitate future studies aimed at studying the function of CRFR1-expressing neurons in different behavioral contexts.

Strengths: 1) The authors included male and female rats throughout the study, with most of the experiments with sufficient statistical power to allow conclusions related to sex as a biological variable. When pooled samples were shown, the sex of the rat used was clearly labeled. This level of transparency is critical for reproducibility and future use of this transgenic rat.

2) The authors go above and beyond in the characterization of the transgenic line by showing not only fidelity of expression in the CeA but also the electrophysiological, synaptic and behavioral characterization of CRFR1-expressing neurons in the CeA of transgenic rat.

Weaknesses: 1) The characterization of the transgenic rat is thorough but entirely focused on the amygdala. Learning about the fidelity of expression in other brain regions is important to assess the potential use of these transgenic rats to study CRF-dependent behaviors and physiology in other brain structures, broadening its applicability to the neuroscience community.

Reviewer #4 (Public Review):

This manuscript took alpine grasslands as a model system and investigated whether lowland herbaceous plants contributed to the short-term dynamics of soil carbon under the context of climate warming. The authors find that warming individually does not render significant changes in alpine soil carbon, but corporately causes ~52% of carbon loss with lowland herbaceous plants in two short periods of field experiments. They further show that alpine soil carbon loss is likely mediated by lowland herbaceous plants through root exudation, soil microbial respiration, and CO2 release. This work adds in an interesting way to the ongoing debate on whether a positive climate feedback will be mediated by plant uphill range expansion in alpine grasslands, where climate warming may lead to a rapid loss of soil carbon.

The claims of this manuscript are well supported, but some aspects of background information in the studied alpine systems and field experiment design need to be clarified.

1) There is an extremely high level of carbon stored in the alpine soils (Figure 1). Climate warming will certainly lead to a great loss of soil carbon in the study systems that could contribute to the positive climate feedback. However, it is unclear for me how the effects of climate warming on soil carbon are relevant to the ongoing climate change in the studied alpine grasslands. It is therefore reasonable to provide more background information about ongoing climate change, and whether the simulated climate warming (i.e., 2.8 oC in central alps and 5.3 oC in western alps, Line 328-329) is realized as real-world climate change in the local systems. In addition, it seems that the manuscript aims to address a question that is of global concern, but my concern is about how the findings could be generalized to other regions.

2) I understand that the manuscript considers elevation as a natural gradient of climate change, which makes it possible to compare soil carbon dynamics in lowlands with alpine grasslands under climate warming. I also understand that the authors have done everything they can to control for the disturbances caused by transplanting that has been well justified by the supplementary data (e.g., Figure S6). However, it is unclear how the authors controlled for the influences of other factors given there are huge differences between lowlands and alpine grasslands, such as differences in wind, solar radiation, humidity, and the length of growing season.

3) It is generally known that different species respond to climate warming differently. Some species may be sensitive to climate warming and have traits aiding to dispersion that could expand their living ranges to some degree, while others may adjust themselves to adapt to climate warming and may not migrate to alpine systems. It is therefore cautious to assume that all the lowland species have the same dispersal ability. In other words, it is unclear how lowland plant species are selected for the field transplanting experiment (Line 284-290). Do all the lowland plant species selected have the potential to migrate to alpine systems?

4) The authors acknowledge that "we did not perform a reverse transplantation (that is, from low to high elevation), so we cannot entirely rule out the possibility that transplantation of any community to any new environment could yield a loss of soil carbon" (Line 318-320). When I read the title "lowland plant migrations into alpine grasslands ...", I thought lowland plant species that were transplanted from low to high elevation. In fact, it is just the opposite to my thoughts. Without performing a reverse transplantation experiment, I am not sure the conclusion will stand that "lowland plant migrations into alpine grasslands amplify soil carbon loss under climate warming". In addition, it is unclear whether lowland plant effects stand alone or depend on climate warming based on the results in Figure 1 that lowland plant treatment is missing, and it is impossible to test the interactions between lowland plant and climate warming.

Public Review:

With interest, I reviewed the manuscript by Morales-Mantilla et al., who report on a novel therapy, namely hematopoietic stem and progenitor cells (HSPCs) for sepsis. They describe that sepsis induced by Group A Streptococcus (GAS) leads to depletion of bone marrow HSPCs and mortality, and therefore attempt to improve outcome using infusion of naive donor HSPCs, which is successful, as it lower mortality. This effect appears not to be mediated by reducing bacterial burden, which is not affected by HSPC infusion, but rather by attenuation of the immune response, reflected by decreased levels of inflammatory mediators and increased abundance of myeloid-derived suppressor cells.

Strengths:

• The manuscript is well written and easy to follow.
• The paper includes interesting novel immunological data on the bone marrow response to severe infection.
• MDSC infusion is an interesting new therapy for sepsis and is shown to be effective in mice infected with GAS.

Weaknesses:

• In the reinfection model (influenza+GAS), it would have been much more interesting to infuse HSPCs in between infections to evaluate whether this therapy decreases vulnerability towards a secondary infection. The authors state in the discussion: "Interestingly, increased MDSCs during sepsis have also been associated with increased development of nosocomial infections (56)." This could still be the case with the therapy studied in the present paper, it was just not assessed in the proper way.
• The data presented in figure 7 is of little value, as (if I understand correctly) these were obtained in non-inflamed/infected mice. Therefore, conclusions such as "Whereas MDSCs did arise directly from infused cells, their numbers were not sufficient to account for the large increase in MDSCs observed in the HSPC-rescued mice. These data suggest that HSPC infusion contributes to MDSC expansion via both direct and indirect mechanisms." are not justified.
• I feel the authors are a bit too optimistic in stating "Our findings could lead to the development of an efficacious new therapeutic approach that could succeed where granulocyte infusions have fallen short". There are so many steps and hurdles that need to be taken before this kind of intervention could be translated to the clinic.

This manuscript is in revision at eLife

The decision letter after peer review, sent to the authors on June 30 2020, follows:

Summary:

This paper addresses a critical issue in neuroscience: what's the question, and can we answer it? The questions the author proposes are ones that have been considered, in one form or another, reasonably often by experimentalists. And the author shows rigorously that there's a reasonable chance that they are simply not answerable.

Essential Revisions:

We believe that this is an extremely important issue, and the approach the paper takes is a reasonable one for addressing it. Our main worry, though, is that mainstream neuroscientists will ignore it, for two reasons. One is that it's not a message they want to hear. Second, the example circuits are sufficiently abstract that they can be dismissed as yet another musing by your typical uninformed theorists. (That is not, we should emphasize, our view, but it's not an uncommon one in the field.) Our goal, therefore, is to fix these potential problems, so that people will have to pay attention.

The premise of the paper is that if you understand a neural circuit, there are certain questions about it that you should be able to answer. The author proposes six such questions, and then shows that in the worst case they are exponentially (in the number of neurons) hard to answer.

The success of this program hinges on two things: a sensible set of questions, and a demonstration that answering those questions is hard. We're not ecstatic about the questions, but we believe that's not an insurmountable issue (more on that below). More problematic is the result that the questions are hard to answer. What's really shown is that there is at least one circuit for which, in the worst case, answering the questions is exponentially hard. While this is certainly correct, it doesn't make a convincing case that answering these questions will be hard in the brain. First, the worst case may not be the typical case. The 3 SAT problem, for instance, is NP complete, but is hard to solve only for a narrow range of parameters. Second, answering the questions for actual circuits found in the brain may not even be exponentially hard in the worst case.

This brings us to two critical comments. First, it needs to be crystal clear that this paper does not demonstrate that answering the proposed questions is guaranteed to be exponentially hard, only that it might be. This was stated in the manuscript, but not emphasized. For instance, on lines 138-140, it says

"Using techniques from Computational Complexity Theory, we ask what is the smallest number of experiments necessary, in general, in order to answer these questions, in typical experimental settings."

Here "in general" means worst-case. For neuroscientists, though, "in general" means "most of the time". It should be clear that you mean worst case, and that the typical case may be very different.

In fact, this needs an expanded discussion. Whether or not it will take an exponential number of experiments to answer the questions depends on the circuit. We might get lucky, and only a small number of experiments are needed. Or we might get unlucky, and a large number are needed. This analysis can't tell us that, and this should be clear in the paper.

Second, what's really needed is the analysis of a more realistic circuit, ideally with both positive examples (for which it is possible to answer the questions) and negative examples (for which it isn't). This is hard, but we have a few suggestions, some of which can probably be done without a huge amount of work.

a. Linear network, y=Ax+noise. For this (and possibly in all realistic) situations, "perform the task" needs to be replaced by "achieve a certain level of performance". For instance, if there's a true mapping y=f(x), then "perform the task" would be replaced with "<(y-y*)^2> below some threshold". The questions should be answerable in polynomial time for this network; otherwise, one should worry.

b. In 2000, Hopfield and Brody came up with a simple circuit which we think of as "understandable" (Hopfield and Brody, PNAS 97:25, 13919-13924, 2000). It would be nice to determine whether the questions can be answered in polynomial time for this circuit. Again, if they can't, one should worry.

c. Deep networks. Again, "perform the task" would have to be replaced with "performance is above some particular threshold". Here we suspect that the questions are not answerable; if that could be shown, it would be a huge step forward.

d. A made-up model of a deep network. Assume that in a deep network, whenever you delete a neuron, performance drops. That's probably not so far from the truth -- and also not so far from what we think would happen in the brain. (With some exceptions; occasionally I hear talks where performance is better when two areas are ablated rather than just one, but let's ignore that.) How much performance drops depends, of course, on which neurons are deleted, so there's not a simple mapping between performance and which neurons are present in the circuit. Can the questions be answered in this case? This sounds like a problem computer scientists have considered, so possibly rigorous analysis could be done.

We believe it's critical to consider a case that is not far from what one might find in the brain. Otherwise, it will be too easy to dismiss this work as irrelevant to real neuroscience. The above are only possibilities, and a and d may be pretty easy, but the author is welcome to come up with his own examples. Note that rigor is not absolutely necessary here, since there's already one rigorous example. Plausible arguments would be fine.

Finally, "understand" needs further discussion. That's partly because the approach here is a little non-standard. Most people try to directly define "understanding". Instead, the statement is "if you understand a circuit, you should be able to answer these questions". This has to be made crystal clear -- especially since people aren't expecting it. In addition, a discussion of the more standard approach, a direct definition, should be included. The usual definition is something like "A short description of what is being computed, along with a description of an algorithm for computing it." It should be clear how this, more standard, definition compares to the one in the paper. For instance, under the standard definition it may be possible to understand a circuit without being able to answer any of the questions. For instance, I believe we can "understand" (by the more standard definition) the synfire chain circuit. This doesn't mean that one definition is better than the other, but their differences should be acknowledged.

Reviewer #1 (Public Review):

This study sought to systematically identify the components and driving forces of transcriptome evolution in fungi that exhibit complex multicellularity (CM). The authors examined a series of parameters or expression signatures (i.e. natural antisense transcripts, allele-specific expression, RNA-editing) concluding that the best predictor of a gene behavior in the CM transcriptome was evolutionary age.<br> Thus, the transcriptomes of fruiting bodies showed a distinct gene-age-related stratification, where it was possible to sort out genes related to general sexual processes from those likely linked to morphogenetic aspects of the CM fruiting bodies. Notably, their results did not support a developmental hourglass, which is the rather predominant hypothesis in metazoans, including some analysis in fungi.

The studies involved analyses of new transcriptomic datasets for different developmental stages (and tissue types in some cases) of Pleurotus ostreatus and Pterula gracilis, as well as the analyses of existing datasets for other fungi.<br> There are diverse interesting observations such as ones regarding Allele Specific Expression (ASE), suggesting that in P. ostreatus ASE mainly occurs due to cis-regulatory allele divergence, possibly in fast evolving genes that are not under strong selection constraints, such as ones grouped in youngest gene ages categories. In addition, a large number of conserved unannotated genes among CM-specific orthogroups highlights the rather cryptic nature of CM in fungi and raises as an important area for future research.

Some of the key aspects of the analyses would need to be better exemplified such as:

– Providing a better description of the developmentally expressed TFs only in CM species

– Providing clear examples of the promoter divergence that could be the underlying mechanism behind ASE. In particular, for some cases, there may be enough information in the literature/databases to predict the appearance or disappearance of relevant cis-elements in the promoters showing the highest divergence in genes depicting the highest levels of ASE.

Reviewer #2 (Public Review):

The evolution of complex multicellularity represents a major developmental reprogramming, and comparing related species which differ in multicellular structures may shed light on the mechanisms involved. Here, the authors compare species of Basidiomycete fungi and focus on analyzing developmental transcriptomes to identify patterns across species. Deep RNA-Seq data is generated for two species, P. ostreatus and Pt. gracilis, sampling different developmental stages. The authors report conflicting evidence for a "developmental hourglass" using a weighted transcription index vs gene age categories. There is substantial allele-specific expression in P. ostreatus, and these genes tend to have a more recent origin, have more divergent upstream regions and coding sequences, and are enriched for developmentally regulated transcripts. Antisense transcripts have low overlap with coding regions and low conservation, and a subset show a positive or negative correlation with the overlapping gene. Comparison to a species without complex multicellular development is used to further classify the developmental program.

Overall the new transcriptional data and extensive analysis provide a thorough view of the types of transcripts that appear differentially regulated, their age, and associated gene function enrichment. The gene sets identified from this analysis as well as the potential to re-analyze this data will be useful to the community studying multicellularity in fungi. The primary insights drawn in this study relate to the dating of the developmental transcriptome, however some patterns observed with young genes and noncoding transcripts are primarily reflective of expected patterns of evolutionary time.

Reviewer #3 (Public Review):

Fungi are unique in forming complex 3D multicellular reproductive structures from 2D mycelium filaments, a property used in this paper to study the genetic changes associated with the evolution of complex 3D multicellularity. The manuscript by Merenyi et al. investigates the evolution of gene expression and genome regulation during the formation of reproductive structures (fruiting bodies) in the Agaricomycetes lineage of Fungi. Transcriptome and multicellularity evolution are very exciting fundamental questions in biology that only become accessible with recent technological developments and the appropriate analysis framework. Important perspectives include understanding how genes acquire new functions and what role plays transcriptional regulation in adaptation. The study gathers a very useful dataset to this end, and relies on generally relevant hypotheses-driven analyses.

Analysis of fruiting body transcriptome in nine species revealed that prediction from the development hourglass model (that young genes are expressed in early and late but not intermediate phases of development) verified only in a few species, including Pleurotus ostreatus. An allele-specific expression (ASE) analysis in P. ostreatus showed that young genes frequently show ASE during fruiting body development. A comparative analysis with C. neoformans, which reproduces sexually without forming fruiting body, indicates that young and old (but not intermediate) genes are likely involved specifically in fruiting body morphogenesis. A number of underlying hypothesis could be presented better, and importantly the connection between the various analyses did not appear obvious to me. Some hypotheses and reasoning therefore need clarification. Some important results from the analyses are not provided and not commented, although they are required to fully meet the manuscript's objectives.

1) I do not clearly see the connection between the developmental hourglass model studied in the first part of the ms, and the allele-specific expression patterns in the second half of the ms. Which "phase" of the hourglass is expected to contain true CM-related genes (by contrast to general sexual processes)? Was P. ostreatus chosen for the ASE analysis because evidence for a developmental hourglass pattern was detected in this species? The conclusion that "evolutionary age predicts, to a large extent, the behaviour of a gene in the CM transcriptome" was established thanks to ASE in P. ostreatus, which was also found to be rather an exception for conforming to the hourglass model of developmental evolution. To what extent is this conclusion transferable to other Agaricomycete/fungal species?

2) The authors acknowledge that fruiting body-expressed genes may relate either to CM or to more general sexual functions, and that disentangling these functions is a major challenge in their study. An overview of which gene was assigned to which function is not explicit in the ms (proposed to be described in a separate publication). Do these functional gene classes show distinct transcriptome evolution patterns (hourglass model, ASE...)?

3.) As far as I understand, major functions of the fruiting body transcriptome are either CM or general sexual functions. Could these genes, notably those showing ASE, play a role in general processes other than sexual development (hyphal growth, environment sensing, cell homeostasis, pathogenicity)?

4) As stated by the authors, "the goal of this study was to systematically tease apart the components and driving forces of transcriptome evolution in CM fungi". What drives the interesting ASE pattern discovered however remains an open question at the end of the ms. The authors appropriately discuss that these patterns could be either adaptive or neutral but there is no direct evidence for any scenario in P. ostreatus. Is the expression of (some of) the young genes showing ASE required for CM? one or two case studies would allow providing support for such scenarios.

Reviewer #4 (Public Review):

This work develops a comparative framework to test genes which support complex morphological structures and complex multicelluarity. This expands beyond simple gene sharing and phylogenomics by incorporating comparison of gene expression profiling of development of multicellular structures during sexual reproduction. This approach tests the hypothesis that genes underlying sexual reproductive structure formation are homologous and the molecular evolutionary processes that control transcriptome evolution which underlie complex multicellularity.

The approaches used are appropriate and employ modern comparative and transcriptome analyses to example allele specific expression, and evaluate an age of the evolutionary ages of genes. This work produced additional new RNAseq to examine developmental processes and combined it with existing published data to contrast fungi with either complex morphologies or yeast forms.

The strengths of work are well selected comparison organisms and efforts to have developmental stages which are appropriate comparisons.

Weakness could be pointed to in how the NAT descriptions are interesting it isn't clear how they link directly to morphology variation or development. I am unclear if these are arising from new de novo promotors, are ferried by transposable elements, or if any other understanding of their genesis indicates they are more than very recent gains in a species for the most part and not part of any conserved developmental process (outside a few exemplars).

The impact of this work will reside in how gene age intersects with variability and relative importance in CM. it will be interesting to see future work examine the functions of these genes and test how allele specific expression and specific alleles are contributing to the formation of these tissues and growth forms. I am still not sure if molecular mechanisms of how high variability in gene expression is still producing relatively uniform morphologies, or if it isn't quantification of morphological variation would be nice to link to whether ASE underlie that.

To my read of the work, the authors achieved their goals and confirmed hypothesis about the age of genes and the variability of gene expression. I still feel there is some clarity lost in whether the findings across the large number of species compared here help inform predictions or classifications of types of genes which either have ASE or are implicated in CM. This is really work for the future as the authors have provided a detailed analysis and approach that can fuel further direction in this research area.

Reviewer #1 (Public Review):

I find the dataset and the questions driving this manuscript very compelling, and the analyses currently presented a very promising start, but I think the manuscript falls short of fulfilling its potential. My chief concern is the sparse amount of detail in both the methods (especially the wrangling of the de novo transcriptomes and all the downstream consequences of choices made then) and results that makes it hard to evaluate what are fundamentally some very tricky analyses. In turn, this undersells the value of this very rich and unique dataset, which again, I commend the authors for collecting.

As might be expected given the singularity of humans' cognitive abilities, much of the paper focuses on comparing gene expression in the human brain relative to other primates, to a degree that sometimes strikes me as somewhat self-limiting. For example, the analyses of evolutionary rate seem to me to only scratch the surface of the sort of questions this dataset makes addressable for the first time and I would have dearly enjoyed seeing explored in more depth. As such, my main concern is not so much with any perceived critical flaws in the analyses presented, but with what has now come almost within reach but hasn't been addressed in this manuscript.

Reviewer #2 (Public Review):

Bauernfeind et al. have created a remarkable transcriptome dataset of unprecedented content, comprising 18 primate species and four brain regions. They analyse the dataset to address questions on divergence in brain gene expression, as well as human-specific expression patterns.

The authors report that both humans and chimpanzees (hominoids) show unexpectedly high levels of brain gene expression divergence relative to the 16 other primates studied, including other great apes. Most previous work in the field had concentrated on human-specific brain expression changes, relative to chimpanzees and macaques, and this result is highly interesting in that it suggests that many unique features of the human brain, reflected in gene expression, are actually shared with chimpanzees. In fact, this may not be so surprising in the light of our growing understanding of chimpanzee cognitive skills.

It is also notable that Bauernfeind et al. find little to no saturation in expression divergence with time among primates in the brain (using human as reference), in contrast to saturation reported at longer evolutionary scale. This suggests a reevaluation of models of drift and selection on brain gene expression.

Meanwhile, the data may deserve deeper analysis both with respect to overall expression divergence across primates and human-specific expression divergence, and also in the context of the correlation between gene expression and brain size, a major question studied by the authors. These could be preferentially performed within explicit phylogenetic frameworks that include positive selection, which could help distinguish drift from adaptive changes.

Without such explicit analysis, what the authors report in the current manuscript's abstract about genes with expression levels correlated with brain size could be simply reflecting phylogenetic divergence. Also, the conclusion that these genes show positive selection signatures in their regulatory regions may not be supported by the data, as multiple testing correction seems to be lacking for the analysis applied here.

Nevertheless, the authors should be commended for compiling this dataset, which holds the potential for significantly furthering our understanding into expression divergence. The dataset could be used effectively to test the roles of cell type composition changes vs. cell type-specific (or cell-autonomous) changes across the identified expression divergence patterns, or to study the relative roles of trans- vs. cis-driven divergence.

Reviewer #1 (Public Review):

In this manuscript, Phansalkar et al., have dissected the endothelial cell (EC) heterogeneity of cardiac blood vessels across development in mouse and human. They describe that the EC heterogeneities of cardiac blood vessels are sequentially governed by the progenitor sources and environmental cues, during initial and late development, respectively. Moreover, authors show that these ECs become homogeneous in adult. They also claim that human fetal hearts take on generally a similar path for establishment of cardiac blood vessels. Overall, although this study is descriptive and lacks mechanical insight, it is novel and intriguing. However, despite the thorough analysis of vast single-cell transcriptomic data, there are a number of interpretations that could be misleading and confusing. Therefore, they need more precise and detailed analysis on the sc-RNA data.

Reviewer #2 (Public Review):

The manuscript by Phansalkar et al. investigates the relationships of endothelial cells (EC) that comprise the coronary vessels of the heart in mouse and humans using state-of-the-art analysis of new scRNA seq data from both mouse and humans, complemented by spatial localization studies. scRNA seq datasets from e12.5, e17.5, and adult mice with lineage markers for endocardial or sinus venosus (SV)-derived EC were analyzed, and the lineage-tagged mice were also used to investigate the relative contributions of EC from the lineages in a cardiac injury model. These studies revealed that initial transcriptional signatures followed the source of EC, but as development proceeded and in adults these signatures were lost, and the transcriptomes reflected the location of the EC, and that EC from both embryonic sources contributed to revascularization after cardiac injury. Finally, new scRNA seq human fetal coronary EC datasets were analyzed and cross-referenced to the mouse data, and the conclusion was that there was substantial overlap in the data.

Overall, the study has many strengths. The experiments were very well-controlled, alternate hypotheses were considered, and the interpretation of the results was appropriate. The study used a variety of techniques to support conclusions, and the scRNA seq data was analyzed using multiple platforms and analytical tools. The impact of this work is substantial, as it indicates that coronary EC exhibit significant plasticity developmentally, a phenomenon that was observed in earlier studies but without the direct comparison of the expression profiles provided here, and it suggests that in this population there is little "memory" of source with time, rather that location and environmental inputs become the drivers of expression profiles. The analysis of a cardiac injury model reveals lack of functional diversification, since both lineages contributed to the healing. The cross-referencing to human scRNA seq data made good use of the mouse data to show overall concordance, with some minor differences. Minor issues with this version of the work center around presentation of the data, which is sometimes confusing, and that the second lineage was not labelled in the injury model but deduced from lack of labeling (although the labeling was rigorously described early in the study).

Reviewer #3 (Public Review):

To vascularize heart, endothelial cells (ECs) originating from both endocardium (Endo) and sinus venosus (SV) form capillary plexus and become subsequent mature vessels from the outside-in (SV) or the inside-out (Endo). It has been unclear how ECs exhibit either Endo-specific or SV-specific gene expression during coronary vessel growth.

Phansalkar R. et al. demonstrate that Endo- or SV-characteristic transcription has been retained in the early coronary capillary formation, while in the later stage Endo-derived and SV -derived cells converge to form capillary ECs. They utilized scRNAseq of lineage-traced mouse ECs during development and those of ECs of the mice with ischemia/reperfusion. They further investigated human coronary vessel development using human fetal hearts and found that Endo-derived ECs and SV-derived EC of fetal human heart converge and differentiate similarly to mice.

The gene expression in Endo-derived ECs and SV-derived ECs is well characterized by scRNAseq and immunohistochemistry using sections of e12, e17.5, and adult mouse hearts. At 17.5e and later, the gene expression of capillary ECs is determined by the localization-dependent character rather than lineage (Endo or SV). Therefore, the authors suggest that localization-driven heterogeneity is observed in plexus ECs. Spatial gene expression implies that it depends on blood flow; less in septum (Cap1 cluster) and more in outer layer (Cap2 cluster).

They demonstrate that during cardiac development and maturation, even after pathological conditions, coronary ECs exhibit convergent differentiation of two distinct lineages (Endo- and SV-derived cells).

Reviewer #1 (Public Review):

Understanding the underlying mechanisms of stromal cell decidualization and cellular interactions in the uterus is vital to improving women's reproductive health and pregnancy outcomes. This manuscript builds on a series of innovative studies interrogating the impact of cell senescence on decidualization and embryo implantation. A novel decidualization co-culture system containing endometrial epithelial organoids and stromal cells (assembloids) was established. The authors utilize this model in combination with single cell RNA-sequencing and receptor-ligand analysis to interrogate the mechanisms underlying decidual cell senescence and their subsequent roles in embryo implantation. Notably, the authors move beyond predictive bioinformatics and utilize pharmacological inhibition to alter the developmental trajectory of decidualizing cells, resulting in an altered assembloid environment and ultimately impeding human blastocyst development. Overall, this manuscript provides foundational information that will help design definitive and mechanistic studies in the future. The data from this paper will be of general interest to those studying cell-type-specific interactions including both reproductive scientists and clinicians.

Reviewer #2 (Public Review):

In this interesting and well written paper, the authors employ organoid culture, single cell transcriptomics and cell-cell interaction mapping and embryo-co-culture to investigate the role of senescent endometrial cells in implantation biology. The organoids consist of primary uterine epithelial cells and stromal fibroblasts. Transition to luteal phase endometrium is induced by MPA (an artificial progestin) and a membrane permeable cAMP derivative as in in vitro stromal cell decidualization. The so treated organoids are subjected to single cell transcriptomic analysis to reveal the cellular diversity induced in these constructs. Most importantly the authors report an unexpected degree of cellular diversity, both in the epithelial as well as in the stromal compartment, both include cells interpreted as senescent cells, and in the stromal compartment also a clearly distinct pre-decidual cell population. A ligand - receptor analysis suggests that the latter two populations are characterized by a strong engagement of the receptor tyrosine kinase signaling pathways, which gave them a chance to specifically address these cells with a tyrosine kinase inhibitor. They were able to produce decidualized organoids without senescent cells which allowed them to demonstrate that embryo implantation into the endometrial organoids is impossible without senescent cells, while it is readily happening in the presence of senescent stromal cells. The lack of uNK cells, necessary to limit excessive senescence, probably limits the stability of these cultures. This is the most direct evidence to date for a physiological role of senescent cells in embryo implantation.

The main strength of the paper consists of the creative combination of organoid culture and single cell technology, revealing both cell state/type heterogeneity and cell-cell communication networks and the experimental test of hypotheses derived from the latter. Naturally this study is a waypoint towards more complete in vitro models of the in vivo situation, by the lack of leukocytes and blood vessels. There are also some questions about the exact details of the experimental protocol, but the robust, biologically interesting and meaningful results speak for themselves.

One aspect that should be justified in the paper is the use of the MPA/cAMP protocol to decidualize the organoids. This is the standard protocol for decidualizing stromal fibroblasts, and circumvents the lack of epithelial cells in standard stromal culture, essentially replacing the effects of epithelial signals with a downstream second messenger, cAMP. In this context it is not clear what this treatment is supposed to be simulating. In humans receptivity is reached with systemic progesterone. A treatment with proteases and/or IL1 could simulate the presence of the embryo. To properly interpret the results using the MPA/cAMP protocol a discussion of this point would be helpful to the reader.

The authors interpret the epithelial compartment of their organoids as representing uterine gland epithelium. It is not clear why the authors do not also expect luminal epithelium (LE) identity to be present, and in particular since some key changes constituting the window of implantation are affecting the luminal epithelium. Is it possible that some of the epithelial diversity revealed in their single cell classification are actually LE cells? In particular the cells called "transitional" could be seen as LE cells, as a loss of polarity towards a mesenchymal phenotype is part of their biology during the window of receptivity.

The experimental protocol also needs a little interpretation: the authors grow their organoids for four days in "expansion media" [simulating the proliferative phase of the menstrual cycle] and some of those samples are then subjected to SC analysis. Another set of cultures is also subjected to differentiation media for an additional 4 days simulating transition to luteal phase and receptivity. Comparing the expansion media organoids only with the treated ones allows us to see what happens in this simulation of the transition to luteal phase, and as such this is OK. However, the result is never the less confounded by the fact that the treated organoids are older than the "expansion media only" samples. A control comparison with organoids treated 4 days with expansion media and then four days with differentiation media but without the MPA and cAMP would be helpful to disentangle the hormone/cAMP effects and the age related changes in culture.

Reviewer #3 (Public Review):

In their study, authors designed a novel human endometrium research model, which they refer to as assembloids, containing not only the endometrium epithelial cells (as standard organoid models do), but also the tissue stromal cells. Once developed and well characterized using, among others, state-of-the-art single-cell RNA-sequencing, the authors showed its application potential by dipping into a candidate cause of endometrial declined function and receptivity, i.e. dysbalanced senescence. Culminating in the study is the addition of human (spare IVF) embryos to the developed endometrial assembloid with and without senescence perturbation, which is a first step toward in vitro deciphering human embryo-endometrium interaction in health and disease.

The study has multiple strengths. Central are the design and detailed characterization of this new endometrium assembloid model, and the demonstration of its applicability for endometrial deficiency studies regarding biology and embryo interaction. The finding that cellular complexity is recapitulated in the assembloid culture and that cell types and states (in particular, senescence) mimic in vivo decidualization are major achievements. The benchmarking with in vivo data is highly interesting for the field. Limitations may be situated in the use of a rather crude method to inhibit cellular stress and senescence (i.e. using a generally acting tyrosine kinase inhibitor) and the premature immediate-generalization of findings to multiple fertility problems without the needed grounds so far. Although definitely a strong advancement in the field, adding still other endometrial cells, most importantly of luminal epithelial cells but also innate immune cells, will in the future further perfect the model.

The authors achieved their aims of establishing and characterizing a new, straightforward endometrium tissue model. Moreover, they achieved to applying this new tool to start unraveling causes of endometrium non-receptivity or ill-performance, in particular regarding (dys-)balanced senescence. Together, the study presents a promising path along which human (in-)fertility research can develop, to provide basic and translational insights in reproductive biology and into (deficient) fertility which may eventually be taken to the clinic to improving pregnancy chances.

Reviewer #4 (Public Review):

Rawlings et al. investigated endometrial mechanisms that may underlie reproductive disorders such as recurrent implantation failure and pregnancy loss. The proper decidualization of the endometrium is essential for correct embryo implantation and this process is tightly controlled by hormone action during the menstrual cycle. The authors hypothesized that acute senescence in the decidualizing endometrium is necessary for successful embryo implantation. They also sought to characterize gene expression changes that occur in stromal and epithelial compartments as a result of decidualization.

The authors used a novel assembloid model of endometrial culture to investigate how endometrial epithelial and stromal cells respond to decidualization and found that both epithelial and stromal cells displayed distinct gene expression signature groups before and after decidualization. They successfully showed that the cellular stress that occurs during decidualization can directly affect the degree to which decidual endometrial cells undergo senescence. By culturing their endometrial assembloids with human embryos, they were able to convincingly demonstrate that endometrial decidual senescence is necessary to allow the embryo to grow and invade after implantation.

This work will be important to the endometrial biology field as well as to clinicians. In addition to offering a mechanism for some types of implantation failure and recurrent pregnancy loss, the authors showed that treatment with a tyrosine kinase inhibitor can modulate pre-decidual stress and decidual senescence, suggesting that endometrial receptivity could one day be manipulated pharmacologically.

Major strengths and weaknesses of method and results.

Strengths:

The major strength of this paper is their assembloid-embryo coculture model, which provides strong evidence that a lack of endometrial decidual senescence results in a lack of embryo growth and failure of the embryo to invade into the endometrial assembloid. Although endometrial organoid models containing both epithelial and stromal cells already exist, the assembloid model allows the visualization of embryo growth and invasion. The single-cell RNA-sequencing of the assembloids convincingly demonstrates the existence of different populations of endometrial epithelial and stromal cells displaying different gene expression profiles before and after decidualization. The separation into these groups is very clean and the gene expression correlates with endometrial gene expression across the cycle remarkably well.

Weaknesses:

Given that the women who contributed endometrial tissue to this study were attending an Implantation Research Clinic and were mostly nulliparous with past first trimester pregnancy loss, it is unclear whether the endometrial samples used in this study is representative of a healthy condition. The rationale for using a minimal differentiation medium rather than the differentiation medium that has been established in the literature is unclear. In particular, the induction of glandular differentiation in endometrial assembloids by NAC, an antioxidant, deserves some discussion. Additionally, some explanation as to why the authors chose to treat their assembloid cultures with decidualization cocktail for only four days, when decidualization in vivo occurs over a much longer period, would be helpful. The authors state that EpS5 is a population of senescent epithelial cells producing SASP based on gene expression data. It would be more convincing if the authors could provide other evidence to characterize these cells as senescent. The authors draw many conclusions based on data from the CellPhoneDB computational tool, but it is unclear how the authors chose the input for this tool and whether the output of this program was validated in any way.

Reviewer #3 (Public Review):

Barone, Paul et al. present a new computational method, named T-REX, to detect changes in immune cell populations from repeated cytometry measurements (before and after infection or treatment). The proposed method is designed to detect changes in rare and common cells with particular focus on the former. T-REX detects subpopulations of cells showing marked differences in abundance between the proportion of cells from different time points (before and after infection) from a single individual. The method relies of a dimensionality reduction step using UMAP followed by a K-nearest neighbor (KNN) search to identify cells that have a large fraction (>0.95) of neighbors from one time point, indicating expansion or shrinkage of certain cell populations. Areas in the UMAP with clustered expanding or shrinking neighborhoods are labeled as hotspots. Cells in these hotspots were further characterized and enriched markers were identified using MEM, a method published earlier by the same authors. T-REX was applied to a newly collected dataset of rhinovirus infection and three publicly available datasets of SARS-CoV-2 infections, melanoma immunotherapy and AML chemotherapy. The results are presented clearly and the authors discuss in details several examples in which the cells identified by T-REX have a phenotypic profile which align with previous knowledge, indicating the relevance of the results.

Strengths:

• T-REX is based on a simple pipeline including UMAP and KNN. This is an advantage especially given the large number of cells collected. Further, the proposed approach has a key advantage since it allows the analysis of one sample at a time, which is practical if one wants to analyze a new sample. There is no need to rerun the analysis on an aggregate of a large number of samples.

• The new rhinovirus dataset is of great value to the community.

Weaknesses:

• The paper lacks a comparison to other methods for differential abundance testing. In particular, it is not clear how T-REX differs from the Differential abundance test proposed by Lun et al. (https://doi.org/10.1038/nmeth.4295). Similarly, there are no experiments or results to support the authors' initial claim that T-REX outperforms current clustering-based methods (SPADE, FLOWSOM, Phenograph,...etc.) in capturing changes in rare (<1%) cell populations.

• T-REX relies on arbitrary cutoffs (0.95 and 0.5 %) to define expansion or shrinkage in the neighborhood of each cell (0.95 and 0.5 %) rather than a formal statistical test. These cut-offs were defined based on the ability to detect tetramer positive cells in one subject only. This greatly limits the generalizability of the method.

• The authors do not motivate the use of UMAP prior to the KNN graph reconstruction. While UMAP is a clearly powerful method to visualize single cell data, the resulting embedding can potentially show distinct groups of points when the high dimensional manifold is more continuous. For this reason, KNN graphs are usually built using the high-dimensional data (or principal components).

• Given that T-REX is mainly developed to detect changes in rare cell populations, the paper lacks an assessment of the method's sensitivity. For instance, cells were subsampled equally from each time point. An assessment of the effects of this subsampling step is necessary. In general, a guide to the users indicating the limitations of T-REX will be greatly helpful.

• Given that the main aim of T-REX is to detect differences in rare cells, the rational to perform a separate analysis for CD4 positive cells is not clear. One would expect these differences to be identified also in the analysis performed using all cells.

• The paper lacks a discussion on the effects of batch effects between the different time points on the performance of T-REX.

Reviewer #2 (Public Review):

This study presents a novel machine learning tool (termed T-REX) for automated analysis of single cell cytometric data that is capable of identifying rare cell populations, such as antigen-specific T cells. This ability to detect low frequency cells is a distinct advantage over existing tools. The demonstration of this ability is appropriately shown by examining antigen-specific CD4+ T cells before and after rhinovirus infection in a challenge study. Useful demonstrations are also included for examining SARS-CoV-2-specific T cells and changes in cellular populations in cancer patients upon treatment. These examples use both mass cytometry and fluorescence-based cytometry. Since both of these are commonly-used single-cell technologies that generate highly complex data sets, new automated analysis methods such as T-REX are needed.

The first data set examined changes in cell phenotype before and 7 days after rhinovirus infection in healthy adults. The flow cytometric staining panel included markers of T cell differentiation and activation as well as rhinovirus-specific tetramers. The results of T-REX convincingly demonstrate "hotspots" that are expanded at 7 days and enriched for tetramer-staining cells. Thus, this study succeeds at demonstrating the utility of this method for identification of rare cells and the authors use this data set to appropriately determine the model parameters. Combining the results of this algorithm with the "Marker Enrichment Modeling (MEM)" method to characterize the markers expressed on those cell populations identified through T-REX is also very informative since this automates the characterization (that traditionally needs to be done by manual investigation).

This first data set is relevant for this demonstration, but in some aspects it represents a best case scenario. "Phenotypic" identification of antigen-specific T cells in this way is only possible because the time point was chosen to capture the relatively narrow window when T cells would be activated, and there was access to a baseline sample for comparison. The authors do address the second point, and perform the analysis comparing day 7 to a later time point, day 28, as an appropriate alternative. The first concern limits the generalizability of this approach. In fact, the second example dataset examining mass cytometry data in patients with COVID-19 does in fact demonstrate limited ability to detect change in cell populations for many study participants.

Reviewer #1 (Public Review):

Sierra M. Barone developed an automated, quantitative toolkit for immune monitoring that would span a wide range of possible immune changes, identify and phenotype statistically significant cell subsets, and provide an overall vector of change indicating both the direction and magnitude of shifts, either in the immune system as a whole or in a key cell subpopulation. The machine learning workflow Tracking Responders Expanding (T-REX) was a modular data analysis workflow including UMAP, KNN, and MEM. T-REX is designed to capture both very rare and very common cell types and place them into a common context of immune change. T-REX was analyzed data types including a new spectral flow cytometry dataset and three existing mass cytometry datasets.

The conclusions of this paper are mostly well supported by data, but one aspect need to be clarified and extend. Cytometry tools like SPADE, FlowSOM, Phenograph, Citrus, and RAPID generally work best to characterize cell subsets representing >1% of the sample and are less capable of capturing extremely rare cells or subsets distinguished by only a fraction of measured features. Tools like t-SNE, opt-SNE, and UMAP embed cells or learn a manifold and represent these transformations as algorithmically-generated axes. The advantages of T-REX tool were not very clear.

Reviewer #3 (Public Review):

This is an important manuscript on COVID-19 convalescent plasma (CCP) that challenges the findings of the larger Mayo Clinic CCP study demonstrating a lack of efficacy. Their main findings are that there is a strong inverse correlation between CCP use and mortality for admitted patients in the USA. Overall this is a well written manuscript without any overt weaknesses.

Reviewer #2 (Public Review):

The use of convalescent plasma (CCP) to treat patients with Covid-19 has changed over the course of the pandemic (from rates as high as 40% of hospitalized patients in October, 2020 to a low of less than 10% by March 2021). To explore the efficacy of CCP therapy and the impact of the drop in CCP use, the authors assess whether there was a link between CCP use and patient mortality rates over time in the U.S. Using information from blood centers to estimate CCP usage and population level information on deaths from public databases, they found a strong inverse correlation between CCP usage per hospital admission and deaths due to Covid-19 after admission. The model estimates that the case fatality rate decreased by 1.8 percentage points for every 10 percentage point increase in the rate of CCP use. The detailed analysis suggests that the observed effect could not be attributed to changes in patient ages over time or the emergence of variant viruses. Other cofounders such as changes in the use of additional therapeutic agents or clinical interventions were not analyzed. The authors acknowledge the main limitation of this type of analysis i.e. that establishing a correlation does not prove a causal role. With that caveat, they conclude that the decline in usage may have resulted in excess deaths, possibly 29,000 to 36,000 over the past year in the U.S. Because the decreased usage of CCP occurred during the time that several randomized clinical trials and some media coverage reported no benefit of CCP, the authors suggest that resultant "plasma hesitancy" may have contributed to increased mortality. These findings add an important perspective to future considerations for clinical care, treatment guidelines and regulatory approvals of CCP. Emphasizing the importance of using high-titer units and administering CCP early in the disease course, the authors urge a more nuanced interpretation of the available evidence and a holistic approach to decisions about the use of CCP in individual patients.

Reviewer #1 (Public Review):

In the early days of the pandemic there was unqualified enthusiasm for convalescent plasma therapy. This enthusiasm shifted dramatically as several trials showed no apparent benefit. Although this manuscript does not show a causal relationship between convalescent plasma therapy and prognosis it is provocative and suggests that further work is needed to assess its utility.

Strengths of the manuscript include the comprehensive review of existing datasets and the use of state-of-the-art statistical methods for examining potential confounders such as patient age, seasonal variation in hospital admissions that might have impacted quality of care, and the emergence of SARS-CoV-2 variants. Weaknesses include lack of data that might have enabled identification of patients who are likely to benefit from convalescent plasma and characteristics of plasma (such as neutralization titers) that may be associated with efficacy. These weaknesses do not indicate a lack of effort on the part of the team; there is simply no way to obtain the data.

Reviewer #3 (Public Review):

In this study, Alhussein and Smith provide two strong tests of competing hypotheses about motor planning under uncertainty: Averaging of multiple alternative plans (MA) versus optimization of motor performance (PO). In this first study, they used a force field adaptation paradigm to test this question, asking if observed intermediate movements between competing reach goals reflected the average of adapted plans to each goal, or a deliberate plan toward the middle direction. In the second experiment, they tested an obstacle avoidance task, asking if obstacle avoidance behaviors were averaged with respect to movements to non-obstructed targets, or modulated to afford optimal intermediate movements based on a commuted "safety margin." In both experiments the authors observed data consistent with the PO hypothesis, and contradictory of the MA hypothesis. The authors thus conclude that MA is not a feasible hypothesis concerning motor planning under uncertainty; rather, people appear to generate a single plan that is optimized for the task at hand.

I am of two minds about this (very nice) study. On the one hand, I think it is probably the most elegant examination of the MA idea to date, and presents perhaps the strongest behavioral evidence (within a single study) against it. The methods are sound, the analysis is rigorous, and it is clearly written/presented. Moreover, it seems to stress-test the PO idea more than previous work. On the other hand, it is hard for me to see a high degree of novelty here, given recent studies on the same topic (e.g. Haith et al., 2015; Wong & Haith, 2017; Dekleva et al., 2018). That is, I think these would be more novel findings if the motor-averaging concept had not been very recently "wounded" multiple times.

The authors dutifully cite these papers, and offer the following reasons that one of those particular studies fell short (I acknowledge that there may be other reasons that are not as explicitly stated): On line 628, it is argued that Wong & Haith (2017) allowed for across-condition (i.e., timing/spacing constraints) strategic adjustments, such as guessing the cued target location at the start of the trial. It is then stated that, "While this would indeed improve performance and could therefore be considered a type of performance-optimization, such strategic decision making does not provide information about the implicit neural processing involved in programming the motor output for the intermediate movements that are normally planned under uncertain conditions." I'm not quite sure the current paper does this either? For example, in Exp 1, if people deliberately strategize to simply plan towards the middle on 2-target trials and feedback-correct after the cue is revealed (there is no clear evidence against them doing this), what do the results necessarily say about "implicit neural processing?" If I deliberately plan to the intermediate direction, is it surprising that my responses would inherit the implicit FF adaption responses from the associated intermediate learning trials, especially in light of evidence for movement- and/or plan-based representations in motor adaptation (Castro et al., 2011; Hirashima & Nozacki, 2012; Day et al., 2016; Sheahan et a., 2016)?

In that same vein, the Gallivan et al 2017 study is cited as evidence that intermediate movements are by nature implicit. First, it seems that this consideration would be necessarily task/design-dependent. Second, that original assumption rests on the idea that a 30˚ gradual visuomotor rotation would never reach explicit awareness or alter deliberate planning, an assumption which I'm not convinced is solid.

The Haith et al., 2015 study does not receive the same attention as the 2017 study, though I imagine the critique would be similar. However, that study uses unpredictable target jumps and short preparation times which, in theory, should limit explicit planning while also getting at uncertainty. I think the authors could describe further reasons that that paper does not convince them about a PO mechanism.

If the participants in Exp 2 were asked both "did you switch which side of the obstacle you went around" and "why did you do that [if yes to question 1]", what do the authors suppose they would say? It's possible that they would typically be aware of their decision to alter their plan (i.e., swoop around the other way) to optimize success. This is of course an empirical question. If true, it wouldn't hurt the authors' analysis in any way. However, I think it might de-tooth the complaint that e.g. the Wong & Haith study is too "explicit."

Reviewer #2 (Public Review):

The authors should be commended on the sharing of their data, the extensive experimental work, the experimental design that allows them to get opposite predictions for both hypotheses, and the detailed of analyses of their results. Yet, the interpretation of the results should be more cautious as some aspects of the experimental design offer some limitations. A thorough sensitivity analysis is missing from experiment 2 as the safety margin seems to be critical to distinguish between both hypotheses. Finally, the readability of the paper could also be improved by limiting the use of abbreviations and motivate some of the analyses further.

Major:

1) The text is difficult to read. This is partially due to the fact that the authors used many abbreviations (MA, PO, IMD). I would get rid of those as much as possible. Sometimes, having informative labels could also help FFcentral and FFlateral would be better than FFA and FFB.

2) The most difficult section to follow is the one at the end of the result sections where Fig.5 is discussed. This section consists of a series of complicated analyses that are weakly motivated and explained. This section (starting on line 506) appears important to me but is extremely difficult to follow. I believe that it is important as it shows that, at the individual level, PO is also superior to MA to predict the behavior but it is poorly written and even the corresponding panels are difficult to understand as points are superimposed on each other (5b and e). In this section, the authors mention correcting for Mu1b and correcting for Sig2i/Sig1Ai but I don't know what such correction means. Furthermore, the authors used some further analyses (Eq. 3 and 4) without providing any graphical support to follow their arguments. The link between these two equations is also unclear. Why did the authors used these equations on the pooled datasets from 2a and 2b ? Is this really valid ? It is also unclear why Mu1Ai can be written as the product of R1Ai and Sig1Ai. Where does this come from ?

3) In experiment 1, does the presence of a central target not cue the participants to plan a first movement towards the center while such a central target was never present in other motor averaging experiment. In the adaptation domain, people complain that asking where people are aiming would induce a larger explicit component. Similarly, one could wonder whether training the participants to a middle target would not induce a bias towards that target under uncertainty

4) The predictions linked to experiment 2 are highly dependent on the amount of safety margin that is considered. While the authors mention these limitations in their paper, I think that it is not presented with enough details. For instance, I would like to see a figure similar to Fig.4B when the safety margin is varied.

The sensitivity analysis is very difficult to follow and does not provide the right information. First, this is only done for exp2 and not exp1. For exp1, it would be good to demonstrate that, even when varying the weight of the two one-target profiles for motor averaging, one never gets a prediction that is close to what is observed. It is unclear in the text that the performance optimization prediction simply consists of the force-profile for the center target. The authors should motivate this choice. For the second experiment 2, the authors do not present a systematic sensitivity analysis. Fig. 5a and d is a good first step but they should also fit the data on exp2b and see how this could explain the behavior in exp 2a. Second, the authors should present the results of the sensitivity analysis like they did for the main predictions in Fig.4b. While I understand where the computation of the safety margin in eq.2 comes from, reducing the safety margin would make the predictions linked to the performance optimization look more and more towards the motor averaging predictions. How bad becomes the fit of the data then ? How does the predictions look like if the motor costs are unbalanced (66 vs. 33%, 50 vs. 50% (current prediction), 33 vs. 66% ). What if, in Eq.2 the slope of the relationship was twice larger, twice smaller, etc. The safety margin is the crucial element here. If it gets smaller and smaller, the PO prediction would look more and more like the MA predictions. This needs to be discussed in details. I also have the impression that the safety margin measured in exp 2a (single target trials) could be used for the PO predictions as they are both on the right side of the obstacle.

5) On several occasions (e.g. line 131), the authors mention that their result prove that humans form a single motor plan. They don't have any evidence for this specific aspect as they can only see the plan that is expressed. They can prove that the latter is linked to performance optimization and not to the motor averaging one. But the absence of motor averaging does not preclude the existence of other motor plans.... Line 325 is the right interpretation.

6) Line 228: the authors mention that there is no difference in adaptation between training and test periods but this does not seem to be true for the central target. How does that affect the interpretation of the 2-target trials data ? Would that explain the remaining small discrepancy between the refined PO prediction and the data (Fig.2f) ?

Reviewer #1 (Public Review):

In this paper, Alhussein and Smith set out to determine whether motor planning under uncertainty (when the exact goal is unknown before the start of the movement) results in motor averaging (average between the two possible motor plans) or in performance optimization (one movement that maximizes the probability of successfully reaching to one of the two targets). Extending previous work by Haith et al. with two new, cleanly designed experiments, they show that performance optimization provides a better explanation of motor behaviour under uncertainty than the motor averaging hypothesis.

Main comments:

1) The main caveat of experiment 1 is that it rules out one particular extreme version of the movement averaging idea- namely that the motor programs are averaged at the level of muscle commands or dynamics. It is still consistent with the idea that the participant first average the kinematic motor plans - and then retrieve the associated force field for this motor plan. This idea is ruled out in Experiment 2, but nonetheless I think this is worth adding to the discussion.

2) The logic of the correction for variability between the one-target and two-target trials in Formula 2 is not clear to me. It is likely that some of the variability in the two-target trials arises from the uncertainty in the decision - i.e. based on recent history one target may internally be assigned a higher probability than the other. This is variability the optimal controller should know about and therefore discard in the planning of the safety margin. How big was this correction factor? What is the impact when the correction is dropped ?

3) Equation 3 then becomes even more involved and I believe it constitutes somewhat of a distractions from the main story - namely that individual variations in the safety margin in the 1-target obstacle-obstructed movements should lead to opposite correlations under the PO and MA hypotheses with the safety margin observed in the uncertain 2-target movements (see Fig 5e). Given that the logic of the variance-correction factor (pt 2) remains shaky to me, these analyses seem to be quite removed from the main question and of minor interest to the main paper.

Reviewer #3 (Public Review):

This is a well-presented study on the development of the CNS in the octopus O. vulgaris. The aim of the study is to identify the origin of the neural progenitors of the brain. The authors provide an excellent gene expression study of conserved neural genes to identify the location of these progenitors. Furthermore, by cell lineage tracing, they confirm the results of a previous study by Koenig et al. showing that the progeny of neural progenitors generated in the so-called lateral lips, a region adjacent to the eyes, migrate to different brain areas. The neural precursor location in the brain can be correlated with their spatial origin from the neural progenitors in the lateral lips. The authors suggest that the spatial map of the lateral lips is conserved in cephalopods. Furthermore, they analyse the mitotic activity in the developing brain by Ov-pcna in situ hybridisation and anti-PH3 immunohistochemistry. The authors conclude that "grossly, the embryonic octopus brain does not contain dividing progenitor cells." Based on the cell lineage studies, the strong expression of neural genes in the lateral lip and the observed mitotic activity, the authors overall conclude that the lateral lips represent the neurogenic zone in the developing brain of the octopus, i.e. that the neural progenitors of the brain derive from this area. I agree with the authors that the lateral lips are neurogenic regions, however, it is also possible that neural progenitors do arise from other regions of the developing brain. Overall this is a valuable contribution to our knowledge of neurogenesis in deuterostomian invertebrates and in a wider context the evolution of neural developmental processes.

Reviewer #2 (Public Review):

The authors established a comprehensive map of neurogenetic sites with evolutionary conserved neurogenic and postmitotic gene expression in a common octopus, Octopus vulgaris that has been a historically important species in comparative neuroscience and behavioral studies. The selected molecules include representative regulatory genes such as achaete-scute, neurogenin, and neuroD, and also proliferating cell markers such as elav and PCNA. In subsequent experiments by using a fluorescent dye, the authors carefully traced the migratory pathways from the target ectodermal sites surrounding eyes to many developing brain lobes of clearing staged embryos with light sheet microscopy for 3D reconstruction.

They found that the special regions called lateral lip and other special ectodermal areas produced a pool of migratory postmitotic neurons that might contribute a novelty for developing octopus large and complex brains as in mammals, in contrast to those of other invertebrates such as flies or worms.

I find the bodies of evidence convincing. A good study usually opens many new questions. Before publication, I found two major points that may enhance the author's conclusions.

1) Are the migratory cells only neurons, or could they also be glia, neurosecretory, blood, or immune cells? The enlarged views of the migratory cells and the cytological features must be clarified.

2) The expression of canonical neurogenic genes disappears during middle and late stages, meaning that octopus has very unique neurogenic mechanisms compared to mammals? Consider the octopus-specific novelty.

Reviewer #1 (Public Review):

The authors first use light sheet microscopy to reconstruct embryonic brain development of O. vulgaris. From these images they note a region adjacent to the eye and the developing brain that initially increases in size and subsequently shrinks. They perform transcriptomics and use phylogenetic analyses to identify 4 classes of genes involved in neurogenesis: those that specify the neuroectoderm, neurogenic genes, and markers for differentiated neurons and mature neurons. They perform spatio-temporal analyses of these genes to demonstrate that the lateral lip is the neurogenic region that harbours neuronal progenitors. This region is distinct from the brain, suggesting that neurons migrate long distances from where they are specified to populate the brain. They perform lineage tracing to provide evidence for this migration and demonstrate that the lateral lip is spatially fated so that regions within it generate neurons specific to parts of the brain.

In summary, this is an elegant study that provides deep insight into embryonic neurogenesis in O. vulgaris.

Reviewer #2 (Public Review):

In this work, Corbett and colleagues investigate how value influences speeded decisions. In a random dot motion task with speed pressure, shortly before motion onset it is indicated which of both choices has a higher value if answered correctly. EEG recordings show a buildup of motor beta in response to the cue (earlier for high value choices, steeper for low value choices) and a dip in LRPs for low value choices in response to stimulus onset. A computational model constructed based on these findings provides a good account of the data. The EEG informed modeling is impressive and deserves merit. The paper is well-written, but rather dense.

• I am struggling with the idea that cue-evoked motor beta reflects urgency. As it currently reads, this is more taken as a given than actually demonstrated. Could this claim be corroborated by e.g. showing that response deadlines modulate this signal? Related to that, how can we be sure that the pre-stimulus patterns seen in motor beta feed into the decision making process itself? It is not hard to imagine why left and right pointing arrows directly trigger motor activity (i.e. simple priming), but does that also imply that such activity leaks into the decision process?

• I had a hard time understanding the choice for this specific design. As the authors write they "primarily focused on the value biasing dynamics in common across these challenging regimes" so I wonder whether conditions with different value differences could have been more instructive (e.g., according to the author's hypothesis different levels of value should parametrically affect motor beta, whereas if this reflect a simple priming process value itself should not matter). Alternatively, it should be better explained why these conditions where crucial for the current findings.

• One of the main selling points of the paper is that we currently lack a model that can explain fast value-based decisions, mostly because the constant drift rate assumption in evidence accumulation models seems invalid. This conjecture is very similar to literature on response conflict, where performance in conflict tasks (such as Stroop, Flanker, etc.) is best modelled using a time-varying drift rate. I wonder to what extent current data reflect the same process, i.e. the value cue "primes" a response, which then has to be suppressed in favor of the correct response. A clear difference is that the value remains relevant here, but could e.g. the motor beta effect just reflect priming?

• If I understand correctly the model was fit to all data effectively ignoring between-participant differences. It is unclear why this was this done (rather than fitting data separately per participant or fitting the data using a hierarchical model), because it induces substantial variance in the fits caused by between-participant differences.

Reviewer #1 (Public Review):

The manuscript has several merits. Most remarkably, Corbett and colleagues developed an alternative to describing biases in decision making by shifting the starting point of evidence accumulation. Instead, they included a linearly increasing urgency buildup rate that was biased by a value cue presented before task onset. Hence, the subsequent evidence accumulation process (labeled the "cumulative bias plus evidence function", p. 5) was affected by this bias in addition to gradually-accumulated stimulus evidence. To allow the estimation of these new model parameters, starting points and urgency buildup rates were constrained to equal the amplitude and temporal slope of the corresponding beta signal captured in simultaneous EEG recordings.

They tested a set of alternative model implementations and found that the bias in stimulus-evidence accumulation was best represented by a concentrated burst of value-biased activity that mirrored voltage changes in the LRP. In comparison, a model with sustained value-biased activity provided an inferior account of the data. Moreover, the authors found that a model gradually increasing evidence and noise provided a better account of the data than a stationary evidence accumulation function. This systematic comparison of alternative model implementations is a great highlight of the paper, because it allows to narrow down on the neurocognitive processes underlying biased decision making.

What limits the generalizability of the authors' results is the sample size and composition. With only 18 participants (one of which was a co-author of this manuscript), the robustness of the authors' modeling results remains an open question. Although 18 participants may provide sufficient power to test a simple main effect in a within-subject design, this does not speak to the issue of the reliability and generalizability of modeling results. Moreover, it is important to note that a sample of 18 participants gives only a power of about 50 % to detect a medium-sized effect with α = .05. Nevertheless, I believe that the generalizability of modeling results is a larger issue than the statistical power. It would have been interesting to assess if the best-fitting model identified in Table 2 provides the best account of the data for all participants or only for a certain percentage of the sample.

Reviewer #2 (Public Review):

In this work, Sobczak et al. suggest that correlations between fMRI and pupil diameter vary over time, and propose an approach to identify distinct clusters of such correlation patterns. The proposed methods are applied to data acquired from anesthetized rats. Based on the clusters obtained, the authors conclude that pupil dynamics are linked with different neuromodulatory centers over different intervals of time.

Overall, I believe that the study is novel and uncovers potential new modes of coupling between neuromodulatory nuclei and pupil diameter. However, additional analysis may be needed to fully support the validity of the derived clusters, and the decoding methods may need some modification before the accuracy values can be properly interpreted. The mechanisms behind the time-varying fMRI-pupil coupling exhibited under anesthesia could also be further clarified. Specifically:

• The clusters appear to involve interpretable brain regions. However, a more formal analysis of reproducibility of these clusters, and statistical testing against an appropriate null model, are not present. Such tests would be useful for establishing the validity of the derived clusters, ensuring that the conclusions are strongly supported. Similarly, the differentiation between power spectral density of each cluster is not yet supported by statistical testing.

• With regard to the decoding models, it appears there could be interdependence between the training and testing data (the PCA step seems to include all scans, and it was not clear if the training/testing sets contained data drawn from the same animal).

• While the paper is motivated by discussion that pupil diameter changes are complex and related to rich behaviors (mental effort, decision making, etc.), this paper examines data from anesthetized rats. The mechanisms behind the time-varying changes in fMRI-pupil coupling in the current data, and the potential impact of anesthesia, were not clear and could be elaborated upon.

Reviewer #1 (Public Review):

Human and animal work over the last couple of years established that fluctuations in pupil size track the activity of a number of neuromodulatory nuclei, including the noradrenergic locus coeruleus, cholinergic basal forebrain, serotonergic dorsal raphe and perhaps the dopaminergic midbrain. In other words, pupil size fluctuations might track a "cocktail" of neuromodulators. The current paper leverages sophisticated data driven analysis techniques to show that pupil size changes can indeed be modulated by different combinations of subcortical nuclei. Doing so, the paper helps laying a solid and nuanced neurophysiological foundation for the interpretation of results from cognitive pupillometry, an area of neuroscience and psychology that is rapidly expanding over the past years. I do have a couple of concerns.

Major issues:

The BOLD hemodynamic response function is slower than the pupil impulse response function. It seems that the authors did not correct for the "lag" between the two (as in Yellin et al., 2015, for example). How much does this matter for the results?

Baseline pupil size was different between the identified clusters. How was pupil size normalized across rats and scanning runs, so that we can meaningfully interpret such a difference?

A substantial part of the literature focuses on the relationship between task-evoked pupil and neuromodulatory responses. I understand that this paper describes results from a resting state experiment, but even in these conditions one typically observes rapid dilations. Right now, it seems that the analysis is somewhat blind to these. See for example Fig. 2C in which frequencies are plotted only until 0.05Hz. Can we see this on log-log axes, to inspect the higher frequencies? Note that there is some work that indicates that the slower pupil fluctuations more reliably track ACh signaling, and faster fluctuations more reliably track NE signaling (Reimer & McGinley et al., 2016).

The authors write "Cluster 2 had the strongest positive weights in [...], but also in brainstem arousal-regulating locus coeruleus, laterodorsal tegmental and parabrachial nuclei." However, the voxel size is very large with respect to the size subcortical nuclei. Because of this, here and in other places, I think the authors should use locus coeruleus region or area, to indicate that their voxel captures more tissue than just LC proper. A discussion paragraph on the spatial specificity of their effects would also help.

The approach is very data driven and the Results section mostly descriptive. I'm personally not at all unsympathetic to this approach, but I do think the authors could aid the reader better by briefly interpreting their results already in the Results section. Related, the authors end each paragraph with "These results verified [...]" or "These results highlight [...]"; however they don't explicitly inform us how.

Rainbow and jet colormaps are confusing because they are not perceptually uniform (https://colorcet.holoviz.org/). Please consider using something like "coolwarm"?

Minor issues:

"Trial" is not well defined. I take this is a 15 minute run?

How many trials in each cluster (Fig. 2)? It would be nice to see a more zoomed in version of Fig. 5 so that we can actually see the subcortical regions in more detail.

Reviewer #2 (Public Review):

The various pathogenic, parasitic, symbiotic, and mutualistic interactions between insects and the microbes they interact with represents a rich area of research. This study by Xiao et al. represents a very interesting example of such a relationship. Overall the study is well designed and executed. The approach they utilize to test their hypothesis is valid and they combined both laboratory and field collected insects to address the question. The RNAseq analysis also provides potential insights into possible mechanisms by which the virus HaDV2 enables enhanced resistance to Bt Cry1Ac. The RNAseq data also represent one of the minor issues. The authors focused on analyzing only development and immune systems, however, they do not report on any other significantly different changes in gene expression other than reporting that there were 1573 significant differences. The authors should at least provide some holistic analysis and report the data in the supplemental results. Focusing on development and immune systems is valid and rationally supported but a complete analysis should be presented. The relationship between H. armigera and HaDV2 is more a mutualistic relationship, thus, the authors should consider changing the titles of the manuscript and the supplementary data. This is an exciting study and is well written and will be of general interest to the field.

Reviewer #1 (Public Review):

Previous reports have provided evidence identifying infection of cotton bollworm with a densovirus as resulting in increased fitness. In the current manuscript, the relevance of this infection towards field resistance to transgenic Bt corn is evaluated by comparing its incidence between regions in China growing non-Bt versus Bt cotton. A clear correlation emerges with infection rates being higher in Bt versus non-Bt cotton growing areas, although its effect on resistance to Cry1Ac and Bt cotton is not as clear.

Strengths:

The manuscript presents evidence for the spread of densovirus infection in field bollworm populations, and that this spread seems to occur at a faster rate in areas of China where Bt cotton is grown versus non-Bt cotton areas. Life table comparisons clearly show increased fitness in bollworms infected with the virus. The study capitalizes on availability of an impressive collection of samples with distinct geographic and historic origin to address relevant evolutionary questions.

Weaknesses:

The suggested role for densovirus infection in resistance to Cry1Ac and Bt cotton is supported by association and the data presented does not necessarily support causation. In fact, the confidence intervals in all the comparisons from bioassays overlap substantially and the resulting resistance ratio is not a good estimate of any significant differences the infection may have on ability to survive Cry1Ac. Infection by a virus is expected to activate the immune system, so the larvae used in bioassays should be considered as "primed" and the slight reduction in susceptibility should not be considered as an effect of the virus itself. The life table data clearly shows that fertility and fecundity are probably the most relevant aspects affecting fitness of infected insects. These differences in reproduction (even more than differences in larval growth) could explain why infection is rapidly spreading in the wild. However, most of the research and analyses are focused on the possibility that the viral infection may make the insects more able to survive Cry1Ac or Bt cotton. There are no conclusive data supporting this hypothesis in the current version, other than increased infection rates in Bt-cotton growing areas. This could be explained by effects on reproduction rather than enhanced survival. Related to this aspect, there should be a more clear distinction between the densovirus increasing fitness versus increasing resistance, the data supports the former but is not so clear in the later. It would be useful to provide a map detailing regions were moths were collected.

Reviewer #3 (Public Review):

The manuscript of Oggenfuss et al presents a comprehensive analysis of TE insertion polymorphisms detected in the genome of ~300 isolates of the wheat fungus Zymoseptoria tritici. The article shows that numerous TE families generated thousands of polymorphic insertions and the authors propose that some of these insertions might potentially be linked to adaptation. They identified a recent burst of transposition in a rapidly expanding population, providing empirical evidence that drastic demographic process shape TE dynamics in nature. Last, they show that intra-specific variation in genome size can be accounted by variation in the number of polymorphic TE insertions, which recapitulate the well stablished association between TE content and genome size variation observed across the diversity of life forms.

The article is well written, present novel as well as relevant results, and provide insights to our understanding of the role of TEs in microevolutionary processes. In addition, it provides an important amount of population genomic data that will serve as a resource. Thus, this manuscript is of potential interest to a broad audience on evolutionary and population genomics.

My major concern is the lack of strong evidence supporting positive selection and/or functional relevance of the TE insertions detected. In particular, the selective sweep scans performed ignored other types of variants (such as SNPs and INDELs), preventing the identification of the actual targets of natural selection.

Reviewer #2 (Public Review):

Transposable elements (TEs) have been shown to play an important role in genome evolution, in shaping genomic organization, structure, and genome size. The importance of TEs in evolutionary processes such as adaptation, has been rather limited so far. Here, Oggenfuss et al. used intraspecies data from six global populations of the wheat pathogen Zymoseptoria tritici to study the process of TE insertion dynamics, to detect candidate adaptive regions associated with TE insertions, and to show that TE expansion has driven to genome size of some populations in about 25 years. Using publicly available short read sequencing, as well as newly sequenced populations, they created a pipeline to specifically identify TE insertions, then used TE frequency insertions to infer patterns of selection, which they hypothesize is under strong purifying selection for regions into genes. They further tried to detect evidence for positive selection at loci associated with adaptation to new environment or resistance to fungicide, and finally contrasted the TE expansion in a window of 25 years to show that two populations have increased their genome size due to TE expansion.

Strengths:

The dataset used in this study with different global populations at different time makes the authors in an ideal position to detect TE expansion in a short timeframe of 25 years. While it has been shown in multiple eukaryotic genomes that TEs contribute substantially to variation in genome size, the evidence provided in this paper is compelling and shows that it is unlikely due to genome duplication events.

The pipeline to identify TE insertions from short read sequencing provides a well detailed path to apply in other genomes, and provide logical reasoning detecting TEs absent from the reference or absent from the isolates but present in the reference. Providing validation for some of the steps would be desired if well-known regions can be used.

The authors explored the interesting perspective of identifying TEs under positive selection by focusing on loci with increased frequency in specific populations. They also added a level of functional validation to better understand specific TE insertions under selection, which is often not included. They identified three loci that did confer resistance to fungicide according to their assay. These results would benefit from additional key details in the methods and the rationale behind the choice of loci in order to fully measure the impact of this finding.

Weaknesses:

While the paper does have strengths in principle to study the evolution of TE dynamics, the weaknesses of the paper reside in the fact that the manuscript in its current form does not directly support the key conclusions presented here. Additional analyses would be required to support them. Such as :

The presence of a large percentage insertions being singleton TEs coupled with low frequency (based on an arbitrary cutoff) is used to conclude strong purifying selection is acting on these new insertions. The authors should have included evidence to convince the reader the presence of a TE in one isolate is not a case of false positive. Additional analyses such as a subset of the singleton TEs to corroborate these single loci or plotting the relationship between how many isolate with one insertion were found and read depth could be informative. Also, understanding the singleton in the context of chromosome locations (core and accessory) could help reinforce the evidence of purifying selection.

The author's conclusion of relaxed selection in accessory chromosomes and between populations are mainly based on TE density. This conclusion may be better supported by adding quantified information of the relationship between the numbers and the type of TE insertions and genomic features such as recombination, which is often found to be negatively associated with TE content. Showing the recombination rate between the chromosomes (core, accessory) would help strengthen the argument that selection acts more strongly in regions of high recombination. To make their conclusion more robust, the authors could have included information about the recombination landscape.

Overall, the authors should have provided the adequate statistical analyses to support many of the insertion frequencies that are often only mentioned qualitatively (e.g. "less than expected") without having the quantitative test. Also, the contrast between low TE insertion frequencies versus high frequency is used without providing details about what is expected, either supported by the literature or by more detailed analyses. Arbitrary threshold can be prone to give artifact results.

Context : This article comes with a number of recent papers exploiting the population genomics dataset of the major wheat pathogen Zymoseptoria tritici (Fouché et al 2020, Krishnan et al. 2018) to show that TE-mediated insertions have in part helped to colonize host plants and tolerate environmental stress. Here, the authors build upon this knowledge to attempt at characterizing the process of TE insertion dynamics, detect signatures of adaptive evolution and changes in genome size at the population level.

Reviewer #1 (Public Review):

The field of genome dynamics is currently very hot and adaptive transposable elements insertions polymorphisms (TIPS) in wild populations are extensively looked for. Here Oggenfuss et al provide evidence that TE activity within a fungus species can vary drastically (1) in different regions of the world and (2) in the same region within a relatively short timeframe (25 years). The data are properly described and both the figures and text are clear. The authors provide examples of candidate TIPS that could adaptive.

Important findings:

• A repertoire of TIPS is provided for 284 genomes. A PCA analysis show that a small number of TIPS can better differentiate two samplings 25 years apart on the same area than the same number of SNPs.
• Increase in TE content is associated with genome size, between areas and within a single area 25 years apart.
• Interesting candidates for adaptative TIPS are provided and discussed.

Limitations:

• The TIPS (or a subset of them) are not validated using another technique.
• The relative expression of the adaptive TIPS is not investigated in this manuscript.
• For genomicists not familiar with fungal genomes the distinction between core chromosomes and accessories chromosomes might be difficult to appreciate.

Reviewer #3 (Public Review):

In "Assembly of higher-order SMN oligomers is essential for animal viability, requiring a motif exposed in TG zipper dimers," Gupta et al. present an impressive amount of data regarding the solution behavior of constructs of the protein SMN1 (or just SMN) from Homo sapiens, Drosophila melanogaster, and Schizosaccharomyces pombe. Defects in the Hs protein are known to cause the neuromuscular disease "Spinal Muscular Atrophy" (SMA). They also present experiments in genetically modified organisms (fission yeast and fruit flies) to test their hypotheses. Bioinformatics are used to generate and refine hypotheses. The potential power of these complementary methods is substantial, if employed well.

The main finding of these researchers is that the oligomerization potential of SMN and its disease-causing variants (usually in complex with the protein Gemin 2 or "G2") mostly correlates with phenotype severity. In humans, this is correlated with the Type of SMA (I/0 for severe disease, ranging to IV for a milder form), and in fruit flies and yeast, it is correlated with viability and, in some cases, animal behavior. The results are extended through the creation of a model that purports to show how higher-order SMN oligomers can form.

Strengths:

The experiments appear to have been carried out competently. There is a virtual mountain of data presented in this paper, and, for the most part, they are summarized in a digestible fashion. The effort to correlate the biophysical solution data with observable phenotypes in human patients or genetically modified organisms is laudable, and it is done in a thoughtful fashion. The authors' structural intuition and savvy enables the generation of testable models that are explored in the paper. A plausible model for higher-order oligomers is presented.

Weaknesses:

The most serious weakness of the paper is that the data cannot support the conclusion stated in the title, i.e. that multimerization of SMN is necessary for organismic viability. Instead, the data support an already-stated, decades-old conclusion (see their reference 21): that multimerization correlates with disease (viability). Even if the reader takes into account the new information about a multimerization interface that is separate from the dimerization one, the advance seems incremental.

The large amount of data leads to numerous difficulties for the reader in the text:

1) Complex biophysical measurements, due to space, are usually summarized by one or two words in tabular format.

2) When these measurements are shown, there is no visual context for the reader to assess the pre-digested conclusions that are included in the figures. For example, all SEC-MALS data show a conclusion ("Tetramer-Octamer"), but there is no visual cue for the reader to know what the theoretical masses for these species are (so that the reader may draw an independent conclusion).

In some cases, the conclusions reached in the paper are not clearly supported by the data or are self-contradictory. An example is the discussion of the residue H273 (human numbering). In Fig. 4B, the mutation H273R is said to have a wild-type "Oligomer Status". But in Fig. 5B, it is "Dimer-Tetramer+". The text says that H273R is "only partially impaired" in forming oligomers; the authors apparently mean the data presented in Fig. 5B but refer to the contradictory result in Fig. 4B. Another example centers on the discussion of the putative "dominant-negative" effect of some human missense mutations. But they do not point to any human data that support this contention (SMA-associated missense mutations are usually discovered in mixed heterozygotes have a deletion in the other copy of the Smn gene), but they cite data that suggest a more nuanced position regarding negative dominance would be appropriate.

Finally, the paper suffers throughout from a lack of precision of language that undercuts its conclusions at numerous points. They continually rely on qualitative statements rather than hard, statistically rigorous facts, e.g. "more intimate," "a bit of a sequence outlier," "very modest."

Reviewer #2 (Public Review):

The current study nicely demonstrates that high-order assembly of SMN protein oligomerization is necessary for animal survival and is dependent on a motif exposed to YG zipper dimers. Mutations in the human SMN1 gene have been shown to cause a neurodegenerative disease named Spinal Muscular Atrophy (SMA). About 50% of the SMA-causing mutations are located in the YG zipper domain. The authors used multi-disciplinary approaches such as biophysical, bioinformatic, computational and genetic approaches to demonstrate that a set of YG box amino acids in SMN protein are not involved in dimerization process and formation high-order oligomers is dependent on these residues. Importantly, mutating key residues within this new structural domain impairs SMN dimerization and causes motor dysfunction as well as viability defects in Drosophila. Overall, this is a well-written paper that offers new insights into the structural and functional aspects of SMN protein. The authors should consider addressing the following issues:

1) The authors should discuss the impact of the YG zipper domain mutations on snRNP biogenesis. SMN protein is a master regulator of snRNP biogenesis. It is a little surprising that the authors did not mention snRNP biogenesis in the whole manuscript.

2) The authors should provide evidence that their transgenic lines express the desired transgene. A WB or qPCR would be great (even as supplementary data).

3) Page 12: The authors stated "Both missense mutations display early onset SMA-like phenotypes". Was it age-dependent phenotype? Did adult animals show a more severe motor dysfunction?

4) There are few statements that the authors should consider making clear. Here is an example "Presumably, the structural changes associated with Cys and Val substitutions do interfere with some aspect of SMN biology, leading to the intermediate and severe SMA phenotypes observed". What do you mean by some aspects? Oligomerization, stability or anything else?

5) There are few typos throughout the manuscript that the authors should correct (western should be written as Western).

Reviewer #1 (Public Review):

Gupta et al. provide a very detailed and in depth analysis of the dimerization / oligomerization behavior of the protein Survival Motor Neuron (SMN). The protein is able to use a modified glycine zipper motif to form tightly packed dimers and additional hydrophobic amino acids for higher oligomeric states. Mutations in SMN cause Spinal Muscular Atrophy and the authors show that mutations leading to this disease affect the oligomerization state of the protein. Overall, this is a very detailed study using several biophysical techniques and extensive mutagenesis. The data are of high importance for researchers working in the field of SMN proteins.

A mechanistic link of how these differences in oligomeric states changes the cellular behavior leading to Spinal Muscular Atrophy is unfortunately missing. The authors stress several times that SMN is part of membraneless organelles. Multivalent interactions are characteristic of such organelles, although they are typically based on "fuzzy" interactions involving low complexity regions (and not all dimerization / oligomerization events can be classified as liquid-liquid phase separation). This limits the impact of this detailed analysis.

While this very detailed analysis is an excellent source for researchers working in this field the interest beyond SMN proteins will be limited. The paper could also be written in a less dense manner, which would make its reading easier. The main weakness is a missing mechanistic model that can explain how differences in the oligomerization behavior relates to the function of the protein and causes Spinal Muscular Atrophy. The impact of oligomerization on the formation of membraneless organelles would be important.

Reviewer #3 (Public Review):

This study investigates the temporal orientation abilities of cerebellar degeneration and control subjects during an orientation discrimination task of visual stimuli with showed a contrast near threshold. Participants were queried to express their discrimination decision with a response only after a random delay following target offset, which decreases the motor preparation component of the task in the interval-based condition. CD subjects showed similar visual discrimination performance to controls when cued by a rhythmic set of stimuli but showed no benefit when the target interval was presented aperiodically. The authors interpret these findings as evidence supporting the notion that the cerebellum plays a role in interval based attentional orienting to proactively modulate perception. This is an elegantly simple experiment providing a novel observation in the field.

Reviewer #2 (Public Review):

The article by Breska and Ivry provides a nice, timely, and relevant continuation of their previous recent work on the role of the cerebellum in interval-based (but not rhythm-based) anticipation in time. While in their related prior work (in particular their recent articles in PNAS and Science Advances) the authors used simple reaction time tasks that made it difficult to attribute the observed effects to visual vs. motor anticipatory mechanisms, in the current work they used a perceptual discrimination task with a delayed response to focus on potential contributions of the cerebellum to temporal anticipation specifically for perceptual sensitivity (where the role of the cerebellum is less obvious, given it has traditionally been implicated more in motor control than in perception). They do so by comparing individuals with cerebellar degeneration to controls, and finding a selective impairment of the individuals with cerebellar degeneration to use interval-based temporal predictions to facilitate visual discrimination, while rhythm-based performance benefits are spared (providing a neat comparison and control).

I have no major comments to detail. The short report is well written, complements related work by the authors nicely, and makes an important and novel contribution to the literature on temporal anticipation (while also having relevant implications more generally for views on the role of the cerebellum in cognition).

Reviewer #1 (Public Review):

Breska and Ivry tested the role of the cerebellum in temporal expectation, specifically in how temporal expectation affects perception. The question is interesting, as the neural mechanisms mediating the substantial effects of temporal expectation on perception are not well understood. The authors found that in a perceptual discrimination task, individuals with cerebellar degeneration (CD) showed reduced effects of temporal expectation on discriminability with interval timing cues, but intact effects with rhythmic cues. This shows that the role of the cerebellum in temporal expectation (which had been previously demonstrated by the authors) is not merely one of motor preparation. Rather, the cerebellum appears to play a causal role in bringing about the perceptual consequences of temporal expectation for predictable intervals. It also reveals differences between interval timing and rhythmic manipulations in terms of the mechanisms by which they affect perception.

This is a straightforward study with a clean experimental approach and clear presentation of the data. However, I felt the manuscript would benefit from a more thorough analysis of the dataset, especially given the rarity of individuals with CD.

Reviewer #1 (Public Review):

The manuscript aims to identify origins of stochasticity ('noise') in mammalian gene expression focused on the case when a single transcription factor controls the expression of a target gene. It also aims to devise strategies to control mean and variance of gene expression independently.

The experimental approach uses a light-induced transcriptional activator in two stimulation modes, namely amplitude modulation (AM: time-constant light input) and pulse width modulation (PWM: periodic light inputs in the form of a pulse train). Perturbation experiments target histone-modifying enzymes to influence epigenetic states, with corresponding measurements of single-cell epigenetic states and mRNA dynamics to dissect mechanisms of noise control. Beyond this synthetic setting, the study is complemented by endogenous gene expression noise in human and mouse cells under the same perturbations.

Major strengths of the study are:

• The experimental demonstration that, and under which conditions PWM can reduce gene expression noise in mammalian cells; the corresponding data sets could be very valuable for further quantitative analysis.
• Providing strong evidence via perturbation studies that the extent of gene expression noise is linked to chromatin-modifying activities, specifically opposing HDAC4/5 histone deacetylase activities and CBP/p300 histone acetyltransferase activities.
• Proposing a positive-feedback model established by these two opposing activities that is consistent with the reported data from perturbation experiments and on chromatin accessibility / modification states.
• Providing evidence that also in the natural (human and mouse cell) setting, the regulators HDAC4/5 and CBP/p300 contribute to the control of gene expression noise.

Major weaknesses are:

• Limited conceptual novelty because noise-reducing effects of PWM have been demonstrated and analyzed previously in synthetic systems in bacteria (with an engineered positive feedback loop; https://www.nature.com/articles/s41467-017-01498-0) and in yeast (with an engineered single transcription factor as in the present study: https://www.nature.com/articles/s41467-018-05882-2#Sec25).
• Insufficient evidence for the postulated bistability caused by positive feedback on chromatin states in the mammalian system analyzed, which has implications for the mechanistic explanations provided (e.g., if PWM allows rapid cell switching between 'high' and 'low' states as postulated).
• Limited theoretical support for the proposed (not directly observable) mechanisms that uses a mathematical model illustrating the potential consistency, but the model is not directly linked to the experimental data and hence of limited use for their interpretation.

Overall, the authors achieved their aim of elucidating mechanisms for noise control in mammalian gene expression by identifying specific, opposing regulators of chromatin states, with clear support in the synthetic setting, and evidence in endogenous expression control. Conceptual advances regarding strategies for the external control of gene expression noise appear limited because of prior work, which includes more in-depth theoretical analysis in simpler (bacterial, yeast) systems.

Hence, the likely impact of the work will be primarily on the more detailed (in terms of histone regulators, etc.) study of noise control in mammalian cells, while the data sets presented in the study could prove valuable for follow-up quantitative (model-based) analyses because they are unique in combining different readouts such as single-cell protein and mRNA abundances as well as histone and chromatin states.

Reviewer #3 (Public Review):

The authors use a synthetic light-controlled transcription factor (GAVPO) to test a model of bistable gene expression that is hypothesized to originate from positive feedback via local histone modifications by trans-activator recruitment of CBP/p300 to facilitate open chromatin, which facilitates GAVPO binding, etc... Their proposed model for the origin of bistability is important because it should apply to any trans-activator that recruits CBP/p300 to modify chromatin and active gene expression. The authors show that periodic modulation of light reduces the bimodal distribution at intermediate light-intensity levels to a unimodal distribution. This is an elegant demonstration of how GAVPO and different temporal patterns of light can reduce cell-to-cell variability in gene expression, if needed.

Strengths:

The authors generate an impressive amount of single-cell data of gene expression and chromatin state (flow cytometry, single-cell sequencing, live-cell MS2-tagging) at different intensity levels. The periodic modulation of GAVPO activity by light is a practical demonstration of how to sculpt the gene expression output in useful ways. This may be a very useful tool for future biologists.

Weakness:

The proposed model for bistability is not convincingly tested or supported by the existing data. Each reporter should exhibit a bistable response because the positive feedback is localized to the promoter via cis-effects on gene expression by local chromatin state/GAVPO binding. The authors show a bimodal distribution of gene expression in a population of cells, which is consistent with a bistable response in a single reporter gene. However, their strain has 9 independent reporters integrated into the genome. Thus, I would expect to see up to 10 peaks, not 2 peaks. Moreover, the mathematical model used to validate their observations does not model the total expression from 9 independent promoters, which is a critical omission given the cis-nature of the positive feedback loop. The fact that these 9 promoters generate 2 peaks at intermediate light intensity suggests that the GAVPO bistability likely originates from a trans-effect, i.e., either all 9 promoters are OFF or all 9 promoters are ON, not a cis-effect.

Reviewer #2 (Public Review):

The manuscript describes a tool to independently tune mean protein expression levels and noise. Light induces dimerization and subsequent activation of transcriptional activator GAVPO. By introducing 5xUAS (a target sequence for dimerized GAVPO) upstream a mRuby reporter gene, the effect of light can be measured on mRuby mean and noise.

By pulsing light at different periods (from 100-400 minutes), the authors reduce the mRuby noise for intermediate average light intensities. Notably, the pulses are all applied at an absolute light intensity of 100 uW/cm2, with the average light intensity being modulated through the light-off time-periods. Therefore, as all periods tend towards 100 uW/cm2 average light intensity, the PWM duty cycles becomes more similar to the 100 uW/cm2 AM case.

Strengths:

The proposed method is an elegant way to independently tune protein mean and noise. This would have a broad application in the field and is much needed to be able to study the consequence of protein expression noise, independently of mean. In addition, the authors use multiple powerful single-cell techniques to try and determine the mechanism underpinning the light-induced noise modulation.

During constant exposure to light, increased light intensity increases the mean expression of mRuby, while decreasing the noise. This high noise is mostly due to observed bimodality in mRuby expression. Through ODEs and by using small molecule inhibitors, the authors show that this bimodality is caused by some cells being stably off, while other cells enter an on state. In this on state a positive feedback can occur where initial binding of dimerized GAVPO induces histone acetylation and chromatin accessibility, and thus stimulates further GAVPO binding. Bistability induced by constant light exposure is disrupted using small molecule inhibitors of CBP/p300 HAT activity, indicating that histone regulation is a cause for this observed bistability. The stable on state is demonstrated to be more active and accessible through ChIP-seq and ATAC-seq respectively.

Weakness:

The single-cell ATAC-seq data indicate that pulsing light induces switching from an accessible (light on) to inaccessible (light off) chromatin state. The authors argue that the switching back into a chromatin inaccessible state prevents the positive feedback to occur and thus reduces noise. However, there are weaknesses in the description of the mechanism by which the pulses modulate (i.e., reduce) noise. Overall, since these sections in the manuscript are not easy to understand, it is difficult to parse what mechanism the authors attributed to the observed noise reduction and to assess if the data supports the conclusions.

The data from the single-mRNA live-cell imaging experiments are somewhat ambiguous and do not necessarily support some of the arguments. The conclusion that transcription, nuclear export, and mRNA degradation flatten the pulsatile chromatin caused by the PWM is not clear from the data. Especially, since most cells do not show any pulsatile behavior both in the single-cell ATAC-seq and the live-cell imaging data.

Reviewer #3 (Public Review):

In this report the authors characterize a mechanism that plays a role in inducing the rhythmic depolarizations that are observed in identified neurons that are part of the feeding CPG in Aplysia. The neurons studied (B63 neurons) are of interest because previous work has established that they play an important role in triggering cycles of motor activity. Further, previous work from this group has demonstrated that activity in the B63 neurons can be modified by operant conditioning.

The authors present this study as though previous work had established that plateau potentials generated in the B63 neurons play an important role in driving network activity. For example, in line 102 they state "This essential role played by B63 is partly mediated by a bistable membrane property, which allows the sudden switching of the neuron's resting membrane potential to a depolarized plateau..." To support this statement, they reference Susswein et al. 2002, which does not support this statement. In the Susswein et al. study it is the B31/32 neurons that are modeled as having plateau properties.

If previous work has not established the role of the B63 plateau potentials, the only data that speak to this issue are presumably in the current report. In this study the authors do provide data that indicate that the B63 neurons generate low amplitude oscillations that are not likely to depend on input from the electrically coupled neurons studied (notably B31). The authors also show that in some instances, these depolarizations do trigger plateau potentials in B63. It is, however, not clear that the B63 generated plateau potentials are then responsible for triggering network activity (e.g., as opposed to a situation where depolarizing input from B63 triggers plateau potentials in B31/32 and the depolarization in B31/32 drives the rest of the feeding circuit). For example, in Figs. 6A and Supplemental Fig. 4A it does not appear that the plateau depolarization in B63 is being transmitted to other electrically coupled neurons to any large extent.

A clarification of this issue is important because it potentially impacts thinking concerning how 'decision making' is occurring. If decision making means induction of a motor program and this does not occur unless the depolarization in B63 is transmitted to B31/32, the process is more complicated than what the manuscript currently suggests.

The title is misleading since there are no studies of behavior in this report.

In part, interest in the mechanisms that drive spontaneous oscillatory activity in the B63 neurons stems from the overall context of this work. Namely the authors have previously established that oscillatory activity can be modified through associative learning. In the Sieling et al. 2014 study they demonstrate that two aspects of plasticity are accounted for by changes in synaptic properties and an effect on a leak current. For readers trying to understand this body of work as a whole, the Discussion should more clearly indicated how the results of the present study integrate with these previous findings.

Reviewer #2 (Public Review):

Motivated behaviors, such as food seeking when hungry, can also occur spontaneously at irregular intervals. Understanding how this irregular expression arises is important for understanding behavior and is relatively little investigated. The present work thus addresses an important and under-investigated area in neurobiology. Its demonstration of a potential cellular mechanism for irregular behavioral production has wide relevance, ranging from how cells make "decisions" to how whole organisms do so.

Intact Aplysia occasionally produce bites even in the absence of food, and isolated buccal ganglia (which contain the biting central generator circuit) will occasionally spontaneously produce fictive bite motor patterns. The activity of central pattern generator networks has almost exclusively been ascribed to the actions of the voltage-gated channels in the network neuron cell membranes and the synaptic connectivity among the network's neurons. Bédécarrats et al. show that a small, highly regular cell membrane voltage oscillation occurs in a neuron (B63) in the biting neural network, and that occasionally this oscillation becomes large enough to trigger a plateau potential in B63 and a single fictive bite from the entire circuit. They show that this oscillation is not due to cell membrane voltage dependent conductances, but instead from process involving the endoplasmic reticulum, mitochondria, or both. Although organelle-driven changes in cellular or tissue activity have been observed in other cell types, this is, to my knowledge, its first observation in a neural network. These data thus are potentially of great importance in understanding how neural networks function, most of which do not show the great regularity of central pattern generated behaviors.

The presented data seem, to me, strong with respect to the small potential oscillations not being generated by voltage-dependent cell membrane conductances, and somehow involving the intracellular organelles. What is less clear to me is how local release of Ca from endoplasmic reticulum or mitochondria would result in changes in ion composition under the cell membrane, which is what gives rise to the cell membrane potential. Ca is highly buffered in the cytoplasm. It is thus unclear to me that free Ca would remain so for any length of time after release. It does, of course, in muscles, but these are evolved for this to occur. The authors themselves raise a variant of these concerns in the Discussion when considering how the B63 cell membrane voltage oscillations are transmitted to neurons electrically coupled to B63, invoking as a possibility Ca activation of second messengers, which would then themselves be responsible for the cell to cell communication. It seems to me that the same concerns arise with respect to how Ca release at sites distant from the cell membrane could charge the membrane's capacitance.

A second remarkable observation is that B63 depolarization and firing does not reset the organelle-derived slow oscillation. B63 firing should result in substantial Ca concentration changes, at least in a shell under the cell membrane, so a possible feedback mechanism can be imagined. Most biological processes contain multiple feedback process that link cause and effect (e.g., the sequential current activations that return a cell to rest after an action potential, the interactions between sympathetic and parasympathetic system activity that maintain functionally proper body activation, the interactions that regulate hormonal levels). One possibility the authors mention is that the organelle-derived oscillation is used only for intermittent bite activities, and in feeding bites are instead generated solely by standard cell-membrane voltage-dependent processes. Regardless, it is a striking observation that merits additional investigation.

These issues, however, do not change the data, which show a clear association of disruption of endoplasmic reticulum and mitochondrial function and cessation of the cell membrane voltage oscillation. Nor is it reasonable to expect an article like this, showing an organelle-driven cell membrane potential oscillation for the first time in a neuron, to describe every aspect of the mechanism by which it occurs. Indeed, it is a measure of the article's interest that it prompts such thinking. It will be very interesting to see the effects of similar organelle-disrupting treatment on the activity of other well-defined neural networks.

Reviewer #1 (Public Review):

This paper presents evidence that membrane potential excursions called plateau potentials are driven by subthreshold oscillation generated by calcium fluxes in mitochondria. Pharmacological and electrophysiological methods were used to deduce that calcium waves were generated in a bilateral pair of electrically-coupled neurons and spread to additional neurons that were coupled to that pair. The identified neurons in Aplysia allow for the detailed measurements needed to determine this mechanism.

Reviewer #3 (Public Review):

Some Gram-negative bacteria synthesize acyl-homoserine lactone molecules, which are secreted into the environment and then transported into nearby bacteria, where they are detected by receptors. Different species make acyl-homoserine lactones that differ in chain length and oxidation state at the C-3 position. The manuscript by Wellington et al. reports an elegant and compelling investigation of the specificity determinants involved in quorum sensing, using a combination of bioinformatics and experimental approaches.

Over the course of evolution, if an amino acid change occurs in one protein, then a compensating change can occur in a partner protein to restore/retain a functional interaction between the two. Analyses of evolutionarily covarying positions between two interacting proteins, or within a single protein, have long been used to identify positions that directly interact. Wellington et al. applied the same approach to two protein families (the synthases and receptors for acyl-homoserine lactones) to identify positions that are connected not by direct physical interaction between the two proteins but rather by interaction with the same acyl-homoserine lactone. The covariation analysis was made possible by the fortuitous case (and reasonable assumption) that genes encoding partner synthases and receptors are located close to one another within bacterial genomes.

The covarying residues turn out to be in the active site of the synthase and the binding site of the receptor, in positions that directly interact with the acyl-homoserine lactone. The authors made a variety of single amino acid substitutions at positions with high covariation scores in the Pseudomonas aeruginosa LasI synthase and LasR receptor proteins. The mutant proteins exhibited altered synthetic and detection specificities for acyl-homoserine lactones. Altering three residues simultaneously resulted in substantial changes in specificity.

This paper constitutes a proof of principle for an approach that could be used to investigate other families of proteins connected by interactions with small molecules (e.g. metabolic pathways). Furthermore, it suggests a path toward rational engineering of quorum sensing systems for synthetic biology, as well as specificity prediction for uncharacterized quorum sensing pathways based simply on the primary amino acid sequences of the synthase and receptor proteins.

Reviewer #2 (Public Review):

Wellington Miranda et al. investigated how acyl-homoserine lactone autoinducer mediated quorum sensing systems evolve. The authors used the statistical covariation method GREMLIN to identify key amino acids that have coevolved in the well-studied LasI/LasR quorum sensing AHL synthase/receptor pair from Pseudomonas aeruginosa PAO1. LasI produces and LasR detects 3OC12-HSL. The authors identify some new and some previously reported residues using the GREMLIN tool; they focus on L157 in LasI and G38, R61, A127, S129, and L130 in LasR as residues that determine selectivity of the acyl-homoserine lactone that is produced and detected, respectively. Quite expectedly, these residues are in or near the ligand-binding pocket of LasI and LasR. The authors further engineer the LasI/R system to produce and detect the non-native 3OC10-HSL autoinducer in addition to 3OC12-HSL, thereby broadening the specificity of LasI/R.

P. aeruginosa is an important pathogen and a powerful system for the study of quorum sensing. The use of GREMLIN to study how autoinducer synthase and receptor pairs coevolve in terms of sensitivity and specificity for a particular autoinducer is impressive. This paper adds an exciting approach to the growing literature on the evolution of sensitivity and promiscuity in quorum-sensing systems. Further, the authors have developed a thin layer chromatography based approach to separate and detect AHLs from nine samples simultaneously. This methodology should be widely useful to researchers.

The current manuscript does not provide any data about the solubitlity and/or stability of the LasI and LasR mutant proteins being studied. For instance, biochemical analyses would be needed to evaluate if the increased sensitivity of LasRA127L compared to wildtype is due to higher affinity for the autoinducer or because the variant is more stable. Further, this work relies solely on reporter assays and does not address the consequences of these LasI and LasR variants to quorum-sensing dependent P. aeruginosa group behaviors such as pyocyanin production and virulence.

Reviewer #1 (Public Review):

Acyl-homoserine lactone (AHL) based quorum sensing systems are an important form of intercellular communication in bacteria. These systems, minimally comprised of a synthase and a receptor, often involve different types of AHLs. This paper uses covariation analyses to try and tease out the determinants of this AHL specificity. Using the GREMLIN pipeline, they identify a series of coevolving residues in LasR and LasI homologs. This is interesting in its own right, and a strength of the paper, as LasR and LasI don't physically interact, and instead interact and covary indirectly by virtue of sharing the same AHL. Through various reporter and biochemical assays, the authors then demonstrate that the residues identified are important for AHL recognition. In the last part of the manuscript, they attempt to use the covariation analysis to guide the 'rewiring' of LasR-LasI to behave like MupR-MupI. This is mostly successful, although LasR-LasI specificity hasn't been 'rewired' so much as simply broadened. The paper could also be improved by fleshing out the description of the results/data obtained - at times it is difficult to assess how the authors have arrived at certain rather sweeping conclusions.

Reviewer #3 (Public Review):

The manuscript titled "The Shu complex prevents mutagenesis and cytotoxicity of single-strand specific alkylation lesions" investigates the biological function of the Shu complex in S. cerevisiae. The Shu complex, containing a DNA binding module comprised of the Csm2-Psy3 heterodimer, is conserved from budding yeast to man, and contributes to the defense against DNA damage caused by DNA alkylation. DNA alkylation occurs due to spontaneous reactions with metabolites and can be greatly increased by exogenous exposure to DNA alkylating agents. Therefore, it is an important question for how the Shu complex acts to detect and direct repair of alkylation damage. It has been well established that loss of the Shu complex sensitizes cells to alkylation damage, but the mechanism by which this complex locates sites of DNA damage and directs repair is not fully understood. This paper measures the methylation-induced mutation spectrum and uses genetic interactions to argue that the Shu complex may be involved in detecting and directing error-free repair of 3-methyl cytosine. This is a plausible hypothesis based on the body of previous work, however the evidence that Csm2-Psy3 directly detects 3-methyl cytosine sites is indirect. It would be highly significant if this complex recognizes many different structures, but future structural information is needed to understand how this could be possible.

The strengths of the paper are in the use of whole genome sequencing to map mutation type and location in different genetic backgrounds and in the systematic testing for genetic interactions between csm2 and other DNA repair factors. It appears that the mutation spectra are very similar in the presence and absence of csm2, which suggests a broad role of the Shu complex in the cellular response to MMS.

The impact of the work is that it could help to explain the cellular program for protection against DNA alkylating agents in budding yeast which has been a very valuable model eukaryotic organism, and raise new questions about how DNA alkylation repair pathways might function in humans that differ from yeast in important features such as in the presence of a direct repair pathway performed by ALKBH2 and ALKBH3.

Reviewer #2 (Public Review):

The manuscript entitled "The Shu Complex Prevents Mutagenesis and Cytotoxicity of Single-Strand Specific Alkylation Lesions" by Bonilla and colleagues reports that the yeast Shu complex promotes repair of 3meC in single-stranded DNA during S phase. Specifically, the authors show that mutations and cell lethality induced by MMS in csm2∆ cells are suppressed by overexpression of the human ALKBH2. Further, the authors find that the Csm2-Psy3 module of the Shu complex has increased affinity for 3meC-containing DNA relative to unmodified DNA. The authors propose a model, where the Shu complex binds to 3meC-containing DNA to facilitate HR-dependent post-replicative gap-filling.

Reviewer #1 (Public Review):

This study shows that the Shu complex is critical for 3meC damage tolerance in yeast, supporting the existence of a new pathway for the removal of an important DNA lesion that seems essential in yeast but likely contributes in other organisms. At the same time, it contributes to clarify the distinctive role of homologous recombination in double strand break repair and post-replicative repair.

Reviewer #3 (Public Review):

The authors tackle an interesting question - whether the dentate gyrus is a locus of pathology in Scn1a+/- mice and uncover a strong phenotype - the granule cells of the dentate gyrus are over-activated and the EC to dentate pathway is prone to seizure genesis. In the discussion, they suggest that their results support the idea that the DG may be a common locus to several different types of epilepsy... an attractive hypothesis! There are several strengths of the paper. The team has done a nice job of presenting 'ground-truth' data that their measurements of dF/F across a large population of granule cells correlates with action potentials in these cells. As the authors point out, this is especially important when working in disease models in which the dF/F-action potential relationship may be altered. Throughout, the authors were also careful about considering the limitations of their various techniques and analyze the data in several ways to account for possible artifacts (e.g. ensuring that differences in activation are not arising because of slicing and consideration of kindling in later in vivo seizure threshold experiments). The experiments were well designed and appropriately interpreted.

One of most intriguing results of the work is that PV interneurons in the DG of Scn1a+/- show only very minor impairments in young adult animals (they show more spike accommodation than in control animals). Rather, it seems that the GCs receive enhanced excitation from the entorhinal cortex. They perform a set of pharmacological experiments to prove that PV interneurons (and more generally inhibition) do not account for the difference in granule cell activation - however, here it would be useful to see the data summarized more consistently. It is difficult to interpret the pharmacological results (both of which are presented as changes in dF/F0) with respect to the initial findings of the manuscript (presented as estimated activation across the entire population). A beautiful aspect of this work is that it goes from cells to circuits to intact brain (in vivo). They nicely show that the heightened excitation from the EC to the DG is sufficient to drive seizures in the Scn1a+/- mice, and finally that since PVs are intact, they can be harnessed to balance out the over activation of GC via optogenetic stimulation of PVs.

Reviewer #2 (Public Review):

Mattis et al have used a hemizygous mutant of the gene Scn1a to study changes underlying the severe epilepsy disorder Dravet syndrome. They describe a change in activation of the dentate gyrus in this mouse model, due to altered excitatory synaptic input. They show that this occurs in the age range after normalization of early inhibitory interneuron dysfunction. This provides an interesting potential mechanism by which neural circuit function is altered even after deficits in inhibition are seemingly corrected. They also report that stimulation of inputs to the dentate gyrus increase seizure susceptibility when body temperature is elevated. Overall these findings indicate a new form of circuit dysfunction that may underlie the etiology of this severe genetic epilepsy disorder.

These findings are not fully complete, and the manuscript suffers from some flaws in experimental design.

The most pressing issue is the lack of a counter-balanced design in experiments testing the ictogenicity of DG stimulation. The authors attempt to justify this stating "there is a theoretical concern that seizure threshold on Day 2 (the second consecutive day of stimulation) could be lowered by a seizure 24 hours prior (a "kindling"-like phenomenon)". In the very next sentence, they cite a study in which this phenomenon has been shown (thus the concern is not theoretical). That said, this is not a semantic argument, but a flaw in experimental design. On day 1, the authors perform experiment A. On day 2, they perform experiment A+B. In an attempt to show that performing experiment A on day 1 does not by itself lead to changes in experiment A+B, they use a separate cohort and show that experiment A does not lead to changes in a repetition of experiment A. Unfortunately, this is not an adequate control. Experiment A+B involves a different set of stimuli, to which the response could very well be altered by the day 1 experiment, but this change would not be revealed with the described experimental design. To determine whether the effect shown in experiment A+B requires a more rigorous, counter-balanced experimental design where one group undergoes experiment A followed by experiment A+B, and a second group undergoes experiment A+B followed by experiment A.

The second major issue is a lack of wild type control groups for several experiments. The experiments presented in Figures 4, 6C and F, and 7 all lack the necessary wild type control measures. Wild type controls were done for Figure 6E, but the data are not presented in the figure.

Some of the cell physiology experiments presented were not optimally designed to provide a relevant mechanistic follow-up to the major findings. For the first major finding of the paper, Figure 2 shows clear and interesting changes in DG activation in the mouse model, and Figure 5 reveals changes to synaptic excitation and inhibition in these neurons. Figure 3 and 4 present data showing changes to PV-interneuron intrinsic properties that only reveal themselves under very intense stimulation. While these findings are interesting and worthy of follow-up, the changes aren't relevant to the synaptic stimulation used in Figure 2.

Finally, Figure 2 has missing data points, seemingly due to cropping of panels. Data visualization is problematic for this vital figure. The fit lines for individual experiments overwhelm the color-filled variance of the mean. Thus, the data in this figure are very difficult to read and interpret. The figure would benefit from including all the individual data points and summary data, but removing the individual fits or putting them into a supplement.

Reviewer #1 (Public Review):

Dravet syndrome is a developmental and epileptic encephalopathy resulting from mutations in a sodium channel subunit that is widely thought to cause disease by affecting synaptic inhibition. Here the authors use a well-established mouse model to show that circuit dysfunction results from excess synaptic excitation in the dentate gyrus, potentially providing new insight into the pathological mechanisms underlying seizure activity.

Strengths of the study include the sophisticated approach of 2P Ca2+ imaging of population activity and whole-cell recording in slices that provide well-supported evidence that circuit dysfunction is independent of GABAergic inhibition. Weaknesses include some oversimplification of the results in the data interpretation such that not all the claims are fully supported and lack of in-depth analysis of the circuit dysfunction with a clear presentation of its developmental time course.

Reviewer #2 (Public Review):

In the manuscript entitled "The Crystal Structure of Bromide-bound GtACR1 Reveals a Pre-activated State in the Transmembrane Anion Tunnel", Li et al. analyzed the effect of bromide binding to GtACR1 by X-ray crystallography and electrophysiology. The authors propose that a bromide ion is bound to the intracellular pocket in the dark, inactivated state and induces a structural transition from an inactivated to a pre-activated state.

I agree that some of the amino acid residues in the current crystal structure change their conformations compared to the previous one reported in 2019 (Li et al., 2019), and it is very impressive that the authors determined the structure using state-of-the-art crystallography technique, ISIMX. However, unfortunately, most of the conclusions and claims described in the manuscript are not well supported by the authors' data.

1) The most serious problem is that the evidence of bromide binding is too weak. The authors showed the composite omit map in Supplementary Figure 1A, but they should present an anomalous difference Fourier map to validate the bromide binding. The authors also claim that they replaced the bromide ion to the water, run the PHENIX refinement, and observed a strong positive electron density at the bromide position in the Fo-Fc difference map (Supplementary Figure 1B). However, when I do the same thing using the provided coordinate and map (I really appreciate the honesty and transparency of the authors), I could not reproduce their result; a weak positive electron density is observed between the bromide position and Pro58 in chain A and there is no positive peak at the position in chain B (Fo-Fc, contoured at 3σ). I am wondering the occupancy and B-factor of the water molecule they show in Supplementary Figure 1B.

In addition to the insufficient evidence, the current models of bromide ions have significant steric clashes. The PDB validation report shows that the top 5 serious steric clashes observed in the coordinate are the contacts between the bromide ions and surrounding residues (PDB validation report, Page 10). I analyzed them and found that the distance between the bromide ion and CG and CD atoms of Pro58 in chain A are only 2.43Å and 2.36Å, respectively. The authors claim that such a close proline-halide interaction has also been observed in the structure of the chloride-pump rhodopsin CIR, but in the structure (PDB ID: 5G28), the distances between the chloride ion and CD and CG atoms of Pro45 are much larger (3.43 and 3.91Å, respectively) and there is no steric clash. Moreover, the authors claim that Pro58 changes its conformation by bromide binding, but it is very possible that the PHENIX program just displaces Pro58 to alleviate the steric clash between the proline and the bromide ion, so the authors should carefully check the possibility.

Overall, the authors should analyze the density again, provide more solid evidence for the bromide binding such as anomalous difference Fourier map, and if they could, they should correct the current significant steric clashes in their models.

2) To analyze the functional importance of putative bromide binding, the authors prepared W246E and W250E mutants and analyzed their electrophysiological properties. Because tryptophan and glutamate are so different in terms of volume and charge, they should analyze other mutants as well. The authors claim that bromide is stabilized by a hydrogen bond interaction formed by the indole NH group of W246, so they should at least test the W246F mutant.

3) The authors claim that the bromide binding in the intracellular pocket induces the conformational change of R94, but the causal relationship is doubtful. As mentioned in the manuscript, R94 forms a salt-bridge with D234 in chain A. However, the arginine has a completely different conformation and does not have any interaction with D234 in chain B. If the bromide binds both in chain A and B and induces the conformational change of R94, why only R94 in chain A interacts with D234? The authors change the pH in the crystallization condition compared to their 2019 study (Li et al., 2019), so the pH may affect the protonation state of D223 and/or other titratable residues and induces the conformational change of R94. The authors should provide more solid evidence for the causal relationship between the bromide binding and the conformational change of R94.

4) The authors assume that the conformational change of R94 creates a functional anion binding site with the Schiff base in GtACR1, but it is too speculative. If the anomalous difference Fourier map does not support the idea, they should delete it.

Reviewer #1 (Public Review):

The dark structure of GtACR1 has been almost simultaneously published at the end of 2018 and beginning of 2019 by the Deisseroth and Spudich groups, respectively. Both groups did not manage to solve a structure with an ion bound and there is very limited information on the open conformation of the channel. Both groups identified a central constriction site as being central for the gating mechanism but the Spudich group proposes two additional constrictions (C1 and C3). In this work Li et al are able to solve the structure of a GtACR1 with a bromide bound near C3, which clearly represents a significant step towards understanding the mechanism of light gated anion channels. The structure reveals that Br binds to the intracellular constriction site (C3) resulting in a small opening of C3. The data support the notion that the partial electropositivity of Pro58 together with two tryptophans play a critical role in anion interaction at C3, which was also confirmed by mutagenesis studies. In addition, there was a noteworthy conformational change in the Bromide bound protein in the extracellular constriction (C1), a 180 degree flip of Arg 94 resulting in a salt bridge to Asp 234 and a slight opening of the C1 constriction.

While the data and conclusions are sound, the lack of discussion of their data in the context of the work of others is a bit surprising.

Reviewer #1 (Public Review):

This paper investigates the structural conformation of BtuB, a membrane protein involved in the intracellular transport of vitamin B12 in bacteria, using DEER techniques through the labelling of targeted pairs of amino acids. Using these techniques on whole cells, they detect structural conformations of the transporter upon binding of its substrate vitB12 that were not detected when BtuB is not in a natural environment.

BtuB belongs to a well-known family of transporters found in the outer membrane of Gram-negative bacteria, called TBDTs (TonB Dependent Transporters). The structure of BtuB has been determined by X-ray crystallography in its apo and vitB12 bound states, as well as interacting with a C-terminal domain of the TonB protein, a periplasmic protein linked to the inner membrane that conveys the energy needed for the transport of vitB12 through BtuB. The TBDTs share a conserved architecture comprising a C-terminal 22 ß-barrel ring occluded by a globular N-terminal domain.

Using EPR techniques, the Cafiso's lab has shown previously that the BtuB structural states are somewhat different or more dynamic than the X-ray structures would suggest. In the present paper they show that the Substrate Binding SB3 loop of BtuB, upon vitB12 binding, adopts some structural conformations in whole cells that are different than observed with BtuB reconstituted in liposomes, showing that a non-natural environment might alter its functionality. By using a set of mutants that supposedly mimics the BtuB-TonB state, they find dramatic conformational changes of the SB3 loop, which might represent an intermediate conformation of the open state of BtuB, in which vitB12 is allowed to move toward the periplasm, bypassing the need of an energized Ton complex. The data presented are convincing and show that the natural environment of BtuB, i.e., an intact outer membrane, but also probably some periplasmic components such as peptidoglycan and the Ton complex, influence the structural state of this protein. Most importantly these states are not detected when BtuB is reconstituted into liposomes, stressing the importance of studying these transporters in whole cells. However, some of the conclusions are premature, especially concerning the mechanism of action of the Ton complex in the catalyzed transport of vitB12. While the data show clear differences between the apo and vitB12 bound states of BtuB, the conclusions on the actual transport mechanism of vitB12 into the periplasm are more speculative.

The main concern of this reviewer is the conclusions reached from the data obtained with the R14A and/or D316A mutants. There is a clear dramatic change of conformation for the SB3 loop for these mutants upon substrate binding, and as shown the natural environment of BtuB is important to detect these changes. However, the authors state that "breaking the ionic lock and eliminate the electrostatic interactions of R14, as we have done here, should mimic the TonB bound state" (lines 487-488). The data presented in the manuscript do not support this statement as they do not allow to monitor the structural state of the N-terminal TonB box.

Later it is proposed that "the movement of SB3 may also drive the movement of substrate" (lines 466-467) and that "this structural change may be sufficient to move the substrate into the periplasm" (lines 501-502). This is highly speculative, as in the structural states observed with the broken ionic lock, it cannot be determined if vitB12 is still bound to BtuB, or released in the periplasm. As noted, the "conversion of the transporter to the apo state does not occur under the conditions of the experiment" (lines 398-399). It is possible that this structural state is locked with a vitB12 bound and unable to complete the transport cycle.

Nevertheless, these mutants affecting the ionic lock seem to represent valuable tools to investigate the structural intermediates during transport. It remains to be seen if these mutants still promote transport in vivo.

Reviewer #3 (Public Review):

The manuscript endeavors to explain the mechanism of action of a Gram-negative bacterial outer membrane (OM) TonB-dependent transporter (TBDT), that acquires metabolites (in this case vitamin B12) from the external environment. The authors use electron paramagnetic resonance spectroscopy to monitor the proximity of different parts of OM protein to one another during the binding of B12. Their data show that different conformations of the target protein occur during the binding of B12.

Reviewer #2 (Public Review):

This study aims at elucidating the substrate-dependent conformational dynamics of TonB-dependent transporter BtuB, which is responsible for vitamin B12 transport in the outer membrane of E. coli. Following the pioneering studies from the same lab, the study employs an innovative approach of in-situ site-directed spin labeling for CW EPR spectroscopy and double electron-electron resonance (DEER) distance measurements in intact E. coli. Despite the intricacy of spin labeling and performing DEER measurements in intact cells, the majority of the obtained DEER spectra are of high quality with impressively long dipolar evolution times and signal-to-noise ratio, enhancing the reliability and accuracy of the data. Despite the limited number of distance constraints on one side of the core domain, experiments are well designed to address the relevant questions and the conclusions are justified by the data. The results fully support the main conclusion that the large substrate-induced structural change on the C-terminal side of the core in the presence of the mutations that mimic the breakage of the R14-D316 ionic lock (i.e., R14A, D316A, D316A/R14A), indicates the shift of the substrate-binding loop 3 towards the periplasm and reproduces the state when the transporter is bound to both substrate and TonB. The authors have utilized the deduced information to assess the currently proposed transport mechanisms. This study provides evidence for a transport mechanism that does not require a mechanical pulling or rotation by TonB as previously proposed. This model is also compatible with the structure of BtuB in complex with TonB that indicates the TonB-dependent release of the ionic lock. It is notable that these results are not seen in reconstituted membranes further highlighting the significance of in-situ structural dynamics studies in general and specifically for the field of EPR spectroscopy. I see substantial advance with respect to, both the mechanism of membrane transport by the TonB-dependent transporters and the application of this innovative approach.

Reviewer #2 (Public Review):

This short report by Yeh et al. reveals the presence of sfRNA in mosquito saliva and that it might enhance DENV infection in human Huh7 cells. By referring to literature, the authors propose that salivary sfRNA is secreted by EVs, and is immunosuppressive. The salivary sfRNA might facilitate DENV transmission and disease prevalence in nature.

Strength: The methods are rigorous, results are clearly presented and the manuscript is well written.

Weakness: sfRNA has long been recognized to interfere with the immune system in the flavivirus field. This study represents a modest advance. Additionally, even as a short report, the study fails to provide sufficient self-standing evidence to support its key claims. The study depends heavily on published literature to support its key conclusions.

Reviewer #1 (Public Review):

The study is focused on the role of noncoding RNA (sfRNA) of DENV in mosquito transmission of the virus. The requirement of sfRNA for efficient transmission of flaviviruses by mosquitoes is well-documented, however the exact mechanisms of this effect are not clearly established. In this manuscript, authors demonstrated that DENV sfRNA is secreted into mosquito saliva within the extracellular vesicles (EV) and can facilitate infection of the acceptor human cells when delivered together with infectious virus in mosquito saliva. This is a novel and intriguing finding that has a potential to expand our understanding of flavivirus transmission and functions of sfRNA.

The data provided are mostly compelling and provide answers to posed questions. However, additional evidence for EV-mediated delivery of sfRNA into acceptor human cells and the effect of this sfRNA on viral replication in acceptor cells are required to further our understanding of mechanistic aspects of how sfRNA is delivered by salivary extracellular vesicles and how it facilitates virus replication in acceptor cells. A number of additional experiments and clarifications has been requested to clarify this.

Reviewer #3 (Public Review):

Inamdar et al. used biochemical and microscopy assays to investigate the role of I-BAR domain host proteins on HIV-1 assembly and release from HEK 293T and Jurkat cells. They show that siRNA knockdown of IRSp53, but not a similar I-BAR domain protein IRTKS, inhibits HIV-1 particle release from 293T cells after transfection of the HIV-1 provirus or HIV-1 Gag in cells. The authors then show that HIV-1 Gag associates with IRSp53 in the host cell membrane and cytoplasm, using biochemical assays and super resolution microscopy. In addition, IRSp53 is incorporated into HIV-1 particles along with other previously identified host proteins. Then using in vitro-derived membrane vesicles ("giant unilamellar vesicles" or GUVs), the authors indicate that HIV-1 Gag can associate with IRSp53, particularly on highly curved structures.

The conclusions are largely supported data, with the virology and biochemical results being particularly strong, but the mechanistic studies in GUVs appear somewhat preliminary and are not entirely clear. The GUV experiments would benefit from better quantification of measurements and manipulation to simulate actual cellular scenarios. In addition, while it is appreciated that the HEK 293T cell line is convenient for biochemical and imaging studies, they are not biologically relevant HIV-1 target cells. While the authors present examples of reproducibility of their results in a CD4+ T cell line, these data are buried in the supplemental figures, whilst it would have been better to highlight them and perhaps include primary CD4+ T cells.

1) Immortalized cell lines do not always recapitulate primary cells. It is unclear what the role of IRSp53 is in the membrane curvature of CD4+ T cells and whether expression levels and localization are consistent with Jurkat T cells.

2) Description of some of the microscopy measurements could be improved. In lines 204-206 of the text and Figure S5, it is unclear how the localization of precision was determined to be approximately 16 nm for PALM-STORM. In Figure 4b, it is understood from the text (lines 252-256) that the red bars denote the Mander's coefficient for colocalization of the GFP-tagged proteins with Gag-mCherry (presumably the average of multiple experiments with standard deviations or errors of the mean, although this is not stated in the figure legend), it is unclear what the green bars are showing. Also, the histograms for IRSp53 and IRTKS colocalized with Gag look similar in Figure S10, suggesting that they are not different in Jurkat cells, but this is not addressed.

3) GUVs are first referenced on page 7 after description of Figure 2, the significance of which is confusing to the reader. However, the actual experimental data are described on pages 12-13 and Figures 5 and S11. A better description of these structures would be warranted for an audience that is unfamiliar with them. In addition, the biologic concentrations of I-BAR proteins at cell membranes are not provided and it is unclear what conditions used in Figures 5 and S11 represent a "normal CD4+ T cell" situation. It appears that the advantage of this in vitro system is that different factors can be provided or removed to simulate different cellular scenarios. For example, relatively low IRSp53 concentrations may simulate siRNA knockdown experiments in Figure 1, which could recapitulate those results that less viral particles are released from the membrane. In addition, the authors state that HIV-1 Gag preferentially colocalizes with IRSp53 as the tips of the GUV tubular structures (Figure 5b,c), but this is not actually shown or quantified. Similar quantification as shown in Figure 1e could be performed to strengthen this argument.

Reviewer #2 (Public Review):

Inamdar and colleagues present a convincing manuscript identifying the unique role of IRSp53 in the successful assembly of HIV-1 particles. The study provides insight into the molecular mechanisms underlying the membrane curvature generation associated with virion budding from infected cells. Notably, the authors postulate a model in which the HIV-1 machinery "hijacks" host functions to generate fully-assembled viral particles by recruiting a central virion-assembly factor, HIV-1 Gag, to the luminal extremities of nascent extracellular vesicles generated by endogenous IRSp53. This is achieved through interactions between the two proteins, resulting in supramolecular complexes containing host and pathogen factors.

A significant positive aspect of this study is the implementation of an experimental approach that encompasses complementary techniques, namely biochemistry, super-resolution microscopy, and advanced computational analysis and modelling. This is particularly relevant because several critical studies in the field of HIV-1 often rely on either one or the other set of methods and consequently lack the depth and cross-validation power achieved here. Also, the authors take advantage of experimental models that fall into opposite sides of the natural-artificial spectrum and use them adequately to test hypotheses and make conclusions.

Reviewer #1 (Public Review):

After infection, new HIV-particles assemble at the host cell plasma membrane in a process that requires the viral protein Gag. Here, Inamdar et al. showed that a component of the host cell, the membrane curvature-inducing protein IRSp53, contributes to efficiently promote the formation of viral particles in synergy with the viral Gag protein.

In cells depleted of IRSp53, the formation of HIV-1 Gag viral-like particles (VLPs) was compromised. The authors showed in compelling electron micrographs that the formation of VLPs was arrested at about half stage of particle budding. Biochemical data (co-IPs and analysis of VLPs and HIV particle content), super-resolution nanoscopy (single molecule localization microscopy) data, and in vitro biophysics measurements (in GUVs), all seem to indicate a functional connection between Gag and the iBAR-domain containing protein IRSp53. The combination of the different techniques and approaches is a clear strength of this manuscript. However, to my opinion, the interpretation of some of the experimental data is somehow limited by the lack of some appropriate controls (that are lacking for different reasons, as the authors state in some parts of the text). These are:

1) Specificity of the IRSp53 siRNA. Although the authors showed that the siRNA used can deplete the expression of the protein (both endogenous and ectopic), they did not presented any rescue experiments of the phenotypes (or corroboration with different siRNA oligoes).

2) In the co-IPs (IRSp53 IP + Gag co-IP) there is no assessment of the IRSp53 IP efficiency in the different conditions. The authors argued that IgG signal masking precluded them from doing that.

3) The authors observed an increase in the membrane-bound pool of IRSp53 when Gag is present (Fig. 2c). It is not clear whether this is specific for IRSp53 or other IBAR proteins can also be more membrane-bound as a result of Gag expression.

Reviewer #3 (Public Review):

Gentile, A. et al. generated snai1b mutant zebrafish embryos and showed that loss of Snai1b led to two mutant phenotypes in the heart: i) hearts with clear looping defects, ii) hearts without looping defects that displayed abnormal cardiomyocyte (CM) extrusion. The authors focused on the second class of mutants and found that loss of Snai1b led to reduction of N-cadherin at cell junctions and basal accumulation of phosphorylated myosin light chain and the α-18 epitope of α-catenin, indicative of mechanical activation. Bulk RNA-sequencing of isolated hearts revealed an upregulation of intermediate filament (IF) genes in Snai1b mutants, and of particular interest, the authors identified upregulation of the muscle-specific IF gene desmin b. Immunofluorescent imaging revealed that Desmin was not only upregulated in Snai1b mutants, but mis-localized away from cell junctions and accumulated at the basal side of extruding cells along with actomyosin machinery. Accordingly, CM-specific overexpression of Desmin was sufficient to promote cell extrusion.

The presented work is particularly interesting because it identifies a new role for the Snai1b transcription factor in maintaining proper tissue structure, independent of its typical function in regulating epithelial to mesenchymal transition (EMT). Overall, the experiments were well designed and controlled, and the data is clearly and logically presented. However, some of the findings could be explained by alternative hypotheses and other interesting aspects of the data were left unexplored.

One hypothesis that was not sufficiently discussed is that loss of Snai1b may prevent cardiomyocytes from undergoing the EMT that is necessary for normal delamination and trabeculation, and thus cells are instead extruded away from the lumen to prevent overcrowding in the developing myocardium. In fact, the authors present evidence that EMT is blocked and acknowledge that extrusion is a known mechanism for preventing overcrowding. It would be interesting to see whether extrusion away from the lumen also occurs if EMT is blocked through other means.

The authors show that extruding cells do not seem to be dead or dying, and that a small number of CMs do extrude in wild type embryos. This raises the intriguing possibility that some amount of CM extrusion is necessary for normal development and that these cells may give rise to epicardial or other cell types. Live-imaging and lineage-tracing studies would inform whether the extrusion observed in mutant embryos is an enhancement of a normal morphogenetic process or an additional abnormal response to loss of Snai1 function.

One particularly interesting observation that was left unexplored was the identification of a second class of Snai1b mutants with defective heart looping. It isn't clear whether these embryos also display enhanced CM extrusion, or if there are other clearly aberrant cell behaviors. Furthermore, it would be very interesting to know whether there is any evidence that the defective looping is due to the same changes in cytoskeletal gene expression and protein organization observed in the class of Snai1b mutants that were detailed throughout the manuscript.

The authors suggest that Snai1b regulates Desmin in two ways: 1) overall expression levels, and 2) post-translationally to control its localization at cell junctions. Although the first claim is sufficiently supported, the second claim lacks experimental evidence. An alternative explanation is that overexpression of Desmin in response to loss of Snai1b leads to mislocalization independent of an interaction with Snai1b. This point could be clarified by examining Desmin localization in the desmb overexpression system. In addition, assaying for co-IP of Snai1b and Desmin could demonstrate a direct interaction between the two and better support a role for Snai1 in regulating post-translational localization of Desmin.

Although the authors convincingly show that Desmin accumulates with other contractile machinery at the basal side of extruding CMs in Snai1b muntants, additional evidence is needed to support a causal link between basal Desmin accumulation and extrusion. For instance, if knockdown or inhibition of Desmin prevents extrusion in the Snai1b mutants, the causal relationship would be much clearer.

Reviewer #2 (Public Review):

An intact myocardium is essential for cardiac function, yet much remains unknown regarding the cell biological mechanisms maintaining this specialized epithelium during embryogenesis. In this manuscript, Gentile and colleagues discover a novel role for the repressive transcription factor Snai1b in supporting myocardial integrity. In the absence of Snai1b, cardiomyocytes exhibit an enrichment of intermediate filament genes, including desmin b. In addition, the authors detect mislocalization of Desmin, along with adherens junction and actomyosin components, to the basal membrane in snai1b mutant cardiomyocytes, and these mutant cells exhibit an increased likelihood of extrusion from the myocardium. Ultimately, the authors put forward a model wherein Snai1b protects cardiomyocytes from extrusion at least in part by regulating the amount and organization of Desmin in the cell, thereby supporting myocardial integrity.

Overall, the authors highlight an important aspect of epithelial maintenance in an environment that experiences significant biomechanical stress due to cardiac function. By generating a promoter-less allele of snai1b, the authors have created a clean genetic model in which to work. Coupled with beautiful microscopy and transcriptomics, this story has the potential to enlighten both cell biologists and cardiovascular biologists on the underpinnings of myocardial integrity. However, clarifications regarding the overall model would be particularly beneficial for the reader.

1) A clearer discussion of the proposed molecular mechanism for Snai1b function would aid a reader's overall contextualization of this work. At one point, the authors suggest that Snai1b regulates N-cadherin localization to adherens junctions, thereby stabilizing actomyosin tension at cell junctions. Later, it is suggested that Desmin activates the actomyosin contractile network at the basal membrane. It is unclear whether the authors believe that these are separate events or whether they may be coupled, perhaps through Desmin disruption at the lateral membranes, leading to modifications in nearby adherens junctions. A more thorough investigation of the phenotype resulting from desmin b overexpression may clarify this relationship.

2) It appears that extruded cells do not bud off from the myocardium, but rather remain on the apical surface of the existing myocardium. However, it is unclear whether this change in tissue architecture affects cardiac function or the overall morphology of the chamber. A brief discussion of these possibilities would have helped to contextualize the significance of this phenotype.

3) The authors show that cardiomyocyte extrusion is most prevalent near the atrioventricular canal, and they suggest that this regionalized effect is due to the different types of extrinsic factors, like biomechanical forces, that this region experiences. However, it is also possible that regional differences in certain intrinsic factors are involved, such as junctional plasticity, actomyosin activity at the basal membrane, etc. To distinguish between these possibilities, it would have been informative to know whether the extent of N-cadherin/α-18/p-Myosin/Desmin mislocalization varies depending on the regional location of cardiomyocytes within the snai1b mutant heart. For example, do cardiomyocytes near the atrioventricular canal exhibit more extreme effects on N-cadherin/α-18/p-Myosin/Desmin localization than cardiomyocytes in further away portions of the ventricle? Or, do these cells exhibit similar degrees of protein mislocalization, but cells near the atrioventricular canal have a lower threshold for extrusion?

Reviewer #1 (Public Review):

Gentile A et al show a novel role of Snai1b in growth regulation of zebrafish myocardial wall. Specifically, authors show that zebrafish lacking Snai1b exhibit cardiac looping defects (~50% penetrance), consistent with previously described morpholino mediated Snai1b knockdown phenotype. Extruding cardiomyocytes away from cardiac lumen, mostly in the atrioventricular canal region were observed in remaining 50% of Snai1b knockout zebrafish. Using RNA-seq, authors identified several dysregulated genes, including enrichment of intermediate filament genes in Snai1b knockout zebrafish. Among these dysregulated genes, authors suggest that increased Desmin expression and its aberrant localization promote cardiomyocyte extrusion in Snai1b knockout zebrafish hearts. Overall, present manuscript describes a novel phenomenon during cardiac development, hence, it is of interest to developmental biologists.

Major concerns are:

1) Snai1 is known to affect cushion formation in atrioventricular canal region. It would be helpful to establish cause and effect relationship for Snai1b in this region. Zebrafish lack global Snai1b expression - so it would be helpful to show if defective cushion promotes cardiomyocyte extrusion in atrioventricular canal region. Tnnt2 morpholino experiments provides some insights, however, it does not rule out role of defective atrioventricular cushion (defective EMT).

2) For Figure 2 - additional histology / immunohistology to show extrusion, cohesion, and orientation of cardiomyocytes at a section level (2D) in Snai1b knockout hearts could help to characterize phenotype at a cellular level. It is assumed that all cardiomyocytes lack Snai1b protein (immunostaining would help), however, only few cardiomyocyte show extrusion. Minor point - Cartoon images in figure 2 are somewhat disconnected from immunostaining images.

3) It is unclear whether Snai1b knockout hearts exhibit defective contractile phenotype and whether there is a cardiac phenotype in surviving adult zebrafish. It is also unclear whether RNA-seq and SEM from adult zebrafish heart represent embryonic extrusion and intermediate filament defects.

4) It is unclear why only few cardiomyocytes show extrusion when most of cardiomyocytes, if not all, are overexpressing Desmin gene.

5) Molecular link connecting Snai1b and cardiac filaments genes is not determined.

Reviewer #3 (Public Review):

This manuscript investigates the structure, electrophysiological and biological functions of a novel mechanosensitive channel from the parasitic protist Trypanosoma cruzi. The channel was identified bioinformatically as being significantly related in sequence to known mechanosensitive channels of the McsS superfamily. Studies on channel proteins in pathogenic protists such as trypanosomes are limited, and this investigation focuses on a previously unstudied mechanosensitive channel which is of potential interest to parasite biology, because trypanosomes are subjected to various mechanical stress forces during their life cycles.

Analysis of the sequence of the TcMscS protein by bioinformatics and structural prediction concludes that it is relatively divergent from previously studied members of the family from other microorganisms. Of particular interest, the divergent C-terminal domain contains a proposed novel cytoplasmic gate that could filter solutes on the cytosolic side. These observations are predictive rather than data driven. The recombinant protein was expressed in E. coli giant spheroplasts and studied by cell-detached patch clamp electrophysiology. The authors have clearly demonstrated that the channel is activated by pressure steps and fluxes K+, Cl-, Ca2+, and they speculate that it may be able to transport osmolytes such as amino acids. Other well supported conclusions are that the channel is located primarily in the contractive vacuole complex (CVC) in insect vector stage epimastigotes, an organelle that expels water from the parasite, but it occupies a wider range of subcellular sites in infective metacyclic and bloodstream trypomastigotes, and it localizes primarily to the plasma membrane of amastigotes. Thus, the channel changes its location during the life cycle. A MscS knockout line was generated and demonstrably shown to have impaired cell volume regulatory responses when subjected to changes in extracellular osmolarity, decreased motility, reduced ability to transform into infective stages such as metacyclic trypomastigotes, amastigotes, and bloodstream trypomastigotes, and altered Ca2+ homeostasis. Hence, the biological impacts of this channel are broad and significant.

A strength of the manuscript is that it is well-executed study and examines many aspects of channel function and biology. Precisely how the channel mediates the biological functions is less clear and will require future investigations. For instance, whether the channel has functions related to shear stresses encountered by the parasites when they enter host cells or extravasate through vasculature is currently rather speculative, albeit of considerable potential interest. How the channel mediates volume changes or affects motility is also unclear. In addition, the manuscript would benefit from some editing to make several points or interpretations clearer to the readers.

Reviewer #2 (Public Review):

In the manuscript 'A novel mechanosensitive channel controls osmoregulation, differentiation and infectivity in Trypanosoma cruzi' the authors show conclusive evidence that TcMscS is a mechanosensitive channel. They also show that TcMscS has additional roles outside of mechanosensation, likely playing a role in the infectivity of T. cruzi. This manuscript is well written with data that clearly supports the authors hypothesis. The evidence provided in the manuscript clearly shows that TcMscS gates in response to tension and that when knocked out of the genome there is a reduction in infectivity. This work will be impactful to both all researchers studying mechanosensation as it shows that mechanosensitive channels have roles outside of tension sensation.

Recommendations:

A) In the section 'Electrophysiological characterization of TcMscS', the authors present compelling evidence that TcMscS gates in response to tension in the membrane. However, it is unclear, both in the text and the caption, if the trace shown in Figure 2 panel C was collected under tension. If it was, please include the applied pressure value in either the text or caption. Additionally, within this section the applied pressure to the patch is frequently unclear. One way to clear this up would be to 1- add the applied pressure to each trace or to 2- add the applied pressure for each patch to the figure caption. -In Panel E: can you comment on the conductance of the channels in the three traces? Why do you see channels that are approximately 1/2 the size of the first trace in the second two traces?

B) In the section 'TcMscS gene targeting by CRISPR-Cas9' the authors utilized CRISPR to KO TcMscS to determine its function, based on the immunofluorescence and qPCR TcMscS has been successfully knocked out. In lines 251-264, the authors complemented the KO with an overexpression vector in an attempt to confirm the role of TcMscS. In this section, it is very unclear what strains C1 and C2 are and how they are different from one another. Neither of these constructs successfully restores the growth rate. The authors can clarify the differences between the two constructs or they can remove this section from the manuscript, particularly Figure 5 supplement 3. The manuscript is strong and compelling without this panel.

Reviewer #1 (Public Review):

The authors found a mechanosensitive channel gene in T. cruzi, and aimed to characterize the functions. The strength of this manuscript is that the channel has been examined from various aspects: the modelled molecular structures; expression and localization during development; electrophysiological characteristics; cell motility; responses to osmotic stress; regulation of Ca2+ homeostasis; infectivity to host cells. The weakness of this study is the assessment of motility and the nature of the recombinant protein. To conclude, this study provides data sufficient for reporting the discovery and initial characterization of the novel mechanosensitive in T. cruzi. The most significant finding is the correlation with infection, but the involvement in the response to osmotic stress is also interesting in the field of cell biology.

Joint Public Review:

Strengths & Overall Comments:

This behavioral study aims to provide an account of the spontaneous behavior of mice as they learn to explore a novel maze in search of a water reward. The authors analyze the trajectories of mice as they adapt to the labyrinth with particular focus on decisions taken at nodes and T junctions. They describe extremely rapid route learning to home and discontinuous exploratory learning or 'light bulb' moments as evident by instantaneous improvements in navigation performance. The authors capture most of the variance in their overall data with a predictive Markov models that could account for the much subsequent actions of the mouse as it moves from one node to the next. The study should be important to anyone who spends their time thinking about decision-making in mice. It highlights the importance of considering ethologically relevant tasks for understanding decision making in rodent species.

In this submission, the authors introduce a new experimental paradigm for the study decision making in naturalistic contexts, presenting an opportunity to observe these dynamics away from the standard two-alternative-forced-choice paradigm. The application of modern tracking and posture analysis to maze exploration by rodents generates rich and interesting data, and allows the authors to do their experiments with many animals, and with nearly no human interference or specific instructions. The design of the maze is clever, using an underlying tree-like structure (with the tree folded so it precisely and fully occupies a rectangular area), and relatively deep (6 branching points from main trunk to a leaf node). Mice explore this voluntarily, and water-restricted mice learn to find water rewards at a leaf of the maze. The authors thus study truly voluntary and highly interesting complex behavior, and in a high-throughput way. By studying the dynamics of a mouse in a maze, the authors perform a careful set of analyses, describing discontinuous learning dynamics and the effects of history on decision-making. These results should be of interest to a wide group of behavioral neuroscientists that are attempting to understand the neural basis of how animals make decisions in complicated natural environments.

The data set released with this submission will be of broad use to the community, and we would not be surprised to see dozens of papers using it moving forward.

Reviewer #3 (Public Review):

The sodium-coupled biogenic transporters DAT, NET and SERT, terminate the synaptic actions of dopamine, norepinephrine and serotonin, respectively. They belong to the family of Neurotransmitter:sodium:symporters. These transporters have very similar sequences and this is reflected at the structural level as judged by similarity of the crystal structures of the outward-facing conformations DAT and SERT. However, earlier functional studies indicated that transport by SERT is electroneutral because the charges sodium ions and substrate moving into the cell are compensated by the outward movement of potassium ions (or protons) to complete the transport cycle. On the other hand, DAT and NET are electrogenic. Moreover, potassium ions are not extruded by these transporters and the Authors set out to investigate if the electrogenicity is related to difference in potassium handling between SERT and the two other biogenic transporters. This was done by analyzing the role of intracellular cations and voltage on substrate transport by the three biogenic amine transporters. This was achieved by the simultaneous recording of uptake of the fluorescent substrate APP+ and the current induced by this process under voltage-clamp conditions by single HEK293 cells expressing the transporters. The Authors found that even though uptake by NET and DAT did not require internal potassium, these transporters could actually interact with internal potassium as judged by the voltage dependence of the so-called peak current. This voltage dependence was very steep in the absence of both sodium and potassium. However, in the presence of either cation this voltage dependence became less steep when either of these cations was present in the internal milieu, indicating that not only sodium but also potassium could bind from the inside. The same result was obtained with SERT. However, uptake by SERT was found to be much less dependent on the membrane voltage than that by DAT and NET and was stimulated by internal potassium, consistent with the proposed electroneutrality of the former. The observations indicate that the structural similarity of the three biogenic amine transporters is also reflected in their ability to bind potassium, even though this cation can translocate to the outside only in SERT.

Strengths:

Development of a sophisticated technique to interrogate the mechanism of sodium coupled biogenic amine transport in single cells. Rigorous analysis of the data. Conclusions supported by the data. The methodology can be used to obtain novel insights into the mechanism of other transporters.

Weaknesses:

The presentation could be made more "user friendly" by explaining in more detail what is happening as we go through the data. For instance, peak and steady state currents are shown already in Figure 1, but an (too brief) explanation is only provided when describing Figure 5. A schematic in the first part of the Results would be useful. Some information of on the structural background should be provided as well as a full description of the transport cycle, namely the number of sodium ions translocated per cycle and the argument why chloride remains bound to the transporter throughout the cycle. The control that in contrast to potassium, lithium is inert should be performed not only for DAT, but also for the two other transporters.

Reviewer #2 (Public Review):

Bhat et al. study transport mechanism of three members of the SLC6 family, i.e. DAT, NET and SERT, using a combination of cellular electrophysiology, fluorescence measurements - taking advantage of a fluorescent substrate (APP+) that can be transported by each of these different transporters - and kinetic modelling. They find that DAT, NET and SERT differ in intracellular K+ binding. In DAT and NET, intracellular K+ binding is transient, resulting in voltage-dependent transport. In contrast, SERT transports K+, and the addition of a charged substrate to the transport cycle makes serotonin transport voltage-independent.

This is an extremely nice and interesting manuscript, based on a series of beautifully designed and executed experiments that are convincingly analyzed via a kinetic model. I have only some suggestions:

1) Fig. 4: I find the description of Fig. 4 extremely difficult to understand. In clear contrast to the introductory sentence "Previous studies showed that Kin+ was antiported by SERT, but not by NET or DAT (Rudnick & Nelson, 1978; Gu et al., 1996; Erreger et al.,2008), SERT appears to be able to transport APP+ without K+ in Fig. 4. I was trying to understand this obvious discrepancy for a long time, until I found the authors coming back to this point in the discussion "However steady-state assessment of transporter mediated substrate uptake is hindered by the fact that all three monoamine transporters can also transport substrate in the absence of Kin+". This is a little late, and the author should address this point more explicitly in the result section, close to the description of Fig. 4.

2) Throughout the whole manuscript I am missing statistical details in comparisons.

3) Since APP+ might also only bind to the transporter or even only bind to the cell membrane, the authors might want to look at how the time course of the cellular APP+ signal depends on the size of the cells or on the ratio of transport currents and capacitance. It is of course possible that the tested cells do not differ sufficiently in size to permit such comparison. The authors should at least comment on this possibiliy.

4) Another set of results one might look at are the time courses of fluorescence decay after the end of the APP+ perfusion (Fig. 2 and 4). Substrate (APP+) outward transport should have a comparable voltage dependence as substrate uptake, moreover it should depend on the amount of substrate that entered to the cell before. Could the authors provide such result and use them to exclude specific/unspecific APP+ binding?

Reviewer #1 (Public Review):

This work is aiming at the characterization of the molecular and kinetic mechanism of how three members of the SLC6 family of transporters, namely for dopamine (DAT), norepinephrine (NET), and serotonin (SERT), transport substrate across the membrane, and how the transport process is affected by cations. The authors use electrophysiology and sophisticated rapid solution exchange methods, in conjunction with fluorescence recordings from single cells, to correlate flux (from fluorescence) with electrical activity (from currents).

The strength of the methods is based on the application of a kinetic method with high time resolution, allowing the isolation of fast processes in the transport mechanism, and their modeling using a kinetic multistep scheme. In particular useful is the combination with fluorescence recording from single cells, which allows the authors to measure flux and current in the same cell under voltage clamp conditions. This is an elegant approach to get information on the voltage dependence of substrate flux, which is difficult to obtain with other methods. As to the strength of the results, the data are generally of high quality, showing the kinetic and mechanistic similarities and differences between the three transporters under observation. Another strength is that the results are quantitatively represented by kinetic simulations, which appear to fit the experimental data well.

The major weakness of the research is related to interpretation of the experimental results. While the authors propose a unified K+ interaction mechanism for the three transporters, DAT, NET and SERT, the proposed K+ association/dissociation mechanism is 1) highly unusual, and 2) not unique in the ability to explain the experimental data. As to point 1), the DAT mechanism (Fig. 7A) proposes a sequence of intracellular K+ association and dissociation steps. Since the intracellular [K+] remains constant, such a sequence requires a change of affinity for K+, which is initially high when K+ associates (33 microM according to the provided rate constants) and then has to be low for K+ dissociation (3.3 mM). Such an affinity change requires input of free energy, to promote K+ dissociation. From the provided rate constants and at room temperature this free energy change can be approximated as 11.4 kJ/mol. This is a large energy amount, in fact larger than what is stored in the physiological concentration gradient for one Na+ ion as a driving force for transport. It appears that the transporter would waste a lot of energy for no apparent benefit, with a futile K+ association/dissociation cycle, that would just generate heat.

Therefore, while the authors have achieved their aim of quantitatively assessing transporter function and thorough description by a kinetic mechanism, their final proposed mechanism does not support all of the conclusions because it is by far from unique in being able to explain the data (point 2) above). While this may be true for other transport mechanisms proposed in the past, the mechanism proposed here is somewhat odd with respect to energy requirements. Thus, it would require extraordinary experimental proof to propose it in exclusion of other, maybe more plausible mechanisms.

Despite these shortcomings, the potential impact of the work is high, because a unifying theory of cation interaction and stoichiometry of the monoamine transporter members of the SLC6 family has been missing in the literature. In addition, the elegant method of combining single cell electrophysiology and fluorescence flux measurements is impactful, especially in the whole cell recording method, allowing the control of intracellular ionic composition.

Reviewer #3 (Public Review):

The authors developed an in vivo model of EBV's contribution to RA that recapitulates aspects of human disease. They examined the role of age-associated B cells and find that they are critical mediators of the viral-enhancement of arthritis.

The manuscript is written in a well-structured form that facilitates the reading and following the incremental experimental setups. The manuscript is appropriate for publication after revisions.

Some of the statistical measures did not show significant values while the author based several statements as if there is a difference (they rather used phrases as increased/fold change). Whether this is strong enough to support their statements is not clear.

Overall, this report provides important insights regarding the association between latency, age-associated B cells, and the enhancement of RA in a mouse model. If these insights are translatable to RA immunology in humans is to be further investigated.

Reviewer #2 (Public Review):

In this study, the authors investigate the long-appreciated but little understood link between chronic infection with Epstein-Barr virus and rheumatoid arthritis (RA). Using a collagen-induced (CI)-model of arthritis and a natural murine analog of EBV (gammaherpesvirus 68, HV68), the authors demonstrate that latent infection with HV68 exacerbates clinical progression of CI-arthritis and is associated with changes in the immune cell and cytokine profile in the spleens and joints of HV68 infected mice. The most compelling finding is that an infection can indeed exacerbate the progression of secondary diseases, and the requirement of age-associated B-cells (ABCs) to the severe disease progression. While this study addresses a timely and important question-how chronic infections affect subsequent or secondary disease progression-additional work as well as a clarification of the experimental design is encouraged to understand some of the key conclusions.

Reviewer #1 (Public Review):

In this manuscript, Mouat et al. investigated the contribution of viral infection to the severity of arthritis in mice. Epstein-Barr virus (EBV) infection is associated with rheumatoid arthritis (RA). By assessing arthritis progression in type II collagen-induced arthritis (CIA) induced mice with or without latent 𝜸HV68 (murine gammaherpesvirus 68) infection, authors showed that latent 𝜸HV68 exacerbates progression of CIA. Additionally, profile of immune cells infiltrating the synovium was altered in 𝜸HV68-CIA subjects - these subjects presented with a Th1-skewed immune profile, which is also observed in human RA patients. Assessment of immune cells in the spleen and inguinal lymph nodes also showed that latent 𝜸HV68 infection alters T cell response towards pathogenic profile during CIA. Lastly, authors showed age-associated B cells (ABCs) are required for the effects of latent 𝜸HV68 infection on arthritis progression exacerbation. Findings presented in the manuscript provides important insights and resource to clinical RA research.

There are some statistical analyses that need to be updated for completeness and appropriateness of use. In addition, the authors will need to highlight that all analyses were conducted in young mice, whereas RA occurs in aged individuals.

Reviewer #3 (Public Review):

Proper sealing of the blood brain barrier (BBB) is essential for viability in many animals, including humans and Drosophila. In Li et al., the authors used Drosophila as a simple genetic system to define the signaling pathways that control BBB formation and maintenance. In Drosophila, the BBB is composed of a thin epithelial sheath of subperineural glia (SPG) that are connected by septate junctions. Previously, the authors found that the G protein-coupled receptor Moody is essential for BBB formation during embryogenesis, but the downstream signaling pathways that facilitate septate junction assembly were not known. Here, they performed a series of genetic screens and epistasis experiments to uncover that Moody and PKA antagonistic signaling drives BBB assembly and expansion throughout organismal development. In the present study, they show that loss of PKA signaling components results in a leaky BBB both during development, and during adulthood. They further show that these functions of PKA are dependent on downstream suppression of Rho and changes in cytoskeletal dynamics. Interestingly, overexpression of PKA also causes BBB permeability, indicating that PKA signaling levels must be tightly regulated for BBB integrity. The authors then use serial section TEM to visualize the intact SPG sheath for the first time at ultrastructural resolution, and show that overexpression of PKA results in an enlarged yet patchy septate junction, accounting for the leakiness. In sum, the authors show that the combined signaling of Moody (apically located) and PKA (basally located) shapes the cytoskeleton to drive efficient assembly and maintenance of septate junctions, and thus, the BBB.

The conclusions of this paper are mostly well supported by the data, but the study would be improved by some expanded analyses and descriptions of statistical assessment.

Reviewer #2 (Public Review):

Here the authors explore the role of PKA signaling in signaling downstream of the Moody GPCR in the BBB. The discovery that PKA is involved is interesting but not entirely surprising, as it functions downstream of many GPCRs to execute function (the really interesting question is how the same signal, changes in cAMP, causes PKA to do different things). The authors make the claim of a monotonic relationship between septate junctions (SJs) and cell-cell contact zones. I do not think they have measured the necessary parameters in a way that allows them to claim a "monotonic relationship between PKA activity, membrane overlap and the amount of SJ components in the area of cell contact." There is a correlation, but that is probably overstating it. There is an interesting analysis of several markers. These cells are very small and it is not clear what do the cytoskeletal markers really tell us. The markers change, no doubt, and do so in a way that correlates with the proposed Moody/PKA antagonistic relationship. The markers do change at the edges of cells and in regions of overlap, but wouldn't that be expected based on the changes in morphology? Again, the claim for "monotonic" changes is probably overstating the relationship. Doesn't the fact the total SJ area covered remains at 30% whether there is more or less overlap also argue against this (i.e. 30% of more overlap is not the same of 30% of less overlap...so more or less SJs are being made)?a

The study would need to be strengthened by more rigorous quantification. There is no quantification in figure 3. This is a primary point in the manuscript-that cytoskeletal markers change (in a claimed "monotonic" way) in subperineurial glia when PKA is altered. There is also no quantification or statistics in Figure 5, which is among the most interesting observations.

The complementary localization of Moody and the PKA catalytic (activated) subunit is very nice. It shows a very interesting cellular polarity. However, it is unclear whether this is altered in Moody mutants (the authors only did knockdown) and whether catalytic (activated) PKA now goes everywhere.

Throughout, the authors use some very nice genetic studies, using loss-of-function, gain-of-function, and enhancer/suppressor approaches, and their findings are consistent with polarized localization of Moody being important.

Reviewer #1 (Public Review):

This work investigates the structure and maintenance of the blood brain barrier (BBB) in Drosophila. Previous work from this lab and others have shown that the BBB is composed of a specialized type of glia called the subperineurial glia (SPG), which enwrap the entire central nervous system (CNS). Furthermore, they previously identified the Moody G protein coupled receptor (GPCR) as being specifically expressed in SPG and required for BBB formation and maintenance.

Here they show that Moody protein is localized to the apical membrane domain (facing the CNS) while Protein Kinase A (PKA) is localized to the complementary basal membrane domain (facing the hemolymph of the body cavity). Not only do Moody and PKA have non-overlapping subcellular localization, but genetic interactions show that Moody and PKA act antagonistically in BBB maintenance. They find that both too little and too much PKA activity disrupts the BBB.

The authors also generate a serial section Transmission Electron Microscopy (TEM) volume to analyze wild type, PKA hyperactivity, and PKA loss of function animals. They find that loss of BBB function is sue to gaps in the BBB, rather than thinning of the BBB. This conclusion is somewhat weak, however, because they did not analyze a genotype that has thin BBB structure but normal BBB function.

Their work raises the question of how does PKA promote BBB integrity. They analyze two potential PKA targets (myosin light chain kinase [MLCK], and Rho1), finding that reduced Moody levels lead to disrupted subcellular localization of both proteins in SPG; that reducing MLCK or Rho1 levels causes failure of BBB function; and that reducing Moody can rescue these phenotypes. They conclude that Moody acts antagonistically to PKA/MLCK/Rho1 to establish distinct apical and basal membrane domains in SPG which are required for BBB function.

Reviewer #3 (Public Review):

The authors have re-sequenced 310 quinoa accessions and carried out field phenotyping of the same set of accessions for two years in order to characterize genetic diversity and analyze the genetic basis of agronomically important traits.

The main strength of the manuscript is that the authors have carefully characterized more than 300 quinoa accessions, achieving a sufficiently large population size for GWAS analysis with good statistical power. It is especially promising that the phenotypes all show high heritability. This indicates that the field phenotyping was of high quality and provides a good starting point for discovering relevant marker-trait associations. In addition, the authors provide convincing evidence for distinct population characteristics of highland and lowland quinoa, adding additional information compared to previous work (Maughan, 2012).

The weak points are related to the genotype data and the conclusions drawn based on the GWAS analysis.

1) An important issue is related to the relatively low depth of coverage (4-10x) that was used for re-sequencing. Across the accessions, there is a pronounced negative correlation between the mean sequencing depth and the heterozygosity level, indicating that heterozygotes are overcalled in individuals with low coverage. This also results in heterozygosity levels that are generally higher than expected for what is assumed to be mainly homozygous inbred lines.

2) Another potential issue concerns SNPs called in repetitive regions. Among the significant GWAS SNPs identified, a very large proportion appears to be found in intergenic regions. While this does not rule out that some of them are genuinely important associations, it does suggest a potentially high level of noise in the GWAS results. In addition to the filtering already imposed, which includes a filter for mapping quality, the SNPs called in intergenic regions with unusually high coverage could be more closely examined to determine the extent of the issue. Masking repetitive genomic regions using RepeatMasker or similar programs could be useful.

3) When the authors discuss their GWAS results, they frequently focus on cherry-picked candidate genes, although, in several cases, the top SNPs in the region in question are not found within these candidates. A more broad focus on all genes within the LD blocks, while still mentioning the candidate genes, would be more informative.

4) The manuscript includes statements that a particular genotype "results in" some phenotypic outcome, although no causal relationship has been demonstrated. In general, there is a tendency to draw too strong conclusions based on the GWAS results.

5) As this is primarily a resource paper, the authors should make the complete genotype and phenotype data as well as the layout of the field trials available. It would not be possible to reproduce the GWAS analysis based on the data included with the current version. They should also clarify how the quinoa accessions described will be made accessible to the community and provide all scripts used for data analysis through GitHub or a similar repository.

Reviewer #2 (Public Review):

A key genomic study on emerging, nutritious, alternative grain crop.

Deep genomic data on hundreds of land races/accessions.

Population structure analysis, could be enhanced.

Agronomic growth and yield traits are correlated and environmentally sensitive.

Genomic dissection via GWAS to multigenic loci with candidate genes add genomic prediction and selection.

Inference on domestication.

Reviewer #1 (Public Review):

The paper details a whole genome re-sequencing of 310 accessions of quinoa. This provides a good glimpse of diversity in this orphan crop, plus the GWAS studies are able to help provide the foundations for identifying key genes in quinoa variation. This will certainly advance our knowledge of this increasingly important orphan crop.

1) One issue that permeates the entire paper is that the analysis is fairly basic and the authors do not make full use of the data. The analysis of population diversity is restricted to PCA, ADMIXTURE and phylogenetic analysis. It would probably broaden the impact of the paper if they can do deeper analysis of quinoa diversity, maybe looking at demographic history, looking at selection of highland vs. lowland, etc.

2) There is a focus on the rapid LD decay, which the authors attribute to the short breeding history and low selection. That seems like a stretch to make this conclusion based solely on LD decay. As they point out, many other factors could account for this, and the authors should provide other lines of evidence to draw this conclusion.

3) The GWAS analysis is good and does provide a good foundation for quinoa genetics. The authors discuss possible candidate genes is these GWAS regions. For the thousand seed weight, the relative small span of the GWAS peaks allows for localization of just a few genes in the GWAS region (CqPP2C5 and the CqRING). The GWAS associated with flowering time is larger - 1 Mb with 605 genes - but the authors focus on the GLX2-1 gene. This is again a stretch, as the large region precludes narrowing the candidate list unless there was a compelling mutation (for example a deletion or insertion of a major flowering time gene).

Reviewer #3 (Public Review):

Cole and co-authors report the development of a novel immunofluorescence technique, where targets of interest are analysed over iterative cycles of staining-imaging-elution(stripping). This method allows for the multiplexed analysis of protein targets, well beyond the usual constraints of such technique (limited by availability of filters and non-overlapping wavelengths of fluorophores). The authors also present several applications of such technique, highlighting how the advantage of being able to record additional parameters (such as cell morphology) can be an advantage over more high-throughput methods such as spatial-resolved transcriptomics.

The technique has been carefully tested. Staining for the same markers after several rounds of stripping/reprobing shows high concordance, indicating that the iterative treatment and staining of the same tissue section is not altering the detection of protein markers.

The authors tested staining with a total of 18 antibodies, and suggest that this number can be increased arbitrarily, as the number of iterations is not limited. Further, they suggest that this technique can be applied to virtually any tissue. It is quite possible that this technique can be readily applied to any other tissue, as the only constraint seem to be the robustness of antibodies. The authors may include the suggestion that previous success of immunofluorescence on a particular tissue type could be a good indication for the success of the iterative staining.

The proposed 4i method is quite interesting, has great potential and is likely to be of very wide interest.

Reviewer #2 (Public Review):

Methods to characterize cell types in intact tissue using large scale analysis of molecular expression profiles are now readily available, with the best example being in situ RNA sequencing (spatial transcriptomics). However, these methods depend on separate immunohistochemical investigations to define the precise cellular and subcellular distribution of the protein products. Cole et al use iterative indirect immunofluorescence imaging (4i, Gut et al Science 2018) to compare the immunoreactivity of an impressive 18 different molecules within the same brain sections containing the dentate gyrus from young and old mice. First, they demonstrate that the method can be applied to not only adult mouse brain tissue, but also to human embryonic stem cell derived organoids and mouse embryonic tissue, which is an advance on the original report (Gut et al 2018). This demonstration is particularly important as it shows the potential for applying 4i to different biological disciplines. The rest of the manuscript focuses on the mouse dentate gyrus (DG) at 2, 6 and 12 months of age in order to map the complex changes and associations in the tissue across age. Various combinations of the 18 molecules are used to define different cell types and it incredibly informative to be able to view so many molecules in exactly the same area and will advance the field. This is the greatest strength of the manuscript. They find that neurogenic, radial glia-like stem cells (R cells) and proliferating cells are reduced in aged animals, as are immature (DCX+) cells, but claim that fluorescence intensity increases for the remaining R cells in 12 month old mice. They report that the density of vasculature also decreased with age, as did the associated pericytes, but astrocytes associated with the blood vessels increased. The last part of the manuscript defines 'microniches' (random or targeted regions of interest within the DG) and attempts to show how cell types, especially Nestin+ R cells, change in their associations with vasculature within these sub-regions at 2, 6 and 12 months of age. It is a commendable approach and the authors use a variety of statistical tests to compare the different cell types. However, there are several parts of the methods, along with insufficient details of the results that prevent full interpretation of the data, meaning that it is difficult to determine whether all conclusions are supported.

1) There are many factors that can affect the measurements of immunoreactive structures (Fritschy, Eur J Neurosci, 2008 vol 28, p. 2365-70). The main limitation is not providing sufficient detail for the immunolabelling design and imaging parameters but providing some unclear details for the imaging analysis (below).

a. In terms of immunohistochemistry, with the impressive number of tested antibodies, there is potential for variation due to antibody antibody penetration, unreported combinations of secondary antibodies, tissue quality (variations in fixation), etc. It is difficult to have confidence in the conclusions based on a total of 3 mice per age group for a single 40 um section per mouse. Ideally, to increase confidence in individual section variability, it is recommended that measurements should be taken from at least 3 sections per mouse then averaged, before averaging for the age group.

b. Assuming there were 3 primary antibodies with 3 secondary antibodies per cycle before elution, were the combinations used consistent for all brain sections and mice? Was the testing and elution order the same (i.e. systematic)? There is a risk of cross-excitation and mis-interpretation of true immunoreactivity if spectrally close fluorophores for the secondary antibodies were selected for primary antibodies that recognize spatially overlapping structures. Can the authors show the cycle number and fluorophore for the examples in figures 1 and 2 to determine which markers were imaged together in the same cycle? This would give confidence to the methods for colocalisation and cell type descriptions. For example, can cross-excitation be ruled out for some of the signals in the images used in Fig 2 (duplicated in Fig 4) such as intensely immunopositive Laminin-B1 cells in the MT3 and Sox2 channels (2A) and Ki167, SOX2 and phospho-histone 3 channels (2C)?

c. For image acquisition, details are required on the resolution (numerical aperture of the lenses) in order to interpret colocalisation measurements in the later figures. Which beamsplitters/filters were used, and was the same laser power used for the same markers over different specimens (important for interpreting figure 4 data)?

d. For the analysis of ROIs (figures 3-6), were the 20x or 40x images used?

e. Details of the antibody specificity controls should be provided.

2) Numerous markers have been used to define different cells, but the proportions are not reported. For example, R cells are defined differently in figures 3 and 4. How many types of R cells (based on combinations of markers) were observed? High resolution examples of each defined cell type (neuronal and glial) would assist the reader in the confidence of the measurements (ideally as single channels side by side, with arrows indicating areas of detectable immunoreactivity that the authors would use to define each cell).

3) The authors use HOPX and GFAP immunoreactivity and a lack of detectable S100beta immunoreactivity to distinguish R cells from triple immunopositive mature astrocytes. In Figure 3, the images are too low power to be able to confirm this. This part would benefit from some single cell examples showing the separate channels.

a. Furthermore, the results (paragraph 2, page 7) report changes in cell number, but rather density is reported. Please either state the numbers or refer to density.

b. Related to Fig 3, there are no details of the number of R cells counted in supplementary table 1. How were the density measurements obtained? How thick were the image stacks and how many R cells per section? Similarly, as stated in methods, for glial cells, 100 cells were randomly counted in each section (presumably the same count for each age), so how was it reported that specifically the numbers of astrocytes were reduced and no significant differences in other glial cell types? (bottom of p.7)

4) An increase in fluorescence intensity for HOPX and MT3 (also marks R cells) was observed with age (Fig 4), with methods stating that the 5 ROIs used to calculate the background intensity were measured at each [optical?] slice for where the cells were measured, to account for unequal antibody penetrance. Several clarifications are required in order to interpret these results: For the example HOPX images in Fig 4A, for the 2 month old mouse, the background is low, whereas for 12 months, the background is far higher, meaning different background ROI values. Can this difference be explained by differences in laser power, contrast adjustments, optical slice thickness, or whether these are maximum intensity projections of different z thickness? These values must be reported, and for each image presented in the manuscript, details must be included as to what type of image (z-projection or single optical slice, z thickness). Was the optical section(s) of the 12 month mouse imaged closer to the surface of the section for this example in Fig 4A? Were cells sampled at all depths of the imaged volume? Did the antibody show better penetration in the 12 month old mice than the 2 month old mice? How many optical slices would a cell soma cover? In these cases, how was the fluorescence intensity measured? If a soma covered several optical slices, which one was selected for the ROI measurement?

5) The described methods for studying cellular interactions are not clear, making it difficult to interpret the associations between vasculature, cell types, and age. How was colocalisation defined, and at what resolution? For example, it is expected that GFAP would be associated with but not directly colocalized with collagen IV (Fig 5). In these cases, the manuscript would benefit from high resolution examples of this colocalization/interaction. How many ROIs were taken, how exactly were the ROIs for cell types associated with collagen IV selected, was this in 2D or 3D?

6) The methods for random microniches are difficult to follow, as are the methods for investigating the associations of other markers to radial processes of R cells. Please provide a definition of a 'spot'. Again, details of the micron per pixel resolution and optical slice thickness would help in the interpretation of results. Additionally, if possible, illustrated examples of the full procedure for niche mapping should be provided in order to follow how the measurements were collected.

Reviewer #1 (Public Review):

Overall the analysis is conducted well and is convincing. The characterisation of neural stem cells using 7 markers as well as their morphology and position, is particularly thorough.

My main criticism is that the study purports to address the effect of aging but the ages analysed only range from 2 months to 12-months. As 12 month-old mice are still middle aged, it is difficult to conclude anything about the process of ageing, which is usually studied in much older mice (18-24 months). Indeed, some of the changes that the authors associate with an "ageing phenotype" appear in microniches already in 2 month-old mice and are predominant at 6 months. This suggest that the authors are documenting the transition from an immature/juvenile state, which is predominant in 2 month-old mice, to a mature/adult state, which already appears at 2 months but becomes predominant at 6 and 12 months. Importantly, this adult state, including the reduced number of neural stem cells, might not be dysfunctional but on the contrary, may perform very well its role of producing small numbers of new neurons as required during adult neurogenesis.

Another, lesser concern is that, based on antibody staining performed in tissues from 2-month and 12-month-old mice, conclusions are made on the different expression levels of HOPX, MT3 and LaminB1 analysed at different ages. This assumes that the efficiency of antibody staining is the same in different samples analysed in parallel but this is not shown.

Joint Pubic Review:

Church et al. carry out a mechanistic study focused on regulation of PKA activity at a specific multiprotein complex nucleated by the scaffolding protein AKAP79. The manuscript presents a rigorous biochemical approach combined with computational modeling to address fundamental issues related to PKA signaling. This is a very important but complex system and the authors have nicely addressed it using in vitro approaches. The in vitro data provide evidence that suggests that the phosphatase calcineurin (CaN), by dephosphorylating the PKA regulatory subunit type II (RII), promotes rapid re-association of the PKA catalytic subunit (C) to RII, leading to PKA inactivation. The model proposed is that this modality of PKA inactivation takes place selectively at the multiprotein complex organized by AKAP79, where CaN, PKA and PKA phosphorylation targets are co-localized: the proximity of CaN to RII at the AKAP79 complex would enhances the efficiency of RII dephosphorylation by one order of magnitude, allowing fast re-association of C and RII subunits. This would reduce the proportion of free C subunits and therefore the level of local PKA substrate phosphorylation. Using purified the FRET reporter AKAR4 as a reporter for PKA activity, they further confirm that the level of phosphorylation of this PKA target at a given cAMP concentration depends on the ability of CaN to interact with AKAP79. Based on these findings the authors conclude that CaN anchored to AKAP79 dephosphorylates AKAP79 anchored RII, leading to fast recapturing on C and inhibition of PKA catalytic activity. They then create a kinetic model for this process where cAMP and calcium are working in opposing ways. Notably, the authors also provide an estimate for the concentration of RII subunits in the hippocampal CA1 neuropil layer and find that this falls within the range at which CaM efficiently dephosphorylated RII in vitro.

In the context of compartmentalized cAMP/PKA signaling, this mechanism would provide yet another regulatory feature to ensure specific control of target phosphorylation at individual subcellular locations. For example, in dendritic spines PKA regulates long-term depression (LTD) of CA3-CA1 hyppocampal synapses via phosphorylation of AMPA-type glutamate receptors, which is facilitated by simultaneous interaction of receptor and kinase with AKAP79. In this context, at a given cAMP concentration, CaN-dependent inhibition of PKA activity would selectively attenuate AMPA phosphorylation and LTD, while PKA may still be able to phosphorylate targets at other sites.

The paper presents very clear biochemical data but can be further strengthened by some additional attention to the following:

While the in vitro data convincingly demonstrate the requirement for CaN to be anchored to AKAP79 for efficient dephosphorylation of RII and confirm that phosphorylation of RII at S98 results in more active PKA, the requirement for RII to be anchored to AKAP-79 for this regulation is not investigated, leaving open the possibility that the more efficient dephosphorylation of RII in vitro may be due increased catalytic activity of CaN when the phosphatase is associated to AKAP79c97.

The authors show convincingly that the pRII subunits are better substrates when the AKAP scaffold is present. However, they need to address the relevance of having the enzyme (CN) and the substrate (pRIIb holoenzyme) scaffolded to the same complex so that diffusion is no longer a rate-limiting factor in the catalytic event. Are MM kinetics relevant for this process? This is a single molecule event that does not necessarily require that the product be released. Instead the product is returned to the active site of the cleft of the C-subunit in the holoenzyme:CN complex where in the cell it is rapidly re-phosphorylated. Also the authors could show what happens when you have a 1:1 concentration of CN and pRIIb. Following this single transfer event does not require dissociation of the holoenzyme and is likely to be more physiologically relevant.

Do the authors know if calcium vs. Mg influences this process? Calcium stabilizes the product whereas Mg stabilizes the substrate in the case of the kinase. If calcium levels are high following release of the phosphate, would this tend to keep the phosphorylated holoenzyme in a more inhibited state until calcium went down and cAMP went up?

This process will take place at membranes which may play a significant role in determining whether the A-subunit is released into solution or not.

Another important question to consider is whether it is even necessary to dissociate the holoenzyme complex at all. Is it sufficient, for example, to simply unleash the linker region of the RII subunit and thereby open up the active site cleft of the C-subunit? Since the tail of the channel is also tethered nearby, it is perfectly reasonable to catalyze this event without dissociating the complex especially given earlier data by Wang, et al showing that the holoenzyme is very stable even when the key arginines in the inhibitor site are mutated. The same motif has access either to the active site of the C-subunit or to the active site of calcineurin in a cAMP/Ca++ dependent cycle. This leaves the phosphorylated tail of the channel free to be dephosphorylated by other phosphatases that are also tethered to AKAP79 and leaves CN committed to recycling of the RII holoenzyme. In principle this does not require dissociation of the RII holoenzyme if CN is tethered nearby. This is a very fundamental question.

One point that is not addressed in the study and is important for the interpretation of the results is whether interaction of CaN with AKAP79c97 increases CaN activity per se, such that the more effective dephosphorylation of RII is not due to the physical proximity of CaN to RII on the AKAP but to a more active CaN. This could be addressed by testing the dephosphorylation rate of a phospho-substrate other than 32P-RII, in the absence and in the presence of AKAP79c97 or by repeating the experiments shown in Fig 1 in the presence of the AKAP79c97 variant where the PKA (391-400) anchoring site has been removed.

AKAR4 is a reversible reporter of PKA activity, so it is surprising that the authors find that its phosphorylation is not affected by CaN. One possibility is that AKAR4 is not a good substrate for CaN. However, multiple studies have shown that AKAP4 can effectively be dephosphorylated. The ability of CaN to dephosphorylate AKAR4 should be investigated further to demonstrate more robustly that, in the in vitro experimental conditions used, the observed reduced phosphorylation of AKAR4 is due to less active PKA rather than more active CaN. This could be done, for example, by repeating the experiments summarized in Figure 3-figure supplement 1C & D using a different phosphatase, to ascertain that the experimental conditions allow for detection of AKAR4 dephosphorylation.

One limitation of the in vitro work is that only AKAR4 is used to measure the level of PKA dependent phosphorylation. AKAR4 is not a natural substrate for either PKA or CaN and the accessibility of the phosphorylation site to these enzymes may be different than for physiological targets. In addition, AKAR4 is not anchored to AKAP79 and may not be the ideal reporter to investigate the effects of CaN-dependent regulation of PKA targets associated to AKAP79.

Stoichiometry of free RII subunits. The authors have shown convincingly that the RII subunits in particular are present in excess of the C-subunits, and this has led to some new concepts for PKA signaling. There are two questions that need to be addressed here. Perhaps in the discussion is adequate but they do need to be addressed. First is whether there are separate pools of free RII subunits and holoenzymes within single cells. This is essential for the model of PKA signaling taking place in the presence of a 10-fold excess free RII-subunits. Are the dissociated R-subunits in the same subcellular location? Second is whether the free RII subunits are bound to cAMP. The cAMP-free subunits are noticeably less stable and degraded more rapidly that the holoenzymes so are these free R-subunits bound to cAMP? If not, are they bound to something else that keeps them stable? RII subunits do not form membrane-less puncta as was recently reported in Cell by Zhang but is there some other mechanism that allows for the sequestration of large amounts of free RII subunits?

Do you need to saturate all four sites to have an active C-subunit that can phosphorylate the tail of a channel? This relates to the question above. Perhaps this would not be measured by the AKAR4 reporter but could it be sensed if AKAR4 were fused to the tail of AKAP79 so that it would be tethered close by similar to the tail of the channel.

Stoichiometry of two calcineurins vs. one RII holoenzyme or one? The authors need to address this stoichiometry question more rigorously. It is quite fundamental for their assays. Does the computational model provide any ability to ascertain stoichiometry of the productive complex?

While it is true that neither S/A or S/E will be substrates for CN, they will in fact have a different effect on the RII holoenzyme. Ser/Ala and Ser/Glu mutants are, in principle, quite different in terms of their accessibility to the active site of the C-subunit vs. the active site of CN. The Ser/Ala mutant, for example, should be locked into the active site of the C-subunit, and this would be presumably strengthened by ATP since this is a pseudosubstrate. Does the affinity for C-subunit change in an ATP-dependent manner? The Ser/Ala mutant should be a good inhibitor that cannot be regulated by phosphorylation. It could be activated by high concentrations of cAMP but not by the cAMP signaling that is being described here. The Ser/Glu mutation would favor docking into the active site of CN but would be trapped in this state as it also could not be dephosphorylated. Is this consistent with the models proposed by the authors?

The in vivo work to assess the physiological relevance on this proposed new modality of PKA regulation is very preliminary. By overexpressing S97A and S97E mutants of RII in hippocampal neurons the authors confirm that modulation of PKA sensitivity to cAMP via RII phosphorylation affects spine density. However, no experimental data directly assess the role of CaN-dependent dephosphorylation of RII at the AKAP79 complex and there is no evidence that this mechanism regulates AMPA phosphorylation or phosphorylation of other physiologically relevant targets. Thus, the caveats that are associated with the system and in particular the physiological relevance of the analyses needs to be addressed. Conclusions based on the preliminary 'in cell' data on physiological relevance should be appropriately tempered.

Reviewer #2 (Public Review):

The paper uses computer modeling and simulations to show how a radially growing circular plant organ, such as a hypocotyl, can develop and maintain its organization into tissues including, in particular, cambium, xylem and phloem. The results are illustrated with useful movies representing the simulations. The paper is organized as a sequence of models, which has some rationale - it presumably depicts the path of refinements through which the authors arrived at the final model - but the intermediate steps are of limited interest. At the same time, mathematical details of the models are not presented to the full extent. Fortunately, the models can be downloaded over the Internet, and the supplementary materials include detailed instructions for executing them (using the VirtualLeaf framework). Consequently, the paper and its results can potentially serve as a stepping stone for further model-assisted studies of radial tissue organization and growth.

Reviewer #1 (Public Review):

The authors try to shed light on how plant stem cells located in a ring-like structure in the (the procambial cells or cambium) can generate two distinct differentiated tissues, one filling the interior of the ring (the xylem) and the other one surrounding the ring (the phloem). To achieve this goal, the authors propose different models increasing in complexity, and perform a series of comparisons between the model outcomes and experimental data in the Arabidopsis hypocotyl.

This work seems to provide for the first time a computational framework to model the radial formation of the cambium, xylem and phloem in the hypocotyl. Some of the features of the wild type and mutants could be qualitatively recapitulated, such as the radial organization of the xylem, cambium and phloem in wild type, and a striking phenotype upon the overexpression of CLE41 transgene.

Although this work is very well written and understandable at the introduction, when paying careful attention to the presented results, there are different aspects that would require further work and investigation, on both experimental and modelling sides:

The authors chose to study different models increasing in complexity, reaching a more complete model (Model 3, Figure 5A-D) that the authors claim it is recapitulating the experimental data and the explored experimental perturbations (Figure 5E-F). This model is substantially more complex than Model 1 and Model 2, and it is difficult to understand all the claims by the authors, and the radial pattern formation capabilities of it. Yet, a feature that is clear to the eye, both in the pictures and in the movies, is that this model seems more likely to present a front instability of the cambium front progression, disrupting the radial organization of the different tissues (see Figure 5B), which does not seem to happen in the wild type hypocotyl from Arabidopsis. This effect is even more extreme when looking at the pxy mutant (Figure 5F) and when the xylem cell wall thickness is explored through the simulations (Figure 6). The authors claim this model is able to recapitulate a basic feature of the pxy mutant, which is the fact that the distal cambium appears in patches. Although these patches appear in the simulations, this effect in the model might be produced by the instability of the cambium front progression itself, which might be fundamentally different from what happens in the experimental data. In the experimental data, the PXYpro:CFP cambium does not seem to present such front instability, but rather is the xylem that gets fragmented. To make a link between the Model 3 and the pxy mutant, a careful study of the different stages of this phenotype could be useful to do, both on the modelling and experimental side.

The authors have a parameter search strategy based on matching the proportion of cell types in Model 3. I am wondering how effective is this strategy in a system where these features are evolving in time, especially in Model 3, which seems to present a front instability. Moreover, this strategy does not tell anything about the model robustness for recapitulating the different features of the pattern.

In the last model, the authors try to link the cell wall thickness with the radiality of the divisions. Although the idea of looking at the division trajectories seems interesting, more clarity is needed to see how helpful is the radiality measure, and perhaps a better measure is needed - note that the proliferation trajectory in Figure 6C might have the same amount of ramifications than in Figure 6B, and therefore, effectively speaking, the amount of periclinal divisions might be the same in both cases. The authors claim that the increase of xylem thickness contributes in having a more radial growth, but this could be related to the cambium front instability, which seems to be more pronounced as well for higher xylem thickness.

On the experimental side, the claims about the proximal and distal cambium, together with the cell proliferation data are not very well supported with the presented data in Figures 2, 3A and S1. Moreover, these different figures seem to show different behaviors - are these sections at different stages of the hypocotyl? Also, seeing more of the H4 marker in a region of the tissue not necessarily indicates a higher proliferation rate (it could also simply be that cells are more synchronized in the S phase in that region of the cambium, and/or the cell cycle lasts for longer in that part of the tissue). A quantification and the proper repeats to support these claims is lacking. A quantitative and more extensive study of the pxy mutant would enable a better comparison with the simulated model. Is there PXYpro:CFP expression between the fragmented xylem?

This work might help progress in the field of understanding radial patterning in plants. The introduction and the first models could attract a more general plant audience, but once the models increase in complexity, the narrative and presented results are more relevant to those scientists more specialized in xylem and phloem formation.

Reviewer #3 (Public Review):

This manuscript seeks to provide mechanistic insight into the role of GJA1-20k in mitochondrial changes that protect against ischemia-reperfusion damage. In previous studies, this group has shown that GJA1-20k protein increases in response to ischemic stress, localizes to mitochondria, promotes mitochondrial biogenesis, and mimics ischemic preconditioning protection in the heart. These changes did not coincide with changes in mitochondrial dynamics proteins, but the increase in GJA1-20k provides protection through an unknown mechanism. This makes it a potentially attractive therapeutic candidate for protection against ischemia.

The evidence in this manuscript shows that mitochondrial size is affected by GJA1-20k, as over-expression of this fragment reduced mitochondrial area. The authors argue that this change in morphology is independent of Drp1 activity, and actin dynamics drive mitochondrial division. These ultrastructural changes coincide with cytoprotective effects during reperfusion following ischemic events by limiting ROS production.

Strengths:

The data on ultrastructural changes is convincing, and GJA1-20k induced a decrease in mitochondrial size. The imaging looks good and quantification is helpful in the evaluating the impact of these changes.

To complement the use of proposed Drp1 inhibitors, the authors use genetic knock-down (KD) of Drp1, and the KD looks robust. Still see some Drp1 colocalization on the mitochondria in the KD, but the levels are diminished.

The decrease in ROS when HEK cells were treated with H2O2 is convincing. And this coincides with the decreased respiration capacity observed in the Seahorse analysis. This provides some mechanistic insight about a specific change in mitochondrial function that contributes to the protective effects observed.

Weaknesses:

With the introduction of GJA1-20k, there is clearly a difference in mitochondrial size, and total mitochondrial content appears unaltered (i.e. Tom20 does not increase). Previously it was suggested that mitochondrial biogenesis was increased with increased levels of GJA1-20k. Is this a difference in the cellular model (HEK) and do the changes in cell culture accurately recapitulate the changes seen in animals? Having more mitochondrial mass despite decrease in the avg. size of these organelles may represent an important difference.

Mdivi-1 is not a selective Drp1 inhibitor. It is a Complex I inhibitor, leading to unintended changes in mitochondrial dynamics in response to ETC stress. Rather than Mdivi-1, a dominant negative Drp1 mutant K38A could be overexpressed to see whether this prevents GJA1-20k-mediated fission. If it still goes through, then I agree that Drp1 is not involved at all.

For the kinetics studies (see Fig 4), I think it is important to measure the timing of the actin recruitment and eventual fission when Drp1 is knocked down and/or when a DN mutant (K38A) is involved. Again, I do not trust the chemical inhibitor (Mdivi-1) data since this does not inhibit Drp1 activity.

The assessment of the impact of ischemic stress with the heterozygous animal (M213L/WT) is hard to interpret. How reduced is the expression of GJA1-20k in these animals and how is mitochondrial function impacted based on Seahorse analysis? The mitochondrial morphology is not altered in these animals, so would mitochondrial function be largely unchanged as well? It is not clear how much GJA1-20k is needed to observe changes in mitochondrial shape and function, and comparisons with the homozygous mutant (M213L/M213L) are not the same, making it difficult to resolve the interpretation of these data.

It is still unclear to me how GJA1-20k is affecting mitochondrial size and function. Based on previous papers, this peptide localizes to the surface of mitochondria, but it is not clear how, or whether, it directly facilitates actin recruitment. The interplay with the endoplasmic reticulum (ER), which can nucleate actin at sites of mitochondrial fission, was not examined. If actin is driving membrane remodeling, is it mediated by ER crossover at these sites?

Reviewer #2 (Public Review):

Shimura et al. have discovered that GJA1-20K may provide protection in ischemic hearts through polymerizing actin around mitochondria and inducing mitochondrial fission. The authors use a series of elegant genetic, chemical, biochemical and cell biology studies including the use of the Gja1 M213L mouse line, which is unable to generate the 20kD Gja1 isoform, in order to determine that the beneficial effects of GJA1-20K. Specifically, the authors discovered that this beneficial effect is due to decreased reactive oxygen species (ROS) generation from smaller mitochondria. The overall work is well done but additional discussion should be provided about the impact of the work, particularly how the work may help realize a goal of therapeutically achieving ischemic preconditioning that has not been achieved in more than 30 years since ischemic preconditioning was first recognized.

Reviewer #1 (Public Review):

Mitochondria hyperfission during ishchemia or hypoxia is generally thought as an index of the severity of cytotoxicity, but this group originally identified Cx43 truncated peptide, GJA1-20K, which induces cardioprotection against ischemia/reperfusion injury in mice through promoting mitochondrial fission. Their proposal that GJA1-20K induces mitochodnrial fission independently of Drp1 activation is interesting, but their indirect results are weak to support it.

Reviewer #3 (Public Review):

Nguyen Lam Vuong et al performed a nested case control study of a multisite, multicountry prospective dengue study (IDAMS) to identify early biomarkers at day 1-3 of illness onset that predicts for severe dengue of ten biomarkers. Ten biomarkers from the inflammatory, immune or vascular pathways (VCAM-1,SDC-1, Ang-2, IL-8, IP-10, IL-1RA, sCD163, sTREM-1, ferritin, CRP) were chosen based on prior literature and understanding of dengue pathogenesis in severe disease. The biomarkers were measured at two time points: at enrollment (illness day 1-3) and after recovery(day 10-31 ). They find moderate-to strong positive correlations for some markers, particular IP-10 and IL-1RA, and IP-10 and VCAM-1, ( Spearman's rank correlation coefficients above 0.6). Interestingly, in their single modal analysis, they also find differences in biomarkers levels in children compared to adults, Associations between SDC-1 and IL-8 and the S/MD endpoint were stronger in adults than children, while the effects of IL-1RA and ferritin were stronger in children than adults. When global analysis was performed, only SDC-1 and IL-1RA were the most stable relative to the single models for both children and adults. And the the differences of the associations between children and adults were more marked, particularly for Ang-2, IL-8 and ferritin. When the biomarkers were combined, for children, the best subset that showed the clearest association with S/MD was the combination of the six markers IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1 with an AIC of 465.9. For adults, the best subset included the seven markers SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 This manuscript certainly provides useful insight into the biomarkers that are involved in the early phase of dengue before onset of vascular leakage or severe dengue which is valuable as most previous publications mainly focused on measurement of these markers after onset of severe disease which was often too late for meaningful interpretation of the disease biology or of limited clinical utility. The conclusions of this paper are mostly well supported by data, but some aspects of study and data analysis need to be clarified in order to improve understanding of the statistical methodology and readability.

Major Strengths:

• More than 7000 participants ( children and adults) in eight countries across Asia and Latin America were enrolled in the IDAMS study
• Prospective and systematic blood sampling starting from day 1 of illness onset
• Cases were laboratory confirmed via PCR or NS1 testing
• Cases and control were fairly well- matched
• Strong rationale for selection of host biomarkers

Weaknesses:

• Three quarter of cases from one country
• Serotype-1 biased

Specific comments to address:

1) For general ease of readership, it would greatly help if the authors can explain the choice of the statistical method used in the data analysis and perhaps briefly explain the model and how AIC should be interpreted in the main rather than the supplementary text).

2) While this reviewer understands that the authors want to focus on host immune and inflammatory biomarkers but it would be helpful if NS1 and viremia data are also shown ( at least in supplementary data) if these have been found not to correlate with disease severity.

3) It is Interesting to note that some biomarkers ( particularly the vascular markers) in severe group do not return to the same baseline as mild cases at convalescence even after >20 days. Whether such individuals already are at higher inflammatory state at baseline (pre-infection) as a result of underlying co-morbidities such as obesity or diabetes? Table 1 did not provide such information but would be interesting to show if there is any difference in health state in the 2 groups especially for obesity.

4) It is rather confusing that the 2nd paragraph of discussion stated "Balancing model fit, robustness, and parsimony, we suggest the combination of five biomarkers IL-1RA, Ang-2, IL-8, ferritin, and IP-10 for children, and the combination of three biomarkers SDC-1, IL-8, and ferritin for adults to be used in practice."

But the concluding paragraph went on to state "The best biomarker combination for children includes IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1; for adults, SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 were selected." This should be clarified further.

Reviewer #2 (Public Review):

Summary:

In this study, Vuong and colleagues conducted a case control study nested within a larger longitudinal and multi-country observational study to evaluate 10 biomarkers. These biomarkers were selected based on the strength of evidence in the literature and the current understanding of dengue pathogenesis. Using a 1:2 ratio of severe/moderately severe dengue cases to uncomplicated dengue controls, the authors examined the trends of the expression of these biomarkers during acute illness (days 1-3 from illness onset) as well as early (10-20 days from illness onset) and late (>20 days from illness onset) convalescence. The identified several biomarkers that were expressed at higher levels during acute illness in cases than controls and showed that these could be used in combination to predict those at increased risk of severe/moderately severe dengue. Notably, the authors identified different sets of biomarkers for paediatric and adult dengue cases, suggesting that the underlying pathophysiology of severe disease may differ in these groups of dengue cases. The authors concluded that the biomarkers they identified would be a major public health benefit to allocate healthcare resources during dengue outbreaks, and suggested that these biomarkers could also be applied as biological endpoints in dengue clinical trials.

Strengths:

This is a fairly sizeable study involving 281 severe/moderately severe dengue cases and 556 uncomplicated dengue controls. The authors combined data clinical observation data with those derived from serum protein measurements and analysed them using sophisticated statistical approaches.

The search for biomarkers predictive of the risk of severe dengue has spanned decades. This study distinguishes itself from others in its study design and the systematic selection of biomarkers for evaluation. Avoidance of untargeted screening reduces the likelihood of chance discovery and makes the findings more statistically robust.

The findings have useful practical applications. Severe dengue manifests typically around the period of fever defervescence at around days 4-7 from fever onset. Application of biomarkers in the acute febrile phase of illness could thus provide a 2- to 3-day lead time to triage those at risk of severe dengue for closer monitoring and management.

Weaknesses:

The main weakness is the exclusion of virological markers, such as plasma/serum viral RNA levels or NS1 antigenaemia. Indeed, previous observations have found severe dengue patients to have higher viraemia in the acute phase of illness compared to those with uncomplicated dengue. More recently, several mechanistic studies have suggested that dengue virus NS1 protein could bind endothelial cells to disrupt its integrity, leading to vascular leakage. Indeed, the authors have pointed out these findings in lines 20-25 on page to lines 1-2 on page 6. Despite these reports, it is curious that the authors have not included either viraemia or NS1 antigenaemia as possible biomarkers for severe dengue.

The manuscript in its present form may favour those with a strong statistical background to fully appreciate the nuances. Clearer explanations on the statistical findings would, I think, be helpful to those without such statistical background but who would nonetheless be in positions to translate these findings into clinical practice.

Most of the cases included in this study had DENV-1 infection. The biomarkers identified in this study may thus be DENV-1 specific and may not be readily applied to triage dengue cases caused by other DENV infection.

Overall impression:

This study provides two interesting findings. Firstly, that there are biomarkers that can be further developed into clinical tests to triage dengue patients for management. Although this possibility will require further assay development - the Luminex platform used for multiplex measurements of these biomarkers is unlikely to be available in most clinical laboratories - this study does show proof-of-concept to justify the development of simpler and perhaps even point-of-care assays. Secondly, the finding that adult and paediatric dengue require different biomarkers to indicate risk of severe disease should also trigger more detailed clinical and basic science investigation into how age influence host response to infection.

Reviewer #1 (Public Review):

Dengue is the most common arboviral infections in humans. Better tools to effectively triage patients at risk for severe dengue are urgently needed to optimize use of healthcare resources. This well written manuscript by Nguyen Lam Vuong and colleagues assessed the associations of a panel of blood biomarkers on day 1-3 from symptom onset with the development of severe or moderate (S/MD) dengue in a large cohort of children and adults. Ten candidate biomarkers were selected, each representing important pathogenic processes in dengue. Overall, higher concentrations of the biomarkers increased the risk for S/MD dengue. Important differences between adults and children were found for the performance of several biomarkers. The performance of the individual biomarkers, as well as the best combination was assessed for children and adults.

Strengths: Particular strengths of this study are the uniqueness of the prospective cohort with a large number of participants from different countries and the availability of blood samples early in the course of infection.

Other strengths include the enrolment of both children and adults, which is important given the observed differences in dengue pathology, the consistent data collection and the use of standardized outcome definitions.

The authors selected the candidate biomarkers based on earlier pathogenesis studies, reflecting different pathogenic pathways in dengue (e.g. activation mononuclear cells, vascular pathology).

State-of-the-art statistical modelling was used to assess the performance of the biomarkers.

Weaknesses: The main aim of the study is to identify biomarkers that predict S/MD dengue early in the course of dengue. This requires biomarkers of which the levels change early after symptom onset. However, levels of several of the biomarkers did not change markedly between the two time points (early vs late), suggesting that the levels of these biomarkers had not yet changed on day 1-3, thereby questioning their use as 'early biomarkers'. The authors selected the biomarkers based on earlier pathophysiology studies. An alternative approach might have been to first measure a larger set of candidate biomarkers in a selection of patients and select only those biomarkers showing a clear change in the early phase.

The predictive values of many of the biomarkers was only modest or absent. In addition, some of the findings appear a bit counterintuitive. Examples include the trend of the association of IP-10 with S/MD dengue that changed from positive to negative in the global model, and the opposite trends of some of the biomarkers (e.g. IL-8, ferritin) in adults and children. The authors acknowledge the existence of differences in dengue pathology between children and adults, but could discuss the possible biological reasons in more detail. For example, why would specifically IL-8 or ferritin have an oppositie effect in children and adults.

The study does not include a validation cohort. The authors conclude that their findings 'assist the development of biomarker panels for clinical use.' Can the authors put into perspective the performance of their current combined biomarker panel to rule out S/MD dengue.

Overall, the authors show convincingly in a unique cohort that biomarkers can be helpful to triage dengue patients already in the first days from symptom onset. Identification of the best biomarkers for this goal, validation in other cohorts, and a better understanding of differences between children and adults are required before such panels can be introduced in daily clinical practice.

Reviewer #3 (Public Review):

In this paper McPherson and Bandres investigated temporal and regional features of spontaneous neural activity in the spinal cord of anesthetized unconscious rats from multi-unit electrophysiological recordings of neuronal activity in the lumbar spinal cord. Spontaneous temporally correlated neural activity in the mammalian central nervous system during unconsciousness is a feature of supraspinal circuits, and recent studies from resting-state fMRI in the spinal cord of non-human primate and in the human spinal cord indicate that spontaneous activity in the absence of sensory stimulus-evoked activity and spontaneous motor output is also a basic property of spinal cord circuits. Here the authors sought to provide more direct evidence of robust and temporally correlated spontaneous neuronal activity in the in vivo mammalian spinal cord by applying correlation-based analyses of activity with single neuron resolution from multi-unit electrophysiological recordings simultaneously in several dorsal and ventral regions of a lumbar spinal cord segment. They successfully demonstrated robust spontaneous activity in these regions, and their correlation analyses of temporal features of this activity, to infer functional connectivity between spontaneously co-active neuronal units, suggests functional connectivity between sensory- and motor-dominant regions of the spinal cord, in addition to intraregional connectivity. This includes finding evidence for mono- and di-synaptic neuronal interactions as well as excitatory and some inhibitory interactions. Evidence is also presented that the spatiotemporal patterns of this spontaneous activity could not be explained theoretically by randomly spiking interconnected neurons, leading the authors to speculate that the spontaneous activity is intrinsic to the spinal cord and may reflect some type of replay of more structured experience-dependent patterns occurring during conscious behavior. The origins and functional significance of this temporally correlated spontaneous activity, however, remain to be determined.

Strengths of the paper include: (1) Clearly presented descriptions of the authors' procedures for recordings of multi-unit electrophysiological activity with a dual-shank 32 channel microelectrode array positioned at lateral and medial regions of the lumbar hemi-cord. (2) Novel reconstructions of functional connectivity maps, from correlation-based analyses, which enabled some topological features of the activity correlations to be represented from microelectrode array geometry and location within the rat spinal cord. (3) Novel results at the single neuron level potentially indicating spontaneous functional connectivity between sensory and motor regions in the unconscious animal. (4) Appropriate discussion of important caveats associated with technical aspects of their correlation analyses including problems for inferring functional connectivity in the presence of polysynaptic connection pathways and shared synaptic inputs as well as limitations of detecting inhibitory connections via correlation-based approaches. (5) Insightful discussion of possible functions of persistent spontaneous connectivity during unconsciousness in the spinal cord including latent activity in spinal central pattern generators or ongoing activity of circuits involved in maintenance/regulation of physiological processes under anesthesia.

Weaknesses of the experimental approach and for potential functional interpretations include (1) the need for more elaboration of technical details about how the temporal correlations of neuronal activity was performed, and (2) electrophysiological measurements were confined to a single lumbar spinal segment, so that origins of the spontaneous activity including interactions between spinal and supraspinal regions, interactions between various spinal segments, and contributions of sensory afferent feedback despite anesthesia, could not be established. While attributing the patterns of spontaneous activity found to reflect intrinsic spinal circuit activity, the authors did not fully explore possible contributions of sensory afferent feedback, for example, by employing local deafferentation.

The results presented in general suggest spontaneous temporally correlated neural activity in the spinal cord during unconsciousness, consistent with the concept that such activity may be a general property of central nervous system circuits.

Reviewer #2 (Public Review):

This manuscript reports a study that sought evidence of patterned inter-areal activity in the spinal cord of anesthetized rats. This could be a very significant finding, with potentially important scientific and therapeutic implications. However, the Methods lack necessary details, and the Results raise substantial issues that need to be resolved. Until these gaps and uncertainties are resolved, it is not possible to evaluate the results and their implications with confidence. Substantial revisions are essential.

Reviewer #1 (Public Review):

This report investigates spontaneous neural activity in the spinal cord of healthy adult male Sprague-Dawley rats under anesthesia. Urethane or isoflurane were used, and the effects were compared.

This manuscript is presented as a research advance that builds upon a 2014 eLife publication. It aims to address unresolved questions regarding the nature of spontaneous neural activity in the cord that give rise to observed resting state spinal cord networks in humans (and other species).

The similarity of results across anesthetic agents is important and I agree with the authors' conclusion that this provides strong evidence for persistent synchronous discharges between spinal cord regions during unconsciousness.

Finally, the authors do an appropriate job of describing the weaknesses of the study and how future experiments may continue this line of investigation.

Reviewer #2 (Public Review):

De and Horwitz deploy a focussed technique for testing the linearity of spatial summation for V1 neurons with spatial opponency, with the emphasis being on the properties of cells that encode chromatic information in a spatially opponent manner - so called double opponent cells. The technique isolates non-linearities of summation from non-linearities that occur after summation, by using an adaptive procedure to home in on stimulus contrasts in different color directions that produce a pre-defined criterion response. The authors conclude that many (but not all) double opponent cells embody linear spatial summation, and discuss implications for our understanding of the cortical circuitry that mediates color vision. The data appear carefully collected and generally well-analyzed. There are some points, elaborated in broad strokes below, where I think the paper would benefit from further elaboration of the data and its implications, and the paper would also benefit from some revisions to improve clarity.

• How are results affected by the cell classification criteria? The authors apply criteria to sort cells into four classes: simple, double opponent, NSNDO, and those not studied further. Response properties are then studied as a function of cell class. Criteria for classification include presence/absence of spatial opponency revealed by the pixel white noise measurements and the adequacy of a linear STA to describe the hyperpixel white noise data. I think more work is needed to clarify for the reader the extent to which these criteria, in and of themselves, affect the results for each class studied. In particular, if a linear STA describes the hyperpixel white noise data, shouldn't we then expect to find linear summation in the spatial receptive field in that sane hyperspectral white noise data? I understand, as the authors point out, that the Phase 3 measurements could reveal failures of spatial summation not seen in the hyperpixel white noise data. But I'm a bit perplexed by the outliers in the NLI indices in Figure 3D. What properties of these cells allow a linear 6D STA to handle the hyperpixel white noise data well, but cause them to summate over space non-linearly for that same hyperpixel white noise data? In terms of the new information provided by the Phase 3 measurements, I wasn't able to get a sense of how much harder these stimuli were driving the cells than the Phase 2 measurements. It seemed like this was the intent of Figure 2 - Figure Supplement 1 and Figure 3 - Figure Supplment 1, but those two figures in the end didn't provide this information in a manner I could digest. Absent this, it was hard to tell how much more we are learning from the Phase 3 data. Could the higher NLI's here than in Phase 2 be a consequence of some stimuli but not others driving the neuron into saturation? And although the authors write on page 15 "Nevertheless, we found that nonlinearities detected in Phase 2 of our experiment were a good indicator of nonlinearity over the greater stimulus duration and range of contrasts in Phase 3, principally for the NSNDO cells (Figure 3E)", those correlations look very weak to me. I was left hoping for a better understanding the commonalities and differences in the data between Phases 2 and 3. I'm also not sure of the reliability of the measured NLI's for each cell with each method. Can anything more be provided about that? I note here that I did study the section of the discussion that nominally addresses some if these issues, and that my comments above remain after that study.

• Implications of the results for models. As the authors summarize in their introduction, the motivation for testing the linearity of spatial summation is that the results can guide how we formulate response models for V1 chromatically sensitive cells. More discussion of this would be helpful. As an example, could cells with the non-linear spatial filtering as shown in Figure 1C be classified as DO, making them relevant to the focussed tests applied in this paper? Or are they necessarily NSNDO? More generally, can the authors spend a little time discussing what classes of response models they would pursue for DO cells that do/don't show linear spatial summation, and for NSNDO cells that do/don't show linear spatial summation. Such discussion would tie the results of the primary data back to the motivating question in a more satisfactory manner, I think. Such discussion could also be used as a vehicle to discuss what the authors think about the DO cells that fail to show linear spatial summation and the NSNDO cells that do, something I found under-treated in the results. As with the comment above, I did read the sections of the paper that speak to this question, but still find it that it would benefit from going deeper.

• Color properties of subfields. The study measures detailed properties of cells that show at least two distinct subfields in the initial pixel white noise analysis. The paper focuses on whether signals from such subfields are combined linearly before any downstream linearities. However, there is another feature of the data that seems central to understanding these cells, and that is what the chromatic properties of these subfields are, and how strong in the data the constraint that the chromatic properties of the two separate subfields be complementary is. It is stated in passing (page 7) that "the two sides of the hyper pixel STA were complementary or nearly so", but it would be nice to see this treated in more detail and also to understand whether there are differences in the distribution of the chromatic properties of the two sides between the DO and NSNDO cells, and between cells with low and high non-linearity indices.

Reviewer #1 (Public Review):

There are very few studies on the spatial integration of color signals of V1 receptive fields, which is a striking gap in knowledge given the importance of color to primate vision and the powerfulness that spatial analysis of luminance contrast integration has proven for understanding how V1 works. This paper helps fill this major gap in knowledge. The main take home is that double opponent cells and simple cells are more likely to be linear in how they integrate signals across their receptive fields than a sample of non-double-opponent/non-simple cells. This conclusion is consistent with the limited data presently in the literature, and I wonder if further analysis of the rich dataset could uncover some deeper insights.

Reviewer #3 (Public Review):

Wodeyar et al. suggest a new method for estimating the phase of oscillatory signals in real-time, based on a state-space objective. They test their approach in simulations and data and present evidence for higher accuracy compared to standard methods based on band-pass filtering. While I especially find the possibility of credible intervals highly interesting in this context, the relationship of credible intervals to an amplitude criterion threshold criterion, customary employed by standard approaches should be elucidated more, it's not clear to whether this practically results in very similar outcomes. In addition, it would be good to see clarifications on the underlying data generating process and physiological motivation for the provided simulations. It would increase accessibility of the manuscript, if the text would be more self-contained & more methodological details were included.

Reviewer #2 (Public Review):

Wodeyar and colleagues describe a new method for phase estimation and compare their method to a range of previously published approaches. Using a state space model, they separately model the signal and noise, and demonstrate accurate phase tracking for broadband signals, and in the presence of multiple rhythms and phase-resets.

The major strength of the manuscript is the ability to track broadband signals without the need to use bandpass filters and to better distinguish between multiple rhythms, even those which are quite close in frequency. The methods and results segments are very well written and describe the approach in great detail. The manuscript also allows the reader to compare multiple methods, commonly used in the field. Processing rhythms without the need for a threshold based method is an added contribution of the method.

The main weaknesses of the manuscript are (1) not being able to compensate for non stationary rhythms (2) and in-vivo phase estimation accuracy. For real-time closed loop phase-locked stimulation, stimulation itself has been shown to speed-up / slow down target rhythms depending on the stimulation angle, and also different rhythms have been shown to drift over time, therefore compensating for non stationary centre frequencies could be critical for such applications. Based on previously published phase-locked stimulation papers, an average 60 degree phase estimation accuracy (in vivo) may not be sufficient to determine effective stimulation parameters.

While the paper makes a great contribution to phase estimation by removing the dependency on filters, whether or not this would actually improve applications (with respect to already trialled approaches) remains unclear.

Reviewer #1 (Public Review):

The phase of a signal (similar to its amplitude) is a significant and informative feature that helps for a better representation of time-series data. In Neuroscience, and in the context of neural oscillations, the phase of neural signals (for example EEG and LFP) plays an important role in understanding mechanisms underlying brain rhythms. The author of this paper proposed a novel approach to track the phase of neural signals in real-time. This approach is inspired by [Matsuda and Komaki, 2017] and employs the well-known state-space modeling framework. Using several synthetic data, it was shown that the proposed approach outperforms other methods in the literature which are based on band-pass filtering (not appropriate for broadband rhythms). The simulation studies were designed to demonstrate the strength of the state-space phase estimation approach vs. two recent methods in the context of common confounds such as broadband rhythms, phase resets, and co-occurring rhythms. As well, the state-space phase estimator was applied to in-vivo data including two datasets: (1) rodent LFP and (2) human EEG. Furthermore, the authors made their proposed method available online in the form of MATLAB code as well as a ready-to-use plug-in for the OpenEphys acquisition system. This effort is very much appreciated as it provides the code available for further theoretical and experimental studies.

While the proposed method is very novel and timely, it would be helpful for the authors to: (i) consider the impact of noise in the phase estimation, (ii) describe specifications of the Kalman filter and its robustness, and (iii) consider the performance of the estimated phase relative to other methods.

Reviewer #3 (Public Review):

The authors effectively utilized Beta5T-iCre to specifically manipulate beta-catenin expression in TECs and definitively showed that careful control of beta-catenin within TECs is needed for the proper development of TEC microenvironments critical for T cell development.

Strengths:

1) The methods used allowed the authors to effectively targeted TECs while avoiding extrathymic side effects of manipulating beta-catenin in ways that impacted skin or other tissues leading to abortive development or improper separation of the thymus and parathyroid.

2) The results showed that beta-catenin GOF specifically in TECs results in thymic dysplasia and loss of thymic T cell development.

3) The results from the analysis of beta-catenin LOF indicate that beta-catenin in TECs is not essential for the generation of functional TECs that support T cell development but the loss of beta-catenin in TECs results in the reduction in the number of cTECs, which leads to the reduction in the number of thymocytes during the postnatal period.

4) The results demonstrated that GOF of beta-catenin in TECs results in trans-differentiation of TECs into terminally differentiated keratinocytes.

Weakness:

The fact that beta5T expression is restricted primarily to cTECs suggests that the models used may not accurately capture the impacts of gain of function and loss of function of beta-catenin to mTECs and the maintenance of the medulla in postnatal mice. Given that beta5T-expressing cells have been shown to give rise to both cTECs and mTECs during fetal development the models may more closely demonstrate the importance of fine-tuning beta-catenin expression during fetal development while missing impacts on postnatal mTECs.

The authors achieved their aims and the results strongly support their conclusions.

The work clearly demonstrates the importance of proper regulation of Wnt/beta-catenin signaling in the development and maintenance of TEC microenvironments and should lead to more interest in defining the specific Wnts and Frizzleds that are important in the development and postnatal maintenance of specific TEC subsets. This work will be important in identifying clinical strategies to counteract thymic involution and the subsequent loss of T cell function.

Reviewer #2 (Public Review):

The authors describe how modulating the levels of beta-catenin in TECs affects thymic organization and thymopoiesis. They use a b5t-Cre to specifically stabilize or ablate beta-catenin in TECs. While stabilization of beta-catenin induces thymic dysplasia and significantly impedes development of thymocytes beyond the DN1 stage, loss of beta-catenin has a milder outcome limited to significantly reduced thymic weight starting and an overall reduction in thymocyte number but no significant effects in thymocytes subset distribution at 2weeks of age. This reduction of thymic weight is associated with a significant and selective reduction in the number of cTECs . On the basis of these findings the authors conclude that fine tuning of beta-catenin levels is essential for postnatal T cell development.

Overall this is an interesting but descriptive study that does not address the physiological and molecular effects of stabilizing or ablating beta-catenin in TECs. The authors suggest that stabilization of beta-catenin function in TECs results in their terminal differentiation to keratinocytes on the basis of increased expression of only two markers involucrin and loricrin, which is a limited definition and further analysis of these cells would be needed for this conclusion. On the other hand the interesting selective effect of loss of beta-catenin function on cTECs versus mTECs has not been analyzed. Are cTECs reduced due to loss of survival/proliferation/differentiation? How is their molecular profile affected by the loss of beta-catenin? Does the selective reduction of the cTEC compartment affect survival/proliferation/differentiation of the individual DN subsets? The paper would benefit from more in depth mechanistic analysis in these directions.

Reviewer #1 (Public Review):

The work by Fujimori et al. addresses the role of downstream WNT signaling in thymus epithelial cell (TEC) differentiation and function. A TEC-specific beta5t-cre driver allowed for the generation of gain of function (GoF) or loss of function (LoF) mouse models. The specificity of the beta5t-cre driver system was key in allowing the authors to focus on TEC effects. Of note, the LoF of beta-catenin showed a smaller thymus with fewer cortical TEC, but generally no changes in the thymus morphology or in the ability to support normal percentages of thymocyte subsets. These results clearly establish that WNT signaling plays a minor role in TEC differentiation and function. Nevertheless, the GoF approach led to thymus dysplasia with a loss of TEC identity, due to a loss of FOXN1 expression, as well as a failure to support T cell development. These results point to a role for WNT signaling in inducing the TEC differentiation into other non-T-cell-development-supporting epithelial subsets.

Reviewer #3 (Public Review):

The manuscript by Xiang and Bartel explores the molecular coupling of poly(A) tail length and translational efficiency (TE) in frog oocytes and various mammalian cell lines. From their experiments they draw several broad conclusions. Firstly, it is that limiting amounts of PABPC in frog oocytes is the basis for coupling between poly(A) tail length and TE. Secondly, in mammalian somatic cell lines PABPC contributes little to TE and transcript with TUT4 and TUT7-mediated uridylation promoting degradation of transcript with short poly(A) tails. Overall, the experimental design is excellent. The conclusions drawn from the frog oocytes are strongly supported by the data provided whereas the cell line studies are more open to interpretation due to the drastic consequences of PABPC depletion.

Reviewer #2 (Public Review):

Poly(A) tails are generally thought to stabilize mRNAs and promote translation. However, the mechanisms of this process have been difficult to experimentally assess due to the essential nature of poly(A) binding proteins, homeostatic mechanisms in gene expression, and the pleiotropic effects of altering the transcription, translation or mRNA decay machinery. The length of poly(A) tails are directly proportionally to translational efficiency in early development - the longer the tail, the more efficiently the mRNA is translated - possibly through a closed loop model. However, experiments in other cells, as well as in vitro reconstitution and imaging of single mRNAs in cells, do not support either coupling of poly(A) tail length and TE, or the closed loop model. Thus, it appears that there is a switch from embryonic to post-embryonic regulation of TE. The mechanistic basis for this switch was unclear.

Here, Xiang and Bartel use reporter assays and transcriptome-wide sequencing technologies, alongside other complementary experiments, to determine the specific circumstances that permit coupling of poly(A) tail length and translational efficiency. The authors are able to synthesize many observations - both from their own lab and from others - to come up with a unified hypothesis. Many of the individual findings have been previously reported or hypothesized but no other work has brought all of these together in one study.

Overall, the data strongly support the conclusions. Importantly, several different cell types and systems are used. In addition, a number of different methods support the work - including reporter assays, global analyses, experiments in extracts, oocytes and cell lines, etc.

A description of events that lead to the switch from embryonic to post-embryonic regulation is still lacking. However, the insight provided here is substantial. It will have influence on many areas of study of gene expression - for example, it helps to explain discrepancies in miRNA function.

Reviewer #1 (Public Review):

This is an excellent manuscript in which Bartel and colleagues use an abundance of approaches to provide compelling evidence relevant to the coupling between poly(A)-tail length and translational efficiency. Without reiterating the results, the data are convincing and the paper is clearly written. Any concerns are too trivial to articulate.

Reviewer #3 (Public Review):

Ma et al investigate the effect of racial and ethnic differences in SARS-CoV-2 infection risk on the herd immunity threshold of each group. Using New York City and Long Island as model settings, they construct a race/ethnicity-structured SEIR model. Differential risk between racial and ethnic groups was parameterized by fitting each model to local seroprevalence data stratified demographically. The authors find that when herd immunity is reached, cumulative incidence varies by more than two fold between ethnic groups, at approximately 75% of Hispanics or Latinos and only 30% of non-Hispanic Whites.

This result was robust to changing assumptions about the source of racial and ethnic disparities. The authors considered differences in disease susceptibility, exposure levels, as well as a census-driven model of assortative mixing. These results show the fundamentally inequitable outcome of achieving herd immunity in an unmitigated epidemic.

The authors have only considered an unmitigated epidemic, without any social distancing, quarantine, masking, or vaccination. If herd immunity is achieved via one of these methods, particularly vaccination, the disparities may be mitigated somewhat but still exist. This will be an important question for epidemiologists and public health officials to consider throughout the vaccine rollout.

Reviewer #2 (Public Review):

Overall I think this is a solid and interesting piece that is an important contribution to the literature on COVID-19 disparities, even if it does have some limitations. To this point, most models of SARS-CoV-2 have not included the impact of residential and occupational segregation on differential group-specific covid outcomes. So, the authors are to commended on their rigorous and useful contribution on this valuable topic. I have a few specific questions and concerns, outlined below:

1) Does the reliance on serosurvey data collected in public places imply a potential issue with left-censoring, i.e. by not capturing individuals who had died? Can the authors address how survival bias might impact their results? I imagine this could bring the seroprevalence among older people down in a way that could bias their transmission rate estimates.

2) It might be helpful to think in terms of disparities in HITs as well as disparities in contact rates, since the HIT of whites is necessarily dependent on that of Blacks. I'm not really disagreeing with the thrust of what their analysis suggests or even the factual interpretation of it. But I do think it is important to phrase some of the conclusions of the model in ways that are more directly relevant to health equity, i.e. how much infection/vaccination coverage does each group need for members of that group to benefit from indirect protection?

3) The authors rely on a modified interaction index parameterized directly from their data. It would be helpful if they could explain why they did not rely on any sources of mobility data. Are these just not broken down along the type of race/ethnicity categories that would be necessary to complete this analysis? Integrating some sort of external information on mobility would definitely strengthen the analysis.

Reviewer #1 (Public Review):

Strengths:

1) The model structure is appropriate for the scientific question.

2) The paper addresses a critical feature of SARS-CoV-2 epidemiology which is its much higher prevalence in Hispanic or Latino and Black populations. In this sense, the paper has the potential to serve as a tool to enhance social justice.

3) Generally speaking, the analysis supports the conclusions.

Other considerations:

1) The clean distinction between susceptibility and exposure models described in the paper is conceptually useful but is unlikely to capture reality. Rather, susceptibility to infection is likely to vary more by age whereas exposure is more likely to vary by ethnic group / race. While age cohort are not explicitly distinguished in the model, the authors would do well to at least vary susceptibility across ethnic groups according to different age cohort structure within these groups. This would allow a more precise estimate of the true effect of variability in exposures. Alternatively, this could be mentioned as a limitation of the the current model.

2) I appreciated that the authors maintained an agnostic stance on the actual value of HIT (across the population & within ethnic groups) based on the results of their model. If there was available data, then it might be possible to arrive at a slightly more precise estimate by fitting the model to serial incidence data (particularly sorted by ethnic group) over time in NYC & Long Island. First, this would give some sense of R_effective. Second, if successive waves were modeled, then the shift in relative incidence & CI among these groups that is predicted in Figure 3 & Sup fig 8 may be observed in the actual data (this fits anecdotally with what I have seen in several states). Third, it may (or may not) be possible to estimate values of critical model parameters such as epsilon. It would be helpful to mention this as possible future work with the model.

Caveats about the impossibility of truly measuring HIT would still apply (due to new variants, shifting use & effective of NPIs, etc....). However, as is, the estimates of possible values for HIT are so wide as to make the underlying data used to train the model almost irrelevant. This makes the potential to leverage the model for policy decisions more limited.

3) I think the range of R0 in the figures should be extended to go as as low as 1. Much of the pandemic in the US has been defined by local Re that varies between 0.8 & 1.2 (likely based on shifts in the degree of social distancing). I therefore think lower HIT thresholds should be considered and it would be nice to know how the extent of assortative mixing effects estimates at these lower R_e values.

4) line 274: I feel like this point needs to be considered in much more detail, either with a thoughtful discussion or with even with some simple additions to the model. How should these results make policy makers consider race and ethnicity when thinking about the key issues in the field right now such as vaccine allocation, masking, and new variants. I think to achieve the maximal impact, the authors should be very specific about how model results could impact policy making, and how we might lower the tragic discrepancies associated with COVID. If the model / data is insufficient for this purpose at this stage, then what type of data could be gathered that would allow more precise and targeted policy interventions?

Minor issues:

-This is subjective but I found the words "active" and "high activity" to describe increases in contacts per day to be confusing. I would just say more contacts per day. It might help to change "contacts" to "exposure contacts" to emphasize that not all contacts are high risk.

-The abstract has too much jargon for a generalist journal. I would avoid words like "proportionate mixing" & "assortative" which are very unique to modeling of infectious diseases unless they are first defined in very basic language.

-I would cite some of the STD models which have used similar matrices to capture assortative mixing.

-Lines 164-5: very good point but I would add that members of ethnic / racial groups are more likely to be essential workers and also to live in multigenerational houses

-Line 193: "Higher than expected" -> expected by who?

-A limitation that needs further mention is that fact that race & ethnic group, while important, could be sub classified into strata that inform risk even more (such as SES, job type etc....)

Reviewer #3 (Public Review):

In this work, Chattaraj and colleagues utilize simulation models to study collective behaviors of molecules with multiple binding sites (multivalency). When the concentrations are low, the molecules do not bind to each other frequently, and they are called free. On the other hand, if the concentrations increase, they start to bind and eventually form a wide network of molecules connected by molecular binding. This transition can be considered as a model for liquid-liquid phase separation. Their major claim is that the solubility product, a simple product of the concentrations of the free molecules, can be used as a proxy to the phase separation threshold (known as the saturation concentration). They observed in various simulation conditions that as the total concentration of molecules increases, the solubility product first increases but eventually converges to a certain value, and the value is consistent over different simulation conditions. The value is the upper limit of the solubility product, after which the molecules start to form a molecular network.

After establishing the model, they tested systems with different valences. Higher valency leads to reduction of the threshold (and phase separation occurs at lower concentrations). The theory was also valid for systems with non-equal valences (e.g. pentavalent A + trivalent B). They applied their models to a three-component system, and found that the results qualitatively explain the published experimental patterns. Lastly, using off-lattice coarse-grained simulations, they show that the linker flexibility and the spacing of binding sites are important determinants of the threshold, which confirms the findings from other computational and experimental works.

The authors successfully defend their claim by using different types of simulations, and their methods to crosscheck the physical validity of their models may be useful for other simulation works. For example, the authors checked if increasing the number of molecules and reducing the system size give the same results for equal concentrations. Also, they employed two different methods (so-called FTC and CMC in the manuscript) to determine the threshold concentrations. However, the conclusions are not easily transferable to real biopolymer systems, since it is hard to determine the valences (and binding affinities) of biopolymers such as intrinsically disordered proteins.

Reviewer #2 (Public Review):

This paper asks whether systems composed of more than one component (heterotypic) that undergo liquid-liquid phase separation will follow the same rules as ionic solutions. The question is motivated by (i) the behavior of homotypic solutions, where after phase separation, monomer concentrations remain fixed despite addition of new components, which is not true for heterotypic systems and (ii) the known behavior of multivalent ionic salts. This idea has not previously been tested. They show quite clearly through simulations that the solubility product, Ksp, can be used as a quantitative metric to delineate phase transition behavior in heterotypic systems. This is a valuable contribution to the understanding of phase separation in these systems, and could be impactful in analyzing experimental observables, at least in vitro, to determine the valency of interacting systems. It provides a relatively straightforward conceptual basis for observed partitioning of components into dilute and dense phases. The result seems robust and likely to be reproducible experimentally and through alternative simulation studies, particularly given its established history in quantifying the related phenomena in ionic salts.

A weakness is the rather qualitative comparison to experiment, which is justified by the authors based on the unknown valency of the experimental system. There is also no quantitative comparison between simulation types (spatial vs non-spatial). However, the simulations do seem sufficiently detailed to test and validate the Ksp concept.

Strengths:

• The paper is very focused, and uses multiple simulation 'experiments' to test the role of the Ksp in delineating the phase transition, showing good agreement for multiple systems, with both matched and distinct stoichiometries between the components. They see typical behavior at the phase transition point, where they observe the largest variability or fluctuations in the formation of the dense phase. Thus the results strongly support the conclusion that the Ksp delineates phase transitions in these 2-3 component systems.

• A comparison is made to a recent experimental result with three components, showing qualitative agreement with an observed lack of buffering, which was unexpected at the time due to the behavior observed for homotypic systems. Here this result is now rationalized via the Ksp, which does plateau despite the monomer concentrations changing.

• Spatial simulations probe the role of structure and flexibility in impacting phase separation, finding general agreement with previously published experimental and modeling work. These observations about flexibility and matched valency are also relatively intuitive.

Weaknesses

• There is no quantitative comparison between the two simulation approaches (spatial and non-spatial), which should be straightforward. By using the same composition and KD in both types of simulations and directly comparing outcomes, it would help explain when and why the spatial simulations differ from the non-spatial ones-see subsequent comments below:

• A related methodological point: On Line 97 it states that NFSim does not allow intramolecular bonds to form, but this is not true. On one hand, they can be written out explicitly. E.g. A(a1!1, a2).B(b1!1, b2)->A(a1!1, a2!2).B(b1!1, b2!2), would form a second bond between an AB complex that already had one bond. While quite tedious, these could be enumerated, allowing for the zippering effect they see spatially, although the rates would not be bimolecular. This would still leave out intra-complex bonds between proteins without a direct link. However, based on the NFsim website, by default it does in fact allow these types of intra-complex bonds to be formed (http://michaelsneddon.net/nfsim/pages/support/support.html) see "Reactant Connectivity Enforcement". So it is not clear to me which option was used in this paper. According to what is written in the methods, no intra-complex bonds are formed, but this is not the default in NFsim and is indeed allowable.

• The spatial simulations do not show the bimodal distribution under the fixed concentrations (Fig S9). This is a significant difference from the non-spatial result. They attribute this to a 'dimer trap', but given they see the dense phase in the clamped monomer simulations, this cannot be the only explanation. What about kinetic effects, due to the differences in initial concentrations of monomers in the two simulation approaches? The rate constants are not listed anywhere. They only seem to see large clusters at fixed concentrations for the mismatched sizes (Fig S12B), where the Ksp behavior does not hold. Can they increase monomer flexibility more and start to see bimodal at fixed concentration, or change the rates and see a bimodal distribution?

• Related-I am surprised that the sterically hindered monomers would not form large clusters at fixed concentration, as it looks like it is impossible for them to 'zipper' up their binding sites and become trapped in dimers. Is the distribution at fixed concentrations bimodal? The data is not shown.

Reviewer #1 (Public Review):

The gist of this work is that the simple concept of a solubility product determines a threshold for phase separation, thereby enabling buffering even in systems where phase separation is driven by heterotypic interactions. The solubility product or SP is determined by the number of complementary interaction sites and the coordination number i.e., the number of bonds one can make per site.

The work appears to be motivated by two questions: Are concentrations buffered in systems where heterotypic interactions drive phase separation thereby negating the presence of a rigorously definable saturation concentration? This question was motivated by work from Klosin et al., showing how phase separation can enable buffering of noise in transcription. They relied on the concept of a saturation concentration. In a paper that followed a few months after, Riback et al., showed that the concept of a saturation concentration ceases to exist, as defined for systems where phase separation is driven purely by homotypic interactions. This was taken to imply that the formation of multicomponent condensates via a blend of homotypic and heterotypic interactions causes a loss of buffering capacity afforded by phase separation. The second question motivating the current work is the apparent absence of a theoretical framework for "varying threshold concentrations" in systems governed by heterotypic interactions.

Using two flavors of simulations, the authors propose that the SP sets an upper limit on the convolution of concentrations that determine phase separation. They show this via simulations where they follow the formation of clusters formed by linear multivalent macromolecules and monitor the emergence of a bimodal distribution of clusters. In 1:1 mixtures of multivalent macromolecules they find that SP sets a threshold beyond which a bimodal distribution of clusters emerges. The authors further find that SP sets an upper limit even in systems that deviate from the 1:1 stoichiometry.

The authors proceed to show that the SP is influenced by the valence of multivalent macromolecules. They also demonstrate that short rigid linkers can cause an arrest of phase separation through a so-called "dimer trap" reminiscent of the "magic number" postulate put forth by Wingreen and colleagues.

Is the work significant, novel, and timely? Effectively the authors propose that the driving forces for phase separation can be distilled down to the concept of a solubility product. Given prior knowledge of the valence, coordination number, and affinities can one predict concentration thresholds for phase separation? The authors suggest that this can be gleaned from either network based simulations, which are very inexpensive, or through more elaborate simulations. They further propose that it is the solubility product that sets the threshold.

It is worth noting that the authors are quantifying what is known in the physical literature as a percolation threshold. The seminal work of Flory and Stockmayer dating back to the 1940s showed how one can calculate a percolation threshold by taking in prior knowledge of valence, coordination numbers, and affinities whilst ignoring cooperativity. These ideas have been refined and advanced in several theoretical contributions by various labs. While none of the papers in the physical literature use the concept of a solubility product, they rely on the concept of a percolation threshold because the transition to large, system-spanning clusters is a continuous one and it is debatable if this is a bona fide phase transition. Rather it is a topological transition.

As for novelty, unfortunately the authors disregard prior work that showed how linker length impacts local vs. global cooperativity in phase transitions that combine phase separation and percolation. Ref. 23 is the work in question and it is mentioned in passing, even though the contributions here are entirely a redux.

The concept of a solubility product, introduced here to model / understand phase behavior of multivalent macromolecules, is an interesting and potentially appealing simple description. It might make the understanding of phase transitions more accessible, but it has problems: (a) it does not define phase separation; rather it defines percolation transitions; (b) without prior knowledge of the relevant quantities, the solubility product cannot be readily inferred, even from simulations, although one can scan parameter space to arrive at predictions regarding the apparent valence and coordination numbers. (c) the solubility product does not tell us much about properties of condensates, interfaces, or the driving forces for phase transitions that are influenced by the collective effects of interaction domains / motifs and spacers.

Finally, as for the absence of a theoretical explanation for the apparent loss of buffering in systems with heterotypic interactions, the authors would do well to see the work of Choi et al., published in PLoS Comput. Biol. in 2019. Figure 12 in that work clearly establishes that the concentrations of A and B species in the coexisting dilute phase are set by the slopes of tie lines - the lines of constant chemical potential. These slopes are set by the relative strengths of homotypic vs. heterotypic interactions, and to zeroth order, that is the physical explanation.

Overall, the two interesting observations are that the percolation threshold can be cast as a solubility product and that this product sets an upper limit on joint concentration thresholds for phase separation, even in systems with heterotypic interactions, thereby rescuing the concept of buffering.

Reviewer #2 (Public Review):

The manuscript by Li et al describes the development of styrylpyridines as cell permeant fluorescent sensors of SARM1 activity. This work is significant because SARM1 activity is increased during neuron damage and SARM1 knockout mice are protected from neuronal degeneration caused by a variety of physical and chemical insults. Thus, SARM1 is a key player in neuronal degeneration and a novel therapeutic target. SARM1 is an NAD+ hydrolase that cleaves NAD+ to form nicotinamide and ADP ribose (and to a small extent cyclic ADP ribose) via a reactive oxocarbenium intermediate. Notably, this intermediate can either react with water (hydrolysis), the adenosine ring (cyclization to cADPR), or with a pyridine containing molecule in a 'base-exchange reaction'. The styrylpyridines described by Li et al exploit this base-exchange reaction; the styrylpyridines react with the intermediate to form a fluorescent product. Notably, the best probe (PC6) can be used to monitor SARM1 activity in vitro and in cells. Upon validating the utility of PC6, the authors use this compound to perform a high throughput screen of the Approved Drug Library (L1000) from TargetMol and identify nisoldipine as a hit. Further studies revealed that a minor metabolite, dehydronitrosonisoldipine (dHNN), is the true inhibitor, acting with single digit micromolar potency. The authors provide structural and proteomic data suggesting that dHNN inhibits SARM1 activity via the covalent modification of C311 which stabilizes the enzyme in the autoinhibited state.

Key strengths of the manuscript include the probe design and the authors demonstration that they can be used to monitor SARM1 activity in vitro in an HTS format and in cells. The identification of C311 as potential reactive cysteine that could be targeted for drug development is an important and significant insight.

Key weaknesses include the fact that dHNN is a highly reactive molecule and the authors note that it modifies multiple sites on the protein (they mentioned 8 but MS2 spectra for only 5 are provided). As such, the compound appears to be a non-specific alkylator that will have limited utility as a SARM1 inhibitor. Additionally, no information is provided on the proteome-wide selectivity of the compound. An additional key weakness is the lack of any mechanistic insights into how the adducts are generated. Moreover, it is not clear how the proposed sulphonamide and thiohydroxylamine adducts are formed. From the images presented, it is unclear whether there is sufficient 'density' in the cryoEM maps to accurately predict the sites of modification. Finally, the authors do not show whether the conversion of PC6 to PAD6 is stable or if PAD6 can also be hydrolyzed to form ADPR.

Reviewer #1 (Public Review):

The authors aimed to develop cell-permeable small molecule probes that can monitor the activity of SARM1, an enzyme that hydrolyzes NAD+ and is thought to be important for axon degeneration. They successfully achieved this goal using the base exchange activity of SARM1 to make a donor-π-acceptor type of fluorophore. The best probe described in the manuscript is PC6. A number of experiments were carried to rigorously test that the probe works as expected. PC6 has a number of nice features. It is cell permeable, gives much stronger signal than any other probes known for SARM1, is specific for SARM1 and does not detect the activity of CD38 (another enzyme that has similar activity), and allows detection of endogenous SARM1 activation in neurons.

Using this probe PC6, the authors was able to monitor SARM1 activity in neurons treated with vincristine and demonstrated that SARM1 activation precedes axon degeneration and is important but not sufficient for axon degeneration. Most importantly, using this probe to monitor SARM activity, they screened a library of about 2000 drug molecules and discovered that a hypertension drug, nisoldipine, could inhibits SARM1. Surprisingly, further studies showed that a derivative of nisoldipine, dehydronitrosonisoldipine (dHNN, present in the nisoldipine compound used ), is actually the inhibitor of SARM1. They then carried nice mechanistic studies (including mass spectrometry and cryo-EM structures) showing that dHNN inhibits SARM1 by covalently modify Cys311 residue in the ARM domain. The dHNN binding site is similar to the previously established NAD+ inhibitory site.

Overall, the probe is novel with many useful features, the study is rigorous and rather complete, and the conclusion is well supported. I believe the study will be important for the field and will be well received by the field.

The only minor thing is that the writing can be further improved, especially in the introduction section.

Reviewer #2 (Public Review):

California health registries were linked to evaluate the relative cancer risks for first degree relatives of patients diagnosed between ages 0-26, Over the period 1989-2015, 29,631 cancer patients were identified and 62,863 healthy family members. The strengths are that this is a large population based study and that the relative cancer risk for specific ethnic groups could be investigated. The analyses were limited to mothers and siblings of children or adults with cancer. Increased relative risk of cancer is well established and these data add to this evidence base.

The major finding that the authors emphasise is increased relative risk of cancer for Latinos (compared to non Latinos). I am not convinced that they have much evidence for this as the Forest plots in the manuscript do not slow large differences between the two ethnic classifications. Their evidence for these differences comes from a separate comparison of the SIRs using an approximate Chi-squared test.

Although the authors comment that the results from the Chi-Sq test are not consistent with the specific group SIRs and 95%CIs, they do not explain how these results can be so different.

I am concerned that there is either an error in the calculations or an error in the assumptions. It is not acceptable to have such contradictory results between the two distinct methods.

For example, for hematological cancers the 95% CI for Latinos is entirely contained within the 95%CI for Non-Latino white, while this gives a p less than 0.05. The authors need to explore why these methods are giving very different answers and be clear that the low p-values are not simply an artifact of poor assumptions.

Reviewer #1 (Public Review):

This is a very well written and comprehensive paper that is a valuable contribution to the literature of childhood cancers. It shows that some childhood cancers have an inherited component and the risk could be to the mother or to the siblings. Although the relative risks are significant, childhood cancer is fortunately rare and the actual risk to the siblings is small.

Can we assume this is less than one percent? i think it would be helpful to provide some absolute risk numbers for the siblings so that parents could be reassured that the risk to other children is small.

Do the authors have a suggestion on what genetic tests should be done on children with cancer? Do you have recommendations to make? i assume that the authors do not recommend screening of siblings for cancer except in rare cases. It would be useful to see what the authors recommend.

Are there some sites where the risk to siblings is there but not to parents which might suggest recessive inheritance?

If the childhood cancer is rare and fatal one might not see it in the parents because of loss or reproductive fitness. Please comment.

Should we assume that the higher risks for Latino children are purely due to genetic influences? Could there be environmental factors at play as well?

Reviewer #3 (Public Review):

This study sought to identify essential features of ESCRT-III subunits, with a focus on the yeast proteins Vps2 and Vps24, in order to reveal the required features of both subunits. The combined genetic and biochemical studies solidified the model that essential functions of ESCRT-III polymers - spiral formation, lateral association, and binding of Vps4 - are mostly distributed between different subunits (with some redundancy) and can be engineered into a single polypeptide. This study also sheds light on the long-standing and initially surprising finding that ESCRT-dependent budding of HIV does not require CHMP3 (Vps24), presumably because the distribution of distinct functions between different ESCRT-III subunits is not absolute.

Inspired by earlier studies, the ability of overexpression of one ESCRT-III subunit to compensate for deletion of another subunit was explored using sorting assays. The demonstration of partial rescue inspired a mutagenesis approach that identified three residues that cluster on one face of a helix that enhanced rescue, and therefore confer functionality that in wt is primarily provided in the deleted subunits, which in this case is binding to Snf7. Extension of this analysis by protein engineering further demonstrated that the essential role of recruiting the Vps4 ATPase is normally performed by Vps2 but can be transferred to Vps24 by substitution of residues near the ESCRT-III subunit C-terminus. Similarly, it is shown that sequences that alter the propensity for bending of a helix at a point where open and closed ESCRT-III subunits differ in conformation contributed to the ability of Vps24 to substitute for deletion of Vps2, presumably by conferring the ability to adopt the open, activated conformation as well as the closed conformation.

I don't have concerns about design or technical aspects of the experimental approach.

Reviewer #2 (Public Review):

The manuscript by Emr and colleagues addresses the important question of how core ESCRT-III members Vps2 and Vps24 interact to form functional polymers using protein engineering and genetic selection approaches.

Major findings are:

Vps2 overexpression can functionally replace Vps24 in MVB sorting.

Helix 1 N21K, T28A, E31K mutations, Vps2, were identified to be sufficient for suppression, concluding that Vps2 and its' over expression can replace the function of Vps24 and Vps2.

Vps24 over expression does not rescue delta Vps2. The authors propose that this is due to the lack of the MIM and helix5 binding sites for Vps4 present in Vps2.

Vps24 E114K mutation was identified to rescue deltaVps2 upon over expression and even better as a Vps24/Vps2 chimera suggesting that auto-activated Vps24 that can recruit Vps4 can functionally replace Vps2.

Analyzing the effect of single ESCRT-III deletions on Mup1 sorting confirmed Snf7, Vps20, Vps2 and Vps24 as essential for sorting.

In summary, the manuscript provides new insight into the assembly of ESCRT-III. It confirms some redundancy of VPS2 and Vps24 and shows how Vps2 can substitute Vps24 but not vice versa.

Comments:

The three minimal principles for ESCRT-III assembly stated in the abstract are not novel. Spiral formation of ESCRT-III has been described before for yeast Vps2-Vps24 as well as its mammalian homologues. The requirement for VPS4 recruitment is also well documented and finally, the manuscript does not provide proof for lateral association of the spirals via hetero-polymerization.

The authors show that 8-fold over expression is necessary to rescue Mup1 sorting to an extent of 40%. The authors hypothesize that over expression of Vps2 can rescue Vps24 deletion because Vps2 may have a lower affinity for Snf7 than Vps24. This is in agreement with data on mammalian homologues which showed that indeed CHMP3 binds with 10x higher affinity to CHMP4B than CHMP2A (Effantin et al, 2012). This could have been included in the discussion, since the function of yeast and mammalian core ESCRT-III proteins is most likely not different.

The authors designed several chimeric Vps24/Vps2 constructs and show that some of the Vps24 chimera including Vps2 helix 5 and the MIM are fully functional in Mup1 sorting in delta Vps24 cells, but lack the ability to functionally replace Vps2 in Vps2 delta cells. It is unclear whether the chimeras are in the closed conformation in the cytosol. It would be interesting to know whether they are activated more easily and possibly prematurely.

The authors show that Vps24 E114K can form some kind of polymers in the presence of Vps2 in vitro while no polymerization is observed for wt Vps24 at 1 µM. It would be interesting to know whether wt Vps24 polymerizes at higher concentrations in this assay.

While the conclusion that E114K shifts the equilibrium to the open state is plausible, there is no evidence provided that this mimics Vps2 as stated. If so, Vps24 E114k should form the same polymers as shown in figure 4 supp 1 in the absence of Vps2 and spiral formation with snf7 should not require Vps2.

The speculation in the results section that Vps24 may not extend its helices 2 and 3 in an activated form due to potential helix breaking Asn residues in the linker region is not backed up by data, and it would have been appropriate to indicate this in the manuscript.

The proposal that Vps2-Vps24 heteropolymers are formed by interactions along helices 2 and 3 is not supported by data presented in the manuscript. The authors would need to use recombinant proteins to test their mutants in biophysical interaction studies.

Reviewer #1 (Public Review):

This Research Advance builds on the findings of this group's 2019 eLife paper which showed that conserved acidic and basic helices associate to enable heteropolymer formation by Snf7 and Vps24. This work provides some general structure/sequence relationships among the homologous ESCRT-III proteins that will be of interest to those in the ESCRT field. While there are no new mechanistic principles obtained from this study, the data allow the authors to propose a model of the minimal or core units needed for ESCRT-III membrane remodeling.

The focus is largely on similarities and differences between the closely related Vps24 and Vps2, where they show that a few key point mutations or chimeric swaps (for Vps4 binding by the C-terminal region of Vps2) can exchange their functions. The last portion of the paper further tests similarities within the subgroups of ESCRT-III proteins to experimentally test functional groupings defined by sequence relationships.

Reviewer #3 (Public Review):

Maltese et al performed 2p imaging of both dSPNs and iSPNs at the same time, while focusing on correlates of forward locomotion. The modulation of dSPN ensembles in response to DA agonism or antagonism was mostly consistent with classic models, although they also observed an 'inverted U-shape' response to D1R agonsists. In addition, they found distinct modulation of dSPN and iSPN ensembles in DA-intact and Parkinsonian mice.

Reviewer #2 (Public Review):

In this manuscript, the authors use in vivo calcium imaging of many individual neurons to investigate how dopamine regulates striatal dynamics. They aim to determine whether manipulations of dopamine signaling, on acute and chronic timescales, change the rate of activity in individual neurons, but also how the number of neurons activated (the size of the ensemble) may also change. This is a very important question, which is challenging to address with traditional methods in mouse models. Single-unit electrophysiology, especially in mice, yields modest numbers of neurons (typically <<50) during any one recording. In addition, acute electrophysiology tends to be biased: higher firing rates make it much easier to detect single units. In relatively quiet brain regions, including the striatum, these factors make it very hard to compare the total number of neurons recruited during a specific behavior across conditions.

One of the major strengths of this manuscript is that the authors make use of a strength of their method (the ability to capture activity across hundreds of neurons), rather than trying to use it merely as a surrogate for a traditional method (by measuring the rate of activity). Another strength is the alignment of neural activity to specific behavior (locomotion), and attempts to control for changes in overall behavior with each of their dopamine signaling manipulations.

Weaknesses, which the authors to some degree acknowledge, include the fact that calcium imaging is not equivalent to action potential firing; changes in the activation across a population may represent a change in the firing rate or pattern. For example, a doubling of the number of neurons that are "active" during a behavior may represent a shift of completely silent neurons to firing above a certain threshold rate, and/or a shift to burst firing mode, without a change in overall rate. Another weakness, partly driven by the head-fixed/treadmill configuration, is the laser focus on locomotion (starts, stops, velocity), though the dorsolateral striatum is likely to regulate other behaviors (grooming, licking, rearing, etc). Finally, again related to their methods, the findings are observational, shedding minimal light on the mechanisms (direct effects on SPN cell bodies? Indirect effects via local GABAergic signaling, dopamine terminals, or glutamatergic inputs?) by which dopamine signaling manipulations lead to changes in SPN ensemble activity.

Despite these weaknesses, I suspect this manuscript, together with other recent studies, will change how other basal ganglia physiologists think about neural activity. Much as the field has emphasized dynamics and synchrony as potential ways neural activity regulates behavior, hard data regarding the spatiotemporal activation of neurons is relatively new. It is also likely to be thought-provoking for investigators working on Parkinson's Disease, as it suggests more cellular/mechanistic lines of research are needed to explain the massive changes in dSPN ensemble size seen in healthy vs 6-OHDA-treated and L-DOPA treated mice.

Reviewer #1 (Public Review):

This study investigates roles of DA modulation in projection neuron ensembles in DA-intact mice and Parkinson's disease mouse model using two-photon calcium imaging of direct and indirect SPNs (dSPNs and iSPNs) simultaneously in head-fixed mice locomoting on a freely rotating or motorized circular treadmill. The study begins with careful validation efforts related to their particular imaging conditions and reporter usage. Major findings are: 1) In DA-intact mice, they found that reducing DA receptor signaling by administration of D1/2R antagonists increased iSPN ensemble size (fraction of imaged iSPN active during locomotion) and decreased dSPNs ensemble size, resulting in an imbalance of striatal outputs in favor of indirect pathway. Consistently, elevating DA receptor signaling by D1/2R agonists yielded a dose-dependent imbalance in favor of direct pathway. Interestingly, at one intermediate dose of D1/2R gonists, iSPN ensemble size remained unchanged while dSPN ensemble size increased, whereas at higher doses, both iSPN and dSPN ensemble sizes shrunk. They also showed that reward-induced and nomifensine-induced DA increase recapitulated the low and high dose effects of DA on SPNs, respectively. 2) In dopamine-depleted Parkinson's disease mouse model, the authors found that 6-hydroxydopamine (6-OHDA) treatment reduced dSPNs ensemble size acutely (within 24h) and chronically (after 30 days) and increased iSPNs ensemble size acutely. However, the active iSPN ensembles returned to pre-lesion levels within one week. Overall, ablation of SNc DA neurons biased the striatum output toward indirect pathway. Lastly, they evaluated the influence of L-DOPA on SPN ensembles in DA-depleted mice and found that l-DOPA increased the dSPNs ensembles by 10 fold and reduced the iSPN ensembles to below pre-ablation levels, resulting in strong bias toward direct pathway, a finding they suggest may relate to levodopa-induced dyskinesias. Together, this study introduces data to support the concept that SPN "ensemble size" may be relevant for long-standing pathway balance ideas concerning striatal circuitry in the control of normal movement and its demise in PD.

Reviewer #3 (Public Review):

Mark and colleagues set out to examine the relationship between neuronal targeting and connectivity and the developmental history of neurons. Specifically, the authors examine if, how and to what extent hemilineage identity combined with temporal birth order can explain neuronal connectivity. To this end they use the fly larval nerve cord with its EM-level resolution of connectivity and prior knowledge about hemilineage identity and birth order as a model.

General comments:

The manuscript represents a comprehensive, thorough and deep analysis of the system. The descriptive elements are outstanding, and the analysis of how Notch activity correlates with hemilineage targeting is of great interest. While understanding the relationship between connectivity diagrams and developmental history, including the role of Notch signaling, is not new, the scale of the analysis presented here is key because it allows - in principle - the drawing of general conclusions. The main "weakness" of the manuscript is not in the work itself but rather some of the key conclusions drawn from the data which often somewhat beyond what the data alone would support, especially in terms of the developmental mechanisms involved in establishing connectivity. With one partial exception (Notch gain of function experiments), the work essentially represents (very important) correlation analysis between the various parameters. While the authors are of course free to interpret their data as per their own views and biases, they do need to either tone down their, often categorical, statements and soften their conclusions, or perform further analysis to examine whether some of the stronger conclusions they draw are justified.

Specific comments:

1) Figure 1; page 3: The authors refer to the "striking" similarity between EM reconstructions and GFP filled clones and yet there are clear differences in some of the clones in the extent and localization of arborization. This may be in part technical but almost certainly also reflects inter individual differences in single neuron morphology. Since EM reconstructions presumably come for, one animal, the use of GFP clones allows the authors to map the degree of variation between clones and it would be interesting for them to show this.

2) Figures 2 and 4; pages 3-5: Along the same lines as above, the authors make categorical statements about the mapping of arbors to dorsal and ventral regions of the nerve cord and correlate that to hemilineage identity. Again, there is clear mixing in almost all neuroblast lineages, that seems to range from 15-30% as a rough estimate, and perhaps a bit more dorsally than ventrally, which the authors do not comment on (except to say it's "mostly non-overlapping"). This is a pity because they obviously have the tools to do so quantitatively and the information is already there in their data.

3) The analysis of Notch activity in hemilineages is excellent and very interesting, as is the new tool they develop. However, the analysis lacks loss of Notch function data and where and when Notch signaling is required to segregate the connectivity space (i.e. in neurons or in precursors such as Nbs and GMCs). Is this a binary fate specification mechanism or lateral inhibition among competing neurons? What about Notch activity manipulation in single neurons? If the authors wish to draw strong conclusions about the role of Notch in segregating target space and its relation to hemilineage identity, these experiments are essential. Alternatively, drawing subtler conclusions and acknowledging these caveats would be very welcome.

4) Figure 7; Page 7: The authors state that 75% of hemilineage neurons correlated by temporal identity are separated by 2 synapses or less, suggesting greater connectivity than expected. How are these data normalized? What is the expected connectivity between neurons that are less related along these two developmental axes?

5) Figure 8; page 7 and discussion: The authors conclude that the combination between temporal identity and hemilineage identity predicts connectivity beyond what would be predicted by spatial proximity alone. This conclusion is problematic at least two levels. First, practically what really matters for proximity is proximity during the time in development when synapses are forming between neuronal pairs, not proximity at the end in the final pattern. Second, conceptually, opposing spatio-temporal mechanisms with proximity-based bias for connectivity makes no sense because that's exactly what spatio-temporal mechanisms achieve: getting neurons to the same space at the same time so connectivity can happen. At any rate, drawing strong conclusions about where and when neurons meet to form (or not form) synapses requires live imaging and absent that authors should refrain from making such a string statement about what their excellent correlative dataset means.

Reviewer #2 (Public Review):

This work presents a comprehensive characterization of seven different neuronal lineages and their connections in the Drosophila CNS. By making use of a previously generated TEM reconstruction of the first instar larval CNS, the authors map de developmental origin of 160 interneurons, providing an unvaluable framework to address the question of how specification mechanisms in neural stem cells, such as temporal patterning, and Notch status of the neurons, correlate with different aspects of neuronal connectivity. The authors show that most NB lineages produce two morphologically distinct hemilineages, each one targeting either the ventral or dorsal VNC neuropil domain in a Notch-dependent manner, which allows the concurrent building of these circuits in a similar number. Importantly, they show that Notch activity is sufficient to target neurons to the dorsal neuropil domain and that hemilineage-related neurons share similar synapse localization. Furthermore, by measuring the cortex neurite length of these neurons, the authors establish neuronal cell body radial position as a proxy of neuronal birth order and assign different temporal cohorts to these neurons. Importantly, they show that neurons that share a hemillineage temporal cohort identity have more similar synaptic positions and shared connectivity than neurons that share hemilineage identity, providing an additional level of partner specificity than that provided by hemillineage alone. They further show that the observed shared connectivity between hemilineages and hemilineage temporal cohorts cannot be explained by proximity alone, further validating the observations presented in this work.

This work is of great value for the field of Developmental Neurobiology as it provides an initial understanding of the link between neuronal specification mechanisms and circuit formation during development. By mapping specific neuronal lineages in a serial section TEM reconstruction, the authors analyze neuronal connectivity with single synapse resolution, allowing a precise characterization of neurite localization and synaptic specificity, which are not offered in most of the works published in the field. The availability (and additional generation in this work) of drivers to label specific NB lineages and hemilineages on the VNC combined with TEM, presents an unvaluable resource to study circuit formation during development. The use of this high-quality framework in the future will continue deepening our understanding of how specification mechanisms in neural stem cells instruct circuit formation and connectivity, and the molecular mechanisms underlying these processes.

The conclusions of this paper are mostly well supported by data, however, there are several points that should be discussed further in the manuscript:

1) The authors state that overexpression of Notchintra transforms Notch OFF neurons into Notch ON neurons. However, since this decision happens at the level of the GMC, wouldn't be more correct to say that Notch OFF neurons were not produced and only Notch ON neurons were generated? Moreover, the authors state that the Notchintra overexpression phenotypes are due to hemilineage transformation rather than to death of Notch OFF neurons, by providing the total neuronal number in both experimental conditions using NB5-2 lineage. I think this statement is too much of a generalization when only one NB lineage has been analyzed and should be addressed in more lineages to claim this as a general mechanism. Moreover, the opposite hypothesis could have also been tested to make the argument stronger: Would depletion of Notch in GMCs make all neurons in a lineage target the ventral neuropil domain?

2) Temporal cohorts described in this work are an approximation to neuronal temporal identity. The authors validate the correlation of early and late temporal cohorts to the expression of the temporal TFs Hb and Cas (Fig 4G). Given the resolution of the TEM dataset and the existence of specific NBs and neuronal drivers for the neurons studied, a correlation between the 4 temporal cohorts presented in this work and the 4 temporal TFs Hb, Kr, Pdm and Cas expressed by these neurons could have been possible and would have presented a more comprehensive view of the relationship between tTF expression and neurite and synapse localization. Does temporal cohort between lineages (cortex neurite length) mean expression of the same temporal TF? For example: would mid-early neurons in different lineages express the same temporal factor? Since shared temporal identity between different lineages on its own does not confer shared neuronal projections, but shared temporal cohort hemilineage does: Does this mean that the expression of a given temporal TF and/or neuronal birth order does not play a role in this shared connectivity? Please clarify these ideas in the text.

3) Although the authors claim so, it is not convincing that the role of spatial patterning in neuronal connectivity has been assessed in this work, since the authors do not present an obvious correlation between specific connectivity features (morphology, axon or synapses localization) and the position of a given NB in the VNC. This should be clarified in the text.

Reviewer #1 (Public Review):

The authors sought to assess the relationship between developmental lineage and connectivity.

This is a tour de force. It relies on detailed EM reconstructions, knowledge of complete neuroblast lineages thus correlating wiring with lineage, and through genetic manipulations of N gene function correlates developmental programs with wiring. The conclusion is important and provides a well described cellular and genetic system for linking the developmental program of a cell to its connection specificity. It provides a framework for considering how to study these questions in other regions of Drosophila and can be extended to the study of more complex mammalian systems where a similar neuroblast-lineage strategy generates different neuron types.

There are no major weakness.

This is an excellent study and, in my opinion, is ready to publish in its current form.

Reviewer #2 (Public Review):

In-frame insertion of fluorescent protein tags into endogenous genes allows observation of protein localization at native expression levels, and is therefore an essential approach for quantitative cell biology. Once limited to unicellular model organisms such as yeast, endogenous gene tagging has become well-established in invertebrate model systems such as C. elegans and Drosophila since the advent of CRISPR technology in the last decade. However, a robust and widely accepted endogenous gene tagging strategy for mammalian cells has remained elusive. This is largely due to the fact that homologous recombination, the method used to create knock-ins in invertebrates, is inefficient (or sometimes doesn't work at all) in mammalian cells, especially those that do not divide rapidly.

Several studies have attempted to bypass the need for homologous recombination by using a different method, non-homologous end joining (NHEJ) to insert GFP tags into vertebrate genomes (e.g. Auer et al. Genome Res 2014; Suzuki et al. Nature 2016; Artegiani et al. Nature Cell Biol. 2020). Such approaches can be orders of magnitude more efficient than homologous recombination, but the generated alleles require careful validation because of the error-prone nature of NHEJ.

Here, Zhong and colleagues improve upon the existing NHEJ-based gene tagging approaches by designing synthetic exons (comprising a FP coding sequence with 5' and 3' splice sites) that can be inserted into native introns using NHEJ. The beauty of this approach is that any mutations (indels) created by the error-prone NHEJ repair mechanism are spliced out, and therefore do not affect the sequence of the encoded protein. A limitation is that tags must be inserted internally within a protein of interest and cannot be targeted to the extreme N- or C-terminus, but this limitation is clearly stated and discussed by the authors. Overall, this is a novel (to my knowledge) and powerful strategy that is likely to advance the field.

Reviewer #1 (Public Review):

In this manuscript the authors show that a designer exon containing a Fluorescent Protein insert can be used to edit vertebrate genes using an NHEJ based repair mechanism. The approach utilizes CRISPR to generate DSBs in intronic sequences of a target gene along with excision of a donor fragment from a co-transfected plasmid to initiate insertion of the exon cassette by ligation into the chromosome DSB.

I like the idea here of inserting FP sequences (and other tags) into introns in this way. Focusing on the N- and C-termini for insertions has always seemed arbitrary to me. In practice these internal sites may even tolerate tag insertions better than the termini. However, this remains to be seen.

My major reservation with this study is that the concepts here are not particularly novel. The approach is very similar to a concept already well established in gene-therapy circles of using introns as targets for inserting a super-exon preceded by a splice acceptor to correct inborn genetic lesions. The methodology employed is essentially HITI (https://www.nature.com/articles/nature20565).

What is new is the finding that FP insertions are frequently expressed and at least partly functional as evidenced by their ability to localize to the expected intracellular structures. However, no actual functional data is provided in this study so it remains to be seen how frequently the insertion of FP exons is tolerated. It would help the study substantilly to have functional information for a few insertions.

The value and utility of this study hinges on whether insertions of this type frequently retain function. The authors speculate that "labeling at an internal site of a gene is feasible as long as the insertion does not disrupt the function of the encoded protein. Many introns reside at the junctions of functional domains because introns have evolved in part to facilitate functional domain exchanges (Kaessmann et al., 2002; Patthy, 1999)." Thus an analysis of how often intron tags are tolerated as homozygotes would be helpful for users who will worry that a potentially "quick and dirty" CRISPIE insertion might not accurately report on the function and localization of their protein of interest.

Other comments:

1) Were homozygotes identified and were they viable in each instance?

2) You say: "The CRISPIE method should be broadly applicable for use with different FPs or with other functional domains, different protein targets, and different animal species." I don't know if you optimized your FP to avoid potential reverse strand splice acceptors, but some discussion of this important point should be made so that those trying to apply the approach will make sure that strong acceptors are not included accidentally in reverse oriented inserts.

3) Would your mRNA sequencing methodologies detect defective transcripts where the splice acceptor and a portion of the upstream FP exon was inserted causing a frame shifted and mispliced mRNA? Such mRNAs would be unstable due to NMD and thus not detected readily in a PCR based approach. Thus disruption of the mRNA by partial insertion of your donor (or fragments of the other co-injected DNA) might be much more widespread than is measured here. This could be tested by recovering clones that partially inserted the donor in the forward orientation and carefully monitoring for defects in mRNA splicing of the inserted allele. Were such clones detected and how frequently?

4) You note that in the case of vinculin the coding sequence of the last exon of hVCL was included in the insertion donor sequence, and a stop codon was introduced at the end of the mEGFP coding sequence. This is essentially the strategy for super-exon insertion into targets for gene therapy, instead of a splice donor on the C-terminus you include a stop codon. You should site these previous studies. Inclusion of a stop codon in frame would be expected to cause NMD, did you also include transcription termination signals?

Reviewer #3 (Public Review):

In this manuscript Lituma and colleagues investigate a potential role for presynaptic NMDARs at hippocampal mossy fiber (MF) synapses in regulating synaptic transmission. The combined use of electron microscopy, electrophysiology, optogenetics, calcium imaging, and genetic manipulations expertly employed by the authors yields high quality compelling evidence that presynaptic NMDARs can participate in activity dependent short term facilitation of release onto postsynaptic CA3 pyramid and mossy cell targets but not onto inhibitory interneurons. Moreover, presynaptic NMDAR activation is demonstrated to be particularly effective in promoting BDNF release from MF boutons. The investigation is well designed with a clear hypothesis, appropriate methodological considerations, and logical flow yielding results that fully support he authors conclusions. The manuscript fills an important gap in our understanding of MF regulation by unambiguously confirming a functional role for presynaptic NMDARs that were first described anatomically at MF terminals nearly 30 years ago. Combined with a handful of other studies describing presynaptic NMDARs at various central synapses this study expands the role of NMDARs as critical players in synaptic plasticity on both sides of the cleft.

Reviewer #2 (Public Review):

Lituma et al. examined the presence and functions of preNMDARs in dentate gyrus granule cells (GCs) in the hippocampus. The authors found that GluN1+ preNMDARs are indeed present at mossy fiber (mf) terminals with electron microscopy. With pharmacological and genetic approaches, the authors showed that preNMDARs are important in low frequency facilitation (LFF), burst-induced facilitation and information transfer at the mf-CA3 synapse. The authors further demonstrated that this preNMDAR contribution is independent of the somatodendritic compartment of the GCs. With 2-photon calcium imaging, the authors found that preNMDARs contribute to presynaptic Ca2+ transients and can be activated by local glutamate uncaging. Separately, the authors showed that GluN1+ preNMDARs might also contribute to BDNF release at mossy fiber terminals during repetitive stimulation. Lastly, non-postsynaptic NMDARs specifically mediates mf transmission onto mossy cells, similar to mf-CA3 synapses, but not interneurons. The authors concluded that preNMDARs mediate synapse-specific transmission originating from the GCs/mf inputs.

Overall, the study provides compelling evidence from a battery of techniques, ranging from EM, pharmacology, genetic deletion, electrophysiology to 2-photon imaging/uncaging. The data supports a coherent story on the presence of preNMDARs at mf terminals and that preNMDARs play important roles in LFF.

In conclusion, this study reveals how NMDA receptors can be found in unexpected locations and how they may have unconventional functions, i.e. outside the narrow textbook view that they primarily serve as coincidence detectors in Hebbian learning. This study thus helps to change the way we think about NMDA receptor functioning, so should be of broad interest.

Reviewer #1 (Public Review):

In this manuscript Lituma et al. provides compelling evidence demonstrating the physiological role of presynaptic NMDA receptors at mossy fiber synapses. The existence of these receptors on the presynaptic site at this synapse was suggested more than 20 years ago based on morphological data, but their functional role was only shown in a single abstract since then (Alle, H., and Geiger, J. R. (2005)). The current manuscript uses a wide variety of complementary technical approaches to show how presynaptic NMDA receptors contribute to shaping neurotransmitter release at this synapse. They show that presynaptic NMDA receptors enhance short-term plasticity and contribute to presynaptic calcium rise in the terminal. The authors use immunocytochemistry, electrophysiology, two-photon calcium imaging, and uncaging to build a very solid case to show that these receptors play a role at synaptic communication at mossy fiber synapses. The authors conclusions are supported by the experimental data provided.

The study is built on a solid and logical experimental plan, the data is high quality. However, the authors would need to provide stronger evidence to demonstrate the physiological function of these receptors. It is hard to reconcile these experimental conditions with the authors' claim in the abstract: 'Here, we report that presynaptic NMDA receptors (preNMDARs) at hippocampal mossy fiber boutons can be activated by physiologically relevant patterns of activity'. We know that extracellular calcium can have a very significant impact of neurotransmitter release and how short-term plasticity is shaped. For this reason, it would be important to explore how the activity of these receptors at more physiological calcium concentrations contribute to calcium entry and short-term plasticity at these synapses.

Reviewer #3 (Public Review):

The current experiment tests whether the attentional blink is affected by higher-order regularity based on rhythmic organization of contextual features (pitch, color, or motion). The results show that this is indeed the case: the AB effect is smaller when two targets appeared in two adjacent cycles (between-cycle condition) than within the same cycle defined by the background sounds. Experiment 2 shows that this also holds for temporal regularities in the visual domain and Experiment 3 for motion. Additional EEG analysis indicated that the findings obtained can be explained by cortical entrainment to the higher-order contextual structure. Critically feature-based structure of contextual rhythms at 2.5 Hz was correlated with the strength of the attentional modulation effect.

This is an intriguing and exciting finding. It is a clever and innovative approach to reduce the attention blink by presenting a rhythmic higher-order regularity. It is convincing that this pulling out of the AB is driven by cortical entrainment. Overall, the paper is clear, well written and provides adequate control conditions. There is a lot to like about this paper. Yet, there are particular concerns that need to be addressed. Below I outline these concerns:

1) The most pressing concern is the behavioral data. We have to ensure that we are dealing here with a attentional blink. The way the data is presented is not the typical way this is done. Typically in AB designs one see the T2 performance when T1 is ignored relative to when T1 has to be detected. This data is not provided. I am not sure whether this data is collected but if so the reader should see this.

2) Also, there is only one lag tested. The ensure that we are dealing here with a true AB I would like to see that more than one lag is tested. In the ideal situation a full AB curve should be presented that includes several lags. This should be done for at least for one of the experiments. It would be informative as we can see how cortical entrainment affects the whole AB curve.

3) Also, there is no data regarding T1 performance. It is important to show that this the better performance for T2 is not due to worse performance in detecting T1. So also please provide this data.

4) The authors identify the oscillatory characteristics of EEG signals in response to stimulus rhythms, by examined the FFT spectral peaks by subtracting the mean power of two nearest neighboring frequencies from the power at the stimulus frequency. I am not familiar with this procedure and would like to see some justification for using this technique

Reviewer #2 (Public Review):

In cognitive neuroscience, a large number of studies proposed that neural entrainment, i.e., synchronization of neural activity and low-frequency external rhythms, is a key mechanism for temporal attention. In psychology and especially in vision, attentional blink is the most established paradigm to study temporal attention. Nevertheless, as far as I know, few studies try to link neural entrainment in the cognitive neuroscience literature with attentional blink in the psychology literature. The current study, however, bridges this gap.

The study provides new evidence for the dynamic attending theory using the attentional blink paradigm. Furthermore, it is shown that neural entrainment to the sensory rhythm, measured by EEG, is related to the attentional blink effect. The authors also show that event/chunk boundaries are not enough to modulate the attentional blink effect, and suggest that strict rhythmicity is required to modulate attention in time.

In general, I enjoyed reading the manuscript and only have a few relatively minor concerns.

1) Details about EEG analysis.

First, each epoch is from -600 ms before the stimulus onset to 1600 ms after the stimulus onset. Therefore, the epoch is 2200 s in duration. However, zero-padding is needed to make the epoch duration 2000 s (for 0.5-Hz resolution). This is confusing. Furthermore, for a more conservative analysis, I recommend to also analyze the response between 400 ms and 1600 ms, to avoid the onset response, and show the results in a supplementary figure. The short duration reduces the frequency resolution but still allows seeing a 2.5-Hz response.

Second, "The preprocessed EEG signals were first corrected by subtracting the average activity of the entire stream for each epoch, and then averaged across trials for each condition, each participant, and each electrode." I have several concerns about this procedure.

(A) What is the entire stream? It's the average over time?

(B) I suggest to do the Fourier transform first and average the spectrum over participants and electrodes. Averaging the EEG waveforms require the assumption that all electrodes/participants have the same response phase, which is not necessarily true.

2) The sequences are short, only containing 16 items and 4 cycles. Furthermore, the targets are presented in the 2nd or 3rd cycle. I suspect that a stronger effect may be observed if the sequence are longer, since attention may not well entrain to the external stimulus until a few cycles. In the first trial of the experiment, they participant may not have a chance to realize that the task-irrelevant auditory/visual stimulus has a cyclic nature and it is not likely that their attention will entrain to such cycles. As the experiment precedes, they learns that the stimulus is cyclic and may allocate their attention rhythmically. Therefore, I feel that the participants do not just rely on the rhythmic information within a trial but also rely on the stimulus history. Please discuss why short sequences are used and whether it is possible to see buildup of the effect over trials or over cycles within a trial.

3) The term "cycle" is used without definition in Results. Please define and mention that it's an abstract term and does not require the stimulus to have "cycles".

4) Entrainment of attention is not necessarily related to neural entrainment to sensory stimulus, and there is considerable debate about whether neural entrainment to sensory stimulus should be called entrainment. Too much emphasis on terminology is of course counterproductive but a short discussion on these issues is probably necessary.

Reviewer #1 (Public Review):

The work by Wang et al. examined how task-irrelevant, high-order rhythmic context could rescue the attentional blink effect via reorganizing items into different temporal chunks, as well as the neural correlates. In a series of behavioral experiments with several controls, they demonstrated that the detection performance of T2 was higher when occurring in different chunks from T1, compared to when T1 and T2 were in the same chunk. In EEG recordings, they further revealed that the chunk-related entrainment was significantly correlated with the behavioral effect, and the alpha-band power for T2 and its coupling to the low-frequency oscillation were also related to behavioral effect. They propose that the rhythmic context implements a second-order temporal structure to the first-order regularities posited in dynamic attention theory.

Overall, I find the results interesting and convincing, particularly the behavioral part. The manuscript is clearly written and the methods are sound. My major concerns are about the neural part, i.e., whether the work provides new scientific insights to our understanding of dynamic attention and its neural underpinnings.

1) A general concern is whether the observed behavioral related neural index, e.g., alpha-band power, cross-frequency coupling, could be simply explained in terms of ERP response for T2. For example, when the ERP response for T2 is larger for between-chunk condition compared to within-chunk condition, the alpha-power for T2 would be also larger for between-chunk condition. Likewise, this might also explain the cross-frequency coupling results. The authors should do more control analyses to address the possibility, e.g., plotting the ERP response for the two conditions and regressing them out from the oscillatory index.

2) The alpha-band increase for T2 is indeed contradictory to the well known inhibitory function of alpha-band in attention. How could a target that is better discriminated elicit stronger inhibitory response? Related to the above point, the observed enhancement in alpha-band power and its coupling to low-frequency oscillation might derive from an enhanced ERP response for T2 target.

3) To support that it is the context-induced entrainment that leads to the modulation in AB effect, the authors could examine pre-T2 response, e.g., alpha-power, and cross-frequency coupling, as well as its relationship to behavioral performance. I think the pre-stimulus response might be more convincing to support the authors' claim.

4) About the entrainment to rhythmic context and its relation to behavioral modulation index. Previous studies (e.g., Ding et al) have demonstrated the hierarchical temporal structure in speech signals, e.g., emergence of word-level entrainment introduced by language experience. Therefore, it is well expected that imposing a second-order structure on a visual stream would elicit the corresponding steady-state response. I understand that the new part and main focus here are the AB effects. The authors should add more texts explaining how their findings contribute new understandings to the neural mechanism for the intriguing phenomena.

Reviewer #3 (Public Review):

Previously published work had shown that small clusters of N protein can be observed at the newly forming interface between pIIa and pIIb and that these clusters are the source of nuclear N intracellular domain (NICD) following N activation by its ligand Delta (Dl). In this manuscript the authors have used live imaging of fluorescently tagged proteins to study the contribution of several regulators of apical-basal cell polarity and of N signaling to the formation of these N clusters. Their analysis revealed differences in the localization of polarity and junction markers between the SOP daughters and surrounding epithelial cells, confirming that the pIIa/pIIb interface has special features different from epidermal epithelial cells. They found that the polarity regulator Bazooka (Baz), the homolog of mammalian Par3, colocalizes with N in the clusters and is required for efficient cluster formation. Two other polarity regulators, Crumbs (Crb) and atypical protein kinase C (aPKC) do not colocalize with N and Baz in the clusters but are found in intracellular vesicles in pIIa and pIIb, in contrast to the surrounding epithelial cells where they localize at the apical plasma membrane. Two regulators of N signaling, Neuralized (Neur) and Sanpodo (Spdo) also localize in the clusters together with N and Baz, whereas the N ligand Dl and the negative regulator of N signaling, Numb (Nb) are not detectable in the clusters.

To test the functional contribution of the proteins mentioned above to formation of the clusters at the pIIa/pIIb interface, the function of the corresponding genes was eliminated by mutation, RNA interference, or ubiquitin-mediated degradation (degradFP). The authors found that Baz and Spdo are required for efficient cluster formation, while Neur, Dl and Nb are negative regulators of cluster formation. In the absence of these three proteins, N clusters are more frequent and show brighter fluorescence signals.

The data also confirm the intimate connection between N signaling and cell polarity. Nonethless, the data do not reveal a novel mechanism but rather describe a cell-biological phenomenon in greater detail than before. Thus, the data are certainly of great interest to specialists in the field, but it is less clear whether they will have a greater impact in the general cell biology or signaling community.

Reviewer #2 (Public Review):

Sensory organ precursor cells of the fly are a strong model system to understand the spatio- temporal regulation of Notch signalling in the context of cell fate regulation. Different signalling competent pools of Notch have been identified previously at the newly formed membrane that separates the two SOP daughters. It is unclear how for instance the Notch signalling pools are restricted to localize exclusively to this membrane.

This study now takes a closer look at one of the Notch pools and finds that SOPs known to remodel PAR-dependent polarity at the beginning of mitosis, seem to remodel polarity once more, this time later, around anaphase when the new membrane is formed. This remodelling is evident with the assembly of intriguing Par3/Baz containing clusters that strikingly co-localize with Notch as well as other members of the Notch signalling pathway. Baz cluster formation is independent of Notch, but negatively regulated by Numb and Neuralized. Notch in turn depends on Baz to some extend to localize to the clusters. The study proposes that the Baz dependent clusters form a "snap button" type of platform to cluster Notch and facilitate directed Notch signalling, which is an interesting idea.

The concept is relevant, especially as the dependency on PAR regulation provides an angle for future research to address the question why Notch accumulates only at the interphase of pIIa/b, but not at other interfaces with other neighbours in the future. The Baz clusters are well-described and the experiments to dissect their origin, dependency and impact on Notch well-designed.

The signalling relevance of the different Notch pools is extremely challenging to address. This has been attempted in the past by the authors and redone in this study. Despite the fact that the sensitivity of these assays is notoriously noisy, the observed tendencies of signalling measured by nuclear Notch levels in the relevant cells support their model. Relevance of the Baz dependent Notch pool appears to be a likely possibility. The fact that this clusters are modulated by Numb, Delta, Neuralized ans Sanpodo are in contrast in strong favour that the here described Baz clusters are under control of this system and relevant.

The study is a little imbalanced in the use of quantification, the phenotypes appear admittedly often evident and convincing, but would need to be backed up by more thorough quantification. Clarity of figures, legends and writing could be strengthened.

Reviewer #1 (Public Review):

The evolutionary conserved Notch receptor cell-cell communication pathway is required in cell fate decisions in many vertebrate and invertebrate cells. In Drosophila, Notch controls (among others) the cell fate decision of the sensory organ precursor cell, SOP. SOPs divides asymmetrically to give rise to an anterior and a posterior cell, pIIb and pIIa, respectively, which ultimately result in the formation of a bristle. In a recent paper form the Schweisguth lab (Trylinsky et al., 2017) is was shown that Notch is found both apical and basal of the midbody at the pIIa/pIIb interface during cytokinesis, and that it is mainly the basal pool of Notch that contributes to signaling.

Houssin et al. now asked how polarity and signaling proteins involved are distributed during cytokinesis and how this distribution could impact on Notch signaling and hence fate decision. The authors show that during cytokinesis of the SOP several polarity determinants are re-distributed. Bazooka /Par3 becomes enriched at the pIIa/pIIb interface, where it occurs in nano-clusters, both apical and basal to the midbody, while aPKC remains in the apical compartment. Bazooka co-localizes with Notch, Sanpodo, Delta and Neuralised (Neur) in these clusters. In the absence of baz, both the apical and the lateral Notch-positive clusters are decreased in intensity and the number of lateral clusters is reduced at the pIIa/pIIb interface. Strikingly, this only slightly reduces the signaling activity of Notch. Formation of the Baz-Notch clusters depend on the Notch-cofactor Sanpodo: in its absence, the lateral Baz-Notch clusters do not assemble, suggesting that Sanpodo supports Notch signaling by promoting lateral clusters. From the data the authors conclude that the Notch/Baz/Spdo/Neur clusters represent the signaling units at the pIIa/pIIb interface.

Major strengths and weaknesses

The authors performed a very detailed analysis to further dissect how Notch signaling at the pIIa/pIIb interface is controlled. They used state-of-the-art live-cell imaging of tagged proteins in wild-type and mutant animals and applied careful statistical analyses of their data. Thereby, they provide a novel link between the role of the polarity protein Bazooka in clustering Notch, and how the particular redistribution of Bazooka/Notch in clusters on the lateral membrane during cytokinesis of the SOP organize putative signaling hubs.

However, in the discussion the authors fall somewhat short to substantiate their main conclusion that these clusters "represent signaling units at the pIIa/pIIb interface." (line 560). First, although in the absence of Baz the number and size of Notch clusters are decreased, Notch signaling is only slightly affected. Second, no suggestion for any molecular mechanism is provided as to how Baz may organize these clusters, e.g. about the molecular interaction between Baz and Spdo, both of which are required to cluster Notch. And finally, the fact that the clusters are similar in composition apical and basal to the midbody does not help to support (or disprove) the conclusions put forward in Trylinsky et al., 2017, showing that Notch signaling mainly occurs by the lateral clusters.

Reviewer #3 (Public Review):

Summary of goals:

In the moth Helicoverpa armigera the authors examined whether projection neurons from different antennal lobe tracts encoding sex-pheromone components with different valence occupy distinct projection areas in the protocerebrum of the midbrain.

Strengths and weaknesses of methods and results:

Methods chosen are adequate and state of the art. In vivo calcium imaging allowed for more easy imaging of a population of neurons, in search for statistically significant responses to pheromone components of different concentrations, quality, and valence. The main, general drawbacks of calcium imaging is the lower temporal resolution that does not allow for detection of single action potentials at the scale of few ms and the inability of fine spatial resolution of projection patterns of single neurons. This was compensated for by excellent intracellular recordings of single antennal lobe projection neurons, stainings of single cells, and embedding in the 3D standardized H. armigera brain. The data a very carefully analyzed with adequate analysis software and adequate statistical analysis and the most relevant results are shown in very good Figures. I also very much appreciate all of the supplementary figures. I do not see any relevant weakness in the methods and the respective results. However, as outlined in detail in the reply to the authors, the wording of the manuscript can be improved, to make it clearer and understandable without the need to read previous publications.

Everybody working with odors knows about the difficulty to precisely control and measure the exact molar concentration of odorants applied. But since the authors showed in previous publications that they take great care to control odor stimuli they should include also in the Material and Methods of this publication more details about concentration of the respective odor stimuli or mixtures employed.

Did they achieve their aims? Do data support conclusions?

Yes, the data support their conclusions as clearly shown in their excellent recordings, their excellent combination of physiological and morphological analysis, as well as their thorough statistical analysis.

Discussion of the likely impact of the work on the field, utility of methods:

This is an excellent, synergistic collaboration of different international experts in insect olfaction. It is still under-estimated how important the combination of single cell analysis in intracellular recordings with neural network analysis via calcium imaging is. Schemes of frequency encoding versus temporal encoding can only be deciphered with a clever combination of these techniques. This manuscript adds important insights into information processing of olfactory stimuli of antagonistic valence. It starts to become clear that in different sensory systems valence of aversive versus attractive sensory stimuli is processed in parallel pathways. Most likely antagonistic pathways connected to different neuronal units in premotor areas of the midbrain, connecting to parallel de- and ascending pathways of central pattern generators in the thorax. In addition, the current work provides relevant new information about processing of pheromone information in the different antennal lobe tracts in another important species. Thus, we may be one step closer to the future manipulation of sexual reproduction of specific insect pests.

Context for others for interpretations:

Sympatric heliothine moths use the same sex-pheromone components but at different concentration ratios, allowing for distinction of species that do not inter-mate. Thus, understanding how pheromone components at defined concentrations with opposite valence are processed in the brain to guide aversive or attractive behavioral interactions is relevant not only for determining principles of higher-order olfactory processing, but also to understand evolution of new species.

Reviewer #2 (Public Review):

Using calcium imaging of mALT PNs in the AL as well as intracellular recordings and subsequent stainings of individual PNs, the authors evaluate the response properties of different PNs to the three pheromone components, including the primary pheromone Z11-16:AL, the secondary component Z9-16:AL and a minor component Z9-14:AL which functions as an antagonist at higher concentrations. The authors conclude from their data that PNs have widespread aborizations in higher brain centers that are organized according to behavioral significance, i.e. with regard to attraction versus repulsion. Although the authors characterize morphologically and functionally a considerable number of neurons, the data are highly descriptive and exhibit a rather large level of variability which impedes, in my opinion, a generalization of response properties for different neuron types. The conclusion that the projection patterns in the higher brain centers, such as the LH, VLP and SIP reflect behavioral significance proves rather difficult from the data presented in this study. Additional data, such as e.g. calcium imaging of pheromone responses in the higher brain areas would support the notion of a valence-based map in these regions.

The intracellular recordings are certainly elaborate, but do not allow drawing a general picture about how coding of pheromones in the individual MGC compartments of the AL is transformed into a representation in higher brain centers. In my opinion the authors could not sufficiently address their major goal which is to understand how the neuronal circuitry underlying pheromone processing is encoding the individual pheromone components that induce opposite valences. The study would highly benefit if the authors would reconstruct their individual PN staining and register them into a standard moth brain (as done in other insect species, such as honeybees and flies) to allow a categorization and matching of morphological properties. Then the different PNs could be compared based on morphological parameters and subsequently be assigned to specific neuron classes, while response properties could be assessed for the different types.

Reviewer #1 (Public Review):

In the manuscript by Kymre, Liu and colleagues, the authors investigate how pheromone signals are interpreted by the projection neurons of the male moth brain. While the olfactory neurons and glomerular targets of pheromone signaling is known, the signaling of the projection neurons (output neurons) that carry pheromone signaling to higher regions of the brain remained unknown. The authors utilized a series of technically challenging experiments to identify the anatomy and functional responses of projection neurons responding to pheromone mixtures, primary pheromone, secondary pheromone, and behavioral antagonist odors. By calcium imaging of MGC mALT neurons, the authors identify that odor responses in PNs are broader than the olfactory neuron counterparts (ie, the behavioral antagonist activates OSNs innervating the dma glomerulus, whereas the antagonist actives dma and dmp glomeruli). The authors then perform a series of elegant experiments by which the odor responses of different mALT PNs are recorded by electrophysiology, and the anatomy of the recorded neurons identified by dye fill and computer reconstruction. This allowed analysis of the temporal response properties of the neurons to be correlated with their axonal processes in different brain regions. The data suggest that attractive pheromone signals activate the SIP and SLP regions, while aversive signals primarily active regions in the LH. Finally, the authors present a model of pheromone signaling based on these findings.

The work presents the first glimpse at the signaling from mALT PNs. The technical challenges in performing these experiments did limit the number of neurons that could be recorded and imaged. As such, the comprehensiveness of the study was not clear, or if additional experiments might alter the findings. The connection of protocerebrum anatomy with functional signaling (as summarized in Figure 6) could have been more clearly articulated.

The manuscript could benefit by revisions to the text and figure presentations that would make it more accessible to a broader audience.

Reviewer 3 (Public Review):

In this work, the authors study the role of Adgrg6 in spine alignment. Using a battery of tissue-specific Cre deleter lines, they show that Adgrg6 activity in intervertebral disc (IVD), ligament and tendon cells is necessary to prevent spine misalignment. The finding that the phenotype appears around postnatal day 20 associates it with the human disorder adolescent idiopathic scoliosis. The authors show reduction in the phosphorylation of CREB in cartilage cells from Adgrg6 KO spine, suggesting a molecular entry point into this regulatory mechanism. Additionally, loss-of-function of Adgrg6 leads to a postnatal phenotype, indicating that its activity is important already in the embryo. Finally, they show involvement of Adgrg6 in regulating mechanical properties of tendons.

It would have been interesting to see if there is early indication for abnormality before the onset of scoliosis by showing histology and gene expression in cartilage, tendon and IVD of P10 mice.

Overall, it is an interesting and important paper, but it would have benefited from better organisation of the Results section.

Reviewer 2 (Public Review):

This work is significant in that it carefully dissects the tissue dependency of the function of Adgrg6 through use of conditional loss of function in different components of the skeleton. The precision of the work, both in characterization of the anatomy through histological, tomography, and genetic analysis of expression is quite exceptional and allowed fine grained dissection of the regionality of Adgrg6 action as it pertains to formation and maintenance of the spine, as well as the temporal manifestation of its phenotypes.

The authors find that although Adgrg6 has important functions in differentiation of chondrogenic cells, its role affecting the susceptibility to AIS stems from its function in dense connective tissues such as ligaments. Notably, the authors do not find an effect when altered in osteoblasts, underscoring a mechanical deficiency model of AIS in which non-osteogenic tissues may drive the presentation and expression of scoliosis. Lastly, although preliminary analysis in KO and WT cells in vitro, the authors show ability to restore Agrgr6 regulated genes by treatment with small molecule mediators of CREB, which functions downstream. Such targeted modulation and restoration of components of Agrgr6 function within skeletogenic cells may prove to be an effective means of prevention in treatment of this disorder - possibly even in cases of diverse genetic, or environmental causes. This however is not directly tested in the animal model presented.

The data is quite clear and directly addresses their attempts to understand the etiology of progressive, and late deterioration of the spine. Weaknesses of the manuscript lie in the integration of their approach and data within a logical framework in which to apply their findings. It is unclear why this gene was a target of analysis, such as its prevalence in AIS, or ability to serve as a unique model for the disease - unique as in why this gene rather than other models of AIS in mouse or zebrafish. The impact of the findings here could be greatly strengthened by discussion of why experiments were initiated and how the data addressed the overall question of cause of AIS by Agrgr6 and by integration within and between sections of the results.

Reviewer 1 (Public Review):

In the manuscript by Liu et al., the authors investigate the role of Adgr6 in spine development in mice and dissect tissue specific contributions leading to late onset scoliosis in knockouts. Furthermore, they implicate Adgr6 in regulating gene expression and the mechanical properties of dense connective tissues via cAMP signaling that are linked to de development of scoliosis.

Overall, this is an interesting and thorough study of the developmental roles of Adgr6 in spine development that contributes both to the understanding of spine morphogenesis and the etiology of common types of scoliosis that are of unknown origin (i.e., idiopathic). Through the use of various tissue specific drivers, the authors generate conditional mouse knockouts that allowed them to dissect the respective contribution of Adgr6's function in each spine associated tissue. In addition to the use of state-of-the-art genetic tools, the authors show beautiful histological and micro-CT data illustrating developmental processes and phenotypes with great detail. Their results also implicate cAMP signaling and CREB activity in the regulation of mechanical properties of dense spine tissues.

Reviewer #3 (Public Review):

The authors have have convincingly demonstrated that modifying residues in the KDEL signal peptide and/or receptor can have a dramatic effect on retrograde targeting and their binding affinities. At a simplistic level, the structure of the electrostatic surface of the KDEL is very positively charged and then transitions into a negatively charged surface. The transition point from negative to positive charge is exactly at the level of the -4 position. As such, a bunch of arginine residues in the receptor progressively engage the signal until its locked in place by salt-bridges (E-117/D50) to the "K/H/R"-residue. In addition, pi-cation interactions between a tryptophan residue and the "K/H/R"- residue in the -4 position are important. To validate and quantify interactions, crystal structures have been solved with the HDEL and RDEL peptides and computational studies have analysed the pi-cation interactions. Moreover, differences are discussed between the tighter (HDEL) vs. weaker (KDEL) binding peptides in context with the differences in pH between the Golgi and ER.

Strengths:

The authors build upon their previous crystal structure of the KDEL receptor and the Newstead and Barr groups team up to provide a strong scientific approach combining structure-function analysis with trafficking and cell biology to yield important molecular insights into the recognition of ER-retention signals by the KDEL receptor. This paper is technically strong and well-written in most parts. They are able to build a connection between the variation of ER-retention signals "K/H-DEL" binding affinities with their pH dependancies and the natural abundance of ER proteins.

Weaknesses:

The authors have not made detailed pH dependent profiles for the "R"-DEL retention signal. This is an important comparative control, because unlike lysine and histidine, arginine has a very high pKa and will therefore always remain protonated. The authors refer to the "acidic" Golgi versus the neutral pH of the ER. However, it would be more correct to refer to the mildy acidic Golgi vs neutral pH of the ER and give the pH values of 7.4 for ER and pH 6.2 for the Golgi lumen. This sets up the scientific question to be more nuanced, as its only a pH difference of around 0.5 to 1.0 pH units. The authors have not included the computational estimates for the pKa values of the "K/R/H"-DEL residues nor the comparative pH dependence of KDEL and RDEL binding affinities, which is needed to properly asses the influence of differences in pH between Golgi and ER organelles and functional significance of the RDEL variant in particular.

Reviewer #2 (Public Review):

This study combines several different techniques to study the binding of the signal sequences of ER-resident protein to the KDEL receptor, which is required for their retrieval from later stages of the secretory pathway. The ER-resident proteins have a C-terminal four amino acid sequence, typically KDEL, HDEL or RDEL, which are bound by the KDEL receptor in the Golgi, leading to their return to the ER for another round of activities. Failure to retrieve the ER-proteins leads their secretion and loss of these valuable chaperones and enzymes. Structural work has highlighted the mode of binding for several variants of the signal sequence, here and in previous work. Binding studies are used to observe differences in affinity between the various signal sequences, showing that the HDEL sequence has the highest affinity, but proteins containing KDEL or RDEL are more abundant. A system is set up where mScarlet proteins are tagged by a range of different C-terminal xDEL sequences to monitor the distribution of these proteins in the cell, looking at their retrieval from the Golgi. Next, a series of mutations are made in the KDEL receptor, targeting residues that are potentially involved in binding of the signal sequence and their ability to retrieve the different tagged mScarlet proteins is studied. Finally, a molecular dynamics simulation is carried out to monitor the binding process of the peptide sequence, showing a relay of positively charged residues involved in the consecutive binding of the negatively charged residues of the signal peptide and the carboxy terminus.

The paper is an excellent example of the use of a large number of different techniques, spanning structural, biophysical, cell biological and computational methods, to provide new and detailed insights into the binding mechanism of signal peptides to the KDEL receptor. It is one of the most complete papers I have had the pleasure to review, as it looks at this problem from so many different angles.

Reviewer #1 (Public Review):

ER retrieval mediated by the KDEL receptor occurs for cargo present from great to minimal abundance and for cargo with different variations of the "KDEL" carboxy terminal sorting signal. Using a detailed structure/function approach, this study provides insight into the mechanism of cargo recognition by the receptor. A significant advance is the new structural data derived from co-crystals of the receptor with TAEHDEL and TAERDEL peptides that is now compared with the previous KDEL co-crystal structure. From there, the investigators use mutation of both receptor and cargo sequences as well as molecular simulation of the binding interaction. Altogether the findings identify charged receptor residues playing a role in specificity based on the -4 position of the signal, a receptor tryptophan that accounts for higher affinity binding to HDEL, and binding pocket arginines that may be sequentially engaged by the carboxy terminus for capture of the three carboxyl groups in the DEL portion of the signal. The work is meticulously carried out and the findings will likely be of significant interest to the field.

Joint Public Review:

Bohm et al. investigated the operating conditions of the soleus and the vastus lateralis muscle during running. They report different roles for the two muscles. The soleus acts as an energy generator characterized by a high force-length potential and enthalpy efficiency whereas the vastus lateralis acts as an energy conservator, characterized by a high force-length and force-velocity potential. The authors show how the decoupling of the muscle-tendon-unit length and the fascicle length, mainly attributable to tendon compliance, allows both muscles to work at a high, almost optimal , force-length potential. Beside this similarity the soleus shortens throughout stance phase (concentric mode) whereas the vastus lateralis shows almost no length changes (isometric mode) and is activated primarily in the first part of the stance phase. These observations in combination with the calculated enthalpy efficiency for soleus and the estimated force-velocity potential for vastus lateralis clarify the role of the two muscles in the optimization of muscle energy production and force generation during the experimental condition. The authors use a complex methodology and calculate the variables of interest in the most sophisticated way. The results of the present study contribute in a comprehensive way to the ongoing discussion on muscle and tendon interaction during human locomotion. The conclusions of this paper are mostly well supported by measured and modeled data, but some aspects of the experimental setup and data modeling need clarification and a more thorough discussion.

Reviewer 2 (Public review):

In this study the authors investigated the effects of maternal obesity on plasma lipid, the cardiac transcriptome and lipidomics in the maternal and fetal mouse heart. Their major conclusions were that maternal obesity has different effects on the maternal and fetal lipidome; the changes are greater in the fetal female heart than the fetal male showing sexual dimorphism in programming and that changes in the transcriptome may reflect alterations in lipids. The study is well conducted and will add significantly to the literature on developmental programming by maternal obesity.

The authors use a well-established model. The methods are all state of the art. I find no problems with any of the data. Maternal obesity is now an epidemic with consequences for mother and fetus. Thus this study is timely and will be valuable in assessing potential interventions and management strategies for the offspring of obese mothers.

Reviewer 1 (Public Review):

The manuscript by Pantaleao et. al., describes the effects of maternal diet-induced obesity on lipid composition in maternal and fetal serum and the fetal heart, and in the fetal heart transcriptome. Lipid composition in fetal serum and heart was analyzed in males and females. This study revealed sex-specific effects of obesity during pregnancy. The authors found changes in the lipidome of both mother and fetus in response to obesity. Many of the lipid profiles exhibit sex specific changes in the fetal sera. Similarly, the authors identified sex-specific changes to the lipidome of the fetal heart. Through the use of transcriptomic analysis on the fetal heart, the authors identified changes in the expression of genes regulating lipid metabolism. The results presented provide insight into the still poorly understood processes influencing the long-term health of the fetus.

The work characterizes an important aspect of the effects of maternal obesity and the results are visually well presented. A limitation is that this is a largely descriptive study. Nonetheless, the authors provide a detailed description of the lipid composition changes in response to maternal obesity and associated with sex.

The introduction provides key information about the effects of obesity during pregnancy in the offspring, and the relevance of lipids in heart homeostasis. However, cardiac transcriptional regulation and sex-specific responses, which are the other key components of this study, could be more cohesively integrated.

Some of the results presented can be analyzed in deeper detail to establish correlation between sex and diet with lipid composition and cardiac gene expression.

Reviewer #2 (Public Review):

In this manuscript, Dalal, Winden, and colleagues perform cell type-specific RNA-seq and TRAP-seq to analyze changes in total RNA levels and mRNA bound to L10/ribosomes in cerebellar Purkinje cells (PCs) that lack Tsc1, which when mutated gives rise to the developmental disorder tuberous sclerosis complex (TSC). These studies were motivated by previous studies by the Sahin laboratory that demonstrated that depletion of Tsc1 in cerebellar PCs alter social behavior in mice. The authors found that transcripts known to bind to RNA-binding protein fragile X mental retardation protein (FMRP) were reduced in the Tsc1 mutant PCs and subsequent bioinformatic analysis suggested that this was due to increased degradation of these RNAs. The TRAP-seq studies of the Tsc1 mutant PCs indicated that there was no change in ribosomal binding for many of RNAs. Finally, that authors found that the FMRP target SHANK2, was reduced in PC synapses the Tsc1 mutant mice, suggesting that compensatory increases in ribosomal binding and translational efficiency is unable to overcome the reduction in transcript levels. The authors conclude their findings further implicate dysfunction of FMRP and its targets in TSC.

The main strength of the manuscript is the data sets generated by the cell type-specific RNA-seq and TRAP-seq in cerebellar PCs that lack Tsc1. In addition, the bioinformatic analysis revealed several interesting findings, including the observation that FMRP target RNAs are reduced in the Tsc1 mutant PCs, which may be due to degradation of these RNAs, and that the translational efficiency of these RNAs is actually increased, which may be a compensatory effect. The authors also observed that 5'TOP containing mRNAs showed increased translational efficiency in the Tsc1 mutant PCs, which is consistent with increased mTORC1 signaling. Finally, the authors show that the protein levels of SHANK2 are decreased in Tsc1 mutant PCs. Weaknesses include not examining the protein levels of additional proteins whose RNA levels are decreased and translation efficiency is increases and the lack of examination for protein levels for 5'TOP mRNAs that exhibit increased translational efficiency in Tsc1 mutant PCs. Given that the FMRP is thought to important for regulating the translation of long genes, it would be important to determine whether any of the differentially regulated genes in either the RNA-seq or TRAP-seq data sets correspond to the length of the gene. Otherwise, this an interesting manuscript that will be of interest to those studying translation, fragile X syndrome, and TSC.

Reviewer #1 (Public Review):

This study reports that deletion of Tsc1 restricted to cerebellar Purkinje cells leads to decreased levels of mRNAs associated with FMRP targets although their ribosomal bindings are increased likely through compensatory mechanisms. However, protein levels of Shank2, one of the core FMRP targets, are decreased, suggesting that the compensatory increases in the ribosomal binding of FMRP target mRNAs do not rescue Shank2 protein expression. The analyses for transcriptional and translational processes were performed carefully and in a balanced manner, and the results are largely convincing and support the key conclusions. Considering the growing importance of mTOR signaling and cerebellar functions in ASD pathophysiology, the present work suggests that FMRP target transcripts are key mediators of Tsc1-related cerebellar dysfunctions, which is an important and timely contribution to the field.

Joint Public Review:

This well-written manuscript describes a spontaneous autoinflammatory phenotype of STAT1-deficient mice, characterized by myeloid hyperplasia, expansion of Th17 cells, microbiota dysbiosis, and inflammatory bowel disease. A similar autoinflammatory condition is seen in humans deficient in STAT1, and in independent colonies of STAT1-deficient mice, but its mechanistic basis is not understood. STAT1 is a critical transcription factor downstream of type I, II (gamma), and III interferon (IFN) signaling. The authors are able to recapitulate the disease phenotype in mice with combined deficiency in both STAT2 (essential for type I and III IFN signaling) and the receptor for IFN gamma, but not in mice deficient in both the type I and II IFN receptors, implying that the phenotype arises from deficiency of all three classes of interferons. Thus the authors conclude that suppression of spontaneous autoinflammatory disease is a redundant function of type I/II/III IFNs. Disease is ameliorated by IL-17 deficiency, indicating a critical role for type 17 responses in pathogenesis. Disease is also ameliorated by treating STAT1 deficient mice with antibiotics, implying a role for the microbiota in the disease progression. The authors' model is that interferons regulate the composition of the microbiota and that dysbiosis of the microbiota then produces the inflammatory disease. The main limitation of the manuscript is that it is not explained how interferons regulate microbiota composition, or how the altered microbiota then produces inflammation. These issues are likely beyond the scope of what could be expected to be fully addressed for this initial paper. However, analysis of disease-free STAT1/IL17-deficient mice could provide insight into whether dysbiosis and inflammation is upstream or downstream of IL-17 signaling.

Reviewer #3 (Public Review):

It is generally thought that the cerebellum is primarily involved in the short-timescale control of movements, while motor cortex is involved in motor planning. The present paper follows classic studies in primates and a recent study in mouse that investigated the role of cortico-cerebellar loops in motor control. To date, studies in both species applied perturbations to the cerebellum to then study changes in cortical activity. For example, it has been long known that cooling deep cerebellar nucleus produces changes in the responses of motor cortex neurons in primate (e.g., Meyer-Lohmann et al., 1975). Further, Gao and colleagues' recent paper (Nature 2018) used optogenetics to perturb responses in the deep cerebellar nucleus before licking movements. The authors of this 2018 nature paper conclude that persistent neural dynamics are maintained during voluntary movements by connectivity in within this cortico-cerebellar loop.

The experiments are well performed, and the results are logically organized and presented. However, a main concern is that the authors have not well justified that these experiments prove a conceptual advance. The conclusions appear to be largely consistent with those of prior work, both regarding changes in the responses of motor cortex neurons, and resultant (subtle) changes in behavior (i.e., altered arm kinematics). The impact of the paper would be improved if the authors adapted a more precise style of reporting the novelty of their results throughout.

Major concerns:

1) The experiments are well performed, and the results are logically organized and presented. However, a main concern is that the authors have not well justified that these experiments prove a conceptual advance. As noted above, prior studies have probed the role of cortico-cerebellar loops by applying perturbations to cerebellar activity (cerebellar cortex and/or deep cerebellar nuclei) and quantifying changes in cortical activity prior to and during movement. The main novelty of the present study is that the authors perturbed the loop at a different locus, namely in the pontine nuclei (PN) which send projections to the cerebellum rather than directly to the cerebellum. The rationale for why this specific perturbation provides a conceptual advance to the field was not adequately motivated.

The authors do clearly review prior literature showing that perturbation of cortico-cerebellar projections impacts the rest of the loop and behavior, they also well explain the application of their exciting new tool to specifically target PN neurons with their optogenetic stimulation. Yet, the authors do not motivate why it is important to specifically perturb the pontine nuclei (PN) to gain new insights into the role of "cortico-cerebellar loops" nor do they provide any reason to expect a difference in changes in loop dynamics for perturbations applied versus to the DCN. Indeed, the conclusions appear to be largely consistent with those of prior work, both regarding changes in the responses of motor cortex neurons, and resultant (subtle) changes in behavior (i.e., altered arm kinematics). Generally, these results are similar to those previously reported in primate DCN cooling experiments characterizing changes in hand movement in in a voluntary tracking task (e.g., Brooks et al., 1973; Conrad and Brooks 1974).

2) The description of the connectivity of the loop illustrated in Figure 1 is straightforward. Motor cortex recipient PN neurons project to PN neurons, which then project directly to the cerebellar cortex and deep cerebellar nuclei, etc. Thus, the effect of any perturbation to PN neurons should be realized rapidly within neurons in the cerebellar cortex and deep cerebellar nuclei if they are part of this direct loop. However, onset latencies for the effect of the perturbations are not documented for these experiments (Figs 3&6 in the test/reaching conditions, and associated text). Similarly, latencies are not reported for the onset of changes in motor cortex neuron responses to PN perturbations in either condition (Figs 4&7 in the test/reaching conditions, and associated text). The only reference I could find to latencies specified the that required to reach the peak firing rate - not latency of the change. Specifically: "these were stereotypical, mostly consisting of transient excitation (Fig. 4B, left; median time of firing rate peak 120 ms)" - 120ms seems very long for the loop in Fig 1. It would be useful to know the latency between optogenetic stimulation in PN and changes in PN firing rate. And then the question is at what latency are the neurons in subsequent nodes altered? Quantification of latencies of the effects that are observes in the different nodes of the cortico-cerebellar loops would strengthen the authors' conclusion that they are actually studying the direct loop in Figure 1 which would then make the study's conclusions more compelling.

3) Overall, there was often a sharp incongruity between the complexity of many of the findings described in results and accompanying figures and the short summary conclusion provided for the Results. Here is one of many examples (bottom of page 5), where the authors conclude "These results demonstrate that the cortico-cerebellar loop does not drive reaching, but fine-tunes the behavior to enable precise and accurate movement." Yet, what the results above describe is considerable heterogeneity and variability across animals and cases. These conclusion should be more aligned with/ justified by the author's description of their actual results.

4) A related issue is the disconnection between description and summary, in the description of Figure 6- 8. The emphasis on correlation, yet the authors' main point here seems to be that there are changes in the activity in cortex and DCN induced by the PN stimulation during movement explain the changes in hand trajectory. For example, Figure 6D and its implications are not effectively described in the text.

5) Finally, the authors conclude that changes in the activity in cortex and DCN induced by the PN stimulation during movement explain the subtle deviations in hand trajectory and conclude that the cortico-cerebellar loop is responsible for fine-tuning movement parameters (bottom pf page 5 and top of page 8). However, i) the statement that this pathway fine-tunes motion is not justified by the analysis, and ii) the novelty is not made clear relative to prior work that has investigated cortico-cerebellar loop (beyond the experimental difference in perturbation site).

Overall, the text that follows in the discussion presented the findings in a far more clear and compelling way than much of the text in the Abstract, Introduction and Results "perturbing cortico-cerebellar communication did not block movement execution: animals were typically able to generate the basic motor pattern during optogenetic stimulation of the PN, and neural activity in cortex and cerebellum largely recapitulated the firing patterns observed during normal movement. Instead, PN perturbation altered arm kinematics, decreasing the precision and accuracy of the reach, and perturbation-induced shifts in neural activity explained these behavioral effects." The paper would be improved if the authors adapted this more precise style of reporting throughout.

Reviewer #2 (Public Review):

Guo et al examine the cortico-cerebellar loop during skilled forelimb movements in mice. The authors use optogenetic stimulation of the pontine nuclei (PN) and recordings in PN, cerebellar cortex, cerebellar nuclei (DCN), and motor cortex to show that PN output is transformed into a variety of activity patterns at different stages of the cortico-cerebellar loop. Stimulation only slightly alters movement-related activity in these structures and degrades movement accuracy. The authors conclude that the cortico-cerebellar loop fine tunes dexterous movement. The study is technically impressive, employing recordings in 4 brain regions, and recordings during optogenetic manipulations and behavior. The experiments are well done and the analyses are appropriate. The comparison across brain regions is comprehensive. The results that PN perturbation alters skilled movement and the perturbed activity could predict perturbed movement are important. The study adds to a long line of work supporting the view that the cortico-cerebellar pathway is required for fine motor control. I have a few comments on the interpretation and analysis which I believe could be addressed with changes to the text and additional analysis.

1) The authors conclude that the cortico-cerebellar loop "does not drive movement" but "fine tunes" the movement. While I generally agree with this interpretation, I wonder if the authors could flush out the concepts of "driving movement execution" vs. "fine-tuning movement" more clearly. Do authors consider them separate processes? How can they be disentangled? I also feel the data on its own has some limitations that should be considered or discussed. First, the data shows that PN stimulation degrades movement accuracy. However, this does not yet reveal the function of the cerebellar loop in fine motor control. Certain places in the text makes stronger assertions (for example, "cortico-cerebellar loop fine-tunes movement parameters") that I feel the data does not support. It is not clear from the data how the loop tunes movement parameters. Second, Fig. 5F shows that stimulating PN blocked movement initiation in some sessions (this is also mentioned in the Methods). Could the authors consider the possibility that stimulating PN at a higher intensity might block movement? This is related to the distinction between "driving" vs. "fine-tuning" movement. At the very least, the authors should discuss these limitations and possibilities.

2) Related to point 1, in Fig. 5F, for stimulation trials in which mice failed to initiate movement, did mice fail to move altogether, or did they move in an abnormal fashion?

3) In the abstract, the authors state that PN stimulation is "reduced to transient excitation in motor cortex". Also in the results (page 5) and discussion (page 8), "pontine stimulation only led to increases in cortical firing rates". These statements are based on the comparison between Fig 3D, 3F, and 4B. But I think the current presentation is somewhat misleading. First, Fig 3D, 3F, and 4B use different neuron selections that make direct comparison difficult. Fig 3 shows all neuron from Purkinje cell and DCN recordings. Fig 4B shows only PN-tagged motor cortex neurons. Furthermore, based on the methods description, it appears that PN-tagged neurons were defined using one-sided sign-rank test. Since the test is one tailed, does that mean neurons shown in Fig 4B are, by definition, neurons significantly excited by photostimulation? Looking at Fig 4B and 4C closely, there appear to be neurons suppressed by PN stimulation. Could the authors organize the rows in Fig 4 in the same way as Fig 3, where neurons that show suppression are grouped together?

4) Fig 7 shows that PN stimulation has only subtle effects on movement-related activity in motor cortex. However, only a small portion (1/8) of the motor cortex neurons show modulation to PN stimulation. Fig 7 shows all neurons. Would the results look similar for PN-tagged neurons?

5) Page 3 "Our observation that the activity of some motor cortex-recipient PN neurons is aligned both to the cue and movement suggests that these neurons might integrate signals of multiple modalities." Presumably, motor cortex neurons also have cue and movement-related activity and PN simply inherits this activity from the motor cortex.

6) Do Purkinje cells follow the 40 Hz PN stimulation like in the multi-unit recordings. The PSTHs in Fig 3 are too smoothed out to see this.

7) For the correlation analysis in Fig 6C top and 7C top, is the correlation computed from z-scored firing rates rather than on raw firing rates? This is not clear from the text. If computed on raw firing rates, one would expect the correlation to be above 0 even before photostimulation, since different neurons exhibit different baseline firing rates that presumably will be the same across control and stim trials.

Reviewer #1 (Public Review):

Guo et al. describes interesting experiments recording from various sites along a cortico-cerebellar loop involved in limb control. Using neuropixels recordings in motor cortex, pontine nuclei, cerebellar cortex and nuclei, the authors amass a large physiological dataset during a cued reach-to-grasp task in mice. In addition to these data, the authors 'ping' the system with optogenetic activation of pontocerebellar neurons, asking how activity introduced at this node of the loop propagates through the cerebellum to cortex and influences reaching. From these experiments they conclude the following: the cerebellum transforms activity originating in the pontine nuclei, this activity is not sufficient to initiate reaches, and supports the long standing view that the cerebellum 'fine tunes' movement, since reaches are dysmetric in response to pontine stimulation. Overall these data are novel, of high quality, and will be of interest to a variety of neuroscientists. As detailed below however, I think these data could provide much more insight than they currently do. Thus below I provide some suggestions on improving the manuscript.

1) Since the loop is the focus of this study, it would be nice if the authors better characterized latencies of responsivity to pontine stimulation through the loop, to address how cortically derived information routed to the cerebellum may loop back to influence cortical function. In the data provided, we know that pontine stimulation modulates Purkinje and deep nuclear firing (but latency to responses are not transparently provided in the main text, if anywhere), while motor cortical responses peak at 120 ms (after stimulus onset?, unclear), and that this responsivity is preferentially observed in neurons engaged early in the reaching movement. Is the idea, then, that cortical activity early in the reach is further modulated by cerebellar processing to (Re) influence that same cortical population? Does this interpretation align with the duration of reaches, the duration of early responsive activity during reach, and the latency of responsivity; or is the idea that independent information from other modalities entering the pontine nuclei modulates early cells? Latency to respond at the different nodes, might aid in thinking through what these data mean for the function of the loop.

2) Many of the figures need work to aid interpretation. Axis labels are often missing (eg 2F); color keys are often unlabeled (2F); color gradients often used but significance thresholds are hard to evaluate (using same colors for z scores and control / laser is confusing 6, 8); and within-figure keys would be useful (5D-h). These issues occur throughout the manuscript.

3) Relatedly, but also conceptually, Figure 3B has particular issues, such as identifying where the neuropixel multiunit activity is coming from. I assume that in the gray boxes illustrating the spatio-temporal profile of spiking band activity that the lower part of the box is the ventral direction, upper, dorsal. This is not spelled out. From the two examples it would seem that the spiking band is in different places in the cerebellum, undermining, I think, the objective of the figure. It would be sensible to revisit this entire figure to identify the key takeaways and design figures around those ideas. As it stands, these examples appear anecdotal. Consider moving this to a supplement. Powerband density strength is missing an axis. More importantly, it would be nice to corroborate the interpretation of the MUA with the single unit recordings, since the idea is that many neurons are entraining to the PN activity. Yet, the examples don't seem particularly entrained. Is the activity being picked up on just axonal firing of the PN axons? Fourier analysis of spiking of isolated neurons in cerebellum should be used to corroborate the idea that cerebellar neurons are entraining, rather than the neuropixel picking up entrained PN axons.

4) The use of the GLM is puzzling. In addressing the question of how cerebellum and motor cortex interact (from the Abstract, "how and why" do these regions interact) it is unclear why these regions are treated separately. I would have expected some kind of joint GLM where DCN activity is used to predict M1 variance (5 co-recordings are reported but nothing to analyze?); or where DCN + M1 activity is used to decode kinematics to see if it is better than one or the other alone. As it stands, we learn that there is more kinematic information in the motor cortex than in DCN. This is not necessarily surprising given previous literature on cerebellar contributions to reaching movements. In principle the idea that 'PN stimulation might perturb reaching kinematics through descending projections to the spinal cord, or by altering activity in motor cortex' is treated as mutually exclusive outcomes, though it is highly unlike to be so.' Analyzing M1+DCN together could address whether DCN activity adds nothing to decoding kinematics that isn't there in M1 or adds something that M1 does not have access to. The main point here is that the physiological datasets could be better leveraged with these fits to derive insight into the interactions of the loop. R2 should be provided in the GLMs (Fig 8) to assess statistically how well they perform relative to one another, not just correlations between the two.

Reviewer #3 (Public Review):

In this article, Odermatt and colleagues report a detailed and quantitative description of the growth in dry mass and volume of single fission yeast cells. They first propose a new method for dry mass measurement and calibrate it. They then use this method to show that, while growth in dry mass shows a rather steady exponential trend, growth in volume changes with the cell cycle stage, depending on the rate of cell tip elongation, which results in changes in cell density. They then use various methods to arrest cells at various stages in the cell cycle, demonstrating that, for each cell cycle stage, the change in density is due to a specific growth rate which does not necessarily matches the growth in dry mass. All these experiments are very convincing. They close the article with two observations which are a bit harder to understand: first they show that there is an internal gradient of density, which corresponds to a difference in the rate of growth of both tips of the cell, and which is maintained on long timescales; second, they show that the differences in densities between the two ends of the cell lead, after the formation of the septum, to a difference in pressure which can be indirectly visualized from the bending of the septum - the denser side having a higher pressure, the septum is bent towards the lower density side.

Overall, the article provides one of the very few available quantitative description of growth from single cell measurements, and shows for the first time that density variations can arise from an uncoupling between volume growth and dry mass growth, which does not seem to be compensated for. I think that this particular finding is significant enough to make this article broadly interesting for the cell biology community, as it concerns a very fundamental aspect of cell physiology.

Reviewer #2 (Public Review):

Odermatt et al. apply quantitative phase imaging to fission yeast and show that the well-established cytokinetic growth pause is not accompanied by a parallel pause in biosynthesis and thus cellular density increases. Interestingly, cellular density does not quickly re-equilibrate after division. Instead, density slowly decrease throughout interphase, suggesting that cells can operate efficiently within at least a 20% range of cellular density.

The work is of high technical quality. Comparisons with other measures of density and mass lend confidence to the robustness of the approach and the fact that it requires no custom hardware makes it accessibly to most workers in the field. However, the biological insight from the work is somewhat limited. The fact that density increases as growth pauses during cytokinesis is not surprising. Demonstrating it is an important contribution to the field, but it will not change the way people think about cell-cycle or growth control.

To me, the most interesting result is that density gradients a stable within cells. This result must have important implications in cytosolic viscosity, which could have been discussed more explicitly. The discussion does claim that the "distributions of large organelles and total protein are not polarized", but it is not clear that the papers cited to support that claim would be able to detect the ~5% reported difference in density. The organelle paper contains no quantitation and the noise in the protein paper looks to be around 10%.

Reviewer #1 (Public Review):

This paper provides quantitative detailed measurements of how dry mass density varies as a function of the cell cycle in fission yeast cells. They find that density decreases during G2 and increases during mitosis/cytokinesis. They also monitor the effects of cell cycle mutants and a drug that depolymerizes actin, and conclude that while dry mass increases continuously, cell cycle-regulated changes in volume growth in fission yeast create the density oscillations. This supports earlier less precise work in the field, and is therefore unsurprising. Overall, the quantitative data convincingly support these conclusions.

More interesting is the discovery of intracellular density gradients in G2 cells, with lower density at faster growing ends. The basis for these gradients is not investigated. The gradients persist through mitosis and cytokinesis, giving birth to daughter cells whose mean densities differ. These findings would appear to suggest that density differences might accumulate with each generation, but this is clearly not the case as the density variation in a cell population is very small. This paper will be of interest to researchers working on fission yeast, and raises interesting questions for future investigation.

Reviewer #3 (Public Review):

Alghoul et. al are attempting to decipher the molecular mechanisms of Hox a3 and a11 TIE elements to inhibit cap-dependent translation. It is known that both TIEs possess an upstream Open Reading Frame (uORF) that is critical. Here they seek to understand the exact molecular mechanism used for inhibition. They were able to show that both a3 and a11 are regulated by different mechanisms to ensure the inhibition of ca-dependent translation. They found that the translation inhibitory mechanism mediated by TIE a3 requires the presence of the translation initiation factor eIF2D. However, the mechanism mediated by TIE a11 contains three elements that enable a highly efficient inhibition of cap-dependent translation, these are: an upstream start codon (uAUG), followed by a stop codon, and a long stable hairpin. These findings show that these TIE elements of Hox mRNAs enable regulatory control between canonical translation and non-canonical translation Internal Ribosome Entry Site (IRES) translation.

The authors use a vast amount of different sophisticated techniques to prove the molecular mechanism of inhibition conferred by the TIE elements. They start by cloning the regions upstream of the beta-globin 5'UTR in a RLuc vector and sequentially deleting regions to identify the region that confers inhibition. By using chemical modification probing, they confirm RNA secondary structure and identify regions of interest that might be responsible for inhibition. Then they focus of each element separately. In TIE a3, they identified an uORF that requires eIF2D for this process. They used MS analysis to identify the binding partners of the two elements and they further confirmed by silencing eIF2D that the inhibition doesn't occur in its absence. They further corroborated this finding by mutating an A-rich sequence found upstream of the uAUG that determines specificity of eIF2D binding. In the a11 case, they use toe-printing and mutagenesis to determine that a 'start-stop' sequence is located upstream of a highly stable stem loop structure which stalls the 80S ribosome and thereby inhibits cap-dependent translation of Hox a11 main ORF.

Reviewer #2 (Public Review):

Non-canonical pathways for regulating protein synthesis serve important roles for controlling gene expression in critical developmental pathways. Homeobox (Hox) genes encode many mRNAs regulated at the level of translation. A general feature for many of these mRNAs has been the proposal they are regulated by Internal Ribosome Entry Sites (IRESs) and possess sequences in the 5'-untranslated regions (5'-UTR) of the mRNA that prevent canonical cap-dependent translation, termed "translation inhibitory elements" or TIEs. However, the mechanisms by which these Hox mRNAs are regulated remain unclear. Here, the authors focus on two Hox mRNAs, Hox a3 and Hox a11, and find they use entirely different means to achieve the same end of repressing cap-dependent translation. Hox a3 uses the non-canonical translation initiation factor eIF2D and an upstream open reading fram (uORF), whereas a11 uses a "start-stop" uORF followed by a thermodynamically stable stem-loop to inhibit translation. Overall, the experiments support the major conclusions drawn by the authors, and nail down mechanisms that have been left unresolved since the Hox mRNAs were first discovered to be regulated at the level of translation. These results will be of wide interest to the translation and developmental biology fields.

Some issues the authors should consider:

1) The mapping of the TIE boundaries are in general well-supported by the luciferase reporter experiments. However, there seems to be a disconnect in the luciferase values in Fig. 1B compared to the western blots in Supplementary Fig. 1D, however. For example, in the a3 case the 106 and 113 bands don't seem to correspond to levels consistent with the luciferase activity. For a11, the 153 band is not consistent with the luciferase activity. Also, the gels at the bottom are confusing. Should 74 in the left gel be 77? It would help to have a clearer explanation in the figure legend.

2) The results in the various sucrose gradients are not entirely convincing as presented. In all these cases, the experiment would benefit from the use of high-salt conditions (See Lodish and Rose, 1977, JBC 252, 1181-ff) in the gradient to remove background 80S not engaged with mRNAs. For the +cycloheximide sample in Fig. 8, this looks more like a "half-mer" between a monosome and disome, rather than a standard polysome.

3) In Fig. 7, it would be helpful to see the absolute level of translation from the reporters, as it is not clear what the baseline level of translation is in the knockdown cell lines. It's hard to judge the eIF4E knockdown case in particular without this information. Also in panel B, the GGCCC147 cell line is missing.

4) From the MS experiments in Fig. 6 and Supplementary Fig. 6, the authors focus on eIF2D, which makes sense. But they don't comment on two other highly suggestive hits in the a3 vs. beta-globin and a3 vs. a11 comparisons. These are eIF5B and HBS1L. Both are highly suggestive of what might be going in with the eIF2D-dependent translation mechanism. They don't show up in the GMP-PNP samples in Supplementary Fig. 6, which is interesting and would deserve a comment.

Reviewer #1 (Public Review):

In this manuscript entitled "Translation inhibitory elements from Hoxa3 and a11 mRNAs use uORFs for translation inhibition", the authors undertake a series of in vitro translation and in cell experiments to characterize the inhibitory features of previously documented Hoxa11 and Hox3 translation inhibitory elements (TIEs). The presence of TIEs within a subset of Hox mRNAs are thought to mediate repression of cap-dependent translation, enabling downstream IRES-mediated initiation to proceed. In sum, the authors report: (i) the presence of an upstream uORF in the Hoxa3 TIE sequence that dampens translation from the downstream ORF and (ii) the presence of a stem-loop structure that appears to block ribosome migration and results in inhibition of the downstream ORF (even thought the 5' UTR of a11 also has 2 uAUGs - these do not appear to play a determining role in a11 TIE activity).

Major Strengths: This study is comprehensive and thorough. The manuscript is well written.

Weaknesses: Some of the experiments lacked internal controls making interpretation of the results preliminary in nature.

For the most part, the authors have defined the inhibitory features of the Hox a3 and a11 Translation Inhibitory Element. The work was placed in appropriate context (Introduction). The work further supports the known concept that uAUGs and 5' UTR secondary structure is detrimental to eukaryotic translation inhibition.

Reviewer #4 (Public Review):

In this paper, the author uses an impressive comparative dataset of 172 species to investigate the relationship between intraspecific genetic diversity and census (actual) population size. They find that even when they use phylogenetic comparative methods, the relationship between neutral diversity and population size is much weaker than predicted by theory and that selection on linked sites is unlikely to explain this difference. The paper convincingly demonstrates that the paradox of variation first pointed out by Lewinton in the 70s remains paradoxical.

This paper is exceptionally strong in multiple ways. First, it is statistically rigorous; this is particularly impressive given that the paper uses methods and data from multiple fields (genomics, macroecology, conservation biology, macroevolution). This is the most robust estimate of the relationship between diversity and population size that has been published to date. Second, it is conceptually rigorous: the paper clearly lays out the various hypotheses that have been put forth over the years for this pattern as well as the logic behind these. The author has done a great job at synthesizing some complex debates and different types of data that are potentially relevant to resolving it. Third, it is exceptionally well-written. I sincerely enjoyed reading it. Overall, I think this is a major contribution to this field and though the paper does not resolve the challenge laid down by Lewinton, I think these analyses (and curated data/computational scripts) will inspire other researchers to dig into this question.

I do however, have some suggestions as to how this paper could be strengthened.

First, in phylogenetic comparative methods (PCMs) there has been a persistent confusion as to what phylogenetic signal is relevant -- when applying a phylogenetic generalized linear model with a phylogenetically structured residual structure (which the author does here), one is estimating the phylogenetic structure in the errors and not the traits themselves. The comparative analysis are well-done and properly interpreted but at some points in the text, particularly when addressing Lynch's conjecture that PCMs are irrelevant for coalescent times and comments/analysis on the appropriateness of Brownian motion as a model of evolution, that there is some conceptual slippage and I suggest that author take a close look and make sure their language is consistent. Strictly speaking the PGLM approach doesn't assume that the underlying traits are purely BM -- only that the phylogenetic component of the error model is Brownian. As such running the node-height test on the both the predictors and the response variable separately -- while interesting and informative about the phylogenetic patterns in the data (including the shift points you have observed) isn't really a test of the assumptions of the phylogenetic regression model. It is at least theoretically plausible (if not biologically) that both Y and X have phylogenetic structure but that the estimated lambda = 0 (if for instance, Y and X were perfectly correlated because changes in Y were only the result of changes in X). To be clear, I am fine with the PGLM analysis you've done and with the node-height test; I just don't think that the latter justifies the former.

One note about the ancestral character reconstruction: I think it is a fine visualization and realize you didn't put too much emphasis on it but strictly speaking the ASR's were done under a constant process model and therefore they wouldn't provide evidence for (a probably very real shift) between phyla. I think it was a good idea to run the analyses on the clade specific trees (particularly given how deep and uncertain the branches dividing the phyla are) but I just don't think you could have gotten there from the ASR.

I am not convinced that the IUCN RedList analysis helps that much here and in my view, you might consider dropping this from the main text. This is for two reasons: 1) species may be of conservation concern both because they have low abundance in general and/or that their abundance is known to have experienced a recent decline -- distinguishing these two scenarios is impossible to do with the data at hand; and 2) there is of course a huge taxonomic bias in which species are considered; I don't think we can infer anything ecologically relevant from whether a species is listed on the RedList or not (as you suggest regarding the lynx, wolverine, and Massasauga rattlesnake) except that people care about it.

This is not really a weakness but I find it notable that recombination map length is correlated with body size. I realize this is old news but I was left really curious as to a) why such a relationship exists; and b) whether the mechanism that generates this might help explain some of the patterns you've observed. I would be keen to read a bit more discussion on this point.

Reviewer #3 (Public Review):

This study is quite directly a follow-up study of the recent work of Corbett-Detig et al (2015) and the commentary by Coop (2016) which aimed to understand the relation between population size and diversity, and the degree to which the shape of the relation could be explained by the action of linked selection. The analysis here scales up the sample size for a large-scale focus on comparative analyses of animals, and introduces the application of phylogenetic correction to control for relatedness.

As the most comprehensive analysis of its type to date, and with the addition of phylogenetic correction, this work's strength primarily lies in confirming the conclusions laid out in the commentary by Coop, notably that linked selection is unable to fully explain the narrowness of the diversity across species with orders of magnitude variation in population sizes. Through an explicit model-fitting of the effects of linked selection, the main conclusions are essentially that Lewontin's Paradox remains unexplained. The Introduction and discussion provide a very nice accounting of the range of possible explanations. I also appreciated the connection of the population size inferences to IUCN status.

I wasn't so convinced that the assessment of phylogenetic inertia (Lambda>0) really provides a way to assess Lynch's argument that coalescent times are too short to have a phylogenetic effect. For reasons outlined by the author in the discussion, it could well be that any phylogenetic inertia signal is due to inertia of life history traits correlated with effective population size rather than with diversity itself. The discussion raises this important point, but I think leaves us with the difficulty of really assessing how important that phylogenetic correction really is: if diversity has no direct phylogenetic non-independence, I am a bit unsure how much we have learned through this analysis alone (i.e. what is lambda telling us), without an explicit assessment of how often divergence times may actually truly be on the same order as coalescent times.

That said, I think it's a very open question whether diversity actually has phylogenetic independence because of short split times relative to effective population sizes. The author mentions the possible effect of large Ne on causing this to be violated; but I also wondered whether many of the small Nc species are still retaining a fair bit of ancestral polymorphism, further homogenizing diversity levels.

Overall a number of possible explanations (such as the effect of variable selected site densities, and variable recombination) were raised, and rather quickly rejected as 'unlikely to explain the qualitative patterns'. In a number of cases these statements were fairly brief, and I wondered whether in aggregate how likely a combination of these COULD explain the patterns. Looking at Figure 5B, it seems like the major effect of phylogeny (or correlated life history) is also apparent for the discrepancy between observed and predicted diversity- Chordates seem to have the largest discrepancy. With that in mind, I do wonder whether some feature of genome structure in Cordates, including a combination of the effects discussed in the paper that could account for the discrepancy (e.g. the effects of variable recombination rates/genome size and functional densities, variation in mutation rates, etc.) could collectively account for the paradox, even though individually the author rules them out as being able to explain the 'qualitative pattern'. Could the genome structure of chordates lead to a major difference in linked selection that's unaccounted for here?

Mei et al (2018) (American Journal of Botany, Volume 105, Issue 1, p1-124) argued that species with larger genomes have greater 'functional space', implying a greater deleterious mutation rate in species with larger genomes. This could potentially be a factor driving those Chordates with intermediate Nc values furthest below the predicted line?

Reviewer #2 (Public Review):

This manuscript presents a thorough reanalysis of estimates of genetic heterozygosity pi, its distribution among animals, and its relationship with the census population size, here estimated from organism body mass and species range. A significant phylogenetic effect on pi is uncovered, and a formal model of linked selection is shown to be insufficient to explain the so-called Lewontin's paradox.

My first and maybe most important comment is that the introduction, discussion and overall writing of the manuscript are really excellent. This might be the most lucid, extensive, balanced overview of Lewontin's paradox and the associated literature I've ever read.

My second comment, somehow counterbalancing the first one, is that the major point made here, that linked selection alone cannot explain Lewontin's paradox, has been made before, e.g. by Coop (2016) and Ellegren & Galtier (2016) commenting on Corbett-Detig et al (2015). The material presented here substantiates this point further, but is perhaps not a major advance per se, so that the manuscript lies somewhere between a review and research article.

I have a few additional, more specific comments below. I think this is a great addition to the existing literature, which clarifies and synthetizes many aspects of a complex question.

1) Phylogenetic inertia

I am not sure I get the point of the phylogenetic inertia analysis. It seems to be intended as a response to Lynch 2011, who, responding to a criticism by Whitney & Garland, stated that the coalescence time is not inherited across the phylogeny. That quote from Lynch is mentioned several times, and as a motivation for performing this analysis. Yet the result reported here, i.e., that pi has some phylogenetic inertia, does not seem to contradict this specific statement, for at least two reasons. First pi might have some inertia via inertia on the mutation rate, not on coalescence time. Secondly, pi might have some inertia because it is in part determined by traits that have some inertia, such has body mass for instance. The text insightfully discusses these aspects (l399-407), but honestly I do think that this analysis invalidates Lynch's (somewhat trivial) point that coalescence time is not a trait that can be inherited.

I still agree that the analysis is worth doing and publishing, but I would suggest putting less emphasis on the Garland/Lynch controversy. Also it might be fair to mention that Leffler et al (2012) and Romiguier et al. (2014) did attempt to correct for phylogenetic inertia when correlating pi to various traits, although they did not analyse the phylogenetic effect as thoroughly as it is done here.

2) Range effect

I was surprised to read that species range alone has a significant effect on pi. The reason is that I suspected species range varied at a shorter time scale than coalescence time - e.g. think of what ranges were 20,000 years ago, when pi was probably, I thought, very similar to current pi; maybe worth discussing?

3) IUCN categories

I found the result that endangered species have a lower estimated Nc and a lower pi than non-endangered species a bit trivial, knowing that lare body sized vertebrates are typically more threatened, and more of concern, than small body sized invertebrates. What would be more relevant to conservation biology is an analysis that controls for body size, e.g., are endangered large mammals less polymorphic than non-endangered large mammals. There is a fairly large amount of literature on this topic.

4) The Methods section (l580-581) states that map length data are available in 41 species, but figure 5A shows a relationship with 131 data points; some clarification needed here

5) abstract line 10: "vary two orders of magnitude", word missing

Reviewer #1 (Public Review):

The standard neutral model, which is our null model for levels of genetic variation, predicts that they should be proportional to census population sizes. In reality census population sizes across metazoan species span several orders of magnitude more than the ~3 orders spanned by levels of genetic diversity. This discrepancy is referred to as Lewontin's paradox, and to resolve it would mean to explain how basic population genetic processes lead to the modest span of genetic diversity levels that we observe. This is a central question in population genetics (which is, after all, concerned with understanding patterns of genetic variation) and is of substantial general interest.

The manuscript addresses Lewontin's paradox through three main analyses:

1) It derives novel estimates of census population size across metazoans, which alongside previous estimates of neutral diversity levels, enables a revised quantification of the relationship between diversity levels (\pi) and census populations sizes (Nc).

2) It quantifies the relationship between \pi and Nc controlling for phylogenetic relatedness.

3) It revisits the question of whether this relationship can be accounted for by the effects of selection at linked loci (e.g., sweeps and background selection). I address each of these analyses in turn.

Novel estimation of census population sizes in metazoans: The estimates are derived by: 1) estimating the density of individuals within their range, based on body size and a previously observed linear relationship between body size and density (Damuth 1981, 1987); 2) applying a geometric algorithm (finding the minimum alpha-shape computationally, sometimes adjusting alpha manually) to geographic occurrence data to estimate the area of the range; and 3) multiplying the two.

The results are sometimes surprising. For example, Drosophila melanogaster is estimated to have a population size > 10^17 (Fig. 1); if the volume of an individual is 1 mm3, this implies a total volume > 1km x 1km x 100 m. Additionally, some species classified as endangered have census estimates > 10^8 (Fig. 3). The author compares his area estimates with estimates for species in the IUCN Red List (focused on endangered species) to find that they largely correlate (although this is not quantified). I think further investigation of the quality of the census size estimates is warranted. Are there are other estimates of census size or biomass that can be used for validation, e.g., for species of economic and biomedical importance (e.g., herring and anopheles)?

If the proposed method proves to work well, I imagine that the estimates of census size may be of broad interest in other contexts. In the context of Lewontin's paradox, it may be interesting to quantify the difference in the relationship between \pi and Nc suggested by the new estimates vs the proxies used in previous work (e.g., Leffler et al. 2012).

Quantifying the relationship between \pi and Nc controlling for phylogenetic relatedness: I am unclear about the motivation for this analysis. As Lynch argued (and the author describes), if TMRCAs of neutral loci within a species are smaller than the split time from another species in the sample, its genetic diversity level was shaped after the split, and it could be considered an independent sample for the relationship between \pi and Nc. There may be underlying factors shaping this relationship that are not phylogenetically independent (e.g., similar life history traits) but it is unclear why that would justify down-weighting a sample. In that sense, I am not convinced by the authors argument that finding a 'phylogenetic signal' justifies the correction. Stated differently, it is not obvious what is the 'true' relationship being estimated and why relatedness biases it. One could imagine that the 'true' relationship is the one across extant species, in which case the correction is not needed (with the possible exception of species in which TMRCAs are on the same order or greater than split times). I don't know what an alternative 'true' relationship would be.

Moreover, I am not sure how a more precise 'quantification' of the relationship between diversity and census size serves us. Regardless of corrections, it is obvious that the null provided by the standard neutral model is off by orders of magnitude. Perhaps once we have alternative explanations for this relationship then testing them may require corrections, but presumably the corrections will depend on the explanations.

One context in which phylogenetic considerations and quantification may be relevant is the comparison of the \pi - Nc relationship among clades. Notably, one could imagine that different population genetic processes are important in different clades (e.g., due to reproductive strategy) and a comparative analysis may highlight such differences. It is less clear whether the corrections that are applied here are the relevant ones. Separating clades makes sense in this regard, but it is unclear why to correct for non-independence within a clade. Furthermore, it seems that in order to point to different processes one would like to control for the distribution of census population sizes in comparisons between clades (to the extent possible). Otherwise, one can imagine the same process shaping the relationship in different clades, but having a non-linear (in log-log scale) functional dependence on census population size (as in the case of genetic draft studied next). In this regard, I am not sure I follow the argument attributed to Gillespie (1991) and specifically how the current analysis supports it.

In summary, I find the ideas of clade level analyses and of using phylogenetic comparative methods (PCMs) to look at census population size (and possibly diversity levels) promising. For example, as the author alludes to in the Discussion (bottom of P. 13), PCMs may be informative about the hypothesis that species with large census sizes have a greater rate of speciation. Yet I find the current analyses difficult to interpret.

Analysis of the effects of linked selection: The author investigates whether the effect of selection at linked sites (e.g., selective sweeps and background selection) can account for the observed relationship between diversity levels and census population size. To this end, he assumes that different species have the same sweeps and background selection parameters inferred in Drosophila melanogaster, but differ in census size and genetic map length.

As justification for using selection parameters inferred in D. melanogaster, the author argues that this is a "generous" assumption in that the effects of linked selection in this species are on the high end. One issue with this argument is that among reasons for the strong effects in D. melanogaster is its short genetic map length. This is not a substantial caveat, given that the analysis is meant as an illustration and it can be resolved by using appropriate wording. Perhaps more troubling is that the author's estimate of the reduction in diversity level in D. melanogaster is much greater than the reduction estimated in the inference that he relies on (several orders of magnitude and less than one, respectively). This discrepancy is mentioned but should probably be addressed more substantially.

The results of the analysis are intriguing. The effects of linked selection `shrink' the ~13 orders of magnitude of census population sizes to ~3 orders of magnitude of diversity levels. This massive effect is largely due to the genetic draft (Gillespie 2001) and to a lesser extent to the decrease in map length with increasing census size: when the census population size becomes very large (Nc~10^9) and coalescence rates due to genetic drift decrease accordingly (~1/2Nc), coalescence rates due to sweeps, which increase owing to the smaller map lengths (and would otherwise remain constant), become dominant. In hindsight this is quite intuitive and aligns with Gillespie's original argument, but this is in hindsight, and using this argument in conjunction with data, specifically with census population size and map length estimates, is novel.

As the author points out, the resulting relationship between diversity levels and census population sizes does not fit the data well. Notably, predicted diversity levels are too high in the intermediate range of census population sizes. Nonetheless, their analysis suggests that linked selection may play a much greater role than previous studies suggested (i.e., the analyses of Corbett-Detig et al. (2015) and Coop (2016) suggests that it cannot account for more than 1 order of magnitude). Maybe the poor fit is due to the importance of other factors (e.g., bottlenecks) in species with intermediate census population sizes?

I also wonder whether the potential role of linked selection may be clearer if the different effects are shown separately, and perhaps with less reliance on the estimates from D. melanogaster. Namely, the effects of background selection can be shown for a few different values of Udel, e.g., between 0.3-3 (this range seems plausible based on many estimates). They can be shown both accounting and not accounting for the relationship between map length and census size. Similarly, the effect of sweeps can be shown for several values of corresponding parameters, and perhaps even for different models for how the number of beneficial substitutions varies with census size (see Gillespie's work to that effect). I believe that such illustrations will be fairly intuitive and less restrictive.