Reviewer #1 (Public Review):

The neuroendocrine system of the maggot has been mapped in parts at both the light and electron microscopic levels in earlier studies. In this manuscript, Hückesfeld et al map the entire endocrine system all the way from its sensory input neurons to the interneurons and secretory neurons and the glands. This is invaluable for many reasons, including because information about external stimuli are likely integrated at the level of interneurons.

The authors use this connectome to model how and to what extent each sensory modality might influence the different neurosecretory cells. They use the CO2 sensing pathway to functionally validate their model in vivo using CaMPARI. Through this they validate a circuitry where CO2 sensing neurons in the trachea influence 4 types of neurosecretory cells via 4 interneuron pathways. Interestingly, they find that the CO2 sensory information is not necessarily what dominates the sensory input onto some these neurons.

Reviewer #3 (Public Review):

It is established that Kinase suppressor of Ras 1 (KSR1) contributes to the oncogenic actions of Ras by promoting ERK activation. However, the downstream actions of this pathway are poorly understood. Here Rao et al. demonstrate that this KSR1-dependent pathway increases translation of Epithelial-Stromal Interaction-1 (EPSTI1) mRNA and expression of EPSTI1 protein. This is significant because EPSTI1 drives aspects of EMT, including expression of ZEB1, SLUG, and N-Cadherin. The analysis is thorough and includes both loss-of-function and gain-of-function studies. Overall, the conclusions of this study are convincing and advance our understanding of cancer development.

Reviewer #2 (Public Review):

KSR1 functions as a critical rheostat to fine-tune MAPK signalling, and identifying modes by which its over-expression promotes tumor progression is clinically important and potentially druggable. Ras is highly mutated in CRC and unfortunately inhibitors of Ras have been challenging to develop. However, small molecules which stabilize an inactive form of the KSR are actively being developed in an attempt to repress RAS signaling. Thus, this study, which seeks to identify how KSR1 promotes oncogenic mRNA translation, is potentially highly clinically relevant, as it may identify novel druggable targets.

In this manuscript the authors performed polysome profiling in colorectal cancer (CRC) cells and proposed that KSR1 and ERK regulate the translation of EPSTI1 mRNA. They go on to characterize the phenotypes associated with knock-down or knock-out of KSR1 in CRC, and show that their defects in invasion, anchorage-independent growth and switch to a less EMT-like phenotype are all EPSTI1-dependent.

The authors succeeded in providing ample in vitro data that KSR1 and EPSTI1 are potential therapeutic targets in CRC. However, the data demonstrating that KSR1 and ERK regulate EPSTI1 mRNA translation is tenuous. Although the authors state that "EPSTI1 is necessary and sufficient for EMT in CRC cells", the data presented are consistent with a more restrained conclusion of a partial-EMT and not EMT per se. Finally, without an in vivo model it is difficult to glean novel insight into the mechanism by which KSR1 and/or EPSTI1 control the invasive and metastatic behaviour of cells.

Reviewer #1 (Public Review):

In this manuscript Rao et al. describe an interesting relationship between KSR1 and the translation regulation of EPSTI1 (a regulator of EMT). They identified this relationship by polysome RNAseq of CRC cells in the context of KSR1 knockdown (KD) which they confirm by polysome QPCR. They then go on to show that KSR KD and add back influences EPSTI1 expression at the protein but not mRNA level and impacts cell viability, anchorage-independent growth, and possibly cell migration. They focus on the cell migration phenotype and show that it is associated with changes in EMT-related genes including E-cad and N-cad. Interestingly, add back of EPSTI1 can reverse the phenotype elicited by KSR1 deletion. Overall, this story is interesting and translation regulation by KSR1 has not been described previously. However, Rao et al. do not provide a mechanism for how KSR1 regulates the translation of EPSTI1, and it is unclear whether this occurs through eIF4E, as the authors suggest.

Reviewer #3 (Public Review):

The authors have studied preclinical models of human small cell lung cancer (SCLC) using characterized SCLC cell lines that have been manipulated to conditionally express mutant EGFR (L858R) or KRAS (G12V) alleles and then assessing their morphology in cell culture, expression of neuroendocrine differentiation markers and transcription factors, and main signaling pathways such as the MAPK pathway. They focus on this because activation of ERK and the MAPK pathways are seen in nearly all non-small cell lung cancers (NSCLCs) including those with EGFR or KRAS mutations but mutations in these driver oncogenes or active ERK and MAPK pathway are essentially never found in SCLCs. In addition, chromatin modifications are assessed after manipulations and functional genomics targeting and pharmacologic inhibition of various components of the MAPK pathway are tested to see their effect on NE expression. Because of the known clinical phenomenon of transformation to SCLC like tumors by lung adenocarcinomas with EGFR mutations that become resistant to EGFR tyrosine kinase inhibitors, findings from the SCLC studies were applied to try to experimentally generate such LUAD to SCLC transformation. Overall, they found that activation of ERK/MAPK pathway by oncogenic mutations led to loss of NE differentiation and that the "ERK-CBP/p300-ETS axis promotes a lineage shift between neuroendocrine and non-neuroendocrine lung cancer phenotypes". They conclude: "In summary, we provide the first reported biological rationale for why alterations in MAPK pathway are rarely found in SCLC and describe the molecular underpinnings of how the central node in this pathway, ERK2, suppresses the NE differentiation program. " The authors conclusions and claims are justified by the experiments and data they present and they provide a mechanistic basis of what happens with MAPK/ERK activation in SCLC, why one does not find MAPK/ERK activation in SCLC, or the presence of related oncogenic driver mutations such as mutant KRAS or EGFR.

Reviewer #2 (Public Review):

Cell fate transitions (such as adenocarcinoma converting to small cell neuroendocrine fate) are an increasing phenomenon observed during therapeutic resistance in lung cancer, prostate cancer, and possibly other cancer types. It is important to understand these mechanisms if we ultimately seek to tailor treatment to a patient's disease and/or to control the pathways that lead to treatment resistance. However, the mechanisms that underly these cell fate changes are not well understood. It has been previously observed (Calbo et al, Cancer Cell, 2011) that activated mutant Kras (commonly associated with adenocarcinoma fate) can promote a non-neuroendocrine fate in SCLC, but the mechanisms are unknown.

Predominantly using three human small cell lung cancer (SCLC) cell lines, Inoue and colleagues use genetic and pharmacological approaches to focus on potential mechanisms by which Egfr/Kras/Mapk signaling can repress neuroendocrine fate. They make a number of interesting observations that extend our understanding of neuroendocrine cell fate regulation including:

1) Kras-induced NE suppression appears to depend mostly on ERK2, and not ERK1 or PI3K signaling.

2) Kras activation induces chromatin changes including increased H3K27Ac in 2/3 cell lines; increased H3K27Ac in response to HDAC inhibition is associated with NE suppression. Pharmacological inhibition of CBP/p300 (a HAT that promotes H3K27Ac) reduces H3K27Ac and restores NE suppression. Altogether, these findings are consistent with the notion that SCLC cannot tolerate high levels of H3K27Ac.

3) Kras induces the MSK/RSK pathway consistently in cell lines but appears to be functionally-relevant to NE fate only in H82 cells.

4) Kras activation induces chromatin occupancy at ERG and ETS family transcription factor (Etv1, 4, 5) binding sites in 2/3 cell lines, and induces ETV4 (2/3 lines) and ETV5 protein levels (3/3 lines). ETV1 and ETV5 overexpression are sufficient to inhibit NE fate markers in context-dependent manner. Ets family induction appears to occur in a CIC-independent manner.

In addition, some interesting negative data is presented, for example, SOX9 is induced upon Kras activation in 3/3 cell lines but it was not functionally relevant for NE suppression; Notch1, Notch2, and HES1 (known NE fate suppressors) are induced by Kras activation in a cell context-specific manner, but they did not appear functionally-relevant to NE suppression based on HES1 knockout and a pharmacological inhibitor of Notch signaling; Rb1 loss was not sufficient to promote NE fate in EGFR/p53 mutant cell lines, despite its known association with adeno-to-SCLC conversion. Overall, the conclusions in the manuscript are well justified. These findings will be of interest to those especially in lung and prostate cancer studying cell fate conversions in the context of EGFR and AR inhibitor resistance, respectively. These observations will be built upon by these fields.

Weaknesses:

1) One recurring issue in the manuscript is that the observations are often not consistent across the three cell lines and are context-specific effects, and the potential reasons could be explained better. The cell lines chosen unfortunately (but interestingly) represent some of the major cell states of SCLC. H2107 represents the ASCL1+ NE-high subset of SCLC (and has some MYCL). H82 and H524 represent the C-Myc (MYC)-high subset of SCLC, with H82 having a MYC amplification, and both representing the NEUROD1 subtype (which tend to be associated with more MYC). Assessment of NE score using a common approach in the field (Zhang et al, TLCR) shows that H82 cells are already considerably NE-low, with H524 as NE-intermediate/high, and H2017 as NE-high. So, this may be related to why H82 seemed to be the most permissive cell line to change NE fate in multiple assays.

In addition, H2107 and H524 appear to have EP300 mutations, which may contribute to their NE-high nature and contribute to the refractory response to A485 treatment based on the author's model. It's known that MYCL and MYC-driven cell lines differ in numerous aspects from transcriptional signatures, super enhancer usage, metabolic regulation, therapeutic response, etc. This information could be mentioned in the results and discussed when mentioned as a factor near line 540.

2) Related to Figure 4, the authors show that p300 pharmacological inhibition can restore NE fate in presence of Kras. Given that drugs can have off-target effects, it would be helpful to know if genetic knockdown/knockout of p300 phenocopies these effects. Given that CREBBP (CBP) or EP300 (p300) mutations are common in SCLC, it is also relevant whether any of these cell lines have CREBBP (CBP) or EP300 (p300) mutations. It appears H2107 and H524 may have EP300 mutations, and it would be good to know whether the authors have tried to restore EP300 function.

Reviewer #1 (Public Review):

The paper is investigating the mechanism of lineage switch in lung cancer. In about 10-15% of lung cancers treated with inhibitors of oncogenic receptors such as EGFR or KRAS, cancer cells emerge over time with newly acquired features of neuroendocrine differentiation. The authors proposed that it is a direct result of inhibition of MAPK pathway signaling so that reduced MAPK activity activates previously silent genes regulating neural crest differentiation. While this theory is of interest, the experiments presented herein are construed on the opposite sequence by way of introducing activated MAPK via oncogenic KRAS or EGFR to 3 neuroendocrine cell lines resulting in lower expression of neuronal transcription factors. The authors propose MAPK-activated ETS family TFs are responsible for the repression of NE lineage.

Several principal issues presented by the authors raise some concerns:

1) Despite presenting some evidence to the effect of suppression of NE transcription factors by overactivating MAPK signaling, the conversion of adenocarcinoma to NE (the opposite transition) is not being addressed in the paper. Therefore, it is rather illogical to investigate the process of transition that is not taking place in the real world.

2) The authors do not consider a possibility of multi-clonality of human cancers and clonal competition as a mechanism leading to acquired resistance and the emergence of NE clones that are not suppressible by the inhibitors of MAPK pathway (e.g. EGFR inhibitors, or KRAS/RAF/MEK inhibitors). Starting the experiments with clonal populations of long-term cultured cell lines may be an insurmountable difficulty to switch these cells between the epithelial and NE phenotypes which proved to be frustratingly non-productive in the hands of the authors. Taken out of context of tumor microenvironment, these phenotypic transitions may be co-regulated by a combination of cell-intrinsic and extrinsic factors.

3) Despite zeroing in on ETVs downstream of ERK1/2, the paper does not go as far as showing the direct effect of these TFs as repressors of NE differentiation (ASCL1, BRN2, NEUROD1 etc.).

4) The line of evidence that Dox-activated MAPK signaling via massive over expression of KRAS or EGFR induces dramatic increase in marks of transcriptionally active chromatin (such H3K27ac and others) is to be expected in this entirely artificial system. Indeed, the addition of doxycycline results in massive burst of proliferation and overexpression of ETV1 and ETV4, the canonical MAPK targets. Again, this switch appears unrelated with the opposite of epithelial-to-NE de-differentiation.

Reviewer #3 (Public Review):

Advances in understanding the biochemical and cellular mechanism of neuronal damage are investigated here and are to be appreciated. The strength of this work on SARM1 is its success in establishing that a concentration-dependent phase change activates the enzyme to degrade NAD, an essential component of neuronal integrity. Cellular significance is demonstrated in C. elegans neuronal damage triggered by citrate. Weaknesses are that high citrate is required for SARM1 effects but low citrate is used in the C. elegans model without establishing concentration dependence in the C. elegans system. The progression on neuronal damage from enzyme activation to neuronal damage in C. elegans is missing the quantitation of NAD change. A strength of the work is to provide a solid stepping-stone to permit the next steps in cementing the biochemical pathways of initiating cellular damage to neurons.

Reviewer #2 (Public Review):

The latest manuscript of Loring and coworkers solves a number of important problems of SARM1 structure and function at once, namely why the purified enzyme has little activity, what size is the active multimer, whether it produces cADPR on the way to ADPR, and how this enzyme may overcome autoinhibition by NAD+ in vivo. In work that is technically sound, the authors describe a phase transition that can be induced by macroviscogens and by citrate in which we are able to see cryoEM images of activated multimers and the induction of SARM1 activity in worms by citrate. Working with concentrated enzyme, the authors are further able to characterize SARM1 activity in detail and clearly show which cations are most inhibitory and that ADPR and not cADPR is the primary product of the reaction.

There is clearly a lot of regulation in the system with NAD+ inhibiting and NMN activating this enzyme and NMNAT, which controls conversion of NMN to NAD+ being localized to the outer Golgi membrane. Golgi and mitochondria are both moved along axons in processes that are totally dependent on cellular energetics. Given the broad contributions that are made by this work, I would not mind if the authors considered whether citrate, either from stressed mitochondria or from inhibition of the cytosolic enzyme ATP-citrate lyase, might be produced at high enough concentration to push SARM1 into the phase transition described herein.

Reviewer #1 (Public Review):

SARM1 is an enzyme that is present in neurons and degrade NAD+. Previous studies have shown that disrupting SARM1 inhibits axon degeneration and thus it could be a target for treating neurodegenerative diseases. NAD+ is also an important metabolite that is required for many biological pathways. Thus, SARM1 activity must be carefully regulated. Recent studies have provided structural and biochemical insights about how SARM1 activity is auto-inhibited in basal states. The manuscript by Dr. Thompson and coworkers provide a nice new model regarding how SARM1 could be potentially activated. They provide strong in vitro data to support that phase transition, promoted by PEG molecules and citrate, could dramatically increase the activity of SARM1 TIR domain (which is the catalytic domain) in vitro. The authors also showed that in the worm, C. elegans, citrate promotes SARM1 puncta formation and axon degeneration, which is consistent with the in vitro data. They also generated multiple mutants of SARM1 TIR domain and showed many of the mutants have decreased phase transition and decreased activity in vitro. One of mutant, G601P, also showed decreased puncta formation when expressed in HEK 293T cells as SARM1 SAM-TIR domains E462A mutant (a catalytic mutant so that expression will not cause toxicity) fused with GFP.

The manuscript has many strengths, including the strong and very careful in vitro characterization of the purified SARM1 TIR domain, which provide a lot of useful information regarding the kinetic parameters, substrate specificity, and inhibition profiles. The worm data with citrate is consistent with the in vitro data, which is also a strength.

The impact of the finding lies in two aspects. First, it provides a new understanding about how SARM1 activity might be regulated in vivo by phase transition. This is especially true given most studies so far focuses on how it is inhibited at basal conditions. It also adds another example to the list of enzymes that are regulated by phase separation. Second, the finding that PEG and citrate strongly activate SARM1 in vitro also provides a much improved assay for the development of small molecule modulators of SARM1 for potential therapeutic applications.

There are two minor weaknesses associated with the studies of the manuscript. One is that all the in vitro studies used just the TIR domain of SARM1, not the full length SARM1. Another minor weakness is associated with the data in Figure 5. Most of the mutants have dramatically lower catalytic activities (>100-fold), but the precipitate formation is only modestly affected (2-fold). Although this does not affect the overall conclusion of the manuscript, it prevents the mutants from being more useful for mechanistic dissection.

This manuscript is in revision at eLife (January 22, 2021)

Reviewer #2:

The authors address the vortex formation of bacteria in circular confinements with a particular focus on the difference of swarming vs. swimming (planktonic) motility of individuals. In the field of active matter, this critical distinction has rarely been studied so far but it is oftentimes ignored in modeling studies. Chen et al. show that qualitatively different patterns emerge for swarming and swimming bacteria. I do therefore believe that the work could have substantial influence on future studies devoted to bacterial pattern formation.

I have two main concerns detailed in the following.

1) A central finding of the present study is that the number of vortices/swirls as a function of the well diameter differs for swarming vs. swimming bacteria. The authors argue and show experimentally (Fig. 2) that the behavior is identical for small and large diameters. For intermediate values, however, they report that a single swirl is observed for swarming bacteria whereas swimming bacteria show multiple swirls.

The fact that the behavior is identical for large wells suggests that the bulk behavior is identical. This is also confirmed by Fig. 2E which shows that the spatial correlation function of the velocity is identical in large wells. To me, that suggests that the boundary conditions play a central role for understanding how the observed phenomenology emerges. [Indeed, it was shown in the past that the interaction of bacteria with boundaries crucially determines the formation of swirls in confinement (Lushi, Wieland & Goldstein PNAS 111 9733 (2014). The authors of this work assume reflecting boundary conditions, which -- to my knowledge -- contradicts the finding of Lushi et al.]. The authors, however, explain the difference of the observed patterns within their modeling study in a different way, namely by a different strength of the (anti-)alignment interactions. Changing the interaction at the level of individual cells will, however, change the bulk behavior too. Accordingly, the numerically observed bulk behavior (Fig. 5B ) is very different in both cases (at a qualitative level). It is difficult to judge the difference in detail because the correlation function was not calculated for the simulations.

In short:

The model (Fig. 5A) reproduces the experimental results partially (Fig. 2C), but the modeling analogue to Fig. 2E is missing. The line of arguments seems to me not to be entirely consistent.

2) Inferring the interactions of active particles from observations of the emergent patterns is a highly non-trivial task. In view of this I am not entirely convinced by the arguments put forward by the authors that "more substantial cell-cell cohesive interaction[s]" are the reason why the swirling patterns formed by swarming/swimming bacteria differ. In this context, I want to raise the attention of the authors to Ref. [Peruani, Deutsch & Bär: Phys. Rev. E 74 030904(R) 2006]. In this work, a clustering transition of self-propelled rods was described. "Rafts", referred to as clusters by Peruani et al., are observed as the aspect ratio of rods is increased. Notably, a kinetic transition towards clustering can emerge even in the absence of any attractive interactions. In short, the observation that cells move in parallel (polar clusters) next to each other does not allow to conclude that cohesive interactions are present. The movies S3 and S4 provided by the authors show that the particle shape of swarming and swimming particles is clearly different. In particular, the elongated swarming bacteria show pronounced clusters (Movie S3) whereas the shorter planktonic cells (Movie S4) do not. The difference in aspect ratio does indeed suggest that swarming and swimming bacteria differ in their alignment interaction. However, this contradicts the observation that spatial correlations in large wells are indistinguishable (see comment 1 and Fig. 2E). Side remark: in the main text, the authors argue that changes of the aspect ratio are not the reason for an increased alignment interaction, however, in the discussion section cell morphology changes (e.g. cell elongation and hyper-flagellation) are mentioned as an indicator that swarming is a different phenotype from swimming.

In summary, I believe that the connection of experimental observations and modeling are not entirely convincing.

Reviewer #1:

In this paper, the authors proposed a new approach by mounting a PDMS microwells of specific sizes on agar surface to confine swarming and planktonic SM3 cell, they found swarming bacteria exhibit a "single-swirl" motion pattern and concentrated planktonic bacteria exhibit"multi-swirls" motion pattern in the diameter range of 31-90 μm. The phase diagram shows that in smaller wells concentrated planktonic SM3 forms a single vortex and in larger wells swarming SM3 also breaks into mesoscale vortices.

After that, they conducted systematic experiments to explore parameters defining the divergence of motion patterns in confinement including cell density, cell length, cell speed and surfactant. They concluded that the single swirl pattern depends on cohesive cell-cell interaction mediated by biochemical factors removable through matrix dilution.

This paper gives a new method to discern swarmers from Planktonic Bacteria and carefully studies the factors that influence the formation of bacterial vortices under restriction. However, major revisions are required to improve the quality of this paper.

Major questions and comments:

1) When the authors put the PDMS chip mounting on the edge of the swarming colony, the PDMS chip is completely attached to agar or suspended in a bacterial solution. The distance between PDMS chip and agar surface should be quantified. It is better to have a schematic diagram of the experimental device.

2) Is the bacteria still expanding outward after a PDMS chip was mounted on agar surface? The effect of PDMS chips on the expansion of bacteria on the agar surface needs to be discussed.

3) "Diluted swarming SM3 show unique dynamic clustering patterns". In the diluted bacteria experiment, the authors found that the diluted swarming bacteria can form bacterial rafts and the concentrated planktonic SM3 disperse uniformly and move randomly. Hence, when bacteria expand and gradually fill up new empty microwells, is there a process of transition from raft to single vortex state?

4) In the experiment of altering the conditions of swarming SM3, the authors diluted the swarming cells in Lysogenic Broth (LB) by 20-fold, re-concentrated the cells by centrifugation and removed extra LB to recover the initial cell density. After these operations, they found the previous single swirl turned to multiple swirls and got a conclusion that matrix dilution can affect single swirl patterns. The authors think centrifugation may wash away some surrounding matrix or polymers on the surface of bacteria. Therefore, the steps of centrifugation need to be presented and the effect of centrifugation on the physiological behavior of bacteria should be discussed.

5) This article covers the PDMS chip directly on the agar surface and finds that swarm and planktonic bacteria have different spatial correlation scales in the restricted microwells. The authors have done a lot of experiments to prove the difference between clusters and planktonic bacteria and explain the reason for the single vortex. However, the conclusion is not clear. Therefore, the authors should focus more on the analysis of this new experimental phenomenon, such as critical length and vortex phase diagram, rather than just describing the experiments they did.

6) The authors mentioned the critical length for swarming SM3 is ~ 49 μm, whereas, for concentrated planktonic SM3, it is ~ 17 μm. Does this quoted data match what you get from their experimental method? I do not see any follow-up discussion and evidence.

7) As shown in Figure 1 and Movie_S1_mp4, the direction of the single vortex motion of bacteria is clockwise. However, the article simply ignores that the single vortexes of bacteria all present the same direction, and there is no analysis and reasonable explanation on the vortex direction. As shown in Movie_S5_mp4 on the numerical simulations of circularly confined SM3, simulated bacteria vortex counterclockwise in completely opposite directions. The influence of the microwell boundary on the direction of the vortex should be clearly explained at the level of bacterial movement and preferentially with theoretical simulation.

8) Swarming and concentrated planktonic Bacillus subtilis 3610 show the same motion pattern across different confinement sizes. However, the authors did not give definitive conclusions and evidence. As shown in Figure S1, bacillus subtilis 3610 show completely different cluster behavior. Therefore, the discussion of 3601WT may cause readers' confusion on the article. It may be better to put it in the supporting material.

Minor questions and comments

9) Figure 1C, 1D, 6A, 6B may be more convenient to have a scale bar.

Reviewer #3 (Public Review):

In this article, Gregory Grecco and colleagues developed a novel translational mouse model of prenatal methadone exposure (PME) that closely resembles the opioid exposure experienced by pregnant women living with opioid use disorder and treated with methadone maintenance pharmacotherapy. The article delineates the impact of prenatal methadone exposure on physical development and motor behavior of the next generation male and female progeny. The authors also relied on a combination of electrophysiological, immunohistochemical and volumetric MRI imaging approaches to investigate the mechanisms underlying PME-derived phenotypes in male and female offspring. Overall, PME produced changes in motor function, motor coordination and growth in progeny. These phenotypes were accompanied by changes in the electrophysiological properties and density of neurons in the primary motor cortex of offspring raised by opioid-exposed dams.

One of the stated goals by the authors was to develop a mouse model that closely mirrored exposure and dosing regimens in clinical populations living with opioid use disorder in order to increase the translational value of the findings outlined in this report. One of the strengths of the article is the experimental design and the longitudinal nature of the studies. The dams were first treated with oxycodone, a commonly abused pain killer to mimic this condition in patients living with SUD. 5 days prior to mating, the animals were switched to methadone to model maintenance pharmacotherapy that is commonly used in SUD patients. The doses of oxycodone and methadone were carefully selected to mimic as closely as possible the suspected exposure experienced by pregnant women and their unborn offspring. The authors demonstrated that the concentrations of methadone and related metabolites were present in the plasma, brain and placentas of dams and offspring in the opioid-treated group during gestation, parturition and up to one week after birth. Another strength of the study was the fact that the authors convincingly demonstrated a lack of change in maternal behavior in the opioid-treated dams, which could have been a major confounding factor. The dams exposed to oxycodone and methadone did develop dependence to opioids as expected, however the amount and nature of maternal care delivered to their offspring was not affected by oxycodone and methadone exposure. This critical finding enabled the authors to delve further into the biological underpinnings of the observed phenotypes. The offspring produced by opioid-exposed dams showed some phenotypes consistent with neonatal opioid withdrawal syndrome (NOWS) in humans, including hyperthermia and twitches or jerks. Together, these findings demonstrate that the authors were successful in creating a novel model of prenatal opioid use and methadone maintenance in mice.

Overall, both males and females produced by opioid-treated dams had lower body weight and length during development and through adolescence. Bone volume was also lower in PME offspring compared to controls at 1 week of age, an effect that dissipated by adolescence in PME progeny. Locomotor activity was reduced at P1 and increased at P7 and P21. Interestingly, ultra sonic vocalization emitted by pups when separated from their mothers, was highest for PME females compared to all groups and this increase in calls also coincided with increased activity. PME offspring also had delays in demonstrated coordinated motor behaviors such as acquisition of surface righting, forelimb grasp and cliff aversion during the early stages of development. Prepulse inhibition, a measure of sensorimotor gating was not disrupted by PME.

At the anatomical level, the largest impact of PME was found in the primary motor region of the cortex, where cell density was reduced particularly in the upper cortical layers. Next, the authors probed the properties of cells and circuits in primary motor cortex and found reduced firing rates in response to injected currents in PME animals compared to controls. The input resistance of these cells was also diminished in the PME group. Together, these findings suggest that the number of cells may be reduced by PME in primary motor cortex and that the remaining neurons are not able to fire as effectively, resulting in blunted transmission within this brain region. Lastly, the authors stimulated local synaptic inputs to M1 using glutamate uncaging and found that the neural circuits connecting the top layers of M1 to layer 5 are enhanced in PME animals.

Overall, the authors identified some electrophysiological correlates of altered motor function and coordination produced by a novel prenatal opioid exposure model and regimen. This article had several strengths highlighted above but also included some areas of potential improvement. The authors included both sexes in many of their analyses but it is not always clear when the sex of the offspring were combined in the analyses and/or whether sex was always included as a factor in the many endpoints described in the paper. The authors acknowledge some of the limitations of their model in better understanding OUD in pregnant women. Including the caveat that many women do not switch to maintenance therapy prior to conception would be worth mentioning. Moreover the use of buprenorphine has increased in recent years and methadone is not the only maintenance therapy available. Lastly, the electrophysiological recordings do not exactly coincide with some of the overt phenotypes reported: at P21, the PME animals are hyperactive but the time window does not match with the coordination deficits reported. Overall, these minor weaknesses detracted only slightly from the overall impact and value of the reported findings.

Reviewer #2 (Public Review):

This manuscript establishes a novel rodent model for prenatal methadone exposure and characterizes various aspects of neurodevelopment in the offspring. Given the global opioid crisis and the rampant rise of drug use by pregnant mothers and incidence of neonatal abstinence/opioid withdrawal syndrome, there is a critical need to determine potential outcomes for children born with this condition. In their model, the investigators use mice that are already taking oxycodone and switched to methadone treatment prior to becoming pregnant, which is a major translational advantage compared to other models where opioid dosing does not start until sometime mid-gestation. The experimental design also included a wide variety of measurable endpoints, including physical development, sensorimotor behavioral tasks, vocalizations, brain imaging, circuit electrophysiology, and histology; this comprehensive approach allows for synthesis of the results that has traditionally been difficult to find in this field, given the vast differences in species, dosing paradigms, etc. Sex differences were also considered, which is especially important given what is known about varying rates of NOWS between males and females. The text is very well-written, including detailed descriptions of statistical analysis.

Despite overall enthusiasm for the study and its findings, there are some concerns regarding the brain volume analyses as well as potential stress confounds with the experimental design. The analysis of structural differences measured by volumetric MRI showed that there were no appreciable differences across grey matter structures with PME (Supp. Fig. 9). This was surprising, given that regional decreases in brain volume are a consistent finding with prenatal drug-exposed offspring (Yuan et al., 2014 [DOI 10.1038/jp.2014.111]; Sirnes et al., 2017 [DOI 10.1016/j.earlhumdev.2017.01.009]; Nygaard et al., 2018 [DOI 10.1016/j.ntt.2018.04.004]). Traditionally, these deficits tend to be more true for white matter than grey, though the authors do not indicate whether this was investigated.

The opioid dosing protocol required twice-daily subcutaneous injections for at least 3 weeks (possibly longer, but it was difficult to determine from the text when exactly the treatments were halted). The effects of maternal/prenatal stress, even in the vehicles, cannot be discounted. The authors rightly noted this caveat in the Discussion, but it remains a critical concern in this otherwise well-designed study.

Reviewer #1 (Public Review):

The authors have succeeded in their attempt to develop and characterize a rigorous preclinical model of prenatal methadone exposure secondary to pre-pregnancy prescription opioid use. The model is a technical advance in terms of the opioid exposure being consistent throughout pregnancy and the outcome measures of methadone impact are rigorous. Many aspects neurodevelopment and key physiological processes are assessed and key knowledge is provided about the effects of prenatal methadone exposure on physical development, sensorimotor behavior and neuronal properties.

Major strengths include the thoroughness and rigor of analyses and the multiple body systems study. In addition, scientific questions are approached using physical, biochemical and behavioral assessments to fully characterize the effects of prenatal methadone exposure.

The strengths of this paper outweighs the weaknesses. Weakness are minor and include an incomplete assessment or discussion of whether withdrawal in the postnatal period may explain the pathophysiology described and changes in circuitry. Similarly, white matter analyses are not included MRI assessments confining the results to gray matter brain regions.

Reviewer #2:

In this study, the authors perform an impressive field phenotyping experiment on three grafted grapevines all with a common scion cultivar 'Chambourcin' alongside an ungrafted control to assess the associations between rootstock and leaf traits. The traits collected include ionomics, metabolomics, transcriptomics, leaf morphology and physiology. In addition, the authors collect these samples at three phenological stages to incorporate seasonal variation. The authors apply a combination of classification and machine learning methods to test whether features within each phenotypic measurement are predictive of genotype. In some cases, such as the ionomics data, certain ions are predictive of rootstock genotype but only at certain seasonal time points. The datasets presented here are extensive and will be of value to the horticulture field since grafting is such a common technique used in cultivating many crops. Considering the scale of this experiment, the manuscript is at times disconnected, in large part because each dataset is analyzed independently without any integration across phenotypes. The results presented do highlight more of an effect of phenology rather than rootstock on the phenotypes measured.

Major comments:

1) It would be very helpful to have a diagram with the layout in the field and the sampling strategy or a more detailed explanation. This would help to associate which phenotypic data was collected at the same time and on the same plants. For example, it would expand on what is mentioned on line 348 "row 8 sampled early in the day". It would help to know what time of day the samples in each row were collected. Additionally, how do the different irrigation treatments factor into the sampling? A better introduction of the experimental design is needed at the start of the results section along with a description of the genotypes and why they were selected.

2) I understand why running a PCA before the LDA can help reduce the dimensionality of the space to be able to invert the covariance matrix (if that was the motivation?) but is this because there were issues with running LDA alone? I wonder if you've lost important discriminating information between the classes by doing this. Was the LDA run on the datasets first prior to the PCA? This may uncover additional classification that was eliminated by the PCA.

3) For the Random Forest analysis, the authors might consider using k-fold cross validation rather than partitioning the dataset, this is especially beneficial when working with smaller datasets and might improve the predictions. Could all the importance scores be reported rather than just the couple mentioned in the text (line 296).

4) In reference to Figure 1B and C, it would be helpful to indicate on the plots which comparisons are significant based on their model tests. The full test results are presumably in the excel spreadsheet referred to in the reporting form although it was not found with the manuscript materials.

5) Throughout the text there is very little mention of the various grafted genotypes and what is known about the lines. The authors should consider introducing these genotypes and why they were selected for the grafting experiment. What is different among these lines? There is very little discussion of the comparisons between genotypes and what phenotypes are significantly different between the lines and what the implications are for the plant as a whole.

6) Line 287 refers to a post-hoc analysis of the ions, do the ions showing significant variation explained by rootstock and phenology match the ions identified in the ML as important classifiers?

7) For such a large metabolomic dataset, it is surprising that the authors do not present any identification of the metabolites highlighted. The identification of the metabolite features that were found to influence the rootstock main effect would be of interest and might reveal interesting biology. How did these metabolites differ between genotypes? On line 501 in the discussion there is mention of flavanols and stilbenes yet these weren't highlighted in the results section.

8) What is the reasoning for not simply applying a linear modeling approach such as limma on the gene expression data first instead of only applying it to the PCs in order to identify differentially expressed genes between the genotypes? If phenological stage is the strongest effect, what if you run the analysis within each stage to look specifically at the differential responses between grafted lines at each stage? The analysis of the gene expression data, similar to the metabolomics data, seems to be missing an opportunity to uncover underlying biological mechanisms contributing to any genotype effects of grafting, a stated goal of the study. What genes are differentially expressed and do they relate to the metabolomic or ionomic data?

9) In the methods, there are three irrigation treatments described yet this is not mentioned in the results section. While it seems as though rainfall mitigated much of the irrigation effect there does appear to be differences in water availability to the vines as described in the provided github page. Were various irrigation treatment sets sampled for all phenotypes? Or were the ionomics, metabolomics and transcriptome analysis done on the same irrigation treatments? If not, was this effect considered in the analysis? This is yet another variable that would greatly influence the response and should be considering when assessing the effects of grafting. Further detail about the sampling and conditions is needed to clarify.

10) In figure 1 there is information about leaf age. For the metabolomics a mature leaf was sampled, transcriptomics the youngest leaf, and physiology it is not specified. Could you clarify the leaves that were sampled and how they relate across phenotypes. This is an important point to mention given the differences observed for the ionomics data.

11) In reference to the vine physiology, were these all collected from the same irrigation treatment? Was the sampling of each genotype spread out over the 3h window to account for time of day variation? It would be helpful to have the significant comparisons indicated in the figure. What are the letters referring to on lines 402-403 with the p. values? This section would be greatly improved by additional clarity in the text.

12) Given the focus on grafting, the analysis presented in Figure 6 does not seem to contribute to this objective. Could this be expanded on to look within and across genotypes to see if different phenotypes covary and to compare the dimensions of variation across genotypes rather than combining them all together? This would complement the previous analyses and hopefully reveal the differences that were highlighted in the earlier sections.

13) The results section is very disjointed and the datasets are presented almost as completely separate studies. To improve clarity in the results section, the authors might consider expanding on the findings of the LDA and ML analysis for each phenotype and connecting them together.

Reviewer #1:

In this manuscript, the authors look at the influence of root stock genotype on a single scion genotype in Vitis. This includes a lovely highly replicated design including differential water availability. While the experimental design is very elegant, I'm less sure that using general PCs or ML is the best approach to grab the signal of interest.

Is there evidence that the top 20 PCs of the metabolome or the top 100 PCs are an end point of gaining new information about the system. For example, if the top 20 PCs are all different descriptions of the water availability, then PC 21 might start to grab more information about the root-scion relationship. For example in this dataset, PC2-10 were largely about temporal block (line 314-316). In large genomic datasets like this, they have an immense amount of variation such that r2 is not a meaningful way to capture what is in a PC. I can understand the desire to minimize the statistical analysis but if the goal is to fully interrogate the dataset, the authors should provide an empirical reason for stopping at pre-ordained PCs. Or possibly better would be to grab the lsmeans for the main factors in the model to exclude factors of blocking and then run the PCs as that is the underlying interest in the experiment.

The focus on PCs or using ML on the full dataset also hinders the ability to get at the underlying root/scion and water availability connection. Given that phenology and blocking are the main sources of variance, using these approaches rather than a direct GLM or PC on lsmeans/BLUPs weakens the authors ability to use the power in their experimental design. PC and ML can only capture the largest components of variance while GLMS that account for these larger sources of variance can begin to dive into the underlying questions. There is a possibility that the authors did attempt these directed GLMS with no luck but that was not stated.

I think the use of PCs is maybe my biggest hindrance on the manuscript as the section on lines 409-430 which is the capstone of the paper but ends up being correlations of faceless PCs. Unfortunately this leaves the reader with the idea that phenology is simply too strong to obtain any information about the root/scion connection or the water availability connection.

Reviewer #3 (Public Review):

In this work Farber and colleagues describe the generation of Fus(EGFP-plin2) and Fus(plin3-RFP) two knock-in zebrafish lines that alllow to study perilipins and lipid droplet biology in vivo at whole animal level. These lines could be important tools to understand how lipid droplet dynamics are affected by different genetic and physiological manipulations.

The article is well written and the work is carries out with a good methodological approach and the results support their conclusions. The weakness is the lack of originality since it does not really go behind the current knowledge in the field. Most of the data are a detailed description of zebrafish lines but I doubt that could be interested to a broad audience.

It also lacks novelty since the work does not add anything compared to what is already known regarding peripilin 2 and 3. I think this manuscript should be submitted to a more specialized journal on lipid metabolism or to a technical "zebrafish" journal.

Reviewer #2 (Public Review):

In this manuscript, the authors generated transgenic zebrafish reporter lines that allow observation of cytoplasmic lipid droplets in vivo. They knocked in GFP or RFP in the endogenous loci of perilipin 2 and 3, and showed that the reporter genes exhibited similar temporal and spatial expression in the intestine in response to acute high-fat feeding as the endogenous perilipin 2 and 3 transcripts. They also characterized the reporter gene expression in the liver, adipocytes, and around neuromasts. These tools open up new opportunities to study lipid droplets dynamics in live zebrafish that is not feasible in mouse models. Overall the manuscript is well written. The authors have discussed in details the strength and caveats of these reporters. The weakness is the descriptive nature of the study - many interesting observations but no mechanistic study. I have the additional comments:

1) It is curious that in plin2 and plin3 reporter fish, the fluorescent tags were inserted at the 5' and 3' of the open reading frame, respectively. The authors did not provide any explanation. Does the location where the fluorescent tag is inserted affect the expression of the reporter genes?

2) GFP and TagRFP-T are not fast folding fluorescent proteins and are very stable, which may not be the best options for studying the formation and degradation of lipid droplets. How the fluorescent tags affect the stability and clearance of the protein should be carefully characterized.

3) Was there any indel being introduced by TALENs in these knockin fish? Is there off target effects of the TALENs?

4) The authors also generated transgenic fish overexpressing human PLIN2 and PLIN3 fluorescent fusion proteins. Is the subcellular localization of these fusion proteins similar to the zebrafish knockin under nofed and fed conditions? In other words, do human PLIN2 and PLIN3 proteins behave similarly as the zebrafish orthologs?

Reviewer #1 (Public Review):

The authors find that plin2 transcript is induced in intestine of 6 dpf zebrafish larvae following a single feeding, while plin3 transcript is expressed in the fasted and fed states in the intestine. They use TALENS to knock-in EGFP and TagRFPt into the plin2 and plin3 loci, with the encoded gene products being the fusion proteins EGFP-plin2 and Plin3-TagRFPt. The EGFP-plin2 protein shows greater induction of fluorescence following a meal. The overall aim of these initial expression characterizations and development of lipid droplet reporter knock-ins is to be able to monitor the life cycle of these organelles in a living whole organism.

Higher resolution photomicrographs of lipid droplets with these knock-in lines concurrently stained with the the fluorescent lipid dyes BODIPY 558/568 C12 and BODIPY FL-C12 are presented with a time series following feeding in intestine; additional cell types beyond enterocytes (i.e., hepatocytes, adipocytes, and cells surrounding lateral line structures) are presented.

The authors have provided a technical advance to the field of lipid droplet biology. With the tractable revisions set out below, their tools set the stage for chemical and genetic screens for factors and compounds that modulate the normal life cycle of these dynamic organelles.

Reviewer #3 (Public Review):

The authors set out to determine the role of interleukin (IL)-33 in the host immune response to the parasite Toxoplasma gondii. They achieve this using a mouse model of infection and a range of genetically modified mice to systematically prove the pathway involved.

A major strength of the study is the use of strategic immune cell/factor-deficient mice in combination with recombinant proteins in vivo. This may be further strengthened by future studies that test the impact off inhibitory antibodies against the primary factor of interest, IL-33. This would allow for a loss and gain of function approach, supporting the exisiting in vivo data with recombinant mouse IL-33.

Overall, the approach taken achieves the goal of the study. The manuscript is well written and systematically addresses the steps in the pathway that are required to mount an effective IL-33-mediate immune response to T. gondii.

The likely impact of this work are new knowledge of the function of IL-33 in response to infection and the interaction between different components of the immune system to achieve a balanced, context dependent response. The study does not highlight new methods or technical advances, but does provide important new information on immune responses to infection.

Reviewer #2 (Public Review):

The authors eloquently showed that IL-33 was produced from stromal cells following Toxoplasma infection and that the absence of IL-33 signaling resulted in increased parasitemia. In agreement with this observation, they found that exogenous IL-33 reduced parasite load and increased the recruitment of inflammatory monocytes that are critical for resistance. The manuscript is well written and data presented here supports the major findings of this work.

Reviewer #1 (Public Review):

In initial experiments, low levels of IL-33 were detected in Toxaplasma-infected mice. How do these levels compare with normal physiological levels? It would help the reader to understand the relative levels to expect.

The authors identify that most IL-33 is produced by stromal cells rather than hematopoietic cells. The frequency of tdTomato parasites appear to be much less than for the distribution of IL-33 producing cells. Does the parasite expression reflect 100% of parasites or are the number of IL-33-producing stromal cells stimulated in the infection much larger than the identifiable parasite number? That is, is the activation of the stromal cells a direct effect of the Toxaplasma infection or does it depend on intermediates to amplify the effect?

Although the data presented are interesting and the authors identify that both stromal cells and hematopoietic cells contribute to the protective effect of IL-33, it is somewhat confusing amongst the hematopoietic cells, which cells are really driving the response amongst those categorized as 'innate'. Within the hematopoietic compartment, a number of associations are delineated but the causal connections are less clear. The provision of exogenous cytokines indeed have the effect they show in their results, but it remains unclear to this reviewer, whether these effects directly act on the hematopoietic cells, or stromal cells which alone are not sufficient to contain the infection and thus develop a higher pathogen load confounding their contributions.

This work would be strengthened significantly by delineating more clearly the contributions of each compartment. Currently, the correlations are modelled on the responses in the omentum and it would be useful to understand if this reflects the broader response.

This work would benefit from a schematic to indicate how the authors believe the different cells are connected and which are the real drivers/where connections have been demonstrated in driving the immune response.

Reviewer #2 (Public Review):

Overall this is a solid and technically sound manuscript, and I have only two relatively minor suggestions for improvement:

1) Tetramer versus dimer

The particles that were analyzed by cryoEM were composed of four THO-Sub2 protomers, yet the authors argue that a dimer is the functional unit. Why? The tetramer versus dimer organization needs to be better discussed, also in light of the observation that the human complex can also form a tetramer.

2) Sub2 activation mechanism

The authors should more carefully discuss how THO 'activates' Sub2 (and how the 'semi-open state' leads to activation) and indicate the RNA binding surface of Sub2 in their model.

Reviewer #1 (Public Review):

1) I found the initial description of the overall structure confusing. At first the authors say the complex is a tetramer, which is not what was seen by the Conti lab and then follow that with a confusing discussion leading to the conclusion that the dimer with a rigid subunit and a flexible one is the functional unit. It rather feels like they arrive at this conclusion because that's what Conti's lab saw, rather than any other reason. Since the human complex is a tetramer, perhaps the tetrameric complex observed here is one possible form and that possibility should be considered more carefully. Please state whether there is any similarity in the arrangements between the human tetramer and the tetramer observed here. I found the figure 2 supp 1C was not easy to follow. Coloring each of the four protomers differently would make things clearer.

2) The authors previously determined the structure of yra1C domain bound to sub2 and several labs have shown this interaction activates Sub2 atpase activity. Are those interaction observed previously between Yra1 and Sub2 compatible with this new structure? If so, perhaps the authors could provide a model showing how Yra1 fits into this larger complex. Also, could Yra1 C domain and Gbp2 bind simultaneously to a single THO-Sub2 protomer or would one protomer bind Yra1 and perhaps another bind Gbp2? This is worth considering because this would strengthen the concept that TREX acts as a general platform engaging with multiple export factors to drive recruitment of multiple Mex67 molecules and eventual export of the Mex67:mRNP complex. In the human system, the SR proteins and Alyref have an overlapping binding site on Nxf1, suggesting they may not act together to recruit a single Nxf1, but rather they recruit different Nxf1 molecules perhaps to the same mRNP via a single multimeric THO platform.

Reviewer #3 (Public Review):

This paper examines the role of neutrophils, inflammatory immune cells, in disease caused by genital herpes virus infection. The experiments describe a role for type I interferon stimulation of neutrophils later in the infection that drives inflammation. Blockade of interferon, and to a lesser degree, IL-18 ameliorated disease. This study should be of interest to immunologists and virologists.

This study sought to examine the role of neutrophils in pathology during mucosal HSV-2 infection in a mouse model. The data presented in this manuscript suggest that late or sustained IFN-I signals act on neutrophils to drive inflammation and pathology in genital herpes infection. The authors show that while depletion of neutrophils from mice does not impact viral clearance or recruitment of other immune cells to the infected tissue, it did reduce inflammation in the mucosa and genital skin. Single cell sequencing of immune cells from the infected mucosa revealed increased expression of interferon stimulated genes (ISGs) in neutrophils and myeloid cells in HSV-2 infected mice. Treatment of anti-IFNAR antibodies or neutrophil-specific IFNAR1 conditional knockout mice decreased disease and IL-18 levels. Blocking IL-18 also reduced disease, although these data show that other signals are likely to also be involved. It is interesting that viral titers and anti-viral immune responses were unaffected by IFNAR or IL-18 blockade when this treatment was started 3-4 days after infection, because data shown here (for IFN-I) and by others in published studies (for IFN-I or IL-18) have shown that loss of IFN-I or IL-18 prior to infection is detrimental.

These data are interesting and show pathways (namely IFN-I and IL-18) that could be blocked to limit disease. While this suggests that IL-18 blockade might be an effective treatment for genital inflammation caused by HSV-2 infection, the utility of IL-18 blockade is still unclear, because the magnitude of the effect in this mouse model was less than IFNAR blockade. Additionally, further experiments, such as conditional loss of IL-18 in neutrophils, would be required to better define the role and source(s) of IL-18 that drive disease in this model.

Reviewer #2 (Public Review):

This manuscript will be of interest to a broad audience of immunologists especially those studying host-pathogen interactions, mucosal immunology, innate immunity and interferons. The study reveals a novel role for neutrophils in the regulation of pathological inflammation during viral infection of the genital mucosa. The main conclusions are well supported by a combination of precise technical approaches including neutrophil-specific gene targeting and antibody-mediated inhibition of selected pathways.

In this study by Lebratti, et al the authors examined the impact of neutrophil depletion on disease progression, inflammation and viral control during a genital infection with HSV-2. They find that removal of neutrophils prior to HSV-2 infection resulted in ameliorated disease as assessed by inflammatory score measurements. Importantly, they show that neutrophil depletion had no significant impact on viral burden nor did it affect the recruitment of other immune cells thus suggesting that the observed improvement on inflammation was a direct effect of neutrophils. The role of neutrophils in promoting inflammation appears to be specific to HSV-2 since the authors show that HSV-1 infection resulted in comparable numbers of neutrophils being recruited to the vagina yet HSV-1 infection was less inflammatory. This observation thus suggests that there might be functional differences in neutrophils in the context of HSV-2 versus HSV-1 infection that could underlie the distinct inflammatory outcomes observed in each infection. In ordered to uncover potential mechanisms by which neutrophils affect inflammation the authors examined the contributions of classical neutrophil effector functions such as NETosis (by studying neutrophil-specific PAD4 deficient mice), reactive oxygen species (using mice global defect in NADH oxidase function) and cytokine/phagocytosis (by studying neutrophil-specific STIM-1/STIM-2 deficient mice). The data shown convincingly ruled out a contribution by the neutrophil factors examined. The authors thus performed an unbiased single cell transcriptomic analysis of vaginal tissue during HSV-1 and HSV-2 infection in search for potentially novel factors that differentially regulate inflammation in these two infections. tSNE analysis of the data revealed the presence of three distinct clusters of neutrophils in vaginal tissue in mock infected mice, the same three clusters remained after HSV-1 infection but in response to HSV-2 only two of the clusters remained and showed a sustained interferon signature primarily driven by type I interferons (IFNs). In order to directly interrogate the impact of type I IFN on the regulation of inflammation the authors blocked type I IFN signaling (using anti IFNAR antibodies) at early or late times after infection and showed that late (day 4) IFN signaling was promoting inflammation while early (before infection) IFN was required for antiviral defense as expected. Importantly, the authors examined the impact of neutrophil-intrinsic IFN signaling on HSV-2 infection using neutrophil-specific IFNAR1 knockout mice (IFNAR1 CKO). The genetic ablation of IFNAR1 on neutrophils resulted in reduced inflammation in response to HSV-2 infection but no impact on viral titers; findings that are consistent with observations shown for neutrophil-depleted mice. The use of IFNAR1 CKO mice strongly support the importance of type I IFN signaling on neutrophils as direct regulators of neutrophil inflammatory activity in this model. Since type I IFNs induce the expression of multiple genes that could affect neutrophils and inflammation in various ways the authors set out to identify specific downstream effectors responsible for the observed inflammatory phenotype. This search lead them to IL-18 as possible mediator. They showed that IL-18 levels in the vagina during HSV-2 infection were reduced in neutrophil-depleted mice, in mice with "late" IFNAR blockade and in IFNAR1 CKO mice. Furthermore, they showed that antibody-mediated neutralization of IL-18 ameliorated the inflammatory response of HSV-2 infected mice albeit to a lesser extent that what was seen in IFNAR1 CKO. Altogether, the study presents intriguing data to support a new role for neutrophils as regulators of inflammation during viral infection via an IFN-IL-18 axis.

In aggregate, the data shown support the author's main conclusions, but some of the technical approaches need clarification and in some cases further validation that they are working as intended.

1) The use of anti-Ly6G antibodies (clone 1A8) to target neutrophil depletion in mice has been shown to be more specific than anti-Gr1 antibodies (which targets both monocytes and neutrophils) thus anti-Ly6G antibodies are a good technical choice for the study. Neutrophils are notoriously difficult to deplete efficiently in vivo due at least in part to their rapid regeneration in the bone marrow. In order to sustain depletion, previous reports indicate the need for daily injection of antibodies. In the current study the authors report the use of only one, intra-peritoneal injection (500 mg) of 1A8 antibodies and that this single treatment resulted in diminished neutrophil numbers in the vagina at day 5 after viral infection (Fig 1A). Data shown in figure 2B suggests that there are neutrophils present in the vagina of uninfected mice, that there is a significant increase in their numbers at day 2 and that their numbers remain fairly steady from days 2 to 5 after infection. In order to better understand the impact antibody-mediated depletion in this model the authors should have examined the kinetics of depletion from day 0 through 5 in the vaginal tissue after 1A8 injection as compared to the effect of antibodies in the periphery. These additional data sets would allow for a deeper understanding of neutrophil responses in the vagina as compared to what has been published in other models of infection at other mucosal sites.

2) The authors used antibody-mediated blockade as a means to interrogate the impact of type I IFNs and IL-18 in their model. The kinetics of IFNAR blockade were nicely explained and supported by data shown in supplementary figure 4. IFNAR blockade was done by intra-peritoneal delivery of antibodies at one day before infection or at day 4 after infection. When testing the role of IL-18 the authors delivered the blocking antibody intra-vaginally at 3 days post infection. The authors do not provide a rationale for changing delivery method and timing of antibody administration to target IL-18 relative to IFNAR signaling. Since the model presented argues for an upstream role for IFNAR as inducer of IL-18 it is unclear why the time point used to target IL-18 is before the time used for IFNAR.

3) An open question that remains is the potential mechanism by which IL-18 is acting as effector cytokine of epithelial damage. As acknowledged by the authors the rescue seen in IFNAR1 CKO mice (Fig 5C) is more dramatic that targeting IL-18 (Fig 6D). It is thus very likely that IFNAR signaling on neutrophils is affecting other pathways. It would have been greatly insightful to perform a single cell RNA seq experiment with IFNAR CKO mice as done for WT mice in Fig 3. Such an analysis might would have provided a more thorough understanding of neutrophil-mediated inflammatory pathways that operate outside of classical neutrophil functions.

4) The inflammatory score scale used is nicely described in the methods and it took into consideration external signs of vaginal inflammation by visual observation. It would have been helpful to mention whether the inflammation scoring was done by individuals blinded to the experimental groups.

5) The presence of distinct clusters of neutrophils in the scRNA-seq data analysis is a fascinating observation that might suggest more diversity in neutrophils than what is currently appreciated. In this study, the authors do not provide a list of the genes expressed in each cluster within the data shown in the paper. Although the entire data set is deposited and publicly available, having the gene lists within the paper would have been helpful to provide a deeper understanding of the current study.

Reviewer #1 (Public Review):

Overall this is a well-done study, but some additional controls and experiments are required, as discussed below. The authors have done a considerable amount of work, resulting in quite a lot of negative data, and so should be commended for persistence to eventually identify the link between neutrophils with IL-18, though type I IFN signaling.

Major Comments:

• A major conclusion of this manuscript is prolonged type I IFN production following vaginal HSV-2 infection, but the data presented herein did not actually demonstrate this. At 2 days post infection, IFN beta was higher (although not significantly) in HSV-2 infection, but much higher in HSV-1 infection compared to uninfected controls. At 5 days post infection the authors show mRNA data, but not protein data. If the authors are relying on prolonged type I IFN production, then they should demonstrate increased IFN beta during HSV-2 infection at multiple days after infection including 5dpi and 7dpi.

• Does the CNS viral load or kinetics of viral entry into the CNS differ in mice depleted of neutrophils, IFNAR cKO mice, or mice treated with anti- IL-18? Do neutrophils and/or IL-18 participate at all in neuronal protection from infection?

• In Figure 3 the authors show that neutrophil "infection" clusters 2 and 5 express high levels of ISGs. Only 4 of these ISGs are shown in the accompanying figures. Please list which ISGs were increased in neutrophils after both HSV-2 and HSV-1 infection, perhaps in a table. Were there any ISGs specifically higher after HSV-2 infection alone, any after HSV-1 infection alone?

• The authors claim that HSV-1 infection recruits non-pathogenic neutrophils compared to the pathogenic neutrophils recruited during HSV-2 infection. Can the authors please discuss if these differences in inflammation or transcriptional differences between the neutrophils in these two different infections could be due to differences in host response to these two viruses rather than differences in inflammation? Please elaborate on why HSV-1 used as opposed to a less inflammatory strain of HSV-2. Furthermore, does HSV-1 infection induce vaginal IL-18 production in a neutrophil-dependent fashion as well?

Reviewer #3 (Public Review):

Mutations in Naa10 are known to be causative in Ogden syndrome, a genetic disorder associated with infantile death. The paper by Kweon et al describes a series of experiments using mouse models of Naa10, an x-linked gene with the function of a major acetyltranferase in a complex accounting for 40-50% of acetylation of all proteins. The lack of complete embryonic lethality in the Naa10 hemizygous mice, leads the authors discover a paralogous mouse gene Naa12. The authors further demonstrate that Naa12 can compensate for Naa10 loss of function and that null mutations in both genes lead to complete embryonic lethality.

Genetic experiments described in this paper involve 2 distinct knockouts of the Naa10 in mice. The resulting hemizygous male mice displayed a variety of developmental defects, and while hemizygous males were underrepresented at birth, some surviving mice experienced early neonatal lethality while a proportion of the hemizygous mice survived to adulthood. Severely affected animals exhibited a variety of development abnormalities but importantly, no major reductions in the acetylation patterns were observed. A similar spectrum of phenotypes were reported in 2017 in a separate paper by Lee et al. The lack of complete embryonic lethality in Naa10 hemizygous males led to the hypothesis that a compensatory gene in mice may exist. The authors then identified the autosomal Naa12 gene in mice. This is a major finding of the paper. Naa12 and Naa10 share 80% sequence identity. The authors continued on to generate a Naa12 knockout mouse that in combination with the Naa10 knockout mice, demonstrate complete embryonic lethality to support the hypothesis that Naa12 is a function homolog to Naa10 in mice. This is strong evidence supporting the functional compensation of Naa12. The authors provided a thorough account of the variety of development abnormalities in the Naa10 hemizygous mice at all stages of development, noting changes in bodyweight, hydrocephaly and significant cardiac defects, pigmentation, skeletal and reproductive abnormalities. The variation and heterogeneity ranged from severe embryonic abnormalities through to milder phenotypes in surviving adults. Importantly, the authors identified several phenotypes in the mice that upon further analysis, we also not in the patients with an assumption of incomplete penetrance.

This reviewer finds this paper to be an important finding worthy of publication. The experiments were well powered and the genetic crosses thoroughly examined. The discussion was thoughtful and considered mechanisms of compensation between Naa10 and Naa12 based on the observed experiments.

Reviewer #2 (Public Review):

This manuscript shows the functional relevance of mNatA catalytic subunit, mNAA10, in mammals' development. Moreover, authors have found a new NatA catalytic subunit in mice, mNAA12, that can compensate mNAA10 inactivation in mice. Interestingly, inactivation of mNAA10 in mice induces some developmental defects similar to those observed in Ogden syndrome (OS) patients including lethality in infants. This study provides several evidences and explains some of the defects observed in OS patients like supernumerary vertebrae and hydrocephaly supporting the relevance of hNAA10 mutations in the development of OS. Moreover, authors have observed in mice some developmental deficiencies not observed previously in OS patients, like supernumerary ribs, that after patient re-examination they have been observed in humans too. Curiously, the results presented in this article show that inactivation of mNatA catalytic subunit does not affects dramatically protein N-terminal acetylation, probably as consequence of mNAA12 paralog function as mNatA catalytic subunit when mNAA10 is not present. Interestingly, gene inactivation supports the biological significance of NAA10 as the main NatA catalytic subunit as mNAA12 inactivation is not associated with any clear phenotype. In spite of being one of the most frequent protein modifications protein N-terminal acetylation has not attracted proper attention, therefore this paper can draw more attention to this important protein modification.

Reviewer #1 (Public Review):

In this paper, the authors investigated the role of the N-terminal acetyltransferase Naa10 in mouse development. In addition, they identified a new paralog, Naa12, and demonstrated that it has a redundant role with Naa10 in controlling mouse embryonic development. The results are very clear and should be of interests to those working on development and N-terminal acetylation.

I have several comments for the authors to consider:

1) It is important to show that N-terminal acetylation is lost in the double knockouts. Only with that, the authors can conclude that they have identified the "the complete machinery for the process of amino-terminal acetylation of proteins in mouse development."

2) Naa12 is new, so if not done yet, the sequence needs to be deposited into Genbank.

3) The presentation needs to be polished.

i) The title "Naa12 rescues embryonic lethality in Naa10-Deficient 1 Mice in the amino-terminal acetylation pathway" is misleading. When I saw the title, I got the impression that Naa10-dficient 1 mice show embryonic lethality. I would suggest to change it to indicate that Naa10 and Naa12 have redundant roles in embryonic development. Also, "Naa10-Deficient 1 Mice" needs to be changed to "Naa10-deficient mice."

ii) In the impact statement "Mice doubly deficient for Naa10 and Naa12 display embryonic lethality...", the word "doubly " is unnecessary.

iii) Too many acronyms, which make the reading a bit difficult. The terms NTA and Nt-acetylation could be avoided. iv) At the end of page 9, please cite the sequence alignment in Fig. S6

v) On page 12, "Naa12 may rescue loss of Naa10 in mice" could be more assertive.

vi) Overall, I feel that the authors could polish the manuscript so that the salient points could be conveyed more easily to readers.

Reviewer #3 (Public Review):

In this study from the Selimi lab, Gónzalez-Calvo and colleagues investigate the role of the uncharacterized complement family protein SUSD4. SUSD4 is expressed at the time of cerebellar synaptogenesis and localizes to dendritic spines of Purkinje cells. Susd4 KO mice show impaired motor learning, a cerebellum-dependent task. Susd4 KO mice display impaired LTD and facilitated LTP at parallel fiber (PF)-Purkinje cell (PC) synapses, indicating altered synaptic plasticity in the absence of Susd4. Climbing fiber (CF)-Purkinje cell synapses show largely normal basal transmission, with the exception of larger quantal EPSCs. Immunohistochemical analysis shows a small decrease in the proportion of CF/PC synapses lacking GluA2. As their data indicates a role for SUSD4 in regulation of postsynaptic GluA2 content at cerebellar synapses, they next explored the molecular mechanism by which SUSD4 might do so. Activity-dependent degradation of GluA2 does not occur in the absence of SUSD4. Affinity purification of proteins associated with recombinant SUSD4 identifies ubiquitin ligases as well as several proteins involved in AMPAR turnover. Finally, the authors show that SUSD4 can bind GluA2 in HEK cells, and that SUSD4 can bind the ubiquitin ligase NEDD4, but that these two interactions are not dependent on each other.

This study provides novel insight in the uncharacterized role of SUSD4 and provides a detailed and well-performed analysis of the Susd4 loss of function phenotype in the cerebellar circuit. The exact mechanism by which SUSD4 affects GluA2 levels remains unclear. However, their findings provide leads for further functional follow-up studies of SUSD4.

Specific comments:

1) Localization of SUSD4. The authors demonstrate localization to spines in distal PC dendrites (Fig. 1C). Does SUSD4 also localize to CF/PC synapses? This is important to establish given the phenotype of increased quantal EPSCs and decreased proportion of synapses without GluA2 at the CF/PC synapse.

2) Figure 4B: There seems to be considerably less surface GluA2 in Susd4 KO cerebellar slices. Is the difference in surface GluA2 between WT and KO significant? Although the mean reduction in surface GluA2 in Susd4 KO following cLTD is similar to WT, the difference with control is not significant. This should be pointed out in the text because it can not be definitively concluded that endocytosis of GluA2 is not altered in Susd4 KO on the basis of this experiment.

3) Figure 4D: The colocalization of SUSD4 with GluA2 is difficult to see in this image. An inset with higher zoom could help. Quantification of colocalization using e.g. Manders coefficient would help.

4) Figure 5B: The negative control used here, PVRL3alpha, lacks an HA tag. This therefore does not control for non-specific interactions of highly overexpressed membrane proteins in co-transfected HEK cells. The authors should use an HA-tagged membrane protein as a control here to demonstrate that the interaction of SUSD4 and GluA2 is specific for SUSD4.

5) Figure 5D: The level of GluA2 recovered in the IP appears normalized to HA-SUSD4. This does not control for the variations in GluA2 input levels shown in Fig. S11. Statements on amounts of GluA2 recovered for various SUSD4 mutants should be adjusted to take this into account.

6) Line 357: binding of SUSD4=is likely meant to be binding of NEDD4.

Reviewer #2 (Public Review):

The authors show that SUSD4 is expressed throughout the brain and is abundant in cerebellar dendrites and spines. Mice with deletion of SUSD4 have motor coordination and learning deficits, along with impaired LTD induction. The also attempt to show that GluA2 AMPA subunits are misregulated, but that is not as convincing. They find Nedd1, along with many other proteins in a proteomic screen for SUSD4 interactors, and try to explain the phenotypes through the regulation of GluA2 degradation by Nedd4 through SUSD4. These are potentially interesting findings, but very preliminary at this point. While the electrophysiology is good, the mechanistic studies are incomplete.

Major comments:

In Figure 1 localization images are shown using exogenous protein. Can the authors visualize endogenous protein?

It appears that SUSD4 is expressed in multiple brain regions, even at higher levels than the cerebellum. The authors should provide a good explanation for why deficits in the KO do not affect other functions, and seem to preferentially affect cerebellar functions.

Figure 4: immunofluorescence data are not very convincing.

Figure 5: The use of the word "could" is not supporting a strong conclusion. The authors should demonstrate whether SUSD4 DOES indeed regulate GluA2.

Overall, while the electrophysiology seems fine, the mechanistic studies are preliminary and speculative at this point.

Reviewer #1 (Public Review):

This is a highly interesting manuscript by Gonzalez-Calvo et al., describing the involvement of the CCP domain containing protein SUSD4 in the degradation of GluA4 receptors at cerebellar synapses. The novelty of this work lies in the specificity of this degradation pathway. In comparison, synaptic proteins involved in AMPA receptor endocytosis, such as GRIP1 and PICK1, play a role in multiple trafficking processes. In addition, CCP domain proteins play a role in synaptic pruning, which is closely related to LTD. We will return later to this point.

The paper will certainly enrich the field and further our understanding of cellular plasticity in the cerebellum. These are exciting findings that should be published. I have three relatively minor comments:

1) Figure 2E: it is surprising that the potentiation shown in WT mice is not longer lasting. Under the experimental conditions used here, plasticity seems to be biased towards depression. In the methods, the authors state that they use 2mM Calcium and 1mM Magnesium in their external saline. A recent study (Titley et al., J. Physiol. 597, 2019) has demonstrated that under realistic conditions (incl. an ion milieu of 1.2 mM Calcium and 1mM Magnesium), LTP results under most conditions - even those involving climbing fiber co-stimulation - while LTD only results from prolonged complex spike firing. Optimally, the authors would establish a real LTP control in their WT mice (using conditions as described in Titley et al or similar) and test for changes in the mutants. As LTP is not the focus of this paper and this might be out of the scope of this work, it should be acceptable to leave it as it is, but this caveat should at least be discussed.

2) Figure 3: The climbing fiber physiology is described in detail, but what is missing is a characterization of potential changes in the complex spike waveform, recorded in current-clamp mode. This should certainly be provided. This is important as it has been shown that changes in the complex spike waveform affect the probability for LTD induction (Mathy et al., Neuron 62, 2009). The CF-EPSC is a rather indirect measure.

3) Is synaptic pruning at parallel fiber synapses impaired in the SUSD4 mutants? The LTD deficit is quite obvious. In the light of the role of autophagy in pruning, and the molecular similarity between LTD and pruning, it would be of interest to see whether activity-dependent pruning at these synapses is altered. This aspect is somewhat addressed by the VGLuT1 measures shown in Figure 2, but should be discussed in more depth.

Reviewer #2 (Public Review):

In this manuscript, Galbraith et al add to our understanding of COVID19 pathobiology by undertaking a cross-sectional survey of 73 hospitalized COVID19 patients with non-severe disease. They perform very broad multi-omics analysis, including plasma proteomics, cytokine profiling and mass cytometry. The authors propose that disease course can be classified by the titer of anti-CoV2 antibodies, which in turn is associated with distinct changes in circulating proteins, cytokines and immune subsets. Interesting correlations with complement and coagulation factors are noted. These findings suggest an alternative way to map disease progression in COVID19 and have implications for broader studies of COVID19 pathobiology. In particular, it will in interesting to extend this framework to analyze a broader spectrum of COVID19 patients, particularly those with poor outcome.

Reviewer #1 (Public Review):

Galbraith et al., using systems immunology approach document in a very detailed manner, provide the textbook example of innate and adaptive immune responses over time following an infection. Here, their clinical assessment is linked to SARS-CoV2 infection. While novelty aspects are not immense, this study is nonetheless well executed, detailed and thorough.

The authors perform association studies and propose that simple seroconversion test should be considered in determining the clinical treatment. While some would argue that is already practiced and perhaps expected, the authors have done an excellent job at detailed immune analyses which they coupled with statistically sound associations. Thus these findings are important to document, and should be considered as experimental ex vivo evidence of what clinical practice may have implicitly already considered.

Reviewer #3 (Public Review):

Bridget A. Matikainen-Ankney et al. discuss the newest generation of their open-source Feeding Experimentation Device (FED3) platform capable of detailed tracking of food pellet intake and dual nose-poke operant behavioral testing. This platform provides a complete solution for these types of studies and includes all necessary open-source hardware, firmware, visualization code, and Arduino and Python libraries for user customization of experiments and analysis. FED3 has a rechargeable battery life of around one week and can operate without any external wires, logging data onto an on-board SD card and allowing for flexible placement in a rodent's home-cage. The platform also includes an on-board display for showing current experimental parameters/data and a variable voltage digital output for synchronizing the system with other external devices such as an optogenetic simulation system. The authors show multiple applications of the FED3 platform including detailed food intake tracking, fixed-ratio operant behavior experiments, and optogenetic self-stimulation. Importantly, they also highlight the ability to do studies across multiple, remote laboratories by leveraging the standardization of such a food intake platform.

Strengths:

The FED3 platform is well thought out and clearly builds off the authors' experience designing and working with their previous generation systems. The specific open-source approach taken by the authors include, not just openly providing design files but, building an understandable and open ecosystem of tools and libraries for laboratories to customize the platform to fit a broad range of experiments. By including data visualization tools and a Python library for working with FED3 data, the authors effectively lower the technical entry point for using such a platform and streamline the process of implanting the system in one's own experiments. The paper provides strong evidence of the FED3's capabilities and relevance of data generated across a range of use cases. There is compelling evidence of the usefulness of developing an open standard for food intake tracking, allowing for multi-site studies and across-laboratory comparisons. Finally, the system is significantly more affordable than other commercial options, lowering the economic barrier for implementing food intake tracking and operant behavior experiments.

Weaknesses:

While this paper presents a very useful, customizable, and flexible approach to food intake and operant behavior studies, certain aspects of the device could be better described in the paper. This is only a minor weakness as all hardware and code is openly available online, allowing for a more detailed understanding of the system beyond what is presented in the paper. It would be helpful to identify the major electronics components on the custom printed circuit board to aid in customization of the system. It would also be useful to provide more details as to the mechanical mechanism used to deliver food pellets and the optical beam breaks for detecting nose-pokes and food pellets.

Some potential limitations of the system include the inability to detect food pellet hoarding, lack of wireless option to access and configure the system, limited battery life, complications when using granular bedding, and no way to identify individual mice. The authors identify and discuss these limitations within the paper which is appreciated.

Reviewer #2 (Public Review):

"Feeding Experimentation Device version 3 (FED3): An open-source device for measuring food intake and operant behavior" describes the third iteration of an open-source automatic feeding device to be used with mice. I have no concerns about this paper and would recommend it as is. The authors have provided an incredible resource for the fields of feeding and reward-related behaviors, and provide all the details needed for assembly and use. Moreover, the data that they have collected using this device constitutes an advance, particularly the circadian rhythms of feeding, as well as the increase in operant responding during the light cycle. This device enables homecage measurement of feeding and training for motivational behavior, enabling most any laboratory to examine feeding behaviors in their experiments.

Reviewer #1 (Public Review):

In this manuscript the authors present a new and improved open-source option for a home cage pellet dispensing device that carries with it the ability to offer continuous monitoring of feeding behavior as well home-cage operant testing. This device solves many issues in the way individuals typically go about studying animal feeding behavior including but not limited to testing at only certain times of the day for limited amounts of time and food restriction in a manner that optimizes cost, functionality, scalability, and customizability over traditional or commercial options. Of note, besides offering the ability to capture massive amounts of home cage feeding and operant data directly in the vivarium of animal housing facilities, a major strength of this approach is that the authors demonstrate that the same amount of learning that would typically require 16 days (one-hour testing sessions) can be accomplished overnight (and with interesting circadian effects on decision-making that are often overlooked). The authors demonstrate useability of this device across institutions in other labs and integration with optogenetics (as well as citing recent studies integrating the device with recording systems).

Reviewer #3 (Public Review):

In this study, van Dorp et al. provide new insights into the structure of the C-terminus of STIM1 in the quiescent as well as the active state. By using extensive smFRET and protein crosslinking techniques, the authors substantially advanced our understanding of STIM1 cytosolic domains orientation and revealed inter- and intramolecular interactions within a STIM1 dimer. Structures have been derived for both STIM1 resting and activated state. Altogether, this study substantially contributes to a mechanistic and structural understanding of the STIM1 activation process, and it paths the way for the comprehensive dynamic resolution of conformational transitions from the inactive to the fully active state.

The single molecule studies represent a very elegant approach to derive novel details on STIM1 structure and dynamics. Utilization of these developed smFRET protein probes of ctSTIM1 in the interaction with Orai1, either reconstituted or even in living cells, would be phantastic, but certainly experimentally challenging based on the low fluorescent background required to resolve single molecule FRET.

Reviewer #2 (Public Review):

Although the major activation steps and general mechanistic underpinnings of SOCE have been reported in a flurry of literatures, they are largely descriptive and lack quantitative information. One topic of greatest interest to the CRAC channel field is the structural basis of CC1-CAD/SOAR-mediated STIM1 autoinhibition. Using single-molecule Förster resonance energy transfer (smFRET) and protein crosslinking approaches, Dorp et al provides a binding model for the CC1-CAD interaction. This model explains the role of CC1 in STIM1 activation, and delineates the activation process of STIM1 CT. It also clarifies the controversy on the two varying structures regarding the packing of the CAD/SOAR domain by favoring the X-ray structure over the NMR structure. The conclusions of this paper are mostly well supported by data. The only minor concern is to reconcile some of the conflicting results (regarding the relative positions of some residues used in the crosslinking study, as well as the CC1-alpha 1 helix), made between this study and a recent structural study, i.e., the NMR solution structure of CC1 reported by the Romanin/Muller's groups (PMID: 33106661). Overall, this study covers a timely topic to address a long-standing question in the ORAI-STIM signaling field, i.e., the structural basis of CC1-CAD association that keeps STIM1 largely quiescent in the resting condition. This work, regarded by this reviewer as a "tour-de-force" by meticulously scanning through many key residues within the multiple CC1/CAD helices, certainly warrants immediate publication.

Notable strengths:

1) smFRET is increasingly being used to determine distances, structures, and dynamics of biomolecules. Full length STIM1 and STIM1 C-terminus have been always difficult to obtain crystal structure due to its tendency for aggregation and the existence of large disordered regions. Herein, the authors selected smFRET as the major tool to overcome this hurdle and illuminated the CC1-CAD binding models to provide novel mechanistic insights into STIM1 auto-inhibition mediated by the intramolecular cis CC1-CAD association.

2) The efforts to extend crosslinking of ctSTIM1 to flSTIM1 are particularly commendable, moving one more step closer to the physiological scenario.

Minor weaknesses:

1) The authors proposed a CC1 model displaying "tandem connection of "CC1α1- CC1α2", that shows notable discrepancies with the recent CC1 NMR solution structure (PMID: 33106661). In the latter structure, the three helices are intertwined to form a bundle like structure. An in-depth discussion is certainly needed to clarify the difference. Some possibilities include: (i) Is this due to the artifact of the CC1 NMR structure (done in the presence of helix-stabilizing reagents)? (ii) is this due to the introduction of cysteine residues for the assays? (iii) is this due to absence of the CAD/SOAR part, or other regulatory components, in the solution structure? Repeating one or two key smFRET/crosslinking experiments in the presence of the similar buffer condition as in the NMR study would provide clues to these possibilities.

2) Another concern, very minor though, is regarding cysteine crosslinking flSTIM1 by 0.2 mM diamide. Will the addition of diamide cause undesired activation of STIM1 in the absence of cyclopiazonic acid?

Reviewer #1 (Public Review):

The authors use smFRET and cross linking to constrain relative orientations of CC1-CC3 helices in STIM1 resting and active conformations. The data are excellent and especially because structures of full length STIM1 are currently lacking they paint an important picture of the structural basis for STIM1 activation. The number of smFRET pairs examined in the inactive state is fairly large and paints a good picture of the relative orientations of helices. In contrast, only a few pairs of sites were examined in activated STIM1 which paint a clear picture of CC1a1 dissociation from CC3, but the remaining postulated conformational changes during activation are inferred primarily from cross linking, and it would have been nice to probe those with smFRET as well. Nonetheless, the data yet provide very useful constraints on STIM1 conformational rearrangements that will be of great value to further structure-function studies.

Joint Public Review:

The manuscript by Tachinawa et al. presents a new method (named RhIP), to study incorporation of recombinant epitope-tagged histone dimers into permeabilized cell nuclei. Using RhIP, the authors demonstrate that both H3-H4 and H2A-H2B and their variants are incorporated in this setup. They proceed with investigating context-specific features of these events, providing evidence that ongoing replication and overall chromatin structure may influence histone dimer incorporation in RhIP. This argues for RhIP having the potential to reveal the mechanisms of chromatin assembly and disassembly genome-wide, and determine how cell cycle and chromatin structure influence these dynamics.

The system is capable of recapitulating major known chromatin assembly pathways and supports existing knowledge of histone dimer dynamics on chromatin. RhIP is also valuable in directly testing histone mutants or variants, as proven by authors.

H3.1 incorporation is shown to be exquisitely dependent on replication, demonstrating that replication itself, as well as replication-dependent chromatin assembly are successfully reconstituted with isolated nuclei, cytosolic extracts and recombinant histones.

The focus of the study is on the incorporation H2A variants, in particular H2A.Z. These data supports known notions about H2A.Z dynamics in chromatin, showing a preference for transcription start sites, and the dependence on the M6 region.

However, the major limitation of the current manuscript is that it remains unclear what properties are driving the observed RhIP effects. This is not fully elucidated and thus limits the ability of RhIP to enable the discovery of new mechanisms.

While replication-dependent mechanisms are well captured by RhIP, it is less clear if transcription and chromatin remodeling is functional in this system and thus if transcription-dependent nucleosome exchange processes are faithfully recapitulated. It is important to improve the comparison of RhIP with 'in vivo' (i.e. existing ChIP-seq datasets) localisation and explicitly develop hypotheses why in some cases the data matches the 'in vivo' situation and in others not. It would be helpful to improve the interpretation of the data to include all existing caveats to the assay setup.

Reviewer #3:

In this paper Werkhoven et al. ask a fundamental question in behavioral neuroscience - what is the structure of co-varying behaviors among individuals within populations. While questions in the context of inter-individual behavioral differences have been studied across organisms, this work represents a highly novel and comprehensive analysis of the behavioral structure of inter-individual variation in the fly, and the underlying biological mechanism that may shape this structure of covariation. In particular, for their experiments they combined a set of behavioral tests (some of them were explored in previous studies) to a 13-day long behavioral paradigm that tested single individuals in a highly controlled and precise way. Through clever analysis the authors interestingly showed strong correlations only between a small set of behaviors, indicating that most of the behaviors that they tested do not co-vary, exhibiting many dimensions of inter-individual variation in the data. They further used perturbations of neuronal circuits and showed that temperature and circuit perturbations can change dependencies among sets of behaviors. In a different set of experiments where they integrated gene-expression data (from the brains of single individuals), they showed that some of the genes are correlated with individual-specific parameters of behaviors. Interestingly, through comparison of inbred and outbred population they demonstrated that also outbred populations are showing relatively low covariance of behaviors across individuals.

Overall, the data in the paper indicate that surprisingly, even for a 'simple' organism, there are many dimensions of inter-individual variation, e.g. many specific characters that can change among individuals in a non-dependent way. The ability of the authors to precisely measure such dependencies in such a highly robust and precise way allowed their investigation of the underlying processes that may generate this variation. The results in this study are highly interesting and novel. They uncover a general picture of the structure of behavioral variation among individuals and open many avenues for further analyses of the underlying neuronal and molecular mechanisms that control variation in sets of behaviors. Furthermore, the methods that were developed in this paper can be of great use by other researches in the field.

However, while the key claims of the manuscript are well supported by the data and analyses methods, some aspects of data analysis need to be clarified or extended:

• It is not clear what the motivation is for using the 'Effective dimensionality spectrum' analysis presented in the paper and how it significantly adds to existing methods of clustering that are relying directly on the correlation/distance matrix (some of them were used in this study).

• While it is clear that the distilled behavioral covariation matrix has many independent dimensions (as the authors indicated, most of the a-priori PCs are not strongly correlated), the number of 'significant' Pcs was not calculated directly for the distilled matrix, and t-SNE analysis is presented only for the original covariation matrix (1L).

• It is possible that some of the behaviors that covary across individuals in the high temporal resolution assay and also tend to be associated over time within an individual, may indicate sequences of behavior on longer time-scales (than the timescales in which parameters are quantified).

• Further analyses are needed for extending the detection of correlations between variation in gene-expression data and the independent behavioral measures in the covariance matrix.

Reviewer #2:

In this paper, Werkhoven and colleagues describe a large-scale effort, using Drosophila, to study variation in behavior among individuals with identical genotypes, and raised in very similar environmental conditions. This addresses the important and basic question of how much behavioral variability exists under such conditions, e.g. due to stochastic processes during development. By looking across many different behaviors, the authors are able also to investigate the nature of this variability. The key conclusion of the paper is that this intragenotypic variability is high dimensional, and cannot be explained by a small set of behavioral syndromes. They find that this observation is robust to the method they use to quantify behavior, and also holds to different degrees in data sets acquired from outbred flies, or files subjected to genetic perturbations of neural activity. Furthermore, they have generated a data set that allows correlation of behavioral biases in individual animals with transcriptomic data. Altogether, this is an impressive study that, beyond its important conclusions, opens up the possibilities for many further explorations in this area, and should be interesting to a broad audience. The experiments are well designed and overall the paper is very nicely written and clear to understand.

Reviewer #1:

The definition of individuality and its neurogenetic basis is a fundamental problem in ethology and neuroscience. Individuals might fall into discrete groups of personality types; alternatively, individuals might be better described by a broader spectrum of independent traits. An unbiased and quantitative analysis of behavioural traits that make up an individual's personality is a prerequisite of investigating the neuronal and genetic basis of individuality. Given the technical challenges in systematically measuring many behavioural traits across sufficiently large and genetically defined populations and over long time-scales, these questions remain unanswered. This manuscript represents a tour-de-force trying to shed more light in these directions. Werkhoven and colleagues aim at characterizing structure in correlations among a large set of quantitative behavioural measures obtained from the model organism Drosophila melanogaster. The authors performed a large number of high throughput behavioural experiments that cover behavioural paradigms ranging from locomotion to perceptual decision-making. Data were acquired from an inbred, hence isogenic fly line, an outbred line, and various neuronal circuit manipulations. In addition, gene expression data were obtained from individuals. In this way, the authors were able to capture hundreds of behavioural metrics from hundreds of flies, while keeping their individual identities over the course of 13 days. They developed a computational analysis pipeline that quantifies the correlation matrix computed from these metrics. In a 2-step procedure, they condense this matrix into a "distilled" matrix, the entries of which contain all remaining behavioural covariates that were not a priori expected by the authors.

A central claim in this paper is that any structure in this distilled matrix should reveal the principal axes along which individuality should be described. Based on these measurements and analyses flies could not be categorized into discrete types. Moreover, behavioral covariates appear rather sparse and derive from a high-dimensional behavioral space. This would mean that each individual fly is better described by a large combinatorial set of parameters. The same qualitative finding was made between inbred and outbred flies, leading the authors to a conclusion that larger genetic diversity does not change the principal organization of behaviour. The authors perform a set of neuronal-circuit manipulations and claim in conclusion that specific neuronal activity patterns underlie structure in behavioural correlations. Some correlations between gene expression and behavioral metrics were discovered, for example gene expression of metabolic pathways can predict some variability found in the behaviour of flies. The behavioural pipeline is sophisticated and presents a great leap forward in enabling researchers to capture a large set of behavioural measures from a large fly population, keeping the identity of individuals. The work is also presenting an innovative and interesting analysis pipeline.

Although we applaud these ambitious experimental paradigms and computational techniques used, we have several major reservations about this work. Reading through the manuscript multiple times, one is left confused whether the major finding is that no structure whatsoever can be found in these data and to what extent the remaining sparse correlations are of biological / ethological relevance. Another major concern arises from the high level of trial-trial variability that is found in the data, which seems to preclude identification of persistent idiosyncrasies in the behavioural traits of individuals and impedes the reproducibility of the data matrices in two repetitions of the main experiment. We feel that most of the authors' conclusions and claims are confounded by these caveats.

1) Distinguishing persistent idiosyncrasies from trial-to-trial variability and reproducibility of decathlon data

A major challenge in measuring personality traits or individuality is to distinguish between persistent idiosyncrasies and trial-to-trial variation; the latter could result from inherent stochastic properties of behaviors, environmental or measurement noise. To identify an idiosyncratic behavioral trait in an animal one needs to show that individuals exhibit a distinct distribution in a behavioral metric that cannot be explained by trial-to-trial variability. Such a distinction cannot be made if a behavioral metric is measured just once or during a short period, but requires repeated measures over longer time-scales from a sufficiently large population of animals. Unfortunately, in this study many measures have been taken during just one 1-2hs episode per individual of a decathlon. For other measures that were taken repeatedly (circadian assays, unsupervised video acquisition) no efforts have been undertaken by the authors to make the above distinction. Hence, the authors' conclusion that there are no "types" of flies seems premature. In Figure S1 we are surprised to see how low most behavioral measures auto-correlate when recorded on two subsequent days; most auto-correlations further drop to meaningless values when compared over time-periods that correspond to the different epochs of a decathlon. This indicates that trial-to-trial variability dominates the data. In our view it makes little sense to ask whether two behavioral metrics are correlated or not, if their autocorrelations measured over the same time-scale are already extremely low. Moreover, Fig S5B shows that the two decathlons generate largely different data matrices (correlation ~0.25), raising concerns that the results are not reproducible. We wonder whether any structure in behavioral correlations was masked by various sources of noise in this study.

Related to above, there should be error bars and number of flies for the plots in Fig S1. This figure undermines the starting point of the paper claiming persistent idiosyncratic behaviors.

2) Given the concerns above, it is not surprising that the outbred fly line delivers another set of covariates which lack otherwise any further structure. If experiments with >100 inbred flies cannot deliver reproducible results, it cannot be expected that a similarly sized population of outbred flies would. Perhaps the needed population size must be orders of magnitudes larger in this case.

3) Figure 3. It is intriguing to observe how the relationship between switchiness and clumpiness is perturbed upon temperature shifts. But, it seems rather uncorrelated at the restrictive temperature in the Iso line, with a slightly positive value. However, the switchiness-clumpiness correlation is not reproducible in both perturbation types at permissive temperatures. Note, that at both temperatures the Shi and Trp datasets show no - or very low correlations: the Trp lines produce correlations from approx. -0.2 (permissive T) to 0.1 (restrictive T); the Shi lines 0, 0.1 respectively. Fig 3D is very misleading in showing the best fits to the combined datasets. We are not convinced that there is a robust sign-inversion in any of these correlation. The authors' major conclusion that " thermogenetic manipulation and specific neuronal activity patterns underlie the structure of behavioral variation" is not supported by these data. The effect of temperature in the control line, although interesting, is a major caveat for interpreting the results from the Shi and Trp results.

4) The authors measure a large set of low- and high-level behavioral metrics, e.g. walking speed and choices in Y-mazes respectively. A fundamental problem is that many of these metrics potentially have common underlying but trivial causes, e.g. covariation between speeds measured in various conditions is expected. Therefore, the authors condense their original correlation matrix (Fig 1E) into a distilled matrix (1G) by making such judgements. In the present form, it is impossible to evaluate how systematic or arbitrarily these choices were. In many cases, where the same measure was recorded repeatedly (e.g. circadian bout length) or across different conditions (e.g. mean speed) it is obvious, but for other cases it is not obvious at all for the non-expert: for example, why are circadian-bout-length and LED-Y-maze-choice-number lumped into one block of expected behavioral covariates? The current manuscript lacks detailed explanations how the authors systematically created the distilled matrix. Can the sparseness of the distilled matrix be a consequence of too generous pre-allocations? See also point (6). The bulk of the analysis in this paper is done on the "distilled matrices" which are produced by removing correlations within previously defined groups of behavioral metrics. This is said to cleanly reveal unexpected correlations, leading to a main result of the paper, the correlations between "Switchiness" and "Clumpiness". However, if the a priori categories were defined differently, then in the extreme case this correlation would have been completely removed. How sensitive is this correlation to the choice of categories, especially given that many of the Switchiness and Clumpiness metrics are from similar assays (Fig. S8)?

5) For the second pipeline that uses t-SNE and watershed (Fig. 2 and S3C), a previous publication from some of the authors [1] appears to show low repeatability of this analysis.Thus, the repeatability and noise levels of the pipeline must be investigated further. These were 3x 1h recordings per decathlon. Related to comments (1-2), the authors need to show that the differences across flies (Fig 2C,D) are not expected from the level of trial-to-trial variability. Perhaps more data from individual flies need to be recorded?

6) 1G: To our understanding, within-block entries to the distilled matrix should indicate zero correlations, because these are correlations between PCA-projections. But we see many nonzero entries. Given the information provided in the methods it is unclear why this is the case; this requires further explanation.

In any case, within-block correlations are expected to be at least very low. Hence, we expect the distilled matrix to be relatively sparse given how it was calculated. Of interest are then the across-block correlations, the authors should make this point more clear to the readers.

7) Some of the author's claims are related to the spectral dimensionality reduction technique described in Fig. S9. However, none of the real data shown in the main paper figures look qualitatively similar to the toy data. Indeed, the histograms from the main figures are on a log scale, and are thus not comparable to the toy data results. Although the technique might be well suited for certain classes of data, one interpretation of the main paper figures seems to be that no structure is revealed whatsoever. More work should be done to exclude this as a possible interpretation, at least by generating toy data that look like the real Datasets; also with respect to point (6) above.

8) Throughout the paper, the authors use the term "independence" for orthogonal / uncorrelated datasets. Correlation/uncorrelation - dependence/independence are not interchangeable terms. To my understanding PCA decomposes into independent variables only under certain circumstances (multivariate normal distributed data). Have the authors tested for independence?

[1] Todd, J.G., Kain, J.S. and de Bivort, B.L., 2017. Systematic exploration of unsupervised methods for mapping behavior. Physical biology, 14(1), p.015002.

Reviewer #3:

The authors propose a new method of focused ultrasound (FUS) neuromodulation namely amplitude modulated FUS that they propose can differentially affect inhibitory and excitatory cells depending upon the intensity employed. Parameter selection is an issue for this field and the introduction of new methods for efficacious modulation are highly desirable. However, this paper does not explicitly test AM FUS against existing forms of FUS thus lending no evidence to its efficacy. While the differential effects are interesting in themselves, we gain no insight if AM FUS is the critical factor leading to this.

Reviewer #2:

Nguyen et al. developed a novel method of transcranial focused ultrasound stimulation and used it to stimulate anesthetized rats while performing extracellular recordings in the hippocampus. They find that the stimulation has different amplitude-dependent effects on putative inhibitory interneurons and excitatory principal cells. This finding is exciting because it suggests that transcranial ultrasound could be used to specifically reduce or increase firing rates in excitatory or inhibitory neurons in a particular part of the brain (resolution in the mm range). In principle, this could also be applied to humans. Simultaneously measured oscillations of the local field potential, particularly (but not exclusively) in the theta band (3-10 Hz) could also be manipulated in a bidirectional manner depending on the stimulation amplitude. Such cortical oscillations have been strongly linked to a wide range of functions including memory, and the potential to manipulate them in an anatomically precise manner is exciting and could even lead to new therapy approaches. Although it is not new that ultrasound can be used to modulate neuronal activity, this paper reaches a new level of precision by demonstrating that bidirectional effects can in principle be limited to one cell class or one frequency band. Thus, it could provide a great alternative to current methods that either provide much less precision (e.g. transcranial magnetic stimulation) or rely on more invasive methods (e.g. deep brain stimulation) or genetics (e.g. optogenetics).

The study is well-designed with stimulation at 3 different amplitudes applied in the same rat, whereby each 3-minute stimulation is compared to a 3-minute sham session where the transducer is 1 cm above the skull. Baseline sessions before each stimulation and sham session did not show any differences, showing no spillover-effects from the previous stimulus. Effects on brain temperature were also measured and shown to be negligible compared to normal variability.

Low intensity stimuli lead to a reduction in firing rates in putative interneurons and a reduction in theta oscillation power, whereas high intensity stimuli lead to an increase in firing rates in putative principal cells, with intermediate intensities having largely no effect.

In principle, these findings could provide novel insights into the mechanisms underlying ultrasound stimulation, but neither of the two discussed main modes of action (mechanical and thermal) appears consistent with the results. Thus, no model could be offered that might give some insight into the underlying mechanisms of ultrasound modulation of neuronal activity. This might be an issue for future work, and if the results were more robust perhaps this would not matter as much. However, the overall size of the effects appears to be too small to be of practical use as a reliable tool for manipulation of neural circuits. Although the authors show statistical significance, some details of the analysis are not fully clear and may need to be further corrected for multiple testing. It remains unclear if perhaps larger or different effects would be achieved when recording through the skin, without anesthesia, in different brain areas, in differently defined subclasses of neurons or with a different stimulation protocol (frequency, duration, amplitude). Thus, although the technique appears promising, more work is needed.

Reviewer #1:

Major points:

1) On the conceptual level, the authors claim that low-intensity amplitude-modulated transcranial focused ultrasound stimulation (AM-tFUS) inhibits local inhibitory interneurons and excites excitatory neurons at high intensity. However, the problem I have with this is that these cell types are highly interconnected within the local circuits, and changing the activity of the inhibitory cell type should have the opposite effect on the excitatory cell type. This has been documented in many experiments (Babl et al., Cell Reports, 2019; Royer et al., Europ.Journ. of Neurosc., 2010), and it unclear why the authors did not see similar effects. Furthermore, it is particularly troubling that the authors observe sustained suppression (five minutes) of the inhibitory neurons yet fail to see any effects on the excitatory neurons (Fig. 3B,D). This conceptual problem raises questions about the experimental setup, which I address below.

2) The authors performed electrophysiological recordings while delivering AM-tFUS with different intensities. To claim the differential effects on the excitatory and inhibitory interneurons, the authors first need to isolate single units in their recordings. However, the authors fail to cluster single units, as documented in the methods section (line 338). There could be several reasons why the authors failed to complete this step. I suggest the following ways to remedy this problem: A. The authors should use a silicone probe with a higher density of recording sites (the distance between the individual sites can be as small as 25 um in some NeuroNexus probes) than the one used in the MS, or use a Neuropixels probe so that the clustering algorithms have a chance to isolate single units. Using NeuroNexus probes with 100 um separation between the recording sites makes it impossible for different channels to "see" the same neuron and severely limits the spike sorting algorithms that separate units based on their unique spatio-temporal waveforms. B. After clustering, the authors should use autoccorellograms to verify that the single units do not violate the refractory period (Hill et al., Journal of Neuroscience, 2011). This is particularly important in areas, such as the hippocampus, which has a high density of neurons, and care should be taken to avoid multiunit recordings. C. The authors should perform one long recording session that comprises all experimental manipulations-the delivery of AM-tFUS, the sham control, and the rest period-to trace how the same units change their firing rate as a function of the experimental manipulations. This would also be very helpful in understanding how the firing rate change in one class of neurons is accompanied by changes in another class. D. Although this might be tricky, the authors could try to perform electrophysiological recordings by lowering the electrode perpendicular to the brain surface. This would allow them to record excitatory neurons and inhibitory interneurons that are connected to each other within the local circuit. This type of recording, would give the authors a greater chance of observing how changes in the firing of the inhibitory cell type affects the activity of the excitatory cell type and vice versa. This type of recording would also be highly desirable for understanding changes in oscillations of the local field potential (LFP) (see below).

3) The authors should report the sites that they have recorded by labelling the electrode with fluorescent dye or performing lesions at the recording sites.

4) When analyzing the effect of AM-tFUS on theta frequency oscillations, the authors should perform current source density (CSD) analysis to verify that the observed effects are local and do not originate from distant sources by volume conduction (Buzsaki et al., Nat. Rev. Neurosc. 2016). Performing electrophysiological recordings perpendicular to the brain surface, as I recommend in 2D, would be necessary for this. The CSD analysis would identify the location in the hippocampus where the change in theta power occurs.

5) The authors argue that temperature changes of 0.2 degrees were not sufficient to alter the firing rate of the neurons. However, the paper to which they refer (Darrow et al., Brain Stim, 2019) shows, in Fig. 7, that heating up brain tissue with a laser even at 0.2C can induce changes in somatosensory evoked LFPs. The authors should perform control experiments that are analogous to those in the cited paper to manipulate the temperature while recording the neurons in order to verify that the observed effects are not due to the changes in temperature.

Minor points:

1) The authors should not use label cells in Fig. 3 as they cannot claim that they recorded single units.

2) In Fig. 5C, Fig. S3B,C, and Fig. S4B,C, the authors should show the full scale of the values. Furthermore, the outliers in these plots (not seen in the figures) may drive the general trends, and removing them should be considered.

3) During AM-tFUS at intermediate power intensity (Fig. 4D,G), the authors observe a very dramatic change in LFP power in the 1-3 Hz frequency range. Although there is no clear underlying change in the firing of neurons at this intensity (Fig. 3E,F,G,H), the authors could speculate on what is happening in this case.

4) Fig. 5B shows a clear reduction of power in the theta frequency range after AM-tFUS in the dentate gyrus as well as in CA1 and CA3. This effect is also seen in Fig. 4G and Fig. S1,2. Although this effect does not reach the level of statistical significance, the authors should report the p-values.

5) Although the suppression of firing rates for a five-minute period after low-intensity AM-tFUS application is interesting, I am not sure if such prolonged after-stimulation effects have ever been documented using other modes of neuromodulation. Therefore, the authors should discuss this effect in line with previous work.

Reviewer #3 (Public Review):

By applying modern viral tracing methods, this paper described in detail extensive input-output connections of Gad1Cre+, VgatCre+, or Ntsr1Cre+ IntA projection neurons.

Because diverse neurons are intermingled in a small region, it is generally challenging to isolate specific excitatory or inhibitory neurons and their circuits in the cerebellar nucleus.

The authors focused on IntA of CN and demonstrated that 1) both inhibitory (Gad1Cre+ and/or VgatCre+) and excitatory (Ntsr1Cre+) neurons comprise extensive input-output connections with many extracerebellar regions, and 2) inhibitory circuits are functionally distinct from excitatory circuits on the basis of projection targets. This work could provide insights into diversity of inhibitory IntA neurons, and thus could be an interesting addition to the field's expanding efforts to identify cell types of CN, their input-output connections, and their functions.

However, interpreting the data is difficult because of technical challenges. Critically, the main conclusion could be compromised by experimental artifacts, which need better characterization. In addition, the text could be revised to make it more accessible to a broad audience.

Reviewer #2 (Public Review):

Judd et al. systematically examine the input/output connectivity of discrete excitatory and inhibitory neuronal subpopulations in the cerebellar interposed anterior nucleus (IntA) using conditional AAV and rabies virus mapping strategies. The authors first define distinctions in the output connectivity of excitatory and inhibitory neurons in the IntA nucleus, and describe a surprisingly much wider projection pattern by inhibitory neurons than previously thought. They also characterize distinctions in projection pattern between identifiable subtypes of IntA inhibitory neurons as well as distinctions in morphology of their terminal fields. The authors next explore the input connectivity of excitatory and inhibitory neurons in the IntA nucleus and found that excitatory output neurons receive fewer, but more organized inputs than inhibitory output neurons, and that many output targets provide reciprocal connections with the CN.

In general, the output analysis is strong and there are only a few questions about interpretation of the distinctions of projections by different subtypes of IntA inhibitory neurons. For instance, the distribution of the initial targeting within the cerebellar nuclei, cerebellar cortex and outside the cerebellum was not analyzed in Ntsr1-Cre and Gad1-Cre similar to the analysis performed for the intersectional output analysis. Clarification on whether and how the distinctions in projections could be due to variability in the specificity of the initial targeting or recombination ability of the two mouse Cre-lines is needed to strengthen interpretation of the different projections patterns observed. As for the input analysis using rabies, there were two major issues identified.

First, the use of conditional GFP-labeled G protein and the use of rabies that is also GFP potentially confounds analysis of input cells.

Next, the low number of starter cells is a concern and the identity of starter cells outside the cerebellar nuclei in Ntsr1-Cre and Gad1-Cre is vague and needs to be clarified. This is important for interpretation of whether input structures observed project specifically into the CN or also into the cerebellar cortex, and whether distinctions observed in number of input structures may reflect amount of starter cells in each Cre line.

Reviewer #1 (Public Review):

In this paper, Judd et al performed intersectional viral-mediated genetics to resolve a projection map from Ntsr1-positive and inhibitory neurons in the anterior interposed nucleus. They show that, in contrast of what is currently thought, inhibitory neurons that project to the inferior olive in fact bifurcate to multiple brainstem and midbrain areas. This is a thorough and timely paper, with valuable information for cerebellar scientists with implications that will be of interest to the general neuroscience audience. As a direct consequence of the vast amount of information, this paper summarizes a lot of data using acronyms and summary schematics, which makes it at times difficult to follow the core story. A bigger concern is that the main conclusion arguing that inhibitory neurons make widespread extra-cerebellar projections relies on the assumption that the Cre-lines used in the study are able to specifically/exclusively mark to those inhibitory neurons – these details were not fully worked out in this study.

Reviewer #3 (Public Review):

The manuscript by Sando et al. describes experiments directed at unraveling how latrophilins (Lphns) orchestrate synapse formation. Lphns are a unique family of adhesion molecules harboring extensive extracellular N-terminal domains with several known interacting motifs coupled to the classical 7 transmembrane architecture of G-protein coupled receptors. In recently published work from the Sudhof group, Lphns were shown to play a surprising postsynaptic role in synapse formation onto CA1 pyramidal neurons with Lphn2 and 3 important for perforant path and Schaffer collateral synapse formation respectively (Sando et al., Anderson et al). However, it remains unclear whether G-protein signaling through Lphns is important for their role as synapse organizers.

To address this issue, the authors use conditional knockout/rescue approaches to convincingly demonstrate an essential role of the GPCR domain of Lphns 2 and 3 both in vitro and in vivo. Replacing the intracellular 3rd loop of the GPCR domain (which is essential for G-protein activation) of either Lphn2 or 3 fails to rescue reduced synapse number in the knockout background (nor does deleting the entire GPCR domain). Thus it appears that cell adhesion properties alone are not sufficient for Lphn-mediated synapse formation. The experiments appear to be robust and convincing and the conceptual advance of Lphn-mediated GPCR signaling during synapse formation is substantial. I have a few specific points outlined below, but overall the authors use a nice combination of imaging, electrophysiology and rabies virus-based synaptic connectivity measurements to support their conclusions. Naturally, I'd like to know more details about the signaling requirement (e.g. how is Lphn signaling spatially compartmentalized compared to other GPCRs present, which G-protein(s) Lphns couple to, how/when/whether GPCR signaling is regulated by ligand engagement etc.) but these questions seem better suited to a separate study.

Reviewer #2 (Public Review):

This manuscript by Sando and Sudhof addresses whether GPCR activity of latrophilin2 and 3 is necessary for the role of these proteins in synapse formation. The key findings are:

— the generation and validation of mutants that lack transmembrane and intracellular domains (but are GPI-anchored instead), the lack only intracellular domains, or that contain all domains but lack GPCR-activity. All mutants work properly in cell aggregation assays and appear to be localized normally when overexpressed in wild type neurons. This also led to the development of an elegant PKA-phosphorylation reporter assay.

— in cultured latrohphilin 3 knockout neurons, latrophilin3 expression restores a decreased synapse density and mini-frequency, but the GPI-anchored, truncated or inactive versions do not restore these parameters.

— in vivo/hippocampal brain slices, latrophilin2 knockout impaired perforant path but not Schaffer collateral transmission onto CA1 neurons, and rescue required latrphilin2 GPCR activity. Conversely, Latrophilin3 knockout impaired Schaffer collateral but not perforant path transmission onto CA1 neurons, and rescue required latrophilin3 GPCR activity.

— finally, monosynaptic tracing confirmed that latrophilin3 knockout reduced inputs onto CA1 starter neurons, and rescue again required GPCR activity.

Altogether, the data are rigorously acquired, the paper is well written, and the finding that GPCR activity is necessary for latrophilins' role is both surprising and important. It is also elegant, as coupling cell-adhesion directly to signal transduction via a single molecule for synapse formation is a compelling way to drive synaptic assemblies. Naturally, the question arises how compartmentalized GPCR-signaling then instructs synapse formation, a topic that will undoubtedly require and attract more research. This is an exciting manuscript that will inspire new research on compartmentalized GPCR signaling at the synapse. Given the central importance of surface trafficking and localization within spines for the conclusions, better description of experimental procedures and quantification, and possibly additional data would clearly strengthen this point.

Reviewer #1 (Public Review):

The general thesis of the work, provided by the authors, is the demonstration that latrophilins 2 and 3 function as classical GPCRs at the synapse and that this activity is necessary for synapse formation at a specific synapse within the hippocampus. The topic is interesting and important for several reasons. First, the knowledge of GPCRs at synaptic connections is focused largely on neurotransmitter receptors in the literature – metabotropic GluR and AChR as well as neuromodulatory neurotransmitter receptors (NPY, Seratonin etc). The mechanism demonstrated in this work concerns the function of a GPCR receptor system that could confer specificity to synapse formation.

The effect sizes that are documented throughout this work are large, giving this reviewer confidence that the effects are robust and will be reproducible and, more importantly, are indeed a biological mechanism related to synapses.

The other major strength of the work is that the studies in neuronal cell culture are recapitulated in vivo providing additional confidence in the validity and importance of the work. Indeed, the concept of specificity requires this type of in vivo work as the identity of synapses in culture systems can not be readily determined.

A further strength is the rational and implementation of three mutant receptors that are used to dissect the signaling modalities of these receptors, validated for their effects on the protein and then used as rescue constructs in synaptogenesis assays.

Reviewer #3:

This is an interesting manuscript in which the authors have investigated the effect of intracellular injection of oligomeric beta-amyloid into hippocampal neurons both in cultures and adult animals. They find that starting from 500 pM, intracellular injection of oligomeric beta-amyloid rapidly increases the frequency of synaptic currents and higher concentrations potentiate the AMPA receptor-mediated current. Both effects were PKC-dependent. Furthermore, they find that following PKC activation there is release of NO which in turn increases release of neurotransmitter not only in the nearby pre-synaptic site, but also in neighboring cells. This suggests that intracellular injections of oligomeric beta-amyloid into the postsynaptic neuron can increase network excitability at a distance. The effect on neuronal excitability would involve AMPA-driven synaptic activity without altering membrane intrinsic properties. The conclusions are sound. However, there are two main aspects of the observed phenomenon that have not been taken adequately into account, or have been avoided by the authors. The authors have not investigated the effects of application of oligomeric beta-amyloid into the extracellular space and the presynaptic neurons, two other compartments of the synapse. They might have performed experiments comparing findings from experiments with intracellular injections of oligomeric beta-amyloid into the post-synaptic neurons, with effects of extracellular application and those of injections into the presynaptic neuron.

Additional minor concerns are related to the following issues:

a) The raw data on Figure 3 suggest that not only excitatory transmission is affected but also inhibitory transmission is somewhat modified. Measurement of the charge might be misleading.

b) This reviewer is not clear on the meaning of the following sentence in the discussion "Contrary to previously published data using extracellular Aβ or with more chronic application models [45-50], we did not find any synaptic deficits". The current work shows synaptic changes!


c) There is a mistake in the numbering of figures in the discussion. The paper has no figure 11. When referring to figure 10, they must mean something else.

d) The model on Figure 10 needs work. The authors should explain what various elements of the drawing mean, or better label them directly on the figure.

Reviewer #2:

Epilepsy is often an early sign observed in Alzheimer patients and there are several mechanisms that may contribute to this hyperexcitability. In this study, the authors focused on an important observation suggesting that intracellular Amyloid beta, a protein often found in plaques in the brain, is found early on inside neurons of the hippocampus, the learning and memory center of the brain. Interestingly, when unique early forms of Ab named oligomers were introduced inside neurons, the cells and surrounded circuits became hyperexcitable. This increased excitability was mediated mainly by the release of glutamate on AMPA glutamate receptors. Remarkedly, these excitatory effects were triggered by intracellular amyloid oligomers through a retrograde signal named nitrous oxide. This manuscript suggests that early stages of the disease may comprise significant increases in network excitability that may trigger a cascade of synaptic dysfunction and cognitive deficits such as memory loss.

Here are my comments to strengthen the manuscript. Overall this is a strong study with an interesting take on the role of intracellular amyloid and how it contributes to increased network excitability in AD.

There is an interest to determine the mechanisms responsible for the hyperexcitability often associated with familial and sporadic forms of Alzheimer's disease. Many have focused on possible reduction in inhibitory interneuron function as essential drivers of the increased excitability of the network. Although there exist a large number of investigations determining the effects of extracellular Ab on synaptic transmission, the intracellular effects of Ab and its contribution to disruptions of synaptic transmission remains less well understood. A couple of studies have shown that intracellular application of Ab (Ab42) induces decreases in long-term potentiation and basal synaptic transmission. In this study, the authors have investigated how intracellular Ab oligomers (iAbo) contribute to enhanced excitability in the CA1 region of the hippocampus. To do so, they have intracellularly applied human brain-derived and synthetic Ab oligomers through the patch-pipette in principal neurons recorded in vitro and in vivo.

In this study, the authors show that intracellular application of intracellular Ab oligomers increased the frequency and the amplitude of excitatory currents and spiking in ex vivo hippocampal slices. Effects that were mimicked by human oligomers. The intracellular amyloid mediated effects were through the amplification of AMPAergic spontaneous activity and currents, and, to a lesser extent, spontaneous GABAA mediated currents. Miniature frequency and amplitude of AMPA-mediated EPSCs were also increased and were sensitive to PKC blockers. Interestingly, since intracellular Ab increased the frequency of EPSCs, which is a presynaptic effect, a signaling molecule is likely to be released postsynaptically to modulate presynaptic terminals. The hypothesis that the retrograde signal NO was involved by determining the sensitivity of NOS inhibitor L-NAME. L-NAME reduced the increased iAbo mediated frequency of spontaneous post-synaptic excitatory currents in cultured neurons. The L-NAME compound was shown to reduce the iAbo -mediated No from both the recorded and neighboring neurons providing further evidence that intracellular Ab oligomers triggered NO release and increased glutamate release. Increases in the excitability of CA1 pyramidal cells were also observed in vivo by intracellular application of AB oligomer. Overall, this is a well written study that demonstrates a novel perspective of the effects of intracellular Ab oligomers on CA1 principal neurons and suggests possible mechanisms underlying hyperexcitability.

Novelty:

1) use intracellular oligomers, synthetics and humans

2) Showing that iAb oligo increased post and presynaptic AMPA-mediated EPSCs.

3) The presynaptic increases in EPSCs were mediated by NOS and NO, this could potentially spread widely across the network.

4) spontaneous IPSCs were also increased (through an undetermined mechanism).

5) the iAbo increase in excitation was also observed in vivo.

Questions:

Intracellular Ab produces both an increase in EPSCs and IPSCs. However, in Fig 3, the IPSCs, measures using a charge transfer quantification, did not show a significant change in response to iAbo, in contrast to EPSCs. This spontaneous inhibition here was measured as charge transfer which depends on the amount of charges in time. I wonder why this was not significant since this measurement should have picked up a possible increase in spontaneous IPSCs?

With regard to the inhibition, In the schematic on Fig. 10, I find this incomplete and slightly inaccurate since it shows one terminal releasing both glutamate and GABA with NO increasing both. While this is obviously an oversimplification, it's slightly inaccurate since NO was not directly shown to increase sIPSCs. Were NOS blockers able to disrupt the increase in sIPSCs? Moreover, there are many papers that have shown that PKC can also phosphorylate GABA receptors and increase their conductance. What could be the reason that this was not involved here? This needs to be discussed.

The experiments were done in cultured neurons, in slices and in vivo. It's not always easily discernible in what conditions the experiments were done when reading the manuscript, especially when looking at the figures and figure legends. This should be at least stated in the figure legends. To help the reader, the conditions in which the currents were recorded (GABA and or excitatory receptor blockers, other ion blockers could be indicated in the figure legends to ease the comprehension of how the experiments were done and what was measured). In relation to this, was the sIPSC iAbo-mediated increases also blocked by L-NAME?

In other studies, investigating intracellular application of Ab, such as the Ripoli et al., 2014 paper, showed that iAb produced significant reductions in EPSCs in their hippocampal neurons. What are the differences explaining this? This should be discussed. Similarly, Gulisano et al., 2019, showed that extracellular, but not intracellular oligo Ab had effects on excitability when it was applied extracellularly but not intracellularly. This should also be discussed.

In the introduction, it's mentioned that the nature of hyperexcitability is unknown. I agree that it's incompletely known, but what is known is that there is a large variety of possible causes. For example, changes in GABAergic interneuron function (see Hijazi et Al 2019) is well known to be a contributing factor. There are many studies that have shown possible contributing causes of hyperexcitability, therefore, something IS known, and this should be identified in the introduction.

How do these increases in synaptic transmission by applying pM concentrations of oligomers fit with the data showing that extracellular Ab oligomers of comparable concentrations decrease synaptic transmission through presynaptic reductions in glutamate release? This needs to be put into context and discussed.

Reviewer #1:

This is an interesting study of the effects of intracellularly-applied amyloid beta (Ab) in primary hippocampal cultures of embryonic rats or in area CA1 of hippocampal slices or anesthetized rats that are less than 35 days old (therefore prepubertal). In vivo, whole cell recordings were made of CA1 neurons which is difficult and therefore a strength. Both synthetic Ab and human-derived Ab were applied by adding them to the internal solution of a patch electrode. Several interesting effects were documented, such as increased evoked and miniature EPSCs (mEPSCs) as well as some effects on IPSCs and neuronal properties. A major question is whether these effects were pharmacological or physiological.

An intriguing finding was that the increased EPSCs was reduced by inhibiting a PKC-mediated effect of nitric oxide (NO). Furthermore, the effect of intracellular Ab on the recorded cell had effects on neighboring cells. Whether those were due to diffusion of NO, synaptic inputs from the recorded cell on neighboring cells, or release of Ab from the recorded cell was not clear. The authors suggested this is 'functional spreading of hyperexcitabiliity' similar to the way prions are spread transynaptically (actually this has been suggested for Ab too; see work by Karen Duff or Brad Hyman's groups) although this seems premature because the work that has been done with prions and Ab involves spread over a long time and a long distance relative to the results of the present study. Still the results are interesting and could be relevant in some way to the development of the disease or hyperexcitability.

MAJOR CONCERNS

One major issue is whether the results are relevant to Alzheimer's disease (AD) or represent interesting pharmacological data about what Ab can potentially do in some of its forms in normal tissue. The cultures are from embryonic rats and it is not clear how well they can predict what occurs in aged humans with AD. This issue is not only a question related to the preparation of tissue but the use of Ab intracellularly. It is not clear that synthetic or human Ab that is prepared outside the animal and used to fill electrodes to dialyze a cell is similar to the Ab generated in a cell of a person with AD. Independent of the methods to determine whether it is oligomeric outside the cell, once dialyzed it is not clear how it may change and where it would go. In AD Ab has a particular location and precursor where it forms and how it travels to the external milieu. As a product of its precursor APP, several peptides are produced besides Ab and many labs think they are as important as Ab in the disease. Although a strength to use atomic force microscopy to attempt to verify the form of Ab being used, it is not clear what form was actually in the dialyzed cell and how that compared to the form in AD.

How this work relates to other studies that are similar is important. It seems that few other studies that have applied Ab are mentioned because few have studied it intracellularly. However, they are relevant because adding Ab has been shown to cause an increase in hippocampal neurons of excitatory activity at low concentration but at higher concentrations synaptic transmission is weakened. Many studies of mouse models of AD pathology suggest reduced synaptic transmission and plasticity, although many others show hyperexcitability, often without adding Ab at all.

PKC and NO do a lot of things throughout the brain and body. How do the effects the authors have identified relate to all these other effects. For example, if PKC is activated by another mechanism, would it occlude the effects of Ab? What are the changes in PKC and NO in AD?

ADDITIONAL CONCERNS

I am not sure of the validation of Ab using the anti amyloid or 6E10 antibodies. The western blot shows a large region that both antibodies detect and the 6E10 antibody shows an even greater band. It is not clear what the large range of bands that are shown imply except nonspecificity. The antigen that the antibodies recognize should be stated exactly.

Clarifying sample sizes throughout the study is needed.

Do the cultures include interneurons? Are the excitatory and inhibitory neurons interconnected? This information will help interpret the results.

The external solution for cultures contains 5.4 mM K+ which is quite high, and can induce hyperexcitability. Therefore it is important to be sure controls did not show hyperexcitability even after persistent recordings. Similarly, the use of 100uM AMPA and GABA seem very high. Justifying these high concentrations is important. They should lead to hyperexcitability and toxicity (AMPA) over time. Another point of concern is that the concentration of K+ for the slice work is 3 mM, much different than cultures. There are also differences in Mg2+ and Ca2+, making data hard to compare in the two preparations.

Line 295 mentions 2 min recording periods were used to acquire sufficient events. One wants to know if this was done throughout the paper and if so, how many events per 2 min was considered sufficient?

Terms related to intrinsic membrane properties and firing need to be explained much more because each lab has a slightly different method.

In the statistics part of the Methods, why is Welch's ANOVA (followed by Games-Howell) used when variance was unequal. Usually the test to determine inequality is provided, so it is clear it was done objectively and with a reasonable test. Then if the data are unequal there is often a choice for a non parametric test, which is common. Some groups transform the data such as taking the log of all data values. If this reduces the variance between groups, sufficient to pass the test to determine inequality, it leads to a parametric test like a one-way ANOVA followed by Tukey's posthoc test.

In the Results, Line 331 suggests that the authors think they know what a low concentration is for Ab. I don't think it is known in AD what is low and what is high. In other studies of Ab, low concentrations were picomolar (Puzzo et al., listed in the references). So it is not clear the term low is justified for 50 nM.

The bursts of activity are not quantified. What was defined as a burst? What was the burst frequency and did it change over the recording period?

In the section about mPSCs in culture, starting on Line 348, were these events EPSCs or IPSCs? It is important because in the section starting on Line 383 there were changes in IPSCs but the authors conclude a major role of EPSCs only. For example, Line 400 suggests that the effects of Ab were on AMPA receptor-mediated activity but it seems from the data there were also some effects on IPSCs.

Line 434. Provide evidence that the fluorescent probe accurately measures NO.

At the top of page 19 there is a section that needs to be moved earlier because it relates to the work in cultures. That earlier section needs to be reinterpreted given changes in membrane properties occurred. Also, if there is increased synaptic activity in cells dialyzed with Ab, TTX needs to be added to be sure of intrinsic properties. The increase in excitability the authors discuss could be due to the synaptic activity or changes in properties, or both and this needs clarification.

The last paragraph on page 20 is not useful because DRG neurons are so different from hippocampal neurons. One could have effects in DRG but not hippocampus, and vice-versa. The paragraph starting on Line 616 should be revised. It is not a series of compelling arguments in its present form. For example, saying that AMPAR are linked to epilepsy seems quite obvious, and does not mean that the work presented here is like epilepsy because AMPAR events increased in several assays. Increased AMPAR events also occur when there is a change in behavioral state, plasticity, etc.

In the conclusions, I don't think the data suggest a synaptic change in AMPAR alone. There are intrinsic changes and changes in GABAergic events. Many sites in the brain could have different effects but were not studied. It is not clear effects of NO were coordinated in the way they affected adjacent neurons to the recorded cell. NO simply could have diffused to an area around the recorded cell. I may have missed evidence to the contrary, but effects could have been mediated by axons of the recorded cell and not NO.

In Figure 1b, there is a representative example. Could the neurons be shown? Then one knows the relationship of the signal to the location of neurons.

Graphs should show points. This is one way to clarify sample size easily also.

MINOR POINTS

Line 169 mentions stable access resistance and one usually provides a number indicating how little it increased over time, such as 10-20%. Similarly the way synaptic events were discriminated by noise is not provided (line 291). Instead, a brief description is provided.

Line 292 mentions noise ~2 pA but it is much higher in the data shown in the figures.

Solvents of drugs are not listed at all, and controls that show no effect of vehicle need clarification in some cases.

On Line 371, Ab-mediated neurotransmission is used. I believe this needs to be modulated rather than mediated, or an explanation is needed.

On Line 381, how do the authors know that EPSCs are mediated primarily by AMPA receptors in this preparation?

On Line 393, what is the comparison of AMPA-mediated events to [where it is stated they are what is mostly changing]?

In all of the sections where drugs were applied, abbreviations need to be spelled out before the first use, concentrations need to be confirmed as specifically action on the intended receptor, and indirect effects on other cells need to be discussed if bath-applied.

The sentence starting on Line 417 is a repetition of a prior sentence on the previous page.

Line 433. Clarify what low concentrations mean here.

Line 444. mPSCs are referred to here. One needs to know what were the values for E and IPSCs.

In this section it is often stated that there is a decrease but actually the dialyzed cells are compared to controls so different language is needed.

Line 461. It is not clear that the hippocampus is the first site to be affected in AD. The entorhinal cortex is earlier in the studies of some, and in the mouse models it is usually the cortex that gets plaque first. In humans, the locus coeruleus may be earlier than the entorhinal cortex.

How the plots of current vs. spikes were done is important. If there were differences in membrane potential, that could affect the spike output. If there were differences in input resistance or threshold, that also could play a role. One can control for these potential confounds, so explanations are needed.

Line 472. Vm does not generate fluctuations in this case. Vm changes, and synaptic potentials get larger or smaller, add new components or lose them, etc.

Line 476. It is not clear why cells are firing at membrane potentials so hyperpolarized to threshold.

The streptavidin/calbindin labeling is good but the morphology of the cell is not like a pyramidal cell of area CA1 because there is a major branch of the dendrites at almost a right angle to the apical dendrites. The electrophysiology of this cell might be like an interneuron, and two of the figures show firing with a large afterhyperpolarization similar to an interneuron.

In Figure 3, what are EPSCs and what are spikes would be helpful to point out. The concentration, 500 nm, may never be reached in the brain of an individual with AD, or do the authors have evidence that concentration is relevant in vivo?

There are typos in figure headings, such as Contro instead of Control and in figure 4g, AMPAergic has the c below AMPAergi

Reviewer #4:

This manuscript by Huss, P., et al, is a major technological step forward for high throughput phage research and is a deep dive into the deep mutational landscape of a portion of the T7 Phage receptor binding protein (RBP). The author’s develop a new phage genome engineering method, ORACLE, that can generate a library of any region of the phage genome. They apply ORACLE to do a deep mutational scan of the tip domain of T7 RBP and screen for enrichment in several bacteria. The authors find that different hosts give rise to distinct mutational profiles. Exterior loops involved in specialization towards a host appear to have the highest differential mutational sensitivity. The authors follow up these general scans in the background of phage resistant hosts. They find mutations that rescue phage infection. To demonstrate the utility of the approach on a clinically relevant task, the authors apply the library to a urinary tract associated clinical isolate and produce a phage with much higher specificity, creating a potentially powerful narrow scope antibiotic.

Overall, the ORACLE method will be of tremendous use for the phage field solving a technical challenge associated with phage engineering and will illuminate new aspects of the bacterial host-phage interactions. It was also quite nice to see host-specialization validated and further explored with the screens done in the background of phage resistance mutations. The authors do a tremendous job digging into potential mechanisms when possible by which mutations could be altering fitness. We especially appreciate how well the identity of amino acids tracks host specialization within exterior loops.

We have no major concerns about the manuscript but have some minor comments to aid interpretation. There are also some minor technical issues. We think this manuscript will be of broad interest, especially for those in the genotype-phenotype, phage biology, and host-pathogen fields.

Minor comments:

P5L20: In the introduction to the ORACLE section the authors mention homologous recombination then they mention using 'optimized recombination' that is done with recombinases. This contrast should be mentioned somewhere perhaps to highlight the benefit of having specific recombinases.

P6L16: Using Cas9 to cut unrecombined variants is clever... Cool! This is a real 21st Century Dpn1 idea.

P6L27 The authors state that there is a mild skew towards more abundant members after ORACLE. Why might this be? In iterations more abundant members simply become even more abundant? To be clear this isn't a substantial limitation and it's common to see these sorts of changes during library generation. Just curious. Overall looks like a fantastic method.

P7L6: Authors mention ORACLE increases the throughput of screens by 3-4 orders of magnitude. How many variants can one screen? Is this screen of a little over 1k variants at about the threshold of the assay?

P8L7: The authors assign functional scores based on enrichment and normalize to wild type. Is a FN=1 equivalent to wild type?

P9L5: Awesome!

P10L7: Authors mention R542 forms a hook with a receptor. There should be a citation here.

P10L21: For N501, R542, G479, D540 there are wonderful mechanistic explanations. However, for D520 there is not. Any hypothesis for why this is distinct from the others? Are there other residues that behave similarly? I feel it would be really helpful to have a color scale that discriminates between FN 1 (assuming wild type) and enriched/depleted w/in figure 3A.

P12L4: Authors note residues that are surface exposed yet intolerant to mutations in the previous paragraph. Authors also calculate free energy changes with Rosetta and state free energy maps pretty well with tolerance. What is the 93% based on? Perhaps a truth/contingency table would be useful here to discriminate/ compare groupings. What residues are in the 7% others. Can the energy scores help understand the mechanisms behind the mutations better?

P12L7: Authors state substitutions predicted to stable and classified intolerant could indicate residues necessary for all hosts. What about those that fall outside of the groupings? Unstable residues can also be necessary.

P14L22L Authors mention comparing systematic truncations, however they do not present any figure. This should be in a figure to aid in looking at the data and would surely be helpful to people in the phage field. A figure should be included here especially because this is one of the main discussion topics at the end of the manuscript.

P16L2: The authors did the selection in the background of a clinically isolated strained and discussed 3 variants that were clonal characterized. Was this library sequenced similar to before?

Figures:

Barplots need significance tests.

Figure 2C-E ; Fig 3A. All figures are colored white to red. With this color scale it's hard to appreciate which variants are neutral vs those that are enriched. A two or more color scale would be more appropriate. Log-scaling might be wise to get a better sense of the dynamic range that is clearly present in fig2F.

FIg 4F: Needs a statistical test between bar plots.

Fig6A-C: These figures have tiny symbols that represent the architecture at an insertion position. It's probably easier to look at if the same annotations from Fig 4B or C for architecture were used.

Fig6D: needs tests for significance

Supp fig 4E: This figure is the first evidence that the physics chemistry of amino acids w/in surface exposed loops determine host specificity. This is followed up by Figure 4D and E. I would consider moving this to one of the main figures.

Supp fig 5: A truth table could be useful here to test for ability to classify based on rosetta compared to FD. It looks like here that the tolerant residues have a distinct pattern

Why are these colored white to red?

Reviewer #3:

Huss et al. describe a phage genome engineering technology that they call ORACLE. This technique uses recombineering of a phage target gene with a variant library to identify both gain and loss of function mutations. The beauty of this method and what makes it superior to other techniques is that it dramatically limits loss of mutants that are less fit during the initial round of library generation. Thus, the pool of variants is vast and is reduced in bias toward more fit species based on the host used for initial library amplification. They use the model coliphage T7 as a proof of principle and show that several previously unidentified residues in the T7 tail fiber play critical roles in both loss and gain of function for phage infectivity and they also identify residues that are major drivers of altered host tropism. Lastly, they apply this library to a pathogenic UTI associated strain of E. coli which is normally resistant to wild type T7 infection and identify tail variants of T7 that can now infect this strain, highlighting the applicability of this method toward the discovery of engineered phages that could be used therapeutically. Altogether this is an important advancement in phage engineering that shows potential promise for future phage therapies.

Reviewer #2:

The authors are reporting a new approach termed ORACLE to develop locus-specific phage variants, which includes a recombination step, whose efficacy is improved by the overexpression of a dedicated recombinase, followed by an enrichment performed using CRISPR/Cas9. They applied this method to create a mutant library containing 1660 variants of the tip domain of the T7 tail fiber. Performance of each variant was determined by quantifying their abundance before and after selection on three E. coli strains compared to the WT phage. Their findings show that single amino acid changes in the tip of gp17 can have major consequences on phage performance on different hosts. Then they tested whether these variants would be less prone to select phage-resistant using an UTI strain. Finally, they searched for variants that would be more prone to infect one host than another and successfully tested their predictions.

The ORACLE approach is overall novel and has some advantages over existing methods, mainly for generation of mutation libraries of genes. Authors did a nice (even if very lengthy) job of showing how mutants have consequences to structure and function of the tail fiber gene and how that influences performance on different hosts, including combating host resistance.

The authors state that ORACLE overcomes three major hurdles that make it better than existing methods, one of which is "generalizability for virtually any phage", while denouncing other systems for being applicable for highly transformable hosts only. This is highly exaggerated since ORACLE requires transformation of two plasmids (helper and donor) including one with tunable gene expression, which is clearly not possible in many bacteria. Furthermore, the enrichment step requires a strain with a functional CRISPR/Cas9 system, which again is not so obvious in the bacterial world.

The authors disregard bias that can be generated at the "O" step if a variant reproduces better than the wt. They should also mention bias arising from non-viable or severely infection hampered variants, which is briefly mentioned later in the manuscript but should be mentioned earlier, would not pass the accumulation step.

The weakest paragraph is the one dealing with the UTI strain. I have the feeling that this paragraph could simply be deleted without changing the overall story. Approaching resistance, selection, and evolution would require more experiments than the very simplistic lysis curves. The authors did not even show adequately that cells growing after 5-10 hours are either genotypically or phenotypically resistant cells. A more appropriate qualification would be "insensitive" instead of resistant.

Reviewer #1:

Huss et al. have developed a novel tool (ORACLE) for generating libraries of phage variants. They go on to apply this tool to study the residues important for T7 host specificity, providing a rich dataset for in-depth functional studies. They validate a subset of hits and use this information to engineer T7 variants that may be able to overcome bacterial resistance against a urinary tract infection associated strain, consistent with their in vitro results. Their approach provides both a valuable new tool and intriguing biological insights prompting future studies.

Major suggestions for improvement:

1) The writing could be much more concise.

2) Claims about generalizability should either be removed or supported by additional data. This study focused on a single phage gene and a single host bacterial species. As such, it is not clear if ORACLE will work well in other contexts.

Reviewer #3 (Public Review):

The authors herein have nicely dissected the role of RNF43 in WNT5A signaling in mammalian cells, with a focus in the context of melanoma. They show that RNF43 inhibits WNT5A activity by ubiquitinating and thereby marking for proteasomal degradation multiple proteins involved in WNT5A signal transduction (i.e., VANGL2). The authors have performed the study in a thorough manner.

Reviewer #2 (Public Review):

In the present manuscript Radaszkiewicz et al. analyze the role of Ring Finger Protein 43 (RNF43) in inhibiting the noncanonical WNT5A pathway. The authors demonstrate that RNF43 can interact with proteins involved in the WNT5A pathway, including ROR1, ROR2, VANGL1 and VANGL2. Specifically, they propose that RNF43 induces: i) VANGL2 ubiquitination and proteasomal degradation and ii) clathrin-dependent internalization of the ROR1 receptor. Considering the role of the WNT5A pathway in melanoma metastasis and resistance to targeted therapy, the authors further explore the role of RNF43 in melanoma invasion and resistance to vemurafenib. The authors ultimately conclude that RNF43 can prevent invasion and resistance to targeted therapy by inhibiting the WNT5A pathway. The data supporting the interaction between RNF43 and proteins involved in the WNT5A pathway are pretty rigorous. However, the study would benefit from additional experiments in the context of RNF43's role in invasion and resistance to targeted therapy in melanoma. Overall, the techniques utilized in the manuscript are appropriate, however additional cell lines and in vivo studies are strongly recommended to strengthen the manuscript.

Reviewer #1 (Public Review):

The authors present data suggesting that RNF43 affects WNT5a signaling through turnover of ROR1 and ROR2 receptors on the cell surface. The strengths of this work are the many overexpression, knockdown and mutant cell lines the authors use to delineate specific protein interactions and localizations. The authors have done a good job of analyzing the interaction of multiple proteins within the Wnt signaling pathways to determine how RNF43 affects expression of proteins associated with non-canonical Wnt signaling. The weakness of this study is that most of these protein interactions were performed in 293 cells and not in melanoma cell lines. One melanoma cell line was used to relate the protein interactions studied in 293 cells to signaling in melanoma. The authors present data that suggest RNF43 decreases invasion and proliferation of melanoma cells in vitro. Analyzing the role of RNF43 in invasion, proliferation and signaling in more than one melanoma cell line would strengthen the authors conclusions about the role of RNF43 in Wnt5A signaling in melanoma.

Reviewer #3 (Public Review):

P2X2 receptor channels do not have a canonical voltage-sensor, yet they display profound voltage-dependence especially when activated by physiologically relevant low ATP concentrations. Understanding the mechanisms of this voltage dependence is not an easy undertaking because there are neither similar proteins as precedent nor clear indications from available structures. In this manuscript, Andriani and Kubo incorporated Anap into 96 residues (separately) in P2X2 receptor channels and performed a comprehensive scanning using voltage-clamp fluorometry technique to probe structural changes during ATP- and voltage-dependent gating. Out of the 96 residues, the authors only observed voltage-dependent fluorescence intensity (F) changes at A337 and I341 in the TM2 domain. The changes are fast and linear, consistent with them being electrochromic effect. When an additional mutant K308R is introduced, the authors were able to detect a small slow and voltage-dependent F change at A337, which could potentially result from structural rearrangements at this position. With a P2X2 model built upon the hP2X3 open state structure, they also proposed that A337 interacts with F44 in TM1, and this interaction is important for activation. The amount of work involved in this study is impressive. The data presented are of good quality. Most conclusions drawn from the results are reasonable and backed with good evidence.

Overall, the identification of a converged electric field around A337 and I341 is new and intriguing. Previously reported functional results and available high resolution P2X receptor structures all suggest that residues A337 and I341 are facing TM1 and they are accessible to Ag+ when mutated to Cys. It is conceivable that the "voltage-sensor" in P2X2 receptor channels involve ion filled crevices between TM1 and TM2 in the membrane. This work is of great value for understanding how membrane proteins sense voltages.

Reviewer #2 (Public Review):

P2X2 activation depends on both ATP binding and voltage. However, the voltage sensor of P2X2 is not elucidated. This manuscript describes the study of voltage dependent conformational changes of P2X2 using voltage clamp fluorometry of the fluorescent unnatural amino acid Anap that substituted P2X2 amino acid residues. 96 positions in different structural domains were scanned by substituting with Anap, and voltage dependent fluorescence signals were detected only at two positions, A337 and I341 in the TM2 domain. A fast and linear voltage dependence of fluorescence suggested that the membrane voltage converged at and around these two positions. With a mutation K308R that was supposed to enhance voltage dependent conformational changes, Anap at the A337 position showed a time and voltage dependent fluorescence. The authors concluded that this result indicated a voltage dependent conformational change. Structure guided mutations suggested that F44 in TM1 might move to interact with A337 in response to voltage. In this study the fluorescence signals were small, but the authors made a great effort and managed to obtain the data that are convincing. The experiments were well designed and the manuscript is clearly reasoned. Considering that among all the positions that were tested only at the two positions in the TM2 segment Anap showed voltage dependent fluorescence, and that the F44 mutations abolished voltage dependence of the P2X2 currents, the conclusion that voltage converges at the A337/I341/F44 and induces a conformational change seems to be well supported.

Reviewer #1 (Public Review):

The study aims to determine the mechanism of voltage-sensing in P2X2 receptor. These receptors are primarily activated by ligand, ATP but their activity is also regulated to some extent by voltage even though they lack a canonical voltage-sensing domain. To address this question, the authors introduce unnatural fluorescent amino acid throughout the structure of the P2X2 receptor. The interaction between excited state dipole and electric fields can cause shift in the fluorescence emission and excitation spectra. For a given probe, the extent of these shifts are directly proportional to the strength of the electric field. The authors exploit this phenomenon to determine the strength of the electric field in the various regions of the P2X2 receptor. The underlying premise is that the regions which sense the largest electric field are likely to be the primary sensors of membrane voltage.

Strengths:

The approach to localize the putative voltage-sensing region is novel and maybe broadly applicable to other voltage-regulated channels which lack canonical voltage-sensors.

Unnatural amino acid, ANAP was introduced and tested at 96 positions in the structure of P2X2 receptor. This is an insane amount of work and has to be a tour de force.

Weakness:

The main limitation of this approach is that ANAP is not going to be incorporated with equal efficiency at all sites and therefore, it is likely that some of the potential where the electric field is strong may remain undetected.

Overall, using ANAP scanning approach, they were able to identify couple of sites in TM2 helix which exhibits large electrochromic signals. Furthermore, they find that the interaction between Ala 337 and Phe44 is critical for voltage-dependent response. These studies lay the groundwork for further investigations of the mechanism of voltage-sensing these physiologically important ion channels.

Reviewer #3 (Public Review):

The authors describe a method for fitting a simple, separable function of contrast and cone excitation to a set of fMRI data generated from large, unstructured chromatic flicker stimuli that drive the L- and M- cone photoreceptors across a range of amplitudes and ratios. The function is of the form of a scaled ellipse – hereafter referred to as a 'Quadratic Color Model' (QCM). The QCM fits 6 parameters (ellipse orientation, ellipse elongation, and 4 parameters from a non-linear, saturating (Naka-Rushton) contrast response curve. The QCM fits the dataset well and the authors compare it (favorably) to a 40-parameter GLM that fits each separate combination of chromatic direction and contrast separately.

The authors note three things that 'did not have to be true' (and which are therefore interesting):

1) The data are well-fit by a separable ellipse+contrast transducer - consistent with the idea that the underlying neuronal computations that process these stimuli combine relatively independent L-M and L+M contrast.

2) The short axis of the QCM tends to align with the L-M cone contrast directing (indicating that this direction is one of maximum sensitivity and the L+M direction (long axis) is least sensitive. This finding is qualitatively consistent with psychophysical measurements of chromatic sensitivity.

3) Fit parameters do not change much across the cortical surface – and in particular they are relatively constant with respect to eccentricity.

This is a technically solid paper – the data processing pipeline is meticulous, stimuli are tightly-calibrated (the ability to apply cone-isolating stimuli to fovea and periphery simultaneously is an impressive application of the 56-primary stimulus generator) and the authors have been careful to measure their stimuli before and after each experimental session. I have a few technical questions but I am completely satisfied that the authors are measuring what they think they are measuring.

The analysis, similarly, is exemplary in many ways. Robust fitting procedures are used and model performance and generalizablility are evaluated with a leave-run-out and leave-session-out cross validation procedures. Bootstrapped confidence intervals are generated for all fits and analysis code is available online.

The paper is also useful: it summarises a lot of (similar) previous findings in the fMRI color literature going back to the late 90s and points out that they can, in general, be represented with far fewer parameters than conditions. My main concerns are:

1) Underlying mechanisms: The QCM is a convenient parameterization of low spatial-frequency, high temporal-frequency L-M responses. It will be a useful tool for future color vision researchers but I do not feel that I am learning very much that is new about human color vision. The choice to fit an ellipse to these data must have been motivated at least in part by inspection. It works in this case (possibly because of the particular combination of spatial and temporal frequencies that are probed) but it is not clear that this is a generic parametric model of human color responses in V1. Even very early fMRI data from stimuli with non-zero spatial frequency (for example, Engel, Zhang and Wandell '97) show response envelopes that are ellipse-like but which might well also have additional 'orthogonal' lobes or other oddities at some temporal frequencies.

2) Model comparison: The 40-parameter GLM model provides a 'best possible' linear fit and gives a sense of the noisiness of the data but it feels a little like a strawman. It is possible to reduce the dimensionality of the fit significantly with the QCM but was it ever really plausible that the visual system would generate separate, independent responses for each combination of color direction and contrast? I suspect that given the fact that the response data are not saturating, it would be possible to replace the Naka-Rushton part of the model with a simple power function, reducing the parameter space even further. It would be more interesting to use the data to compare actual models of color processing in retina/V1 and, potentially, beyond V1.

3) Link to perception. As the authors note, there is a rich history of psychophysics in this domain. The stimuli they choose are also, I think, well suited to modelling in the sense that they are likely to drive a very limited class of chromatic cells in V1 (those with almost no spatial frequency tuning). It is a shame therefore that no corresponding psychophysical data are presented to link physiology to perception. The issue is particularly acute because the stimulus differs from those typically used in more recent psychophysical experiments: it flickers relatively quickly and it has no spatial structure. It may, however, be more similar to the types of stimuli used prior to the advent of color CRTs : Maxwellian view systems that presented a single spot of light.

Reviewer #2 (Public Review):

The goal of this work is to advance knowledge of the neural bases of color perception. Color vision has been a model system for understanding how what we see arises from the coordinated action of neurons; detailed behavioral measurements revealed color vision's dependence upon three types of photoreceptors (trichromacy) and three second stage retinal circuits that compute sums and differences of the cone signals (color opponency). The processing of color at later, cortical stages has remained poorly understood however, and studies of human cortex have been hampered by methodologies that abandoned the detailed approach. Typical past work simply compared neural responses in two conditions, the presentation of colorful (formally, chromatic) vs grayscale (luminance) images. The present work returns to the older tradition that proved so successful.

The project's specific goals were to measure functional MRI responses in human cortex to a large range of colors, and equally importantly, capture the pattern responses with a quantitative model that can be used to predict response to many additional colors with just a few parameters. The reported work achieved these goals, establishing both a comprehensive data set and a modeling framework that together will provide a strong basis for future investigations. I would not hesitate to query the data further or to use the QCM model the paper provides to characterize other data sets.

The strengths of the work include its methodological rigor, which gives high confidence that the goals were achieved. Specifically:

1) The visual presentation equipment was uniquely sophisticated, allowing it to correct for possible confounds due to differences in photoreceptor responses across the retina.

2) The testing of the model was quite rigorous, aided by distinct replications of the experiment planned prior to data collection.

3) The fMRI methods were also state of the art.

The work was well-situated within the literature, comparing its findings to past results. The limitations and assumptions of the present work were also clearly stated, and conclusions were not overstated.

Weaknesses of the current draft are relatively minor, however, I believe:

1) The data could be presented in a way to make them more comparable to prior fMRI work, e.g. by using percent change units in more places, comparing the R^2 of model fits reported here to those reported in other papers, and explaining and exploring how the spatially uniform stimuli, used here but not in other fMRI studies, limited responses in visual areas beyond V1.

2) Comparison between the two models, the GLM and QCM is not quite complete.

3) The present results are not discussed in context with past results using EEG, and Brouwer and Heeger's model of fMRI responses to color.

4) Implications of the basic pattern of response for the cortical neurons producing the data are discussed less than they could be.

Reviewer #1 (Public Review):

This manuscript presents new data and a model that extend our understanding of color vision. The data are measurements of activity in human primary visual cortex in response to modulations of activity in the L- and M-cone photoreceptors. The model describes the data with impressive parsimony. This elegant simplification of a complex data set reveals a useful organizing principle of color processing in the visual cortex, and it is an important step towards construction of a model that predicts activity in the visual cortex to more complex visual patterns.

Strengths of the study include the innovative stimulus generation technique (which avoided technical artifacts that would have otherwise complicated data interpretation), the rigor of experimental design, the clear and even-handed data presentation, and the success of the QCM.

The study could be improved by a more thorough vetting of the QCM and additional discussion on the biological substrate of the activation patterns.

Reviewer #3 (Public Review):

Myotonia congenita is a heritable disorder of muscle fiber excitability in which a severe reduction of the resting chloride conductance (gCl, CLCN1 mutations) produces susceptibility to involuntary after-contractions and transient weakness. Fifty years ago, Bryant, Adrian and colleagues showed that loss of > 50% of gCl is sufficient to cause myotonic bursts of after-discharges. Much less is known about the mechanistic basis for the transient weakness (several seconds, up to 1 minute) that occurs with initial contractions after rest. This study elegantly confirms what has long been suspected; that sustained depolarization of the resting potential is the basis for the transient weakness. The experimental approach employed several new techniques to achieve this demonstration. First, the use of repeated in situ contraction tests every 4 sec (Fig. 1) clearly shows the coincidence of myotonia and transient weakness, both of which exhibit warm-up. This animal model for the transient weakness in a low gCl state was essential for the success of this study. Secondly, the remarkably stable measurements of membrane potential (Vm), without the need to apply a holding current to achieve the normal resting potential (Figure 2) is necessary to convincingly demonstrate the plateau depolarizations are a consequence of the myotonic condition, and not a stimulation artifact. Moreover, a severe reduction of fiber excitability was directly demonstrated by application of brief current pulses during the plateau depolarization (Figure 2E). Third, the authors have used the ncDHPR mouse (non-conducting CaV1.1) to show the Ca current has some role in prolonging the duration of the plateau. This is an important contribution because the sluggish, low-amplitude Ca current in skeletal muscle has not previously been implicated in the pathogenesis of myotonia. Finally, the authors built upon their recent work showing ranolazine suppresses myotonia in low gCl muscle to also show this drug abolishes the plateau potential. Taken together, this excellent study provides the most definitive experimental evidence to date for the mechanistic basis of transient weakness in myotonia congenita and also suggests ranolazine may be beneficial for prophylactic management.

Major Points:

1) The major experimental limitation that prevented prior studies from establishing the mechanism for the transiently reduced excitability and weakness in MC was the concern that plateau depolarizations frequently occur as an artifact in studies of skeletal muscle membrane potential (e.g. secondary to leakage current from electrode impalement or failure to completely suppress contraction with motion-induced damage). The authors are to be commended for including many records of Vm (absolutely necessary for this publication) and for explicitly stating that a holding current was not applied to maintain Vrest. The confidence of these observation could be further increased by addressing these questions:

— Were recordings excluded from the analysis if the plateau potential was not followed by a subsequent return to Vrest? Was a criterion used to define successful return to the resting potential?

— If fibers that failed to repolarize were excluded, was this a frequent or a rare event, and importantly, was the likelihood of failure different for control versus myotonic fibers?

2) The data clearly show a large variance for the duration of the plateau potential (e.g. horizontal extent of data in Figure 3B), which is interesting and may provide additional insights on the balance of currents that contribute to this phenomenon. The authors also point out that the distribution was skewed toward briefer plateau periods for the 9-AC model than the adr mouse. It is suggested this difference may be a consequence of life-long reduced gCl in adr mice with some chronic compensation versus the acute block of ClC-1 in the 9-AC model. What about the possibility that the reduction of gCl is more severe in the adr fibers than in 9-AC treated animals? A residual Cl current could foreshorten the duration of the plateau potential. Another question with regard to the variable duration of the plateau potential is a "duration of 0". In other words, as shown in Fig 3C, how frequently was the absence of a PP encountered?

3) The possibility that activity-dependent accumulation of myoplasmic Ca may contribute to the PP is suggested (page 9 line 175), but this is not further commented upon in the Discussion. Namely, is the reduction of PP duration in ncDHPR fibers proposed to be a consequence of less inward charge movement or of less myoplasmic Ca accumulation (i.e. is it a balance of ionic currents or an intracellular signaling factor)? Moreover, with regard to an activity-dependent process that influences the likelihood and/or duration of the PP, the authors quantify the "mean firing rate" and the "mean membrane potential", both quantified during the preceding myotonic burst. Both of these factors may contribute to an activity-dependent process, but another factor has been omitted; namely the duration of the antecedent myotonic run. It would be interesting to test whether the duration of the myotonic burst had an influence on the PP.

Reviewer #2 (Public Review):

The manuscript by Myers et al provides new insight into the mechanism of transient muscle in myotonia congenita, a question that has escaped understanding since its first description over >40 years ago. The authors use a complementary set of approaches (including measurements of in situ muscle force production, membrane voltage and ion currents) to determine the membrane conductances that underlie transient weakness in muscle from both genetic (Clc1-/- adr mice) and pharmacologic (9-AC-treated WT mice) models of myotonia congenita. The authors utilize a combination of a non-conducting Cav1.1 mouse and treatment with ranolazine to dissect the relative contribution of Cav1.1 and persistent Nav1.4 conductances, respectively, to sustained plateau membrane depolarizations observed following myotonic runs, which are proposed to underlie the transient weakness observed following myotonic runs.

Reviewer #1 (Public Review):

Patients with myotonia congenita caused by loss-of-function mutations in ClC-1 experience muscle stiffness (due to hyperexcitability) as well as transient muscle weakness. This study examines the mechanisms underlying the transient muscle weakness seen myotonia congenita. The authors show that a ClC-1 null mouse exhibits the transient weakness after muscle stimulation observed in humans. Current clamp recordings of muscle fibers from ClC-1-null mice showed indicated myotonia after electrical stimulation that often terminated in a plateau potential for varying periods, during which the muscle was unexcitable, before repolarization to the resting membrane potential. The myotonia and plateau potentials could be recapitulated in wild type muscle fibers with acute pharmacological inhibition of ClC-1. Experiments in fibers from a non-conducting Cav1.1 knockin mouse indicated Ca2+ influx is important for sustaining, but not initiating, plateau potentials. Ranolazine blocked both the myotonia and development of a plateau potential in isolated muscle fibers, as well as the in vivo transient muscle weakness observed in ClC-1-null mice, implicating Na+ persistent inward currents through Nav1.4 (NAPIC) as the molecular mechanism.

Overall, the experiments presented in this work are well-executed and the results convincing. While the role of NAPIC in the development of myotonia in ClC mice has been previously reported this work provides the new insight that it is also responsible for the development of plateau potentials that underlie muscle weakness in myotonia congenita.

Joint Public Review:

The presented manuscript takes a very comprehensive look at the molecular underpinnings of the differential outcomes of IL-27 and IL-6 signaling. Both cytokines engage GP130 as a cellular receptor, however while IL-6 uses homodimers of this signal transducing receptor, IL-27 signals through a heterodimer of GP130 and IL-27Ra. Both receptor complexed lead to the phosphorylation and activation of STAT1 and STAT3 and, hence, to a similar transcriptional program. Strikingly, however, IL-27 responses lean more towards an anti-inflammatory nature (suppressing Th17 and supporting Treg responses), and IL-6 stimulates a classical inflammatory response (inhibiting Treg differentiation, supporting Th17 generation). The presented study deals with elucidating this functional pleiotropy of similar or identical signal transducers.

The authors follow a comprehensive and elaborated approach, combining in vitro experiments in cell lines and human Th1 cells with (phospho-)proteomics, transcriptome sequencing and mathematical modeling, which gives rise to an impressive data set presented in this manuscript. The large body of experimental work is complemented by mathematical modelling of the signaling pathway(s), which is used to discriminate feasibility of distinct hypothesis in terms of mechanisms behind differential STAT activation.

The major finding of the study is that IL-27, at least in certain cells (Th-1), leads to the stronger and more sustained activation of STAT1 as compared to IL-6, and that this higher activation of STAT1 is the basis of the differential transcriptional result. The subsequent -omics analyses support differences in signaling outcome between IL-6 and IL-27, and provide an interesting data base for the community. Finally, data re-analysis in a cohort of patients suffering from the autoimmune disease Systemic lupus erythematosus (SLE), reproduced the effects expected by the mathematical model, potentially pointing to differences in their response to different cytokines.

Overall, the extensive and complex study presents a comprehensive analyses of IL-6 and IL-27 signaling, puzzling together pieces that may have been around before but not put into meaningful context. It provides a compelling overall idea and model of how cytokine receptors make differential use of STAT proteins.

Reviewer #3 (Public Review):

Lee et al. report results from an fMRI experiment with repeated viewings of a single movie clip, finding that different brain regions come to anticipate events to different degrees. The findings are brief but a potentially very interesting contribution to the literature on prediction in the brain, as they use rich movie stimuli. This literature has been limited as it has typically focused on fixed short timescales of possible anticipation, with many repetitions of static visual stimuli, leading to only one possible time scale of anticipation. In contrast, the current video design allows the authors to look in theory for multiple timescales of anticipation spanning simple sensory prediction across seconds to complex social dynamics across tens of seconds.

The authors applied a Hidden Markov Model to multivoxel fMRI data acquired across six viewings of a 90 second movie. They fit a small set of components with the goal of capturing the different sequentially-experienced events that make up the clip. The authors report clusters of regions across the brain that shift in their HMM-identified events from the first viewing of the movie through the (average of the) remaining 5 viewings. In particular, more posterior regions show a shift (or 'anticipation') on the order of a few seconds, while more anterior regions show a shift on the order of ~10 seconds. These identified regions are then investigated in a second way, to see how the HMM-identified events correspond to subjective event segmentation given by a separate set of human participants. These data are a re-analysis of previously published data, presenting a new set of results and highlighting how open sharing of imaging data can have great benefits. There are a few important statistical issues that the authors should address in a revision in order to fully support their arguments.

1) The authors report different timescales of anticipation across what may be a hierarchy of brain regions. However, do these timescales change significantly across regions? The paper rests in part on these differences, but the analyses do not yet actually test for any change. For this, there are multiple methods the authors could employ, but it would be necessary to do more than fit a linear model to the already-reported list of (non-independently-sorted) regions.

2) The description of the statistical methods is unclear at critical points, which leads to questions about the strength of the results. The authors applied the HMM to group-averaged fMRI data to find the neural events. Then they run statistical tests on the difference in the area-under-the-curve (AUC) results from first to other viewings. It seems like they employ bootstrap testing using the group data? Perhaps it got lost, but the methods described here about resampling participants do not seem to make sense if all participants contributed to the results. Following this, they note that they used a q < 0.05 threshold after applying FDR for the resulting searchlight clusters, but based on their initial statement about the AUC tests, this is actually one-tailed? Is the actual threshold for all these clusters q < 0.10? That would be quite a lenient threshold and it would be hard to support using it. The authors should clarify how these statistics are computed.

3) Regarding the relationship to annotated transitions, the reported difference in correlations at zero lag don't tell the story that the authors wish they tell, and as such it does not appear that they support the paper. While it is interesting to see that the correlation at zero lag in the initial viewing is often positive in the independently identified clusters, the fact that there is a drop in correlation on repeated viewings doesn't, in itself, mean that there has been a shift in the temporal relationship between the neural and annotated events. A drop in correlation could also occur if there was just no longer any correlation between the neural and annotated events at any lag due to noisy measurements, or even if, for example, the comparison wasn't to repeated viewings but to a totally different clip. The authors want to say something about the shift in in the waveform/peak, but they need to apply a different method to be able to make this argument.

4) Imaging methods with faster temporal resolution could reveal even earlier reactivation, or replay, of the movies, that would be relatively invisible with fMRI, and the authors do not discuss relevant recent work. E.g. Michelmann et al. 2019 (Nat Hum Beh) and Wimmer et al. 2020 (Nat Neuro) are quite relevant citations from MEG. Michelmann et al. utilize similar methods and results very similar to the current findings, while Wimmer et al. use a similar 'story' structure with only one viewing (followed by cued retrieval) and find a very high degree of temporal compression. The authors vaguely mention faster timescale methods in the discussion, but it would be important to discuss these existing results, and the relative benefits of these methods versus the benefits and limitations of fMRI. It would be interesting and puzzling if there were multiple neural timescales revealed by different imaging methods.

5) The original fMRI experiment contained three conditions, while the current results only examine one of these conditions. Why weren't the results from the two scrambled clip conditions in the original experiment reported? Presumably there were no effects observed, but given that the original report focused on a change in response over time in a scrambled video where the scrambled order was preserved across repetitions, and the current report also focuses on changes across viewings, it would be important to describe reasons for not expecting similar results to these new ones in the scrambled condition.

Reviewer #2 (Public Review):

Aly et al. investigated anticipatory signals in the cortex by analysing data in which participants repeatedly watched the same movie clip. The authors identified events using an HMM-based data-driven event segmentation method and examined how the timing of events shifted between the initial and repeated presentation of the same video clip. A number of brain regions were identified in which event timings were shifter earlier in time due to repeated viewing. The main findings is that more anterior brain regions showed more anticipation than posterior brain regions. The reported findings are very interesting, the approach the authors used is innovative and the main conclusions are supported by the results and analyses. However, many cortical regions did not show any anticipatory effects and it is not clear why that is. In part, this may be due to a number of suboptimal aspects in the analysis approach. In addition, the analyses of behavioural annotations are open to multiple interpretations.

Methods and Results:

1) The paper shows that across multiple regions in the cortex, there is significant evidence for anticipation of events with repeated viewing. However, there are also many areas that do not show evidence for anticipation. It is not clear whether this is due to a lack of anticipation in those areas, or due to noise in the data or low power in the analyses. There are two factors that may be causing this issue. First, the data that were used are not optimal, given the short movie clip and relatively low number of participants. Second, there are a number of important issues with the analyses that may have introduced noise in the observed neural event boundaries (see points 2-4 below).

2) Across all searchlights, the number of estimated events was fixed to be the same as the number of annotated events. However, in previous work, Baldassano and colleagues (2017) showed that there are marked differences between regions in the timescales of event segmentation across the cortex. Therefore, it may be that in regions such as visual cortex, that tends to have very short events, the current approach identifies a mixture of neural activity patterns as one 'event'. This will add a lot of noise to the analysis and decrease the ability of the method to identify anticipatory event timings, particularly for regions lower in the cortical hierarchy that show many more events than tend to be observed in behavioural annotations.

3) If I understand correctly, the HMM event segmentation model was applied to data from voxels within a searchlight that were averaged across participants. Regular normalization methods typically do not lead to good alignment at the level of single-voxels (Feilong et al., 2018, Neuroimage). Therefore, averaging the data without first hyperaligning them may lead to noise due to functional alignment issues within searchlights.

4) In the analyses the five repeated viewings of the clips were averaged into a single dataset. However, it is likely that participants' ability to predict the upcoming information still increased after the first viewing. That is especially true for perceptual details that may not have been memorised after watching the clip once, but will be memorised after watching it five times. It is not clear why the authors choose to average viewings 2-6 rather than analyse only viewing 6, or perhaps even more interesting, look at how predictive signals varied with the number of viewings. I would expect that especially for early sensory regions, predictive signals increase with repeated viewing.

5) In the analyses of the alignment between the behavioural and neural event boundaries, the authors show the difference in correlation between the initial and repeated viewing without taking the estimated amount of anticipation into account. I wonder why the authors decided on this approach, rather than estimating the delay between the neural and behavioural event boundaries. The finding that is currently reported, i.e. a lower correlation between neural and annotated events in the repeated viewing condition, does not necessarily indicate anticipation. It could also suggest that with repeated viewing, participants' neural events are less reflective of the annotated events. Indeed the results in figure 5 suggest that the correlations are earlier but also lower for the repeated viewing condition.

6) To do the comparison between neural and annotated event boundaries, the authors refit the HMM model to clusters of significant voxels in the main analysis. I wonder why this was done rather than using the original searchlights. By grouping larger clusters of voxels, which cover many searchlights with potentially distinct boundary locations, the authors may be introducing noise into the analyses.

Discussion:

7) To motivate their use of the HMM model, the authors state that: "This model assumes that the neural response to a structured narrative stimulus consists of a sequence of distinct, stable activity patterns that correspond to event structure in the narrative." If neural events are indeed reflective of the narrative event structure, what does it mean if these neural events shift in time? How does this affect the interpretation the association between neural events and narrative events?

Reviewer #1 (Public Review):

In this study, Lee et al. reanalyzed a previous fMRI dataset (Aly et al., 2018) in which participants watched the same 90s movie segment six times. Using event-segmentation methods similar to Baldassano et al. (2017), they show that event boundaries shifted for the average of the last 5 viewings as compared to the first viewing, in some regions by as much as 12 seconds. Results provide evidence for anticipatory neural activity, with apparent differences across brain regions in the timescale of this anticipation, in line with previous reports of a hierarchy of temporal integration windows.

– One of the key findings of the paper – long-timescale anticipatory event reinstatement – overlaps with the findings of Baldassano et al., 2017. However, the previous study could not address the multiple time scales/hierarchy of predictions. Considering that this is the novel contribution of the current study, more statistical evidence for this hierarchy should be provided.

– The current hierarchy of anticipation is closely linked to (and motivated by) previous studies showing evidence of a hierarchy of temporal integration windows. Indeed, the question of the study was "whether this hierarchy also exists in a prospective direction". This question is currently addressed somewhat indirectly, by displaying above-threshold brain regions, but without directly relating this hierarchy to previous findings of temporal integration windows, and without directly testing the claimed "posterior (less anticipation) to anterior (more anticipation) fashion" (from abstract).

– The analysis is based on averaging the data of the 5 repeated viewings and comparing this average with the data of the first viewing. This means that the repeated viewing condition had much more reliable data than the initial viewing condition. This could potentially affect the results (e.g. better fit to HMM). To avoid this bias, the 5 repeated viewings could be entered separately into the analysis (e.g., each separately compared to the first viewing) and results averaged at the end. Alternatively, only the 6th viewing could be compared to the first viewing (as in Aly et al., 2018).

– Correlation analysis (Fig 6). "we tested whether these correlations were significantly positive for initial viewing and/or repeated viewing, and whether there was a significant shift in correlation between these conditions". It was not clear to me how we should interpret the correlation results in Figure 6. Might a lower correlation for repeated viewing not also reflect general suppression (e.g. participants no longer paying attention to the movie)? Perhaps comparing the correlations at the optimal lag (for each cluster) might help to reduce this concern; that is, the correlation difference would only exist at lag-0.

– Correlation analysis (Figure 6). "For both of these regions the initial viewing data exhibits transitions near the annotated boundaries, while transitions in repeated viewing data occur earlier than the annotated transitions" How was this temporal shift statistically assessed?

– Not all clusters in Figure 2/6 look like contiguous and meaningful clusters. For example, cluster 9 appears to include insula as well as (primary?) sensorimotor cortex, and cluster 4 includes both ventral temporal cortex and inferior parietal cortex/TPJ. It is thus not clear what we can conclude from this analysis about specific brain regions. For example, the strongest r-diff is in cluster 4, but this cluster includes a very diverse set of regions.

– In previous related work, the authors correlated time courses within and across participants, providing evidence for temporal integration windows. For example, in Aly et al., 2018 (same dataset), the authors correlated time courses across repeated viewings of the movie. Here, one could similarly correlate time courses across repeated viewings, shifting this time course in multiple steps and testing for the optimal lag. This would seem a more direct (and possibly more powerful) test of anticipation and would link the results more closely to the results of the previous study. If this analysis is not possible to reveal the anticipation revealed here, please motivate why the event segmentation is crucial for revealing the current findings.

Reviewer #3 (Public Review):

In a previous study, the authors had shown that germline tumors that accumulate in the C. elegans gonad because of the lack the RNA binding translational repressor GLD-1, have an increased propensity to differentiate and express somatic proteins in response to ER stress induced by tunicamycin or the absence of the TRK kinase protein tfg-1 (a process the authors call GED). Using this as a model, here, the authors investigate the mechanisms by which the abnormal nuclei accumulate in the tumorous gonad of glp-1 animals by manipulating genes in the soma and germline.

The key message of this paper is, then, the identification of neurons and neuromodulators that suppress or enhance this accumulation of abnormal germline cells in the glp-1 germline. While the results of this analysis could potentially provide an interesting advance, the validity of the many of the conclusions are difficult to evaluate because of limitations posed by the experimental methods and ambiguity in defining the GED.

Weaknesses:

A key issue is the identity of the abnormal germline cells that accumulate in glp-1 gonads. Modulation of the neuronal circuits examined (FLP-6, serotonin, cholinergic) change the germline, alter ovulation rates, modulate somatic gonad contraction rates etc. in wild-type animals. The effects of these circuits on a glp-1 germline are not known, but some of the same effects are likely to continue even if germ cells turned tumorous. Therefore, how neurons and neuromodulators alter the accumulation of abnormal cells in the gonad may or may not be surprising or novel, based on what is actually happening to these cells (the phenotype scored as GED). However, this is unclear as all the abnormal effects on the germline are assessed using DAPI at some steady state. Therefore, GED (ectopic differentiation) needs to be better demonstrated separate from the simple accumulation of abnormal nuclei, which could happen for a number of different reasons.

Strengths:

One strength of this paper is the identification of the neuropeptide FLP-6 as a suppressor of GED and a possible RIDD target. However, there is insufficient analysis conducted to fully support this claim.

Reviewer #2 (Public Review):

Levi-Ferber and colleagues showed in their previous paper that ER stress regulates germline transdifferentiation in a way that is IRE-1 dependent, but XBP-1 independent. An open question at that time was how IRE-1 activation could mediate this signaling. The authors present several experiments in this manuscript that support the idea that neuronal Ire-1 can cell non-autonomously control germline differentiation through regulation of the neuropeptide FLP-6. Mechanistically, the authors characterize that FLP-6 is a target of IRE-1 RIDD activity. This is the first demonstration of RIDD in C. elegans, an important finding given that no RIDD targets have yet been identified in this organism. Using a wide range of mutants, the authors were also able to identify a neuronal circuit that can control the germline ectopic differentiation (GED) phenotype, involving the sensory neuron ASE, the interneuron AIY, and the motor neuron HSN. The data presented in the manuscript are sound, the mapping of a pivotal three-neuron circuit is impressive, and the findings are likely to be of high interest to a broad readership. However, some more evidence is required to support some of the conclusions made, in particular the characterization of flp-6 as a substrate for RIDD.

Reviewer #1 (Public Review):

In this manuscript, Levi-Ferber et al use C elegans to study how germline cells maintain pluripotency and avoid GED (germline ectopic differentiation) before fertilization. The authors previously showed that activation of the ER stress sensor Ire1 (but not its major downstream target Xbp1) enhances GED, and here they explore the mechanism of this effect.

The authors convincingly – and surprisingly – show that the Ire1-mediated GED increase results not from Ire1 activity in the germline but in the nervous system, specifically in certain sensory neurons. Worms lacking a specific neuropeptide (FLP-6) or a particular neuron that produces this peptide (ASE) also displayed increased GED. Although FLP-6 deficiency did not induce ER stress, ER stress did lead to a reduction of FLP-6 transcript (and protein) levels in an Ire1-dependent manner, suggesting this RNA is a target of Regulated Ire1-dependent decay (RIDD). The authors then go on to map out the signaling cascade that begins with FLP6 reduction in ASE by Ire1 and is transmitted to the gonad via an ASE-AIY-HSN circuit, including serotonin produced by HYE.

This paper is quite interesting and for the most part the data are very convincing and support the model. The demonstration that Ire1 and the ER stress response have non-cell autonomous effects is of particular interest, and is very well supported here. The description of this circuit linking particular neurons and signaling molecules to gonad pluripotency is also very strong.

A weakness of the paper is the link between RIDD of FLP6 and the disruption of this circuit. The data presented do clearly support the model. However, additional information would strengthen this considerably. The authors show that FLP6 mRNA levels are reduced in Ire1+ but not Ire-/- animals subjected to ER stress. They also show that GED results from the nuclease activity of Ire1 in the ASE; and that loss of FLP6 can also induce a similar effect. However, they do not show as clearly that Ire1's effects on GED are mediated primarily through FLP6.

Reviewer #3 (Public Review):

This is a very interesting and well conducted study that addresses a question of crucial importance and will make a very valuable contribution to the literature. The question of the vulnerability of newly generated oligodendrocytes in an inflamed environment has not previously been examined with anything like the sophistication of the current series of experiments. The paper is excellent and the data convincing. I only have a few relatively minor issues that the authors might want to consider.

The first results section on sephin1 in EAE is a little confusing. If I have understood the rationale correctly, it is to activate the ISR to protect oligodendrocytes, newly generated from OPCs, in the face of a hostile inflammatory environment. If that is correct, then perhaps this could be explained more explicitly, and the concluding sentence re-worded so as not to give the impression that sephin-1 is able to enhance remyelination (which I realise is not what is stated but is the conclusion that might be drawn).

The effect of the BZA-sephin combination of g ratio of remyelinated axons is very interesting. This could, of course, be because the process is accelerated with this combination rather than enhanced given that g ratios in the CC will eventually return to normal after cuprizone induced demyelination (eg Stidworthy et al., Brain Pathology 2003). This could perhaps be addressed in the discussion.

The authors could make the point in the discussion that regenerative medicines are very unlikely to be given in the absence of effective drug-mediated suppression of aggrieved inflammation.

Reviewer #2 (Public Review):

This is an interesting paper showing that prolonging the integrated stress response provides protection to oligodendrocytes in the presence of an inflammatory cytokine. For their experiments, the authors use the cuprizone model in transgenic mice overexpressing IFNg in an inducible manner in combination with a genetic and pharmacological approach to enhance the integrated stress response. The experiments are well conducted and the results clearly presented in the text. The Popko lab has previously demonstrated in a series of papers the importance of the integrated stress response for oligodendrocyte function. The novel aspect of this work is that targeting the integrated stress response requires a neuroinflammtory environment for the protective effects to occur.

It is important to improve the introduction. As written it is not clear what was known before and how this paper goes beyond the existing literature.

The rational for combining for combining BZA and Seph needs to be explained.

The figures and legends could be improved according to the following suggestions:

The evidence that Sephin1 promotes remyelination in the EAE model shown in Figure 1 is only based on differences in g-ratio with the overall number of myelinated axons being unchanged. It is difficult to make conclusion based on these results. It is difficult to obtain accurate g-ratios in lesions. Maybe the authors could extend the analysis by performing histology and counting the number of oligodendrocytes.

Figure 2 contains only a scheme. Figure 2 should be combined with Figure 3. In addition, a scheme showing the time line of the cuprizone treatment and recovery from the treatment would be helpful. I assume W0 is at the time of treatment, W5 after 5 weeks of cuprizone and W8 represents 5 weeks of cuprizone and 3 weeks of recovery. If yes, it is not clear why the ASPA cell count shown it not reduced between W0 and W5. The numbers seem to be similar for W0, W5 and W8 in the absence of IFNg. In addition, the comparison shown in Figure 3 are incomplete. W0 is only shown without IFNg but not with. Does IFNg affect ASPA number in the absence of cuprizone?

Panel B and C in Figure 5 could be combined to be able to compare the analyses and to evaluate the recovery of cell number by Seph at W8. The number of mice per group is borderline (only 3 mice).

Same issue as above: Panel B and C in Figure 6 should be combined and a multiple comparison should be performed between W0, W5 and W8.

The rational for combining BZA and Seph as shown in Figure 8 should be explained in the text. The figure and legends should be improved to clarify at which time point the analyses were performed. The panel number stated in the legends do not match with what is shown in the figure. I assume the analyses were done at W8. Only g-ratios change, whereas the number of ASPA cells and amount of myelinated axons are not affected by the combined treatment. The interpretation of this result is not easy, and the emphasis of this result should be removed from the abstract.

Reviewer #1 (Public Review):

Drs. Chen and colleagues report that augmentation of the integrated stress response (ISR) increases the oligodendrocytes and myelination during recovery after experimental demyelination in the presence of inflammation. Homozygous GADD43 KO mice or Sephin1 are used, respectively, to genetically and pharmacologically augment the ISR. Sephin1 treatment in mice with experimental autoimmune encephalomyelitis (EAE) shows increased remyelination in the spinal cord after inflammatory demyelination. Cuprizone administration to GFAP/tre;TRE/IFN-gamma double transgenic mice produced corpus callosum demyelination and CNS inflammation, with release of interferon-gamma initiated by removal of doxycycline from the drinking water. GADD43 KO did not change overall severity of cuprizone demyelination based on loss of oligodendrocytes and demyelination in corpus callosum after 5 weeks of cuprizone with ectopic interferon-gamma. The authors state that GADD43 KO enhanced the recovery of oligodendrocytes and remyelination during the 3 weeks after removal of cuprizone from the diet, but an incorrect figure prevents evaluation of this result. In double transgenic mice, with initiation of CNS inflammation, but without the GADD43 null mutation, pharmacologically enhancing the ISR with Sephrin1, increased recovery of oligodendrocytes and remyelination at 3 weeks after removal of cuprizone from the diet. These effects of genetically or pharmacologically enhancing ISR were not observed in the absence of ectopic interferon-gamma. Genetic and pharmacologic enhancement of the ISR did not appear to significantly alter the progenitor or microglial response to cuprizone demyelination. The combination of Sephin1 with bazedoxifene (BZA) enhanced the oligodendrocyte density and remyelination during the recovery period to a similar extent as either treatment alone. The authors provide several results supporting their interpretation that augmenting the ISR can overcome inhibitory effects of inflammation to enhance oligodendrocyte density and remyelination. Clarifications of the methods, correction of missing data, and additional experiments are needed to support the authors' conclusions that the potentially significant findings that combination of Sephin1 and BZA protects remyelinating oligodendrocytes and promotes remyelination even in the presence of inflammation.

Major concerns:

1) The experimental design and interpretation of the results would be strengthened by examining an indicator of the ISR to allow the reader to interpret the extent of ISR activation and the effect of the genetic and pharmacologic modulators of the ISR. This analysis would be particularly helpful in the corpus callosum in conditions with and without cuprizone.

2) Cuprizone is started at 6 weeks of age which is designated as week 0 (W0). The studies use W0 for comparison to the treatment groups that are analyzed at W5 or W8. The authors refer to W0 as pre-lesion or baseline levels, which is appropriate. The authors' statements related to the vehicle condition are appropriate as is. However, it is not clear why the W8 age-match (non-cuprizone and non-IFN-gamma) was not used to more directly interpret the extent of recovery. Using W0, the comparison is 6 versus 14 weeks of age. Myelinated axons continue to significantly increase during this age interval in mice.

3) The data graphed in panel 3C for the KO genetic prolongation of the ISR is exactly the same and the data graphed in panel 5C for the Seph pharmacologic enhancement of the ISR. The graph in 3C is actually labeled for Seph and so must have been inadvertently inserted when the graph of the KO data was intended.

4) The combined Sephin1/BZA treatment does not appear to work through remyelination, based on the definition of thinly myelinated axons (g-ratio >0.8) as used by the authors. The authors state that the data shows the after cuprizone demyelination, mice treated with Sephin1/BZA "reached myelin thickness levels comparable to pre-lesion levels" and "restored myelin thickness to baseline levels". To support this interpretation, the authors would need to include analysis of the Sephin1/BZA mice at 5 weeks of cuprizone to show that the combined treatment, which is initiated at 3 weeks of cuprizone, did not protect oligodendrocytes or reduce demyelination during weeks 3-5 of cuprizone and Sephin1/BZA treatment.

5) Conditions during which augmenting ISR is protective of mature oligodendrocytes or protecting remyelinating oligodendrocytes should be more clearly presented in the Discussion. The prior EAE results are reported as protecting mature oligodendrocytes. The results (Figures 3B and 5B) show that genetically or pharmacologically augmenting the ISR did NOT protect from mature oligodendrocyte loss at 5W cuprizone. The results (Figure 5B) show increased oligodendrocytes at 8W cuprizone. The current results are interpreted as protecting remyelinating oligodendrocytes, which are presumably mature as well.

Reviewer #3:

In this manuscript, the authors investigated roles of PSD95 in the hippocampus for contextual fear extinction. The authors showed that PSD95 levels in the spine and density of PSD-95-positive spines in the dorsal CA1 (dCA1) are changed following contextual fear conditioning and extinction learning. Interestingly, overexpression of PSD95-S73A mutant or chemogenetic inhibition of dCA1 impairs only the second extinction learning at 24 hrs following the first extinction learning. Importantly, these manipulations also blocked the changes of PSD95-positive spines following the first extinction learning. These observations suggest that phosphorylation of PSD95 at S73 in the dCA1 of hippocampus contributes to contextual fear extinction. This manuscript suggests the importance of PSD95 phosphorylation in the hippocampus in some aspects of mechanisms of contextual fear extinction at the molecular and spine levels. However, the title, abstract and conclusions do not well reflect observations and experimental designs in this manuscript. I have several concerns as follows.

Major concerns:

1) The authors used viral overexpression of PSD-95 S73A mutant that may function as a dominant negative mutant, but not knock in mutation. Therefore, the function of phosphorylation of PSD 95 at S73 on spine morphology and contextual fear extinction have been not yet investigated well. The experimental design in this manuscript made limitations to understand behavioral results. It is better to use knock-in mutation strategy than overexpression of the mutant. Alternatively, the authors can examine the phosphorylation levels of PSD95 following contextual fear conditioning and extinction learning and/or function of this mutant at the molecular and cellular levels using biochemistry/molecular biology/cell culture.

2) Overexpression of S73A or chemogenetic inhibition of CA1 impaired additional extinction learning. These observations are interesting. However, the authors have not well characterized these findings at the behavioral levels. In other words, the authors should clarify the effects of these manipulations on contextual fear extinction at the behavioral levels. According to abundant knowledge of fear memory extinction, the behavioral results in this manuscript raised a lot of questions to understand the impact of those genetic manipulations on "contextual fear extinction". How about effects on extended extinction learning (60 min), additional 30 min extinction learning at the same day after first extinction training, spontaneous recovery, renewal, and reinstatement? Some answers of these questions will help to understand behavioral observations in this study and enable us to identify roles of PSD95 and its phosphorylation in extinction of contextual fear memory. It is also important to examine PSD95-positive spines just after the additional extinction learning to understand behavioral observations.

Reviewer #2:

Ziółkowska et al. investigate synaptic processes in the dorsal hippocampal CA1(dCA1) region with the goal of testing the role of postsynaptic density protein 95 (PSD-95) dynamics in contextual fear extinction. They conclude that 1) extinction increases synaptic dCA1 PSD-95 levels and induces remodeling of dendritic spines, 2) extinction-related PSD-95 changes are mediated by phosphorylation of PSD-95 at serine 73, and 3) phosphorylation of PSD-95 at serine 73 as well as dCA1 activity are required to "update a partially extinguished fear memory". The experiments provide new insight and address a timely and important issue. The major strengths of the paper lie in the use of a wide range of complementary technical approaches, and the significance of addressing specific molecular mediators of fear attenuation. However, some of the analysis is based on inadequately justified or inappropriate measures (e.g. that do not directly assay the phenomenon under investigation), and there are concerns about independent effects of viral overexpression in this system as well as the relevance of the behavioral analysis. The conclusions from the paper, if true, would appear to support a very intricate model involving PSD95 phosphorylation and synaptic accumulation after extinction, but because of weaknesses in the underlying evidence, these mechanisms and their relationship to extinction memory were not persuasively demonstrated. Following are some specific concerns:

1) The mean intensity of PSD95 labeling per spine appears to be affected in some hippocampal layers (Fig. 1), but this might be attributable in some cases to elimination of spines that have relatively lower PSD-95, rather than a change in PSD-95 levels, per se.

2) The quantification of overexpressed PSD-95 in Fig. 2 makes unclear what specifically has been measured. The methods suggest that % area is defined as the total area of mCherry labeling divided by the total image area. This is not a direct measure of PSD-95 levels, rather than morphological or protein localization changes. Furthermore, the localization of overexpressed PSD-95 (Fig. 2) is clearly very different from that of endogenous PSD-95 (Fig. 1) in that it accumulates throughout the dendrites. This makes it unclear what a "puncta" represents, or whether the analysis implies anything about synaptic function.

3) The authors argue that S73 phosphorylation is required for synapse elimination during extinction, but Fig. S2 (which is not referenced or discussed in the manuscript) and Fig. 3 indicate that the effect of S73A overexpression is to dramatically reduce spine density in both behavioral groups. It is therefore not clear whether the manipulation interacted with extinction to prevent spine removal, or simply occluded such an effect because spine density was already at an artificial floor prior to any behavioral training. Overexpression of the wildtype construct also reduced spine density to a similar degree. Furthermore, the S73A mutant protein dramatically increased PSD area (Fig. 3d), which apparently contradicts the notion that phosphorylation of this site is required for synaptic accumulation, when applying the same logic used elsewhere in the paper. These are serious confounding issues because the central claim of the paper is that S73 phosphorylation mediates PSD95 synaptic accumulation and synaptic strengthening.

4) The authors suggest that successive days of extinction represent a distinct process called updating of a partly extinguished memory, which they seem to imply has different molecular requirements. There appears to be no basis in the literature for this idea.

5) The analysis of extinction relies on measurement of within-session decreases in freezing. However, within-session extinction has been shown to be neither sufficient nor essential for between-session extinction. It is not even clear that within-session extinction is really even extinction at all, rather than, for example, habituation. It is essential to examine the retention of decreased freezing across days in order to establish that the formation of long-term memory is involved.

6) Finally, numerous comparisons are made between animals that received FC, with no further manipulation, and extinguished animals. This design leaves open the possibility that any differences are attributable not to an extinction process but instead to context exposure independent of fear regulation. A behavioral control in which animals receive context exposures, but no shocks, would be very useful.

Reviewer #1:

Patients with posttraumatic stress disorder show impaired fear extinction that leads to persistent fear memories. The CA1 subregion of the hippocampus has been implicated in the acquisition and extinction of contextual fear memories, and both mechanisms depend on glutamatergic synaptic plasticity in this region. Postsynaptic density protein 95 (PSD-95) is known to regulate structural and functional changes in glutamatergic synapses, but whether PSD-95 participates in the acquisition and extinction of contextual fear memories remains unclear. To address this question, here Ziółkowska and coworkers used nanoscale-resolution analyses of PSD-95 protein in the CA1 combined with genetic and chemogenetic manipulations in mice exposed to a classical Pavlovian contextual fear conditioning paradigm. The study revealed that PSD-95-dependent synaptic plasticity in the dorsal CA1 area is not necessary for fear acquisition or the initial phase of fear extinction, but is critical for updating a partially extinguished fear memory. In addition, phosphorylation of PSD-95 at serine 73 is necessary for contextual fear extinction-induced PSD-95 expression and remodeling of dendritic spines in this region, suggesting a potential mechanism for fear memory persistence.

This timely study provides important and novel findings with regard to the role of PSD-95 protein in fear extinction formation and helps to advance our understanding of how dendritic changes in the hippocampus regulates fear maintenance. The present findings should be of general interest to the scientific community because extinction-based therapies are the gold-standard treatment for many fear-related disorders. The manuscript is clear, and the experiments were well-designed and executed. While the study is elegant, there are several important points including data interpretation that need to be clarified.

Major points:

1) The authors identified changes in PSD-95 expression levels and spine density after both fear acquisition and fear extinction. Similarly, S73-dependent phosphorylation of PSD-95 and changes in spine density were also reported following both phases. How do the authors explain the lack of effects on fear acquisition and extinction after the infusion of S73-deficient PSD-95 expressing virus? Does this suggest that the observed dynamics of PSD-95 are not important for the fear memory expression? The interpretation of these findings should be clarified in the discussion.

Previous studies have demonstrated a key role of dorsal hippocampus CA1 area on fear retrieval and extinction acquisition using either lesion (e.g., Ji and Maren 2008, PMID: 18391185), or optogenetic tools (e.g., Sakagushi et al, 2015, PMID: 26075894). However, in the present study, chemogenetic inhibition of this same region had no effect on fear retrieval or extinction acquisition (Figures 5 and 6). How do the authors reconcile the lack of effects on fear retrieval and extinction acquisition with the previous literature? Similarly, previous studies on the role of hippocampal PSD-95 protein in extinction memory should be described and the main differences in the experimental design and findings should be discussed (e.g.; Nagura et al, 2012, PMID: 23268962; Cai et al, 2018; PMID: 30143658; Li et al 2017, PMID: 28888982)

2) The authors have used scanning electron microscopy to analyze the ultrastructure of dendritic spines and determine whether PSD-95 regulates extinction-induced synaptic growth. In addition, the authors complemented these studies by investigating the effect of PSD-95-overexpression and fear extinction training on synaptic transmission in the dorsal CA1 ex vivo. However, it is hard to understand what does the observed changes in dendritic spines and amplitude of EPSCs mean if the behavior of the animals was the same. This point should be discussed in the article.

3) In Figure 5, the authors showed that chemogenetic inactivation of CA1 changed PSD-95 expression in all the 3 subregions of CA1 (stOri, stRad and stLM). However, the extinction training behavior in Figure 1 demonstrated an effect only in 2 subregions (stOri and stLM). The authors should clarify this discrepancy. In addition, in the same series of experiments (Fig. 5Ciii), it is unclear whether the reduction in PSD-95 expression induced by chemogenetic inactivation is sufficient to bring the PSD-95 expression to the same post-conditioning levels.

4) The authors showed an interesting behavioral effect in the second part of the extinction phase (Figure 6C), similar to the results in Figure 4C. However, to confirm that phosphorylated PSD-95 is crucial for the maintenance of extinction memory, the authors may want to consider a direct comparison between the levels of phosphorylated PSD-95 right after extinction 1 and extinction 2. Differences in the expression would clarify whether the phosphorylated PSD-95 expression is further increased after additional extinction training, which would help to link the effect of chemogenetic inactivation on behavior. At least some discussion is needed for this part.

5) The authors used immunostaining and confocal tools to analyze 3 domains of dendritic tree of dorsal CA1 area in Thy1-GFP(M) mice (stOri, stRad and stLM) on different fear phases (conditioning and extinction). They found a significant decrease of PSD-95 expression, spine density and spine area in stOri and stRad during conditioning and a rescue of such decrease during extinction. However, the authors’ interpretation is that extinction resulted in an upregulation of PSD-95, which doesn't seem to be the case if you compare the numbers with the naïve group. Please clarify this point.

Reviewer #3 (Public Review):

In the article "Widespread premature transcription termination of Arabidopsis thaliana NLR genes by the spen protein FPA", the authors describe the function of FPA as a mediator of premature cleavage and polyadenylation of transcripts. They also focused their study on NLR-encoding transcripts, as that was their most novel observation, describing an additional layer of control.

In general, the article is well written and clear. The experimental design is good, they didn't seem to over-interpret the results, the controls were solid, and the nanopore data were quite informative for their work. It is rather descriptive, but the results will be helpful for those working on NLRs, and demonstrate the utility of bulk long-read transcript data. The authors were able to string together a number of descriptive observations or vignettes into an informative paper. Overall, it is solid science.

One minor complaint is that the authors don't focus on NLRs starting on line 436, and then they have extensive results on NLRs; by the time I got to the discussion, I'd forgotten about the early focus on the M6A. While the first part of the article is necessary, I would suggest a more concise results section to give the paper more focus on the NLR control (since that is emphasized in the abstract and the title of the manuscript).

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public Review):

The manuscript by Parker and colleagues presents an extensive body of work on characterizing the role of FPA in the choice of polyadenylation sites in transcripts of A. thaliana. Investigation on the mechanistic details that FPA engages on the mRNA processing was first initiated with the in vivo pull-down followed by LC-MS/MS, which revealed the its protein interactome relevant for 3'-end processing. The main dataset pertaining to the manuscript title comes from the comparative transcriptome analysis of Col-0, fpa-8 mutant and the overexpressor of FPA, 35S:FPA:YFP. The strength of this work lies in the use of nanopore DRS by demonstrating the layers of FPA-dependent transcripts, including its own, and its comparison to datasets by Illumina RNA-Seq and Helicos DRS. The systematic analysis uncovered unexpected complexity in the A. thaliana NLR transcriptome under the control of FPA and thus delivers a new insight on NLR biology. Several studies anecdotally have reported the importance of using genomic DNA, but not a single cDNA species, for addressing full functionality of NLR genes. Recent advances in NLRome sequencing from multiple genomes of a species and NLR structure/function studies also highlight the importance of understanding modular nature of NLR. As alluded with the modular diversity of NLRs kept in the genomes of a species in recent studies, NLR genes are prone to reshuffle in the genome to generate different variants, including partial entities with the loss of some parts of the proteins or even chimeras, supposedly maximizing the repertoire for defense. This work adds the level of transcript diversity on that of genomic diversity; FPA, an essential factor for transcription termination determinant, targets numerous NLRs to control the layers of NLR transcriptome of an individual plant. Although it is yet to be clarified for the regulatory significance of FPA-mediated NLR transcript changes under biotic or abiotic conditions, the authors succeeded in employing fine genetic schemes utilizing FPA-defective vs. -overexpressing lines along with long-read nanopore DRS technology for the first time to uncover the breadth of differential transcript generation focused on 3'-end choices. This work is timely and impactful for NLR research owing to the above-mentioned recent advances in NLR field.

As this work is the first of its kind in utilizing nanopore DRS to address NLR transcriptome, several technical concerns can be addressed to corroborate the claims made in the manuscript, which authors can find in the following section (1-8). Regarding the organization of the manuscript, the authors may consider to rebalance the two parts: FPA interactome vs. FPA targets and NLRs. Overall, the manuscript can be seen as combining two stories; first to characterize FPA function in 3'-end processing of transcripts inferred by interacting proteomes and meta-analysis of ChIP-seq data; second part includes detailed analysis of NLR transcripts and others. Although the first half of the analysis is a necessary prelude to the following NLR analysis, the current title and academic novelty mainly lies, or were intended by the authors, on the NLR analysis. However, current manuscript has relatively enlarged section of the first with NLR analysis packed into a series of supplementary dataset. If authors wishes to opt for highlighting NLR analysis, the following suggestions would help (9-14).

1) Earth mover distance (EMD) has been applied to identify a locus with alternative polyadenylation. What is the basis of using EMD value of 25 as a cutoff? According to Figure 4 B,D, EMD can range from 0-4000. One would also wonder if the distance unit equals bp. In addition, EMD values of some genes (e.g. FPA and representative NLRs) can be specified in the main dataset so that significance of the cut-off values shall be appreciated.

2) Regarding the manual annotation of alternatively polyadenylated NLR genes (L1160-): Genes with alternative polyadenylation were identified and the ending location was supported when there were minimum four DRS reads. It would be relevant to provide the significance of "the four" based on read coverage statistics, for example, with average read number covering an annotated NLR transcript with the specification of an average size.

3) Figure 4E shows that Ilumina-RNAseq dataset detects the number of loci with a different order of magnitude compared with the other two methods. Reference-agonistic pipeline shall be appreciated, however, the method engaged might have elevated the counting of paralogous reads mapped to different locations than they should be. Along with paralogous read collapsing, this is always a problem with tandemly repeated genes, such as NLRs by and large. For example, NLR paralogs in a complex cluster with conserved TIR/NBS but diversified LRRs would have higher coverage in the first two domains but drop in the diversified parts. The authors need to specify their bioinformatic consideration to avoid such problems.

Although the tone of the Illumina read section was careful and the main 3'-end processing conclusion was made by nanopore DRS, the authors are also advised to clearly state the limitation of using Illumina-RNAseq to address alternative polyadenylating sites at the beginning of the section, for example what to be maximally taken out from Figure 4 E and 4F. This will give relative weights to each dataset generated by different methods. One advantage of using Illumina data would be that the expression level changes can be associated with changes in processing, it seems.

4) At the RPP7 locus, At1g58848 is identical in sequences with At1g59218 as is At1g58807 with At1g59214 (two twins in the RPP7 cluster by tandem duplication). It would be good to check whether the TE At1g58889 readthrough indeed occurs in the sister duplicate with a potential TE in the downstream of At1g59218. If not, it can be used as an example of duplication and neofunctionalization through an alternative polyadenylation site choices.

5) HMM search shall be revisited to confirm if they are to detect the TIR domain. Given that a large proportion of NLRs in A. thaliana carry TIR at their N-terminal ends and the specified examples included TIR-NLR, it is surprising to see no TIR domain in Figure 5.

6) L659-668: how does the new data relate to the previously TAIR annotated At1g58602.1 vs At1g58602.2 (Figure 6, Inset 1)? It would be good to see these clearly stated in the main text as compared to newly identified ones. From the nanopore profiling, At1g58602.2 appears to be the dominant form.

7) One thing to note is that in the overexpressor of which Hiks1 R is suppressed, there was hardly any At1g58602.1 produced in addition to the large reduction of At1g58602.2. Thus, relative functional importance of the two transcripts shall be discussed in line with the Hpa resistance data. Accordingly, L740-741 phrasing shall be revised to include the possibility of absolute or relative "depletion" of functional transcript(s) contributing to the compromise in Hpa resistance.

8) It would be necessary to state in the main text the implication of phosphorylation on the two Ser residues on Pol II at L245. A clear description distinguishing the effect of the two phosphorylation and the specificity of the antibodies is desirable, as the data was interpreted as if the two sites made differences, such that Ser2 was heavily emphasized (e.g. subtitle). Albeit low level, Ser5 data also shows an overlap with FPA ChIP-seq coverage at the 3' end. If there is a statistical significance to be taken account to interpret the coverage, please state it. Given that elongation occurs progressively, I wonder how much should be taken out from the distinction.

9) Figures presentation for RPP4 and RPP7 are great in detailing the FPA-dependent NLR transcript complexity. To make the functional link more evident, the authors may consider bringing up parts of the Figure 5-supplement to a main Figure to detail the revised annotation of NLRs. Given recent advances in NLR structure and function studies, extra domain fusion, fission and truncated versions of NLRs require a great deal of attention. For example, potential functional link to the NMD-mediated autoimmunity and revised annotation of At5g46470 (RPS6) needs a clear visual guidance preferably with a main figure (Figure 5-Supplement 3).

10) The section "FPA controls the processing of NLR transcripts" includes dense information and can be broken down to several categories. To this end, Supplement File 3 (NLR list) shall be revised to deliver the categorical classes and further details and converted to a main table.

For NLR audience, for example, it would be important to associate the information to raw reads to assess where the premature termination would occur. At least, the ways to retrieve dataset or to curate the termination sites shall be guided.

On the contrary, there is no need to include other genes in Figure 4 Sup4-8 under this section. They are not NLRs.

11) Figure 7 and IBM1 section can be spared to the supplement.

12) The list of "truncated NLR transcripts" in particular, either by premature termination within protein-coding or with intronic polyadenylation, should be made as a main table. The table can be preferably carrying details in which degree the truncation is predicted to be made. With current sup excel files, it is difficult to assess the breadth of the FPA effect on the repertoire of NLRs and their function. This way, functional implication of differential NLRs transcriptome can be better emphasized.

13) FPA-mediated NLR transcript controls, as to promote transcript diversity, is expected to exert its maximum effect if FPA level or activity is subject to the environmental stresses, such as biotic or abiotic stresses. The discussion on effectors targeting RNA-binding proteins (L909-918) is a great attempt in broadening the impact of this research. In addition, if anything is known to modulate FPA activity, such as biotic or abiotic stresses or environmental conditions, please include in the discussion.

14) NLR transcript diversity as source of cryptic variation contributing to NLR "evolution" is an interesting concept, however, evolutionary changes require processes of genic changes affecting transcript layers or stabilizing transcriptome diversity. In the authors' proposition in looking into accessions, potential evolutionary processes can be further clarified.

Reviewer #3 (Public Review):

The manuscript of Anchimiuk and colleagues investigates the mechanism of translocation of Bacillus subtilis SMC-ScpAB, a well characterized bacterial condensin. First, the authors use several SMC constructs where the coil-coiled region has been extended and /or the hinge exchanged and test what are the effects on growth and on the organization of the chromosome. They find highly altered conformations for most of the mutants. Particularly, these altered SMCs are unable to bridge two arms in the presence of the naturally-occurring parS sequences. Interestingly, they are partially able to restore arm pairing if a single parS sequence is provided.

Next, the authors used Chipseq to compare the binding pattern of wildtype SMC and SMC-CC425 (a mutant with an extended coil coiled region and a different hinge). They observe that the binding of wt-SMC is only midly affected by removal of most parS sequences, whilst that of the mutant is highly affected. In time-lapse experiments where ParB is depleted and then re-expressed, the authors show that in a strain with a single parS wt-SMC loads in the origin region and then redistributes over the chromosome while the mutant can only partially achieve redistribution and to a large extent remains concentrated on the origin region.

The authors then use wt-SMC and investigate how the conformation of the chromosome changes with two different parS sites located in different positions. They observe that each parS site is able to produce arm-pairing. They observe a decrease in the strength of arm pairing when both parS sites are present.

Finally, the authors increase the expression level of wt-SMC, and observe decreased levels of arm-pairing in the presence of all the naturally-occurring parS sites. More normal levels of arm-pairing are observed when only one parS is present, despite the higher wt-SMC levels. When two parS sites are introduced, more complex structures appear in the contact map.

These observations are new, interesting and intriguing. However, there are multiple possible interpretations, models and mechanism that are not discerned by the data presently presented in the manuscript.

At times, there seem to be inconsistencies in their interpretation of results, and at times the models proposed do not seem well supported by data.

Finally, the presentation of previous models and results from the literature could be improved.

Major issues:

In Fig. 1 the authors make several mutant SMC constructs with larger or shorter arms and different hinges and use Hi-C to explore the changes in 3D chromosome organization. Is it not clear to me why the arc is still visible in the mutants, nor what happens to the overall organization of the chromosome in the mutants? Is chromosome choreography normal?

In Fig. 1C the authors show that strains with parS-359 only display a secondary diagonal and conclude "chromosome arm alignment was comparable to wild-type". A quantification of the degree of pairing for each mutant normalized by the wild-type is necessary to evaluate the degree of pairing and its dependence on genomic distance to the origin.

In Fig. 2, the authors use HiC and chip-seq to quantify the effects of changes in SMC arm length on chromosome organization and SMC genomic distributions. It would be important to verify that the expression levels of these SMC mutants are the same as wt, as as they show in Fig. 4 changes in protein levels can change also 3D chromosome organization.

In Fig. 2C, what is the distribution of SMC at t0? Showing this result would support their claim that SMC can load in absence of ParB.

In Fig. 2C it is claimed that SMC-CC425 moves at a slower rate than WT. Can the authors provide a quantification?

In Fig. 2, the authors focused on one of the mutants with longer SMC arms (CC425) and performed HiC and Chip-seq in time-lapse after induction of ParB in a ParB-depleted culture. These experiments clearly establish that SMC-CC425 can redistribute from the origin and can achieve arm pairing but to a lesser extent than the WT. The authors speculate that a slower translocation rate and/or a faster dissociation rate explain the experiments. However, other possibilities exist: for instance that the mutant SMC is defective at passing through road-blocks (highly expressed genomic regions, e.g rRNA sites) or at managing collisions with RNAP/ DNAP/ other SMCs, it makes different higher-order complexes than wt-SMC, etc. This could could be due to the change in the length of the SMC, or to the use of a hinge/coiled-coil region different from that of the wt-SMC. Thus, I am not convinced that the text explores all the possible models or that the data shown discerns between any of them.

In Fig. 3B, the authors show that use of two parS-opt sites at -304kb and -9kb lead to the formation of two secondary diagonals. They argue that these can be rationalized in terms of the diagonals formed by the strains harboring single parS-opt (either -9kb or -304kb). However, I cannot see how these can happen at the same time! If a cells makes arm pairing from -9kb then it cannot make it from -304kb right? I do not understand either how the authors can conclude from these experiments that ParS may act as unloading sites for SMC. Again, the authors are speculating over mechanisms that are not really tested.

If parS sites triggered the unloading of SMCs, then one would assume that ~5-6 natural parS sites in the origin region are unloading the SMC complexes loaded at other parS sites? This makes little sense to me, or there is something I clearly do not understand in their explanations.

In their text, the authors explain that "A small but noticeable fraction of SMC complexes however managed to translocate towards and beyond other parS sites apparently mostly unhindered". I am confused as to where is the evidence supporting this statement. I do not think the ensemble Hi-C experiments provided in Fig. 3 can provide conclusive evidence for this.

The authors often hypothesize on a mechanism, but then assume this mechanism is correct. For instance, the disruption in the secondary diagonals in Fig. 3B when experiments are performed with two parS sites are initially hypothesized to be due to roadblocks (e.g with highly transcribed regions) or to collisions between SMCs loaded at different parS sites. These possibilities cannot be discerned from their data. However, the authors then assume that collisions is what is going on (e.g. paragraph in lines 274-284). I think they should provide evidence on what is producing the changes in the secondary diagonals in mutants with two ParS sites.

Why is the ChIP-seq profile for a strain with all the natural parS sites and for a strain with only parS-9kb the same? even with the same peaks at the same locations? Does this mean that SMC peaks do not require the presence of parS? But, then SMCs do not load equally well in all naturally occurring parS sites? This is then in contradiction to their assumption that parS cannot be selectively loaded?

Do we really know that it is a single SMC ring that is responsible for translocation? The authors assume so in their models and interpretations, but if it were not the case it could drastically modify the mechanisms proposed. For instance, SMC may be able to load on a ParS site without pairing arms (i.e. only one dsDNA strand going through the SMC ring).

In Fig. 2C-D it is shown that a large fraction of wildtype SMC and SMC-CC425 accumulate at the origin region at early time points (Fig. 2C) however this does not seem to lead to an increased Hi-C signal in the origin region (compare early time points to the final t60). Also, despite small amounts of wt-SMC in the chromosome at the latter time points, the intensity of the secondary diagonal is very strong. Why is this? These results would be consistent with many SMCs loading at the origin region but only a fraction of them being responsible for arm-pairing. Is this not in contradiction to their assumption that SMCs pair two dsDNA arms when they load?

The authors state that: "If SMC-CC425 indeed fails to juxtapose chromosome arms due to over-enrichment in the replication origin region, collisions may be rare in wild-type cells because of a high chromosome residence time and a limited pool of soluble SMC complexes, resulting in a small flux of SMC onto the chromosome. If so, artificially increasing the flux of SMC should lead to defects in chromosome organization with multiple parS sites but not with a single parS site (assuming that most SMC is loaded at parS sites)". However, this assumption seems inconsistent with their results in Fig. 2 that show that the peaks of SMC do not change upon removal of most parS sites.

I am a bit confused about the interpretation of the results in Fig. 4D. The authors talk about 'loop contacts' and point to the secondary diagonal (yellow ellipses). But these are not loop contacts, but rather contacts between arms that have surpassed the two parS sequences, right? Also, it is not clear what they mean by paired-loop contacts (red ellipse). Do they mean contacts between the two loops originating at parS-359 and parS-334? If this where the case, then it means SMCs are bridging more than two dsDNA segments? Or that there are multimers of SMC linking together? Or that and SMC can circle one arm from one loop and another from the other...? But in this case, how can it load? For me it is very unclear what these experiments really mean. The explanations provided by the authors seem again highly hypothetical.

Reviewer #2 (Public Review):

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

Reviewer #1 (Public Review):

The authors investigate the role of Condensin and its loading in ensuring appropriate chromosome dynamics in the model organism Bacillus subtilis. The data are of high quality and generally support the ultimate conclusions.

The demonstration of collisions between ectopically-loaded Condensin and their negative impact on cellular viability are important insights, particularly in light of the recent single-molecular in vitro experiments demonstrating the ability of 2 Condensins to pass one another and thereby form Z-structures on DNA.

The main caveat is that the work lacks direct quantization of the levels of chromosome-associated Condensin—inclusion of experiments to evaluate this parameter would go a long way to validating (or refuting) the authors' conclusions.

Reviewer #3 (Public Review):

The authors of this manuscript combine electrophysiological recordings, anatomical reconstructions and simulations to characterize synapses between neurogliaform interneurons (NGFCs) and pyramidal cells in somatosensory cortex. The main novel finding is a difference in summation of GABAA versus GABAB receptor-mediated IPSPs, with a linear summation of metabotropic IPSPs in contrast to the expected sublinear summation of ionotropic GABAA IPSPs. The authors also provide a number of structural and functional details about the parameters of GABAergic transmission from NGFCs to support a simulation suggesting that sublinear summation of GABAB IPSPs results from recruitment of dendritic shaft GABAB receptors that are efficiently coupled to GIRK channels.

I appreciate the topic and the quality of the approach, but there are underlying assumptions that leave room to question some conclusions. I also have a general concern that the authors have not experimentally addressed mechanisms underlying the linear summation of GABAB IPSPs, reducing the significance of this most interesting finding.

1) The main novel result of broad interest is supported by nice triple recording data showing linear summation of GABAB IPSPs (Figure 4), but I was surprised this result was not explored in more depth.

2) To assess the effective radius of NGFC volume transmission, the authors apply quantal analysis to determine the number of functional release sites to compare with structural analysis of presynaptic boutons at various distances from PC dendrites. This is a powerful approach for analyzing the structure-function relationship of conventional synapses but I am concerned about the robustness of the results (used in subsequent simulations) when applied here because it is unclear whether volume transmission satisfies the assumptions required for quantal analysis. For example, if volume transmission is similar to spillover transmission in that it involves pooling of neurotransmitter between release sites, then the quantal amplitude may not be independent of release probability. Many relevant issues are mentioned in the discussion but some relevant assumptions about QA are not justified.

3) The authors might re-think the lack of GABA transporters in the model since the presence and characteristics of GATs will have a large effect on the spread of GABA in the extracellular space.

4) I'm not convinced that the repetitive stimulation protocol of a single presynaptic cell shown (Figure 5) is relevant for understanding summation of converging inputs (Figure 4), particularly in light of the strong use-dependent depression of GABA release from NGFCs. It is also likely that shunting inhibition contributes to sublinear summation to a greater extent during repetitive stimulation than summation from presynaptic cells that may target different dendritic domains. The authors claim that HCN channels do not affect integration of GABAB IPSPs but one would not expect HCN channel activation from the small hyperpolarization from a relatively depolarized holding potential.

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public Review):

This manuscript by Gabor Tamas' group defines features of ionotropic and metabotropic output from a specific cortical GABAergic cell cortical type, so-called neurogliaform cells (NGFCs), by using electrophysiology, anatomy, calcium imaging and modelling. Experimental data suggest that NGFCs converge onto postsynaptic neurons with sublinear summation of ionotropic GABAA potentials and linear summation of metabotropic GABAB potentials. The modelling results suggest a preferential spatial distribution of GABA-B receptor-GIRK clusters on the dendritic spines of postsynaptic neurons. The data provide the first experimental quantitative analysis of the distinct integration mechanisms of GABA-A and GABA-B receptor activation by the presynaptic NGFCs, and especially gain insights into the logic of the volume transmission and the subcellular distribution of postsynaptic GABA-B receptors. Therefore, the manuscript provides novel and important information on the role of the GABAergic system within cortical microcircuits.

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public Review):

This paper focuses on the role of historical evolutionary patterns that lead to genetic adaptation in cytokine production and immune mediated diseases including infectious, inflammatory, and autoimmune diseases. The overall goal of this research was to track the evolutionary trajectories of cytokine production capacity over time in a number of patients with different exposure to infectious organisms, infectious disease, autoimmune and inflammatory diseases using the 500 Functional Genomics cohort of the Human Functional Genomics Project. The identified cohort is made up of 534 individuals of Western European ancestry. Much of this focus is on the impact and limitations of certain datasets that they have chosen to use such as the "average genotyped dosage" to be substituted for missing variants and data interpretation. Moreover, some data pairings in the data set are not complete or had varying time points . Similarly, a split was done to look at before and after the Neolithic era and the linear regression correspond to those two eras. However, the authors do not comment or show the data to demonstrate why they choose that specific breakpoint as opposed to looking at every historical era transition, i.e., from early upper paleolithic to late upper paleolithic to Mesolithic to Neolithic to post-Neolithic to modern. Lastly, the authors should highlight additional limitations of this current study in terms of the generalizability to other populations or to clearly state that this is limited to the European population at the specified latitude and longitudes used.

Reviewer #3 (Public Review):

In zebrafish embryo development the surface epithelium, the enveloping layer (EVL), proliferates and migrates along with the yolk sac during epiboly. This process requires the simultaneous proliferation and migration of cells, which must undergo cell shape changes. Co-ordination of these processes is regulated by proliferation, whereby cell number and shape perturb tissue-scale forces necessary for epiboly. This paper investigates explicitly the importance of successful cytokinesis, through abscission of cytokinetic bridges, on regulating these forces and epiboly progression. They show that Rab25, a GTPase belonging to the Rab11 subfamily, regulates abscission through endomembrane trafficking in the EVL. Through their detailed analysis of cellular-level phenotypes, including qualitative and quantitative approaches, this paper presents convincing evidence for this novel role of Rab25. The authors should be congratulated on excellent time-lapse movies of cytokinesis in early zebrafish development.

Reviewer #2 (Public Review):

The authors examined the role of Rab25 during cell division within a developing epithelia. Strikingly, they found that the RabGTPase, Rab25, localized to mitotic structures such as centrosomes and cytokinetic midbodies in dividing cells of the developing zebrafish embryo. They went on to create maternal-zygotic Rab25a and Rab25b mutant embryos where they clearly demonstrate that apical cytokinetic bridges fail to undergo abscission leading to anisotropic cell morphologies that likely contribute to a delayed epiboly.

The major strengths of this study is the clear cell biology defects found in a developing embryo that lead to downstream developmental defects (delayed epiboly). The rab25 localization is beautiful. The examination of the viscoelastic properties is also compelling. The main improvements would be to expand upon the spatio-temporal localization of Rab25a and Rab25b during cell division at different stages of epiboly, present Rab11 localization patterns in the Rab25 mutant embryos, and clearly demonstrate that changes in viscoelasticity are also in their multinucleated cells that occur in Rab25 mutant conditions. These additions will help the authors support their conclusions that Rab25 localization/regulation of endomembranes (potentially recycling endosomes) regulates abscission and subsequently the viscoelastic properties of the developing tissue.

This study has identified novel roles for Rab25 in cytokinesis/abscission and opens the doors for examining it in regulating mitotic centrosome function. It is paradigm shifting in that it creates a new way to think about Rab25 and potentially its relationship with Rab11 and recycling endosomes during division in the early embryo.

Reviewer #1 (Public Review):

In the manuscript by Willoughby et al. the authors examine the role of Rab25 in early embryogenesis in zebrafish. They implicate Rab25 activity in abscission and show various defects including delayed epiboly and altered cell behaviors associated with defective acting dynamics. This is an interesting and well-written paper that uses reverse genetics and microscopy to analyze the function of Rab25, a GTPase previously implicated in membrane recycling, in vivo. Their work illustrates how defects in cytokinesis affect epiboly and establish an interesting link to acto-myosin regulation of the mechanical properties of the EVL. While these pehnotypes are described and demonstrated clearly, the implication of membrane recycling is not fully supported in the present work. It is also unclear whether Rab25 plays a role in oogenesis that may account for some of the observed phenotypes.

Reviewer #2 (Public Review):

This manuscript set out to address several outstanding questions concerning the impact of 'eusocial' behaviour in mammals, here represented by the experimental model of the Damaraland mole-rat, on skeletal remodelling. Specifically, the transition to breeding status (queen) for some individuals in the colony is accompanied by changes that support high fecundity. The authors investigate the extent to which changes are localised in the skeleton and the underlying regulatory changes that are associated with these morphological features. The paper is well-written, the experiments have been planned thoughtfully and described carefully, and the panel figures convey information without over-crowding. Overall, I thoroughly enjoyed reading this manuscript, which represents as a multi-pronged approach to advancing understanding of the unusual biology and phenotype of queen mole rats.

Reviewer #1 (Public Review):

The authors provide a novel case-study of the skeletal consequences of queen-only breeding in Damaraland mole-rats, one of the few eusocial mammals. Out of a population of adults, a queen will be selected as the sole female to breed with a male, and the non-breeders will provide support in the highly cooperative society. Once selected, a new queen will undergo a rapid skeletal transformation in which lumbar vertebrae expand. Supporting closely-timed pregnancies and lactation, mineral reserves will be excavated by bone-specific macrophages along the inner, or endosteal, lining of some limb bones. Unlike most other mammals, the skeletons of queens do not typically recover to their pre-pregnancy phenotype as rapid sequential pregnancies continually erode the limbs, leaving them vulnerable to fracture.

To understand the molecular mechanisms driving these phenotypic changes associated with breeding in queens, the authors artificially selected queens in captivity, recreated a eusocial society, and then tracked gene expression along with skeletal phenotypes throughout breeding cycles. After lumbar expansion in queens had completed only long bones showed gene expression consistent with breeding status. Specifically, results showed upregulation of differentiation and activity of bone-specific macrophages, call osteoclasts. These cells liberate minerals from bone and make components of the extracellular matrix available metabolism and development of embryos.

To understand if these changes were driven by the presence of sex-steroids, multiple cell types were harvested from the marrow of lumbar vertebrae and limb bones and treated with estradiol. No significant effect was found. Data, therefore, suggest that mechanisms shaping the postcranial skeleton were not consequences of sex-steroid mediated signaling pathways.

Non-recoverable bone loss in queens is unusual among mammals and is a vulnerability that potentially limits the number of pups a queen can produce. Vulnerable queens may therefore be protected in cooperative societies in which non-breeders can work more and offer queens more rest.

This study furthers the field of skeletal biology by exploring how enduring bone resorption contributes to the greater fecundity of one of the world's few eusocial mammals but has a potentially life-long consequence on limb performance and fracture resistance. The authors weave together multiple lines of evidence to better illustrate the enormous and rapid changes that occur as a female ascends to queen status, and what she sacrifices to build her colony. Results offer compelling and transdisciplinary insights into an extreme skeletal strategy and the impact of this work can be bolstered by only minor changes.

Reviewer #3 (Public Review):

The manuscript by Turner et al. employs a transcriptome-wide approach to study the effects of mutants of the 3'-end processing machinery and the anti-cancer drug cordycepin (3' deoxyadenosine) on alternative poly(A) site selection in budding yeast to better understand alternative polyadenylation (APA) mechanism(s). In particular, poly(A) test sequencing (PAT-seq), a 3'-end focused deep sequencing technique, is employed to determine cleavage/poly(A) site choice in seven mutants of the core 3'-end processing machinery – three cleavage factor IA (CFIA) mutants (rna14-1, pcf11-2, clp1-pm), one cleavage factor IB (CFIB) mutant (nab4-1), and three cleavage and polyadenylation factor (CPF) mutants (ysh1-13, fip1-1, pap1-1). Six of the 3'-end processing factor mutants exhibit increased distal poly(A) site usage and lengthening of 3'-UTRs, with rna14-1 and pcf11-2 showing the greatest effect, but clp1-pm exhibiting little effect. Notably, 3511/7091 genomic annotations (49.5%) have two or more poly(A) sites and 422 genes have significantly changed poly(A) sites in all the 3'-end processing factors mutants except clp1-pm. APA is also examined in 41 genes in a full spectrum of 3'-end processing mutants (22) using a multiplexed poly(A) test (mPAT) method and most of the mutants alter poly(A) site choice, with a predominant shift to distal site usage. In addition, APA analysis of cells treated with cordycepin using PAT-seq indicates that cordycepin alters poly(A) site choice in 1959 genes, with predominant distal cleavage site usage and lengthening of 3'-UTRs. Cordycepin is also shown to increase nucleotide abundance. Interestingly, impairment of transcription elongation, using mycophenolic acid (MPA), which reduces GTP levels, or an RNA polymerase II mutant, rpb1-H1085Y, in cells treated with cordycepin promotes proximal poly(A) site usage and shorter 3'-UTRs, reversing the effects of cordycepin. Finally, comparison of genes altered in APA by cordycepin to a dataset of yeast nucleosome occupancy suggests that 3'-end nucleosome positioning and length of intergenic regions in convergent genes correlates with cordycepin responsiveness. The data presented in the paper suggest a kinetic model for cleavage/poly(A) site selection in yeast that involves a balance between the concentration/availability of the cleavage and polyadenylation machinery and transcription elongation rate.

The strengths of the study include the generation of transcriptome-wide datasets for poly(A) site usage in numerous mutants of evolutionarily conserved, essential cleavage and polyadenylation factors using the PAT-seq method. In addition, the study indicates that almost 50% of the annotated genes in budding yeast exhibit alternative polyadenylation. The study also indicates that impairment of numerous 3'-end processing factors, irrespective of subcomplex, predominantly causes an increase in distal poly(A) site usage and lengthening of 3'-UTRs. Interestingly, the study also suggests that the choice of poly(A) site is regulated by the availability of cleavage and polyadenylation factors and transcription elongation. Finally, the study shows that anticancer drug cordycepin causes transcriptome-wide changes in alternative polyadenylation, predominantly elevating distal poly(A) site usage.

The weaknesses of the study revolve around basing some conclusions solely on the transcriptome-wide data without additional small-scale experiments. In addition, the effects of 3'-end processing mutants and cordycepin on alternative polyadenylation have been examined in two different strain backgrounds, which could impact direct comparisons of the data. The proposed kinetic model for cleavage site choice in yeast seems only to be tested in cells treated with cordycepin.

Overall, the authors achieved their aims of providing greater insight into the mechanism of alternative polyadenylation and its links to transcription and more understanding of the biological effects of cordycepin in cells. At present, most of the conclusions are supported by the results, but some conclusions require additional experiments.

This study will be of enormous interest to the RNA processing field and to the wider community, especially given that alternative polyadenylation regulates so many aspects of mRNA function, the 3'-end processing factors studied are evolutionary conserved, and cordycepin is an anti-cancer agent.

Reviewer #2 (Public Review):

The authors investigated how alternative polyadenylation (APA) is modulated in yeast using appropriate transcriptomic methodologies.

The authors found that mutants for mRNA 3' end formation factors and cordycepin treatment alter alternative polyadenylation in the same manner, generating transcripts with longer 3'UTRs, due to a switch to distal polyadenylation sites (PAS). Most mutants analyzed cause a PAS switch, in particular mutants for RNA14, PCF11, YSH1, FIP1, NAB4 and PAP1. They also found that MPA and a rpb1 mutant, with a slower transcription elongation rate, reverts the cordycepin effect of distal PAS selection. This implies that in yeast, as in higher organisms, APA is modulated by RNAPII elongation. There is nucleosome depletion in the 3' end of convergent genes that undergo cordycepin-driven APA alterations, which is a new finding.

On the basis of their data, the authors propose a kinetic model for APA in yeast that is regulated by the concentration of core mRNA 3' end factors and nucleotide levels, which in turn modulates RNAPII elongation. This integrative model has been already described in higher organisms, but not in yeast, and overall this study covers an impressive body of work that makes an important contribution to the field.

1) The authors show that cordycepin have the same effect in APA as most of the 3' end factors mutants used, but there is a lack of integration between the two sets of PAS-seq data. The cordycepin APA effect may be due to decreased expression of mRNA 3' end factors but this hypothesis was not fully explored. Treating those mRNA 3' end mutants with cordycepin could shed some light on this.

2) A new role for SEN1 in APA for a subset of protein coding was observed. The SEN1 mechanism could be clarified if the authors show that SEN1 is within the subset of convergent genes analyzed, and also if SEN1 expression changes upon cordycepin treatment.

Reviewer #1 (Public Review):

The authors set out to test a variety of factors that could impact poladenylation site (PAS) selection in yeast. To that end, they rigorously tested a collection of temperature-sensitive mutations in polyadenylation machinery components and utilized a custom 3'-end sequencing method to assess PAS selection genome-wide. The most common result associated with polyadenylation machinery dysfunction was global switching to a more distal PAS. Further, the authors test an interesting phenomenon of cordecypin-induced switching to the distal PAS and reveal through metabolomics that enhanced nucleotide biosynthesis may be the root cause. The enhanced nucleotide pools was found to alter elongation rate leading to alterations in PAS choice. Finally, the authors find that convergent genes are influenced by the nucleosome landscape to impact APA events.

Overall, this is a rigorous and thorough study that brings together multiple regulatory components that impact PAS selection. The model presented by the authors is supported by their work and provides the field with a clear picture of the complex nature of cleavage and polyadenylation in yeast.

Reviewer #4 (Public Review):

The authors have studied the effects of microstimulation in a single subject with 2 microelectrode arrays in the somatosensory cortex. They aimed to investigate the how altering frequency, current amplitude and train duration affected the elicited percepts. They report three new findings:

1) Increasing stimulus frequency did not increase the intensity of the percept, in fact there was frequency selectivity of cortical regions and these were somewhat topographically organized on the cortical surface.

2) The intensity of the subject's responses were similar using suprathreshold (higher) currents but using lowest electrical currents (perithreshold) required higher frequencies for detection similar to other somatosensory brain regions.

3) Frequency-intensity variation could evoke different types of sensations, with higher frequencies more likely to evoke tingle or buzz (less natural), and lower frequencies eliciting more pressure, tap, or touch (more natural type sensations).

The major strength of this work is the detailed testing performed over multiple sessions through the same microelectrodes, demonstrating consistent effects. It provides new methods to alter sensations by changing the parameters of stimulation to optimize the type of percept that they are trying to produce.

Reviewer #3 (Public Review):

Microstimulation of the somatosensory cortex is a very promising approach to restore sensory feedback in disabled people. Hughes and colleagues performed cortical microstimulation experiments in a spinal cord injured subject to characterize the relationship between the stimulation parameters (frequency and amplitude) and the perceived sensation (type and intensity). This type of experiment is very important to better understand the potentials and limits of this approach. The results achieved by the authors are very interesting and can represent a first step towards the development of more effective and personalized approaches to restore sensory feedback. These results need to be confirmed with additional subjects and during closed-loop experiments.

Reviewer #2 (Public Review):

This study induced tactile percepts through microstimulation via two multi-electrode arrays implanted over a quadriplegic's primary somatosensory hand region. The report focuses on manipulation of the stimulation frequency of microstimulation, though further manipulations were tested and are briefly reported.

For different stimulation sites, the perceived intensity was highest at different stimulation frequencies. This result contradicted the expectation that higher stimulation frequency would be related to higher perceived intensity. This expectation derived from previous work in non-human primates that showed lower detection thresholds for higher-frequency stimulation. The authors show that the same result is obtained in their human patient, suggesting that differences exist between near- and supra-threshold perceived stimuli and that, accordingly, generalizing from non-human primate work has its traps.

The authors grouped stimulation sites according to optimal stimulation frequency into low, intermediate, and high frequency preferring sites. These three classes were spatially clustered, and related to different patterns of reported perceptual qualities (such as vibration, pressure etc).

The paper's results are important for practical developments of sensory feedback in brain-machine interfaces. Understanding the perceptual result of brain stimulation requires reports by human participants, as underlined by the differences uncovered here between near- and supra-threshold stimulation. They furthermore reveal new aspects of the cortical organisation of primary somatosensory cortex.

The conclusion of clustered patches sensitive to specific frequencies is tentative. As an inherent limitation of intracranial recordings, the total number of stimulation sites is small, and some electrodes did not produce significant results, further reducing the number of analysable sites. Therefore, it is possible that stimulation doesn't truly fall into three distinct clusters (even if such clustering is statistically supported with the current data set), but are actually continuous or divide into a larger number of classes. Notably, this critique does not invalidate the main finding that different patches of cortex show specific frequency preferences.

Reviewer #1 (Public Review):

This manuscript reports data from unique experiments in which a paralysed person reported sensations evoked by microstimulation of the somatosensory cortex. The main emphasis of this paper is on the effects of increase in stimulation frequency. It was discovered that depending on the electrode used, the peak intensity was felt at different frequencies. Accordingly, the electrodes and stimulation sites were divided into three groups-Low, Intermediate and High frequency preferring. Overall, it was noticed that in most electrodes increasing stimulation frequency beyond about 100 Hz led to less intense sensation. Without knowing the exact somatosensory circuits involved in processing, the connection with recently discovered human vibrotactile psychophysics phenomena and cortical recordings in mice are speculative, but are in close agreement with the current observation and thus the manuscript would benefit from expanding discussion on this. I personally don't think there is any contradiction with non-human primate studies, as the authors state, rather it should be viewed as a significant extension to those studies and warrants viewing them in a new light.

A very interesting observation is that three types of frequency-intensity effects are associated with different perceptual qualities. However, types of seemingly distinct sensations might be attributed to semantics describing sensation of periodic stimulation at different intensities. Subjective reports of one subject are very valuable to set future directions for this kind of investigation, but may not be enough to generalise those findings just yet.

The location of electrodes belonging to three different frequency-intensity effect groups appeared to be not at random, but whether it reflects cortical organisation or some other factors like systematic variation in electrode depth might have influenced the result, needs to be confirmed. Only a small number of electrodes was tested - 8 in the Medial Array and 11 in the Lateral Array.

Three frequency-intensity effect group electrodes also differed in median intensity reported across all frequencies, which cautions that the reported perceptual quality differences at least partly might be attributed to the overall level of intensity sensation. It has to be noted that the overall frequency-intensity response profile did not change by changing the stimulation current, however some shifts seems to be present. Alternatively, such frequency-intensity effect profiles represent circuits tuned to detection of specific features of stimuli. This possibility is indeed very intriguing.

As those experiments performed on a human subject with implanted electrodes are absolutely unique, the data are exceptionally interesting regardless of limitations generalising those findings. Unlike animal experiments humans can describe sensations evoked by cortical microstimulation so there is no substitution for these experiments and every piece of evidence is highly valuable. These results give ground for new hypotheses to better understand how the somatosensory system works and generate ideas for designing future human psychophysics and animal model experiments. From a practical point of view, it is exceptionally valuable for informing the design of stimulation protocols for bidirectional brain-computer interfaces (BCIs).

Reviewer #3 (Public Review):

Hallast and coworkers identify a potentially novel complex Y chromosome structural rearrangement that is associated with male infertility in a carefully phenotyped European cohort. The authors interrogate the Y chromosome AFZc region in 1190 Estonian idiopathic male infertility cases of varying severity and 1134 controls (healthy young men or proven fathers). They replicate partial AFZc deletions and replicate a known gr/gr deletion association with comparable effect sizes. After conditioning on gr/gr deletion status, they identify an association with secondary b2/b4 duplications on case status, but with no accompanying observed effect on andrological sub-phenotypes.

The authors identify multiple non-syntenic DAZ/CDY1 deletion patterns that are consistent with a large inversion followed by deletion. The authors further infer that this putative inversion is fixed in a Y chromosome sub-lineage. Based on population haplotype frequency estimates they infer that a surprisingly large number of individuals harbouring the r2/r3 inversion have a subsequent deletion. They show through detailed phenotyping shows that r2/r3 inversion+deletion cases in their cohort have more severe disease.

Strengths:

1) Despite being a very common disease, idiopathic infertility is severely understudied, due in large part to difficulties in sample acquisition. More generally, sex chromosome genetic associations for common disease as a whole are understudied owing to their structural complexity and other technical issues. The authors should be applauded for attempting to overcome these challenges.

2) The putative finding of a large-effect common variant conferring risk to a common genetic disease is of great interest. The authors leverage the advantages of a logistically coherent health care system. The level of phenotypic detail of andrological parameters in both cases and controls is impressive and aid in biological interpretation of the genetic findings. For example, the distinction between azoo- versus oligozoo-spermia shed light on a potential meiotic disease aetiology. The endocrine values add important context.

3) The authors imply that the combination of the inversion+deletion risk allele favours a meiotic failure disease aetiology as opposed to a gene dosage aetiology. This is a potentially disruptive finding.

Weaknesses:

1) The authors do not replicate their association, raising the possibility of a false positive finding.

2) The study is underpowered to reliably detect variants of small effect, and underpowered in general. This is a common challenge in reproductive genetics.

3) The logical inferences (as opposed to direct measurement) made by the authors are elegant but add substantial uncertainty to the findings. Most notably, cytogenetic or long-read sequencing based validation of the inversion genotype would strengthen confidence in the study considerably.

If the genetic association is robust and the allele frequency estimates are well calibrated, the implications of this work are considerable. The locus could become a genetic biomarker for infertility. The locus could potentially account for a huge amount of variance in polygenic risk associated with infertility. The findings also raise a fascinating evolutionary conundrum as to how an allele associated with such an evolutionarily destructive phenotype could occur at such high frequencies. The authors briefly raise the possibility of age-dependent effects, but with extremely sparse data.

Reviewer #2 (Public Review):

Hallast et al. have performed an extensive genetic analysis of a cohort of men with idiopathic infertility, for whom many have accompanying phenotypic data in the form of andrological parameters. The complex genetic architecture of repeating sequences on the Y chromosome gives rise to recurrent AZFc deletions that affect male infertility. However, while partial deletions of AZFc are reasonably frequent, they have less clear phenotypic effects. gr/gr and b2/b3 deletions seem to be a risk factor for spermatogenic impairment in some populations but are fixed in others. Hallast et al. focus on these partial AZFc deletions in a reference cohort and a cohort with idiopathic infertility from the same geographic population, characterising further structural and sequence variation, Y-chromosomal haplogroups, and gene dosage.

While the gr/gr deletion is present in the reference group of individuals with normal andrological parameters, Hallast et al. show that this deletion is enriched among patients, with 2.2-fold increased susceptibility to infertility. As observed in other European populations, the prevalence of b2/b3 deletion was similar in the reference group and the patient group, suggesting that it is not a spermatogenic impairment risk factor for this Estonian population either.

A quarter of Estonian gr/gr deletion carriers belonged to the Y chromosomal haplogroup R1a1-M458. Within this Y haplogroup, an inversion has occurred that promotes subsequent deletion, likely causing severe spermatogenic failure in the majority of carriers as this complex rearrangement is enriched 8.6-fold in individuals with severe spermatogenic impairment.

Some major strengths of this paper are the size of the groups recruited (1,190 patients and 1,134 reference individuals) from a single national population, the extensive accompanying andrological data, and the genomic characterisation of many individuals to elucidate the relationship between specific structural variants and effects on fertility.

The discovery of the fixed inversion infertility risk factor on a specific Y haplogroup is a useful contribution that could aid genetic counselling efforts through carrier identification and risk mitigation.

However, I am seeking clarity on multiple testing correction for microdeletion association with specific andrological parameters. Besides this, the main conclusions of this paper are supported by other data presented.

Reviewer #1 (Public Review):

In this study, Hallast and colleagues performed a detailed genetic analysis of the AZFc region of the Y-chromosome in a large cohort of 1190 Estonian men with idiopathic infertility and >1100 controls from the same population. They focused on partial deletions of the AZFc regions, because their clinical significance remains controversial and published reports are often contradictory. The authors performed a comprehensive genetic analysis, which in addition to a standard AZFc deletion protocol with gene dosage of the key AZFc genes, included also Y-haplogroup determination and re-sequencing of the retained DAZ, BPY2 and CDY genes. The authors showed that gr/gr deletions were enriched in infertile men, thus confirming that this deletion is a risk factor for impaired spermatogenesis. An important novel finding is identification of a previously unknown structural variant: a long r2/r3 inversion, which likely destabilizes two palindromes and leads to deletions. This variant is fixed in the Y lineage R1a1-M458, which is common in some Central European populations. In the Estonian study group, nearly all patients with this variant and a gr/gr deletion, had a severe impairment of spermatogenesis. The authors mentioned that the variant largely 'destroys' two palindromes, P1 and P2. One would like to see more discussion what are the structural and functional consequences - e.g. are any loci for e.g. non-coding RNA affected by a deletion in men with this inversion in comparison to those without?

The authors also speculated in the discussion that deletion on this background might lead to progressive worsening of the reproductive phenotype. This is based on just one control individual, a young man with borderline reproductive parameters, and corroborating this hypothesis would require further studies, including repeated evaluation of the same individuals over a long period of time.

This is a high quality study, performed by collaborators from the UK and Estonia, with an excellent track record in the analysis of the Y-chromosome structure and evolution, and in reproductive genetics and clinical andrology, respectively. The data presentation and figures are very informative and convincing. Among the strengths of the study, I have to emphasise a detailed phenotypic evaluation of the study subjects, including several parameters of testis function, semen analysis, and reproductive hormone profiles. Hence, the results and conclusions are valuable and add to the understanding of the consequences of the partial AZFc deletions. The authors also provided useful guidelines how to identify men with this variant in labs performing genetic analysis of infertile couples.

Joint Public Review:

Hsiang-Chun Chang et al. investigated the role of ALR, component of the mitochondrial MIA40/ALR protein import apparatus, in cytosolic Fe/S cluster biogenesis performing loss-of-function (silencing) and gain-of-function (over-expression) experiments with MEFs (mouse embryonic fibroblast) and HEK293 (human embryonic kidney) cells. They find that downregulation of ALR impairs maturation of cytosolic Fe/S cluster proteins, while activities of mitochondrial Fe/S cluster proteins such as complex I and II are unaffected. Furthermore by reducing ALR expression cells up-regulate cellular iron transporter transferrin receptor 1 (Tfrc) and consequently cellular iron levels increase. The authors reveal that ALR down-regulation post-transcriptionally regulates Trfc through stabilization of Trfc mRNA mediated by IRP1, which is activated by absence of its mature Fe/S cluster. Additionally they demonstrate that only over- expression of full-length ALR, mainly located in the mitochondria and not the cytosolic short from ALR can reverse cytosolic Fe/S cluster maturation and therefore IRP1 activity and cellular iron levels. In the last part of their manuscript the authors present evidence about the mechanism by which ALR carries out this function. They find that ALR enables mitochondrial import of ABCB8 but not ABCB7, two mitochondrial proteins involved in the maturation of cytoplasmic Fe/S clusters. This transport into mitochondria requires functional MIA40/ALR in the IMS and further the TIM23 complex to the inner mitochondrial membrane. ABCB8 interacts directly with MIA40 by 5 cysteines (difulfide bond formation) and therefore these conserved cysteins are necessary for recognition and binding, which is not the case for ABCB7. These data add an interesting view on how ALR expression is linked to Fe/S cluster protein maturation, cellular iron homeostasis and their potential impact on related dieases.

The strength of the manuscript are the well designed and performed experiments presenting evidence of how mitochondrial function of ALR is linked to the sulfur redox homeostasis and cellular iron regulation. Interestingly, reduction in cytsolic Fe/S cluster maturation and therefore increased cellular iron levels is also associated with increased sensitivity of cells to oxidative stress and this might be a plausible explanation for the previously described impact of full length ALR expression on oxidative stress in various disease models (PMID: 30579845).

The drawn conclusions that the mechanistic studies about the role of ALR for Fe/S cluster maturation and cellular iron uptake may parallel the disease phenotype of patients with mutations in ALR gene GFER may be in parts speculative. The reported ALR mutations are varying and result either in partial functional or truncated protein expression (PMID: 20593814, PMID: 25269795). ALR is expressed in several isoforms (varying between two or three depending on the organ) of different size (15kDa, 21kDa, 23kDa). Most of the data showing the short form ALR (15kDa) solely in the cytosol and the full length ALR (23 kDa) as wells a second immuno-reactive band of 21 kDa ALR, both in cytosol and mitochondria (PMID: 30579845). While over-expressing full length ALR the authors show in the manuscript higher expression level in the cytosol than in the mitochondria fraction (w-blot, which is not reflected in the graph of Fig. S3 B). It was reported earlier that continuous over-expression of full length ALR in mammalian cells leads to the accumulation of full length ALR not only in the mitochondria but also in the cytosol (PMID: 23676665), which is also in agreement to observations of cytosolic occurrence of full length ALR (see above). This raises the question whether the conclusions made in the manuscript may be due to its cytosolic accumulation rather than or in addition to its mitochondrial localization. The presented study refers at several points to a study by Lange et al 2001 demonstrating that ALR rescues cytoplasmic Fe/S cluster maturation defects in Erv1- null yeast. There has been contradictory evidence published about the role of ALR in the maturation and export of cytosolic Fe-S cluster proteins. Lange et al. claimed that ALR interacts with Atm1 (an ABC transporter in the inner membrane of the mitochondria) and facilitates the export of Fe-S proteins to the cytosol. However, later it was suggested that, in yeast cells, ALR plays neither a direct nor an indirect role in cytosolic Fe-S cluster assembly and iron homeostasis. It is claimed that Iron homeostasis is independent of Erv1/Mia40 function in various yeast strains (Erv1 mutant) and that the finding by Lange et al. is based on only one Erv1 mutant strain, mainly due to strongly decreased glutathione (GSH) levels (PMID: 26396185).

Additionally, this statement is reinforced by a study in human cells, demonstrating that depletion of ALR does not impact the maturation of cytosolic Fe-S proteins assembled via the CIA pathway (PMID: 25012650). Furthermore, this study in mammalian cells has pointed out the role of ALR in exporting MitoNEEt to the outer mitochondrial membrane (OMM). MitoNEEt is a Fe-S protein that is synthesized in the mitochondrial matrix. Upon synthesis, MitoNEEt translocates through the inner membrane of the mitochondria by ABCB7 and then through the IMS by ALR to the OMM where it contributes to cell proliferation (PMID: 25012650).

Reviewer #3 (Public Review):

These authors report the identification of the function of a genetic determinant (dev1, formerly ydcO ) carried by the ICEBs1 element that increases fitness of the host strain by delaying the entry into the normal developmental pathway leading to biofilm formation and ultimately sporulation, such that the subpopulation expressing the product of dev1 increases in a mixed pool. An interesting novel aspect of the dev1 system is that it is co-regulated with ICEBs1 conjugation, and thus is only activated when the host strain is a minority of a mixed population; in this scenario the Dev1+ subpopulation is essentially cheating on the Dev-. Since expression of the Dev1 phenotype in an entire population would likely cause a crash, the ICE- population density-dependent regulation ensures that the fitness advantage disappears before the crash can occur. I think that the gene is interesting and this report adds a significant aspect to our understanding of the biology and evolution of ICE elements. Overall I am positive about this paper.

Reviewer #2 (Public Review):

This manuscript provides convincing evidence that the ICEBs1 conjugative element confers a fitness advantage on the model bacterium B. subtilis during biofilm formation and sporulation. This effect is frequency dependent and is effected in large measure via an element gene, named devI, by an unknown mechanism that probably decreases the concentration of Spo0A-P. The data are well presented and successfully make the case for a fitness advantage conferred by the mobile element during biofilm formation and sporulation. It is likely that a mechanistic exploration of DevI will follow and will provide another facet to the regulation of Spo0A, a gift that keeps on giving.

Delaying sporulation in a mixed culture confers an advantage for the delayers. This has been convincingly shown. But I wonder about the effects in a clonal population of cells carrying ICEBs1 in competition with a null population. I appreciate that the delay in sporulation is transient, as pointed out in lines 404-407. But a delay of a few hours may be critical in this type of competition between populations as resources become limiting. This is presumably why sporulation is so exquisitely regulated on so many levels and in response to many external an internal signals. If so, ICEBs1 would have a deleterious effect and the element might be in danger of extinction. I suppose that an analogous discussion could be considered for biofilm formation.

Reviewer #1 (Public Review):

Mobile genetic elements like phages, transposons, plasmids, and conjugative elements are widespread in prokaryotes and confer important traits to their hosts, including antibiotic resistance and virulence. In this study, the authors convincingly demonstrate that the mobile element ICEBs1 of Bacillus subtilis confers a fitness advantage to its host by delaying entry into metabolically costly developmental processes (biofilm formation and sporulation). The gene devI is identified as being responsible for delaying initiation of development, but the mechanistic basis for this could be further explored. Their results show that, in addition to conferring novel phenotypes, mobile elements exert influence by tuning existing host pathways, a paradigm that could be extended to many other prokaryotes.

Strengths:

The paper is written very clearly, the experimental data is convincing, the interpretations and conclusions are justified by the data.

The authors implemented clever genetic approaches to quantitatively compare the fitness of strains harboring or lacking ICEBs1 in co-culture. I appreciated the use of the conjugation mutant (comEK476E) to prevent ICE transfer that would confound the analysis. Similarly, the authors genetically separate the developmental pathways under which ICEBs1 confers an advantage (biofilm formation and sporulation), by deleting the spo0A promoter under sigH control to prevent sporulation but retain biofilm formation. Finally, to assess the contribution of ICE-encoded genes to fitness, the authors take advantage of a "locked-in" ICE variant (∆attR, oriT*) that cannot excise and replicate - thereby eliminating the confounding variable of gene dosage from ICE replication.

As mentioned above, the effects of ICEBs1 on development set an important precedent for how mobile genetic elements interact with their hosts. They are often regarded as autonomous elements, but the authors provide an example of how these elements can influence host pathways.

Suggestions for improvement:

The authors show that the gene devI is necessary and sufficient for ICE-mediated delay of development initiation. Gene expression analyses suggest this delay affects the earliest stages of development (genes under control of spo0A, the master regulator of sporulation, are affected). I think the authors could investigate the mechanism of spo0A inhibition in more detail. Which aspect of spo0A function is affected by DevI? Starvation sensing, spo0A expression, activation of upstream kinases (KinA?), phosphorelay, or binding of Spo0A~P to promoters?

Ectopically expressed DevI (Fig 5) seemed to have a stronger inhibition of sporulation than ICEBs1 alone (Figure 2) - does the constitutively expressed protein block rather than delay sporulation? I wonder if the authors would like to comment on how, in the wild-type ICEBs1 context, DevI activity is eventually overcome by cells that eventually do sporulate after a delay. Furthermore, will cells that successfully transfer ICEBs1 be relieved of DevI-mediated sporulation inhibition?

The data in Fig 4 suggest that devI is not the only ICEBs1-encoded factor providing a fitness advantage. Do the unknown factor(s) also delay development, or do they work via other mechanisms: i.e. does the ∆devI mutant have a sporulation delay? Any idea what the other factors might be (from bioinformatics for example)?

Reviewer #3 (Public Review):

In "KLF10 integrates circadian timing and sugar signaling to coordinate hepatic metabolism", Anthony Ruberto and colleagues characterize the role of the transcription factor KLF10 in circadian transcription and the transcriptional and physiological responses to hexose sugars in mouse hepatocytes. They confirm earlier reports that Klf10 is expressed rhythmically in mouse liver, with peak expression at ZT9. They show that Klf10 expression is induced by glucose and fructose and that hepatocyte-specific deletion of Klf10 exacerbates hyperglycemic and hepatosteatotic responses to 8 weeks of elevated sugar consumption. They use RNA sequencing and ChIP sequencing to define the complement of Klf10 target genes in hepatocytes and how they are regulated by glucose and fructose. Together their data support a model in which KLF10 limits the transcriptional induction of rate-limiting enzymes involved in gluconeogenesis and lipogenesis in response to elevated sugar consumption, thus mitigating the pathophysiological impact of high sugar diets. The experiments are mostly well designed, presented, and interpreted but several points require additional investigation and/or clarification. While the current manuscript suggests an integration of circadian timing and sugar signaling by KLF10, additional experiments to establish how some of the molecular and physiological effects are modulated by time of day are needed to better support that claim.

Strengths:

This study uses a combination of genetic, biochemical, and physiological approaches to investigate the hepatocyte-specific function of the transcription factor KLF10. Deletion of KLF10 specifically in hepatocytes distinguishes this study from other related work. Further, the characterization of global daily gene expression patterns in mouse liver is well designed and analyzed and establishes that hepatocyte-specific deletion of Klf10 remodels daily rhythms of gene expression in the liver. The combination of that analysis with ChIP sequencing provides powerful evidence to establish the hepatocyte-specific KLF10-dependent transcriptome and highlights its targeting of rate-limiting enzymes in lipogenic pathways. Together, the molecular and physiological analyses in this study provide compelling evidence that KLF10 plays a protective role in the context of excessive sugar consumption by limiting lipogenic gene expression pathways and thereby suppressing hepatic steatosis.

Weaknesses:

In its present form, this study does not thoroughly connect the in vitro and in vivo findings and misses the opportunity to fully characterize the role of KLF10 in circadian regulation of lipogenesis in response to excessive sugar consumption in vivo. It is unclear whether the concentrations of glucose and fructose used to stimulate primary hepatocytes are similar to those experienced in response to the dietary stimulus in vivo and there is no examination of the impact of sucrose on Klf10 expression or downstream gene expression. This omission complicates the interpretation of the response to the combined sugar stimulus in vivo, especially in light of a recent report that KLF10 deletion protects against hepatosteatosis caused by consumption of a high sucrose diet. It also does not examine how time of day influences KLF10-dependent gene regulation in response to sugar consumption. Without these analyses, it falls short of connecting the circadian and sugar-response pathways through KLF10.

Reviewer #2 (Public Review):

This study builds on a previously published paper from this group showing that KLF10 is under circadian control, and it in turn affects the oscillation of a set of metabolic genes in the liver. While the previous study utilized a systemic Klf10 KO mouse model, here, Ruberto et al. generated a conditional hepatocyte-specific Klf10 KO mouse model (Klf10Δhep).

The authors find that the absence of hepatocyte KLF10 alters the circadian oscillation of a number of metabolic genes. In response to sugar consumption, Klf10Dhep mice demonstrate exacerbated adverse effects as well as significantly increased hepatic expression of many glycolysis, gluconeogenesis, and lipogenesis related genes. They conclude that Klf10 normally acts as a "transcriptional brake" to protect animals against the effects of high sugar consumption and show via ChIP-seq that KLF10 is present at a wide range of metabolic genes, particularly at those involved in acetyl-CoA metabolism. The findings are interesting, particularly in the context of the burgeoning burden of metabolic disease and its relation to high sugar consumption, and are supported by the experimental findings.

Reviewer #1 (Public Review):

Ruberto et al. utilize hepatocyte-specific Klf10 knock-out mice to demonstrate expression changes of rhythmic transcripts, highlighting dysregulated glucose and lipid metabolism as an enriched gene set. They demonstrate that KLF10 is necessary for proper glycemic control in mice and that KLF10 coordinates suppression of metabolic gene expression in the liver in response to high sugar diet. The authors corroborate their findings by analyzing gene expression changes of primary hepatocytes stimulated with fructose and high glucose. Finally, the authors identify KLF10 target genes using ChIP-seq and validate Acss2 and Acacb as target genes suppressed in mice following a high sugar diet. Novel aspects of this work include the metabolic characterization of a hepatocyte-specific Klf10 knock-out mouse, identification of KLF10 target genes in hepatocytes using ChIP-seq, and description of circadian transcript expression with Klf10 loss.

Reviewer #3 (Public Review):

The authors sought to directly compare manipulations of different signaling pathways for their ability to induce cell cycle activation and proliferation in cardiomyocytes from various species and maturation levels. The manipulation consisted of peristent lentiviral expression of beta catenin, cyclin D2, rat Erbb2, human Erbb2, and Yap8SA.

A major strength of this study is that it shows that most of above expressions appeared to induce negative feed-back responses at the post-transcriptional level to limit protein overexpression, illustrating how difficult it is to manipulate cardiomyocyte proliferation. By contrast, human Erbb2 did induce prominent proliferative effects in both rat and human cardiomyocytes. However, this finding has been shown before. The novelty here is limited to interspecies differences of the effects of Erbb2 overexpression. Multiple studies in oncology have shown that Erbb2 overexpression increases cell proliferation and is sufficient to induce cancer growth. It has also been shown that transient overexpression of Erbb2 in vivo in the heart results in dedifferentiation and proliferation of cardiomyocytes. The observation that Erbb2 overexpression induces cardiomyocyte dedifferentiation alongside mitosis is not unexpected; in general, stimuli that induce cardiomyocyte proliferation also induce cardiomyocyte dedifferentiation and sarcomere disassembly.

In this study, in a 3D model of rat neonatal cardiobundles Erbb2 overexpression also led to formation of a necrotic core. It also led to loss of sarcomeres and contractile force and tissue stiffening. These effects appeared to be mediated by mTOR-independent, Erk-dependent mechanisms. Although experiments in this study are of a high technical level, and results interesting, the likely impact of this work is minor. Indeed, the overall picture of Erbb2-induced pathologic hypertrophy is likely related to the applied methodologies, i.e., a persistent as opposed to temporally controlled Erbb2 overexpression and the use of an avascular 3D model lacking the cellular complexity of the intact heart.

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #1 (Public Review):

This manuscript titled "Human Erbb2-induced Erk Activity Robustly Stimulates Cycling and Functional Remodeling of Rat and Human Cardiomyocytes" directly compared a number of previously identified candidate mitogenic genes in different cardiomyocytes and different maturity status and investigated the pathway involved. The authors found that the human Erbb2 triggers the strongest proliferative effect in both human-induced Pluripotent Stem Cells and Neonatal Rat Ventricular Myocyte, and was associated with the Erk pathway. The authors then proved this association by demonstrating that inhibition with Mek inhibitor and Erk inhibitor attenuates the human Erbb2-induced response. In addition, the authors found that Yap8SA failed to trigger proliferation in the cardiomyocyte tested due to negative feedback loop. Thus, this study provides helpful information regarding the relative effectiveness of a number of candidate genes.

Strengths:

— This study investigates five candidate genes in different species and different maturation status of cardiomyocyte. In each setting, all genes are studied. Therefore, direct comparison regarding their effectiveness can be made.

— Furthermore, this study demonstrated the mechanism on how the differing responses arose, providing in-depth information.

Weakness:

— Although this study showed induced proliferation of cardiomyocyte following candidate genes expression, the authors did not present sufficient proof that the function would improve. Cardiomyocyte harbor differing functions and parameters that represents it should ideally be investigated.

Joint Public Review:

The manuscript by Liu and colleagues is a very elegant study demonstrating the emergence of ectopic beta cells after beta cell specific ablation in zebrafish pancreas in a context in which vascularization of the larvae was altered in either npas4l mutants or etv2 morphants. Provocatively, the authors demonstrate the mesodermal origin of ectopic and functional beta cells using 2 mesodermal mapping strategies. This study is very well conducted with appropriate controls and rigorous statistical analyses. This study will likely impact the field of pancreas regeneration providing a novel source for beta cells within the adjacent mesodermal tissue.

Joint Public Review:

Although sensory neurons are thought to be the primary detectors of environmental stimuli in skin, it is more and more appreciated that non-neuronal cell types also play important roles. Previous work from the Stucky group (and others) has shown stimulation of optical excitation of keratinocytes can evoke action potentials in sensory neurons and behavioral responses suggesting functional connectivity. Earlier work from the Stucky group provided evidence that keratinocytes are thermosenstive and required for normal temperature sensation.

Here, they look into whether these cells are also important for mechanosensation. Using K14-Cre-dependent conditional KO mice, functional assays and behavioral analysis, Moehring and collaborators report that the mechanosensitive channel Piezo1 is expressed in keratinocytes in mice and humans and claim that it contributes to normal touch sensation. The in vitro data convincingly show that keratinocytes have mechanically evoked currents mediated by Piezo1. Interestingly, this work shows that recruitment of epidermal, non-neuronal Piezo1 by mechanical stimulation of keratinocytes could contribute significantly to touch through activation of cutaneous sensory fibers (mechanoreceptors). Specifically, they provide evidence that removing Piezo1 from keratinocytes reduces the frequency of spiking in select types of sensory neurons to punctate and dynamic touch stimuli. Finally, they supply quite surprising data documenting significant behavioral deficits in Krt-conditional knockout mice.

Overall, this work provides an intriguing series of observation and potentially fundamental discovery. However, concerns remain as to how the relatively subtle differences in the skin-nerve recordings result in such profound behavioral effects? Similarly, it is hard to understand how loss of the related channel Piezo2 in sensory neurons completely abolishes many touch responses if mechanosensitivity of keratinocytes is sufficient to evoke touch behaviors (as their experiments applying Yoda-1 to the hindpaw of mice would suggest). Altogether, this work suggests a novel role for epidermal Piezo1 in normal touch but the key neuro-epithelial signaling remains to be identified.

Reviewer #3 (Public Review):

The authors clearly demonstrate the effectiveness of optimized tools to generate precise C to T point mutations in zebrafish F0 embryos. The demonstrate germline transmission and an associated mutation for one mutation. There is sufficient data for members of the community to consider adopting these tools to generate mutation in their own laboratories

Reviewer #2 (Public Review):

Rosello et al. present very compelling evidence that Cytosine base editors can be used to introduce G:C to A:T base conversions with high efficiency in zebrafish. Furthermore, they describe engineering and validation of a base editor targeting the NAA PAM sequence. Finally, they have developed a potential novel model of the Noonan syndrome. The manuscript represents an important and much needed advance in precision genome editing in the zebrafish model system.

Reviewer #1 (Public Review):

The manuscript by Rosello et al., describes the application of cytosine base editing to efficiently introduce known and predictable mutations into disease genes in vivo in zebrafish, and examine signaling pathways and model disease. The majority of the data presented is analysis of editing precision and efficiency in somatically targeted embryos, with one example of a precise edited germline allele recovered. A direct comparison of the cytosine base editor BE4 and an improved version ancBE4max indicates both are highly efficient at somatic base editing. ancBE4max reduces alteration of bases outside the base editing window, and the data suggests loci for which BE4 base editing has failed can be targeted with ancBe4max. The authors demonstrate efficient base editing in embryos at multiple cancer genes (up to 91%), introducing activating mutations into oncogenes and nonsense mutations in a number of tumor suppressors. A S33L allele was introduced into the b-catenin gene ctnnb1 to activate the wnt signaling pathway as evidenced by expression of the wnt reporter Tg(tcf:GFP). Another novel aspect of this study is that the authors have expanded base editing target site selection by switching out the ancBe4max SpCas9 PAM-interacting motif domain with the domain from Spymac, which recognizes an NAA PAM. ancBe4maxSpymac editing efficiency was modest (16-19%). The method reported here has strong potential for effective combinatorial mutagenesis to map complex genetic interactions that underly disease pathogenesis. Overall, this study demonstrates cytosine base editing is an efficient and powerful method for introducing precise in vivo edits into the zebrafish genome.

Reviewer #2 (Public Review):

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

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

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

Considerations:

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

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

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

Reviewer #1 (Public Review):

While I am not sufficiently qualified to comprehensively assess the molecular dynamics simulations, all interpretations seem careful and remain within the described limitations of the various metrics that the authors report.

The experiments are well executed; the results are presented clearly and interpreted carefully. This is a rigorous and important biophysical study that provides a solid foundation for the investigation of archaeal genome biology. The authors' new findings raise interesting questions, and although addressing them is outside the scope of this study, the article would perhaps benefit from a more detailed discussion of the biological implications of the results. The manuscript does not indicate whether the cryo-EM maps and atomic models were deposited in the EMDB and PDB. I strongly encourage the authors to do that: it would add a lot of value not only for the readers of this study, but also for the wider structural biology community.

Reviewer #3 (Public Review):

This is a well written and elegant study from a collaboration of groups carrying out models based on high resolution imaging. I think the study also serves as a prime example for where modeling and simulation bring added value in the sense that the insights revealed in the study would not likely be gained through other methods.

1) As the authors point out, clinical studies have revealed that the fibrotic burden in ESUS patients is similar to those with aFib. The question is why then, do so few ESUS patients exhibit clinically detectable arrhythmias with long-term monitoring. The authors hypothesize and their data support the notion that while the substrate is prime for pro-arrhythmia in ESUS patients, a lack of triggering events may explain the differences between the two groups.

2) I think the authors could go further in describing why this is surprising. Generally, severe fibrosis is thought to potentially serve as a means or mechanism for pro-arrhythmic triggers. This is because damage to cardiac tissue typically results in calcium dysregulation. When calcium overload occurs in isolated fibrotic tissue areas, or depolarization of the resting membrane potential due to localized ischemia allows for ectopic peacemaking, we might expect that the diseased/fibrotic tissue is itself the source of arrhythmia generation. I think the novel finding here is that this notion may be a simplification, and the sources of arrhythmia generation may be more complex and may need to come from outside the areas of fibrosis. I think this is a big deal.

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

Reviewer #1 (Public Review):

Previous research showed a close link between sub-clinical AFib (Atrial Fibrillation) and ESUS (Embolic Stroke of Undetermined Source). As such, current established clinical care for ESUS patients is long-term monitoring for evidence of AFib and anticoagulant treatment for an individual with high risk for AFib. Nevertheless, questions are still unanswered about who the individuals with high-risk for ESUS are and how to properly identify this population.

This research tries to identify the fibrotic properties of ESUS patients and its pro-arrhythmic potential using computational modeling of patient's left atria reconstructed from cardiac LGE-MRI (Late-Gadolinium Enhanced Magnetic Resonance Imaging). Ultimately, their results of the comparison between left atria of ESUS and AFib patients revealed that the fibrotic substrate that could induce arrhythmia in ESUS and AFib patients are indistinguishable, raising more questions that would need to be addressed in further studies.

This study uses a sophisticated personalized computational modeling approach that has been validated in previously published papers. This study is also well designed, clearly written, with robust data and proper statistical analysis.

What is left unclear is what is unique about the fibrotic substrate in ESUS patients in comparison to AFib patients in the future.

Reviewer #3 (Public Review):

In this manuscript, Sheng et al. have demonstrated that Langerhans cells (LCs) do not exit the skin both under steady-state conditions and after skin sensitization, using newly generated DC-SIGN DT mice and others. In addition, through a combined use of genetic fate mapping and novel inducible LC ablating mouse models, they show that the originally described lymph node LC fraction is actually an independent LClike cell population that originates from the dermis, not from the epidermis. Moreover, these LClike cells, which are replaced over time by bone marrow-derived counterparts, are characterized by their slow turnover rate and trafficking to the LN. This study contains novel and important findings. This reviewer has several comments on the manuscript.

Major comments:

1) The functional roles of LClike cells remains unclear, especially in the relationship with conventional LCs. Thus far it has been considered that LCs play essential roles in OVA-induced atopic dermatitis like models. But it remain unclear whether conventional LC s or LC like cells play important roles.

2) The authors stated that a fraction of CD11bhiF4/80hi cells co-expressed CD326 and CD207 are detected in the dermis (Fig. 1b, upper panel), which is likely to be derived from the epidermis. But it remains unclear whether this subset is just a contamination through the separation process or a truly migratory one from the epidermis. The authors can demonstrate clear localization of LCs in the epidermis, LClike cells, and LCs in the dermis.

3) Related to the above question, the authors claim that LCs can emigrate from the epidermis to the dermis. Given that LCs can emigrate from the epidermis to the dermis by transmigrating through the basement membrane, why LCs cannot migrate into the lymphatic vessels. LCs are known to express CCR7 highly that is important for migrating into the lymph nodes from the skin. What is the functional (APC, migration, etc) difference between LCs and LClike cells?

4) I agree that the novel subset exists as CD207+CD326+ LClike cells in the dermis, which is different from conventional LC. But the term, "LC-independent" CD207+CD326+ LClike cells, which the authors used often through the manuscript, is a bit confusing, because it is not totally clear whether LClike cells are completely independent of LCs or not. It would be informative if the authors can demonstrate whether LClike cells contain bierbeck granules (this is also a hallmark of LCs) or not, since bierbeck granule-positive cells were detected in the LN (https://pubmed.ncbi.nlm.nih.gov/4758275).

5) The authors can discuss the relationship between LClike cells and short lived LCs that were previously described (https://pubmed.ncbi.nlm.nih.gov/23159228).

6) Where do LClike cells locate by FACS plots analysis using CD11b and CD103?

Reviewer #2 (Public Review):

The authors used several approaches to define a discrete population of Langerhans cell-like (LC-like) dendritic cells (DC) in the dermis of mice. By flow cytometry, these cells expressed langerin/CD207 and EpCAM/CD236 and were found in the CD103-CD11b- fraction of dermal cells. It was also shown that LC-like cells, rather than LC, are the main contributors to CD103- langerin+ cells in the lymph node. By single cell RNAseq of dermal cells they clustered with Langerhans cells but lacked expression of DC-SIGN/CD209. Fate-mapping with Kitmercremer/Rosa26loxPSTOPloxPeYFP showed a similar origin to other dermal DC, with no yolk sac signal as observed in LC. Bone marrow chimeras, however, indicated a much slower turnover compared with other dermal DC. Independence from LC and other DCSIGN+ DC was also demonstrated by unchanged kinetics during continuous ablation of DCSIGN+ fractions in a DTR model.

The results explain the expression of langerin on two fractions of dermal DC observed several years ago (CD103+ and CD103-). The demonstration that epidermal LC do not contribute to LN populations in the steady state is completely unexpected and raises an important question of the in vivo function of the LC-like subset. LC-like cells appear to be related to LC but a more in-depth analysis of their gene expression differences compared with LC would be interesting. Also, their potential relationships with other DC (cDC1 or cDC2), was not defined. Langerin expression by human cDC2 is well described and possibly correlated with these observations in mice. Finally, the turnover of LC-like cells was slow, yet they contributed as many langerin+ cells to the resting lymph node as cDC1, which turnover quickly. Proliferation in situ might explain this observation but there were no data on this.

Reviewer #1 (Public Review):

In the days of the COVID-19 pandemic vaccines, mechanisms of vaccine administration are important and of broad interest. Vaccines are most often given into the skin. Antigen-presenting cells of the skin are responsible for eliciting the immune response in draining lymph nodes. Langerhans cells, the dendritic cell variant of the epidermis, are one of these cutaneous antigen presenting cells that are believed to do this job. They migrate from the skin, the site of antigen/vaccine uptake to the draining lymph nodes, where lymphocytes are located and where the immune reaction will be initiated. With their sophisticated experiments, the authors challenge this view. They use leading edge methodology (mouse models) that strongly suggest that there may be yet another subset of skin antigen presenting cells, that is responsible for carrying antigen from skin to lymph — at least in the steady-state skin. This population resides in the dermis (the connective tissue part of skin), as opposed to the classical Langerhans cells, which sit in the epidermis. This may be relevant to the maintenance of immunologic tolerance to innocuous substances in the absence of an overt inflammation. The data suggest that Langerhans cells may not play the crucial role they were thought to play. This is certainly a conceptual advance that — like always in science, especially when experimental systems are complex, as they are here — needs to be underpinned by future studies. In the long run, it will be very interesting (but much more difficult to study) to see whether this also holds true for human skin.

Reviewer #3:

Labelling strategies for electron microscopy have so far lacked the ability to clearly visualise genetically expressed probes such as GFP for light microscopy. Building on previous studies by the group of Ellisman, the Parton group have made significant adaptations and improvements to the system. Especially addressing the issue of diffusion of the DAB precipitate and the low visibility of it by silver enhancing the particles is a key step forward. The authors have tested their system in a wide variety of EM workflows and show that it works.

The quantification part of the manuscript is to me potentially the most interesting part. Quantitation of proteins at physiological levels at the ultrastructural level would be a significant achievement. This part is a bit under represented although there are some issues with that. The silver enhanced particles on the added external standard appear to be larger than the ones inside. Does that result in lower detection?

Overall, this is a manuscript that is very clearly written and very easy to follow for non-experts.

Reviewer #2:

In this study, entitled “APEX-Gold: A genetically-encoded particulate marker for robust 3D electron microscopy” Rae et al. describe a method to improve the visualization of the staining for genetically encoded probes that they described in previous studies (namely APEX2 constructs).

These techniques are very powerful as they increase the sensitivity for detecting low level of expression of the tagged proteins (e.g. compared to GFP tagged proteins). The novelty in this study is that the reaction product (DAB precipitates) is revealed by the nucleation of silver/gold precipitates. Such enhancement has been used extensively in the past for pre-embedding immuno-peroxidase techniques, but has never been combined with the use of APEX2. This has one major advantage: that the contrast of the positive staining can stand out from the contrast of the surrounding ultrastructure, making the sample preparation more adapted to 3D EM techniques, especially volume SEM where contrast is a bottleneck. Moreover, the sensitivity of the technique is shown to be compatible with the detection of endogenous levels of expression.

The technique is very well detailed and elegantly illustrated by convincing applications on cultured cell systems. The apparent simplicity of use, together with a growing interest in the community for the APEX2 based techniques (also in correlative imaging), significantly raises the potential for it to become a standard in the field, and should thus be shared with the community.

Reviewer #1:

The manuscript by Rae et al. reports the development of a new protocol for labeling genetically-tagged proteins of interest with heavy atom particles for visualization by electron microscopy. The optimized protocol builds on the established use of the enzyme APEX fused to the target of interest. APEX oxidizes diaminobenzidine, DAB, which in turn converts silver and gold metal salts to particulates in close proximity to the APEX-fused protein of interest. The optimized protocol is related to the contrast-enhancement method reported by Sedmak et al., 2009 and Mavlyutov et al., 2017. The changes to the method may improve the proportionality of the signal such that the number of APEX tags present in a sample is better correlated with the number of heavy atom particles. While the study appears to be sound, it is an extension of an established labeling method.

This manuscript is in revision at eLife

The decision letter after re-review, sent to the authors on February 2 2021, follows.

Summary

The reviewers concur that this article offers an interesting conclusion regarding optimal foraging and chemosensory valence. However, they also agree that it would benefit from a second round of revision, aiming at an improved precision of language and a better discussion of the assumptions of the model and experimental conclusions.

Public Review 1:

The authors present experiments that demonstrate how C. elegans worms bias their foraging decisions depending on feeding history and sensory cues (here, called pheromones) that reflect the density of worms. Navigational preference for these sensory cues is found to change from attractive to repulsive depending on the time at which worms leave a food patch, and additional experiments that condition worms under different combinations of conditions (with/without the sensory cues, with/without food, with/without repellent) indicate that associative learning is involved in this inversion of preference. A mathematical model is provided to argue that this inversion represents an optimal foraging strategy that is also evolutionarily stable.

Public Review 2:

The authors use the nematode C. elegans to reveal how animals associate social signals with specific contexts and modify their behaviors. Specifically, they show that C. elegans leaving a food patch are attracted to pheromonal cues, while those leaving later are repelled from pheromones. The authors using a behavioral model to suggest that the switch from attraction to repulsion is likely due to a change in learning. This study links learning with social signals providing a framework for further analysis into the underlying neuronal pathways.

Reviewer #3:

This paper is primarily about modeling the ERK pathway during the induction of synaptic plasticity. This pathway has been previously modeled, and this is cited in the paper. The main addition here is the addition of the effect of SynGap which is necessary in some form of LTP. This is a very detailed study, and what it seems to primarily show is that the ERK pathway favored spaced vs. massed stimulation protocols. This is a very detailed paper, but no conceptually new ideas are presented here. The paper adds to an existing foundation, but fails to make the case that this is a very significant addition. What is the significant consequence at a higher level of these added details?

The ERK pathway is just one component of a much larger set of pathways that control synaptic plasticity, how much do we learn from studying this pathway in isolation? Also, the paper cites the importance of this pathway to L-LTP, is it the induction phase of L-LTP? It seems so because ppRRK decays in less than an hour. How then does this pathway contribute to the maintenance of L-LTP? These processes, such as a possible upregulation of protein synthesis, are not part of this model either.

This paper studies in detail different pathways that influence ERK activation in synapses. This is a very detailed study, but how many details do we actually know? For a detailed paper though it seems that many of the details are missing. Is there a detailed diagram of reactions, or set of equations for all these reactions? Some coefficients are named in figure 1, and this might be sufficient for a schematic description of the model in the paper, however there must be somewhere a detailed description of all reactions. How many species are there here, how many coefficients? How are coefficient values known? How many coefficients are directly estimated? The paper does carry out an extensive robustness analysis, though it is not well explained.

What are the major takeaways from this paper, and what experiments could test this model?

To summarize, the paper is very detailed, carefully constructed and executed, but it fails to convince that the problem it addresses is very significant, and it makes no conceptual breakthroughs.

Reviewer #2:

Miningou and Blackwell in their manuscript titled "Temporal pattern and synergy influence activity of ERK signaling pathways during L-LTP induction" explored the contributions of upstream pathways to ERK activation during LTP. The authors expanded on their previously published LTP model to assess the influence on ERK activation of each of the upstream pathways originating from cAMP or Ca+2 activated with differing temporal patterns. This manuscript's aim is quite germane, since 1) ERK plays such a central role in learning and memory and its cellular proxy LTP; 2) the Ca+2/cAMP/PKA system is highly complex and nonlinear, with multiple feedback loops. The resulting manuscript has the potential to be impactful. The approach of using a stochastic reaction-diffusion model is state of the art and appropriate for the modeling of these subcellular events in spines. And the modeling insights are very intriguing as the authors predict that ERK activation by cAMP/PKA or Ca+2 pathways differ in their linearity, these pathways can synergize during LTP and this may involve a novel feedforward loop containing synGAP. The authors do a marvelous job placing their findings within the huge body of LTP literature.

There are, however, a couple of points that I feel should be addressed:

1) There needs to be additional technical detail on how the original models were expanded. The model presented here was developed by merging Jȩdrzejewska-Szmek et al., 2017 and Jain and Bhalla, 2014 models. These models were developed based on experimental data and validated with independent experimental datasets in a rigorous manner. It is not clear how the combining these two models, and the additional molecules and reactions added have affected the dynamics of ERK activation, and how comparable they are to the original experimental data used for model development in the previous modeling efforts. It is not clear if the model was reparameterized.

2) Beyond the ERK activation traces, it would be useful for clarity sake to also include the simulated traces for the activation of the upstream molecules (PKA, RAS, RAP, etc). Given how additional changes have been made additional information should be provided to ensure that the contribution of each pathway is accurately represented.

Reviewer #1:

This study takes on the question of the roles of the many pathways leading to ERK activation in long-term potentiation. This is an advance: few models consider more than a couple of input pathways. The authors consider two aspects: how pathways sum to give strong responses, and distinct temporal pattern selectivity. They show that both summation linearity, and pattern selectivity, are strongly governed by which pathways are engaged in driving the response.

The model and analysis is potentially interesting, but the paper would be much strengthened if there were more convincing validation of the properties of the model by way of simulations to compare with experiments. Further, the pathways chosen are already one step into the synapse. Thus the actual combination of pathway activations would not be quite as cleanly separated if they were driven by synaptic input.

This manuscript is in revision at eLife

The decision letter after peer review, sent to the authors on January 29 2021, follows.

Summary

This paper builds on recent studies that have made the connection between chronic endothelial damage and cellular senescence among endothelial cells in PH. Here, using a transgenic mouse that expresses in endothelial cells a dominant negative form of the TRF2 protein needed for telomere maintenance, the authors induce cellular senescence in the endothelium and show that these mice demonstrate worse PH characteristics following exposure to chronic hypoxia. They go on to test the effect of this dominant negative protein on human pulmonary artery endothelial cells in vitro and show that transfected ECs increase expression of secreted and surface-bound signaling molecules, and that when co-cultured in direct contact with pulmonary artery smooth muscle cells the SMCs increase their proliferation, an effect blocked by pharmacologic inhibition of Notch signaling. Notch blockade in vivo attenuates pulmonary hypertension in both transgenics and wild type controls. These data provide an intriguing framework for understanding how endothelial damage alters signaling to neighboring cells in the vascular wall and provides further evidence that Notch signaling plays a key role in the development of PH vasculopathies.

Essential Revisions

1) It's unclear where within the pulmonary vasculature the TRF2DN transgene is expressed, and therefore which vessels are effected by senescence. That the transgene is driven by a well-established endothelial promoter (VEcad) is not sufficient to demonstrate universal expression. Especially in the case of a transgene whose expression is intended to result in chromosomal abnormalities, DNA damage and a halt to cell proliferation, significant mosaicism is to be expected. In situ hybridization with probes specific to TRF2DN or an antibody stain that specifically recognizes the transgenic protein on lung tissue sections would address this problem. Both representative images and a careful characterization of the classes of arteries (subdivided by diameter, for example), capillaries, veins, and lymphatics that express the transgene and with which levels of mosaicism would be ideal.

2) Validation of the EC expression changes, specifically of the Notch ligands identified as upregulated in vitro, need to be validated in situ to ensure they are upregulated in the endothelium of arteries where the PH phenotype (increased muscularization, increased SMC proliferation) is observed in this model. Whole lung dissociation followed by enrichment for CD146+ ECs will result in an overwhelming number of capillary ECs and a tiny number of artery ECs (Figure 3E). Similarly for the in vivo validation of Notch reception in SMCs through qPCR for indicators of Notch reception (Figure 3F, 4I) - this experiment was done on whole lung lysate and does not demonstrate increased expression of these genes in the artery wall. In situ hybridization with probes specific to TRF2DN or an antibody stain that specifically recognizes the transgenic protein on lung tissue sections would address this problem. Both representative images and a careful characterization of the classes of arteries (subdivided by diameter, for example), capillaries, veins, and lymphatics that express the transgene and with which levels of mosaicism would be ideal.

3) The method by which PAs are identified (Figure 1D, 4F) and the metrics by which artery muscularization from images of tissue sections is quantitated (Figure 1F, 1H, 4H) are somewhat unclear and appear to be made from very few fields from an unspecified number of animals. There appears to be significant variance in artery response to hypoxia (comparing Figure 1E with vehicle in 4G), which is not a problem and very much to be expected, but means there must be absolute clarity in how the data for graphs summarizing imaging data were obtained. A supplementary figure with representative images demonstrating how arteries were scored would be very helpful. The number of independent mice for each analysis must appear either in the figure legends or in the relevant sections of the methods. A reader's understanding of how the PAs were identified in Figure 1D and 4F would be helped by using a vascular specific antibody stain. And a supplementary figure with a large panel of artery images from Tg and Wt animals before and after hypoxia exposure, with and without DAPT, so the reader can grasp the range of effects on vessels in each case would be immensely helpful.

4) Please describe the in vivo relevance of endothelial progeria induced by decreased TERF2 function in patients with PAH.

5) While endothelial senescence leads to decreased proliferation and apoptosis of EC, which have been shown to occur in PAH, clonal proliferation of EC is also a hallmark of advanced disease in PAH. The study does not comment on this varied phenotype of EC in the pulmonary circulation in PAH patients and the relationship of senescence of EC to SMC migration.

6) Increased levels of Jag1 have been linked to excess proliferation in several cancer cell lines. In the context of senescence with decreased EC proliferation, increase in Jag1/Jag2 levels is surprising and the paper does not comment on this phenotype as being distinct from cancer cells.

7) The mechanism for increased notch ligand expression in response to progeria was indirectly addressed with 5-Aza studies which presumably leads to inhibition of DNA methylation. However, it is unclear how this inhibits increase in notch ligand expression. In the discussion, the authors mention (Line 17, page 10) that DNA hypomethylation promotes specific transcriptional programs as a result of senescence. However, 5-Aza prevented the induction of Notch ligand expression in senescent EC (Suppl Fig 2). The discussion of these results needs further clarification. It is unclear what specific epigenetic modifications occur to increase the expression of Jag1/2 and Dll4 in senescence associated changes.

8) The study did not report whether aorta and other systemic vessels demonstrate senescence changes in endothelial cells-endothelial progeria in the TG mice would involve all vasculature. Presumably, vascular remodeling was limited to the lung, given the unique response of the lung to hypoxia. However, examination of a systemic vascular bed would have strengthened the conclusions of the study. Do the EC and SMC derived from aorta or coronary vessels show similar responses in vitro compared to human PAEC with DN-TERF2 transfection?

Reviewer #2:

In this work, Hofmann and colleagues conduct a study investigating the relationship between EEG alpha and subjective arousal in naturalistic (as opposed to controlled experimental) settings. Participants completed an immersive virtual reality experience while EEG was recorded, and continuously rated their subjective arousal while a video of the experience was replayed. Three different decoding methods were evaluated (Source Power Comodulation, Common Spatial Patterns, and Long Short-Term Memory Recurrent Neural Networks), each of which demonstrated above chance levels of performance, substantiating a link between lower levels of parietal/occipital alpha and subjective arousal. This work is notable because the roller-coaster simulation is a well-controlled, yet dynamic manipulation of arousal, and in its comparison of multiple decoding approaches (that can model the dynamics of affective responses). Indeed, this is an interesting proof of concept that shows it is possible to decode affective experience from brain activity measured during immersive virtual reality.

Major concerns:

The authors advocate that naturalistic experiments are needed to study emotional arousal, because "static" manipulations are not well-suited to capture the continuity and dynamics of arousal. This point is well-taken, but no comparisons were made between static and dynamic methods. Thus, although the work succeeds in showing it is possible to use machine learning to decode the subjective experience of arousal during virtual reality, it is not clear what new insights naturalistic manipulations and the machine learning approaches employed have to offer.

The methods used to assess model performance are also a concern. Decoding models were evaluated separately for each subject using 10-fold cross-validation, and inference on performance was made using group-level statistics. Because time-series data are being decoded, if standard cross-validation was performed the results could be overly optimistic. Additionally, hyperparameters were selected to maximize model performance which can also lead to biased estimates. This is particularly problematic because overall decoding performance is not very high.

Reviewer #1:

Hofmann et al. investigate the link between two phenomena, emotional arousal and oscillatory alpha activity in the cerebral cortex, which is of central interest in their respective fields. Although alpha activity is tightly linked to the first reports of electric activity in the brain nearly 100 years ago, a comprehensive characterization of this phenomenon is elusive. One of the reasons is that EEG, the major method to investigate electric activity in the human brain, is susceptible to motion artifacts and, thus, mostly used in laboratory settings. Here, the authors combine EEG with a virtual reality setup to give experimental participants a roller-coaster ride with high immersion. The ride, literally, leads to large ups and downs in emotional arousal, which is quantified by the subjects during a later rerun. Next, the authors decode the degree of emotional arousal as stated in the rerun based on the EEG signals recorded during the VR session. They demonstrate convincingly a negative dependence of alpha activity with the degree of emotional arousal. Further, they demonstrate the differential involvement of parietal and occipital regions in this process. The sequencing of the description of methods and results could be improved upon, is, however, as such perfectly ok. This investigation comes timely, makes an important contribution to our understanding of the relation of emotions and sensory processing.

Summary: This research makes important, incremental contributions to the fundamental understanding of propofol interactions with bacterial voltage-gated sodium channels.

Public Review:

The reviewers agree that this research adds to the fundamental understanding of propofol interactions with bacterial voltage-gated sodium channels. Here an objective avenue to binding site mapping is taken involving a photoactivated azide propofol derivative. The strategy identifies two adjacent sites at the intracellular face of Nav channels. These sites are provocative as they settle into a mechanistically rich channel region where the voltage-sensor is coupled to the pore. The manuscript is well-written and referenced, and the conclusions are aligned with the data presented. The methods are appropriate, the data appear to be of high quality. The manuscript is internally consistent and well written. The findings are quite interesting.

The primary concern is that these results were deemed to add incrementally to recently published studies (Yang et al., JGP, 2018) which came to similar conclusions, without the support of the photoaffinity ligand results. Additionally, there were questions about whether voltage-gated sodium channels are involved in the anesthetic actions of propofol, technical questions about molecular simulations, and suggestions for control experiments.

Reviewer #2:

This paper proposes a novel and relevant evolutionary model that explains many aspects of replication origin statistics in a family of yeast species. It is a step forward in our understanding of the evolutionary pressures that affect the distribution of replication origins in Eukaryotes. I recommend the authors address the following issue:

1) Many of the conclusions of the paper are based on the claim that the extending the model by adding an efficiency bias to the origin death rate makes the model fit the data better; in particular, they say in line 213 that "the observed huge divergence in efficiency between lost origins and their neighbors is absent in the model simulations." This is reinforced in line 243, and in other parts of the text. But inspecting Fig 3, the two models (with and without a death rate bias) yield almost identical box-plots; if anything, the box-plots for the lost/nearest fractions of the pure double-stall aversion model seem visually to match the data marginally better. So why do the authors claim that the model with death rate bias is a much better fit? This is far from clear by just inspecting the data. I see no "huge difference" in the plots. There is a difference, but it is far from huge - the differences in the mean are much smaller than the size of the boxes. It seems to me unjustified to use this to choose one model over another. One way to ascertain this is to do rigorous statistical tests to determine if the differences in the means of the simulated and observed data are statistically significant; for example, a t-test.

Reviewer #1:

The manuscript entitled "An evolutionary model identifies the main selective pressures for the evolution of genome-replication profiles" is an examination of the principles shaping evolution of replication origin placement. Overall I found the manuscript to be engaging and interesting, and the topic of general importance. It is quite compelling that with just two parameters, origin efficiency and distance between origins, a good model can be built to describe the dynamics of origin birth and death. While this work on its own is sufficiently important for publication, it would be very interesting to see whether the model can be updated in the future to address whether there are fork-stalling or origin-generating mechanisms that shape evolution of specific inter-origin spaces. This work provides a very good foundation for such efforts.

I have a few major, general concerns I would like the authors to address.

If I'm interpreting the methods correctly, it seems the parameters used in these simulations, such as mean birth rate, mean death rate, gamma, and beta, were fit to the data once, and used as point estimates during simulation. If true, I expect the simulations to be yielding estimates of birth and death rates with a much narrower distribution of outcomes than is likely to be realistic given what an appropriate level of confidence in those parameter estimates would be. Could the parameters be fit to data in such a way that we attain an estimate of confidence in the parameter values, from which a distribution could be generated and sampled from during simulation?

Closely related to my prior concern, I would like the authors to demonstrate the general predictive value of their model on out-of-sample data. Can the model be applied to other data on replication timing? Without such an attempt to demonstrate the model's applicability to out-of-sample prediction, the reader cannot ascertain whether the model is overfit to the Lachancea data from Agier et al, 2018. Also, keeps the parameter estimates here from being overfit to better predict origin birth and death events in closely related branches of the Lachancea tree in Figure S1? Are gamma and beta inferred in a way that accounts for the higher correlation in birth and death events in closer-related branches than in distal branches, or has the fit ignored those correlations?

The authors state that their model identifies selective pressures. The authors imply, and specifically state in lines 238-242, that increased death rate of origins which happen to be nearby highly efficient origins represents selective pressure against the less efficient origins. It isn't until the discussion that the authors raise the possibility that there may simply be a lack of selective pressure to retain inefficient origins that are near highly efficient origins. In my view, it's more likely that selection for the existence of an inefficient origin is simply lower than the drift barrier, so mutagenesis and drift can passively remove such origins over time without the need to invoke selection against inefficient origins.

Figure 3 is intended to show that the stall-aversion and interference model performs better at predicting correlations between efficiency of lost origins and their nearest neighbor. I agree, but I do not think Figure 3 presents a strong case for this conclusion. Fig S6 presents stronger evidence to me. While fig 3 does qualitatively suggest that the joint model may predict the correlation between neighboring origin efficiency and origin loss better than the double-stall model alone, it almost appears to me that the model with fork stalling and interference has significantly overestimated the correlation. Is there a quantitative way, perhaps using information criteria, though I admittedly am not sure how one would go about doing that with simulations such as these, to demonstrate that the model with both effects has better predictive value than the one with only fork stalling?

There are a couple of assumptions of the model that I would like the authors to examine in further detail. First, that origin birth events occur in the middle of an inter-origin space. I am not aware of evidence pointing to this being a good a priori assumption. Can you re-run the simulations, allowing origins to arise at a random site within the inter-origin space into which it is born? Second, is it reasonable to expect origin firing rates to reshuffle to a new value randomly, without any dependence on their prior rate? Perhaps I'm mistaken, but it seems to me that an origin's firing rate should evolve more gradually, and should have a higher probability of sampling from values near its current value than from values very far from its current value.

Reviewer #3:

In this manuscript, the authors utilize single-cell/single-nucleus RNA-sequencing to perform a comparative analysis of the cellular composition of the dorsal lateral geniculate nucleus (dLGN) in mice, non-human primates, and humans. This topic is important for a number of reasons, including (1) the dLGN is a critical center of visual processing about which we know relatively little; (2) the dLGN has emerged as a widely used experimental model of neural circuit development; and (3) in general, the integration of cross-species data at the transcriptomic level is important for identifying conserved mechanisms of brain function that may shed light upon neurological disease states. By employing a relatively deep RNA-sequencing approach (Smart-Seq) the authors identify major excitatory and inhibitory dLGN cell types within each species. While the multiple inhibitory neuron subtypes were relatively similar across species, excitatory neurons displayed major differences particularly between mouse and both primate classes. The authors identified four major excitatory cell types in primate and human dLGN corresponding with known functional heterogeneity that places these neurons into magnocellular, parvocellular, and koniocellular populations. Interestingly, koniocellular neurons could be broken into two distinct subtypes. Somewhat surprisingly, the authors noted a lack of excitatory neuron diversity in the mouse, despite prior evidence suggesting these neurons can have different morphological and physiological features. Yet, although all excitatory neurons in the mouse clustered together, there were subtle differences in excitatory neurons in the mouse that aligned with different regions of mouse dLGN (shell vs core), suggesting that excitatory neuron heterogeneity may still exist along a more subtle continuum. Consistently, neurons in the shell region in mouse dLGN more strongly resembled koniocellular neurons in primates versus the core region, suggesting some level of conservation between excitatory neuron identity across species. While the study is largely descriptive, the authors are creative in their use of bioinformatics to uncover particularly interesting observations that the transcriptomic analysis yielded, and the paper is very interesting because of that. The major weakness of the paper is a paucity of robust FISH analyses to quantitatively validate the transcriptomic findings in all species. Overall, it is my opinion that this work is very important and that, at a broader level, it may help to define the relationship between transcriptomic cell type, functional/physiological cell type, and anatomical cell type within a brain region that is critical for visual function and that has emerged as a fascinating model of neural circuit development in the mouse.

Strengths:

The Smart-Seq transcriptomic technique chosen is appropriate to address the authors' questions.

The data were generated rigorously and subjected to an in-depth quality control pipeline prior to analysis. As a result, the quality of the transcriptomic data is high.

The paper includes a detailed, transparent description of the approach taken in the Results and Methods. The authors point out caveats and weaknesses - and how they were addressed - throughout the text.

The inclusion of tissue from thalamic nuclei surrounding the dLGN as a way to control for the unintentional inclusion of non-dLGN tissue in the experimental dissection was well-designed and effective.

Despite a couple of exceptions, the authors do an excellent job of placing their findings within the context of what is already known about dLGN cell types across different species, and how these cell types function differently in physiological, morphological, and anatomical terms.

The study is descriptive in nature but the authors do a nice job of laying out several interesting findings, such as the observation that GABAergic neurons are more conserved across species than relay neurons, with mouse neurons being particularly distinct. Another fascinating observation is that shell-located neurons in mouse dLGN are transcriptomically related to koniocellular neurons suggesting the possibility of a close relationship between thalamocortical connectivity and molecular identity across species.

Weaknesses:

The characterization of gene expression patterns through sequencing-based transcriptomics has emerged as a powerful tool for dissecting the brain, but it is important to couple such approaches with techniques like fluorescence in situ hybridization (FISH) to verify sequencing results in a histological context. While here the authors show 3 - 4 validations of mouse genes that seem to be restricted to or excluded from the shell versus the core dLGN regions (Figures 4G and S4E), the conclusions of the study would be better supported by a more extensive and rigorous analysis of cell-type-specific gene expression within all species described.

It is not entirely clear from the manuscript how the authors dissected the shell from the core region of the dLGN, given these regions are not as clearly distinct as the dLGN lamina in other species. One possibility would be to take advantage of the fact that the shell receives input from specific RGCs that can be targeted genetically by crossing a Cre driver to the TdTomato line, but I do not believe that that is what was done here. I also noted that the authors use ventral LGN (vLGN) as one of their controls for the precision of their micro-dissections, but given that the vLGN does not directly contact the dLGN, this had me wondering exactly how cleanly the shell and core regions of the mouse dLGN were isolated.

On lines 101 - 103, the authors state "...differentially expressed genes between donors were related to neuronal signaling and connectivity and not to metabolic or activity-dependent effects." Table S2 is cited, but the columns are not labeled such that a common reader could interpret them and confirm the statement in the text. Moreover, the text does not state how the authors made the determination that these differentially expressed genes are not related to "activity-dependent effects".

Reviewer #2:

The conclusion was quite surprising from their anatomical differences and connectivity to the cortex, however, implies different mechanisms underlie for species specific circuit organization.

The manuscript is well-organized and well-written with strong figures. I have only a few comments/suggestions to further improve the overall quality of this manuscript.

I understand obtaining human and NHP tissue is difficult and hard to perform numbers of ISH. Therefore, there is a database that provides additional information on gene expression in NHP LGN (https://gene-atlas.brainminds.riken.jp/). From this database, it is possible to obtain parvocellular specific and magnocellular specific gene expression by fine structure search, which may be worth comparing with the results in the current paper. Many researchers have realized that marmoset is one of the good animal models to understand human brain function and dysfunction, therefore, it is worth including marmoset for comparative analysis for community interest.

Reviewer #1:

In this manuscript, Bakken et al use single cell and single nucleus RNA-sequencing to conduct comparative analysis of dLGN in humans, macaques and mice. dLGN exhibits a dramatic reorganization and lamination in primates relative to mice. Other components of the visual system (retina, V1) have previously been explored with cross-species transcriptomic analyses to reveal species-specific or evolutionary modifications. How dLGN fits in this picture, and the extent to which differences amongst previously identified cell types can be discerned from transcriptomic data, is an important question.

The conclusions are supported by the data, but the paper could better motivate what the main questions or debates are.

Strengths:

The authors use highly sensitive SMART-seq v4 to collect and analyze thousands of cells from dLGN and some adjacent nuclei. The gene detection rate using this method is impressive, and the plate/strip-based workflow has distinct advantages in terms of lower ambient contamination and risk of doublets compared to microfluidics-based single cell platforms. Cells or nuclei are sorted to enrich for neurons, which are the main focus of this paper. Key results are validated by smFISH or by examining publicly available Allen Brain Atlas ISH data. By examining conservation and divergence of cell types and evolutionarily conserved thalamic nucleus that has nonetheless undergone dramatic anatomical reorganization, these data and analyses add to our understanding of how cell types evolve in mammalian brains. They also contribute nuance to the ongoing debate of the extent to which transcriptomic data alone can be used to identify and discriminate cell types that have been described using other methodologies.

Weaknesses:

The Introduction does a nice job of describing what is known about the anatomy and cell types of the dLGN in each species, but it is less obvious what the motivating cross-species question is. Similarly, the Discussion focuses on technical details but the take-away is not clear.

dLGN is collected from all species, but in some species (macaque, mouse), additional thalamic nuclei are also collected. These are useful for examining cell type correspondences across regions or shifts between species, but their inclusion in cross-species integrations can also distort results (e.g. with some integration approaches, inclusion of very different, dataset-specific cell types can distort integration of more similar types). Analyses could be done to better distinguish the evolutionary comparisons within dLGN itself vs. what is additionally learned from inclusion of extra-dLGN nuclei.

One major evolutionary difference can involve differences in cell type proportions. Some proportion results are described but mainly for individual species (some of which include extra-dLGN regions) rather than in the integrated maps, so they can't be compared across species. The FISH results could also be used to corroborate proportion changes when such data are available.

Parameters for clustering analysis (using CCA/Seurat) are not described. Often changes in parameters can change the clusters, and it would be important to know if species integration results robust across a range of parameters and inclusion of extra-dLGN regions.

Some expected genes (PVALB) are barely detected in the macaque neurons, raising the question of whether this is due to tissue or annotation/alignment quality.

This manuscript is in revision at eLife

The decision letter after peer review, sent to the authors on December 14 2020, follows.

Summary

This study examined osmolarity-dependent dendritic signaling in oxytocin magnocellular neurosecretory cells (OT-MNCs). The authors show that repetitive depolarizations evoke larger calcium responses in proximal dendrites relative to distal dendrites. When these neurons were exposed to hyperosmotic stimuli, the distal calcium responses were found to be inhibited to a greater extent compared to proximal dendritic calcium responses. Propagation of glutamate evoked depolarizations from the dendrite towards the soma were also found to be reduced following increases in osmolarity. These effects of hyperosmotic stimuli are likely mediated by changes in membrane resistance of dendrites. A non-selective blocker of the channels, ruthenium red, blocked these effects of hyperosmolarity, indicating the non-selective cation channels (e.g. TRPV types) may be responsible.

All three reviewers agreed that the finding is potentially important and could address fundamental questions about MNC dendritic physiology. However, the reviewers identified a number of technical concerns, as summarized below. These concerns need to be addressed for further consideration.

Essential Revisions

1) The title and abstract are not exactly reflecting what this study is about. The title of the paper is "Dendritic membrane resistance modulates activity-induced Ca2+ influx in oxytocinergic magnocellular neurons of mouse PVN". However, dendritic membrane resistance is never actually measured. As such, a title that does not mention membrane resistance may be more appropriate. Also, the purpose and rationale of this study are not clearly communicated in the abstract and introduction. The implication to the regulation of soma-dendritic release of oxytocin, but not hyperosmotic responses, was mentioned in Introduction, while the entire Results and Discussion sections are about hyperosmotic stress.

2) Figure 3: The reviewers believe that stimulation paradigm is not physiological (neurons voltage-clamped at -70 mV with repetitive voltage steps to +50 mV for 5 ms). It is important to show that action potentials in the current clamp, instead of the +50mV voltage step in the voltage-clamp, can produce similar signals.

3) A major focus of the manuscript is on Ca2+ elevations in MNC dendrites. However, the authors have not performed the essential experiments to identify what the Ca2+ entry/release pathways are. It is important to show that Ca2+ is through voltage-gated Ca2+ channels for their main conclusions. In addition, it should also be established whether dendritic propagation is active or passive.

4) It is essential to report the effect of the osmotic stimulus alone on dendritic resting Ca2+, as this would affect the interpretation of the Ca2+ data.

5) Figure 8: What is the effect of RR on proximal EPSCs? This information is needed to interpret the effect of RR on distal EPSCs. It would be required to also test the effect of RR on the modulation of Ca2+ responses in distal dendrites to see their effects on the dendritic conductance.

Statistical handling:

Please provide the statistical methods (t-test, 2-way ANOVA with Hom-Sidak corrections, 2-way repeated-measures ANOVA, etc.) used for each measurement in the text or figure legend (not just in the method section). For repeated measures ANOVA, please indicate how measurements were repeated.

For the statistics of sex differences (Fig. 2-1, 4-1 etc), it is required to use 3-way ANOVA to assess variability by cells, animals, and sex. The number of males and females used is not clear in some cases, but it appears that only 2 females and 2 males are used (Line 203-204). If this is the case, the statistical comparisons between males and females are not meaningful and should be removed.

Reviewer #2:

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

Major Concerns:

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

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

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

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

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

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

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

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

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

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

Reviewer #1:

This is an interesting manuscript which covers an important topic in the field of computational neuroscience - the 'temporal signatures' of individual neurons. The authors set out to address several important questions using a single-neuron electrophysiology dataset, recorded from monkeys, which has previously been published. The behavioural paradigm is well designed, and particularly well suited to investigating the functional importance of different temporal signatures - as it simultaneously requires the subjects to monitor feedback across a short timescale, as well as integrate multiple outcomes across a longer timescale. The neural data are of high quality, and include recordings from lateral prefrontal cortex (LPFC) and mid-cingulate cortex (MCC). First, the authors modify an existing method to quantify the temporal signatures of individual neurons. This modification appears helpful, and an improvement on similar previously published methods, as the authors are able to capture the temporal signatures of the vast majority of neurons they recorded from. The temporal signatures differ across brain regions, and according to the neurons' spike width. The authors argue that the temporal signatures of a subset of neurons are modified by the subjects' degree of task engagement, and that neurons with different temporal signatures play dissociable roles in task-related encoding. However, I have several concerns about these conclusions which I will outline below. The authors then present a biophysical network model, and show that by varying certain parameters in their model (AHP and GABAB conductances) the temporal signatures of the monkey data can be reproduced. Although I cannot comment on the technical specifics of their models, this seems to be an important advance. Finally, they perform a Hidden-Markov Model analysis to investigate the metastability of activity in MCC, LPFC, and their network model. However, there are a few important differences between the model and experimental data (e.g. neurons recorded asynchronously, and the network model not performing a task) that limit the interpretation of these analyses. Overall, I found the manuscript interesting - and the insights from the biophysical modelling are exciting. However, in its current form, the conclusions drawn from the experimental data are not supported by sufficient evidence.

Major Comments:

1) The authors use a hierarchical clustering algorithm to divide neurons into separate groups according to their spike width and amplitude (Fig 1C). There are three groups: FS, RS1, and RS2. The authors ultimately pool RS1 and RS2 groups to form a single 'RS' category. They then go on to suggest that RS neurons may correspond to pyramidal neurons, and FS neurons to interneurons. I have a few concerns about this. Firstly, the suggestion that spike width determined from extracellular recordings in macaques can be used as an indicator of cell type is controversial. A few studies have presented evidence against this idea (e.g. Vigneswaran et al. 2011 JNeuro; Casale et al. 2015 JNeuro). The authors should at least acknowledge the limitation of the inference they are making in the discussion section. Secondly, visualising the data alone in Fig 1C, it is far from clear that there are three (or two) relatively distinct clusters of neurons to warrant treating them differently in subsequent analyses. In the methods section, the authors mention some analyses they performed to justify the cluster boundaries. However, this data is not presented. A recent study approached this problem by fitting one gaussian to the spike waveform distribution, then performing a model comparison to a 2-gaussian model (Torres-Gomez et al. 2020 Cer Cortex). Including an analysis such as this would provide a stronger justification for their decision to divide cells based on spike waveform.

2) The authors conclude that the results in Fig 3 show that MCC temporal signatures are modulated by current behavioural state. However, this conclusion seems a bit of a stretch from the data currently presented. I can understand why the authors used the 'pause' periods as a proxy for a different behavioural state, but the experiment clearly was not designed for this purpose. As the authors acknowledge, there is only a very limited amount (e.g. a few minutes) of 'pause' data available for the fitting process compared with 'engage' data. Do the authors observe the same results if they constrain the amount of included 'engage' data to match the length of the 'pause' data? Also, presumably the subjects are more likely to 'pause' later on in the behavioural session once they are tired/sated. Could this difference between 'pause' and 'engage' data be responsible for the difference in taus? For instance, there may have been more across-session drift in the electrode position by the time the 'pause' data is acquired, and this could possibly account for the difference with the 'engage' data. Is the firing rate different between 'pause' and 'engage' periods - if so, this should be controlled for as a covariate in the analyses. Finally it is not really clear to me, or more importantly addressed by the authors, as to why they would expect/explain this effect only being present in MCC RS neurons (but not FS or LPFC neurons).

3) At many points in the manuscript, the authors seem to be suggesting that the results of Fig 4 demonstrate that neurons with longer (shorter) timescales are more involved in encoding the task information which is used across longer (shorter) behavioural timescales (e.g. "long TAU were mostly involved in encoding gauge information", and "population of MCC RS units with short TAU was mostly involved in encoding feedback information"). However, I disagree that this conclusion can be reached based on the way the analysis has currently been performed. A high coefficient simply indicates that the population is biased to be more responsive depending on a particular trial type / condition - i.e. the valence of encoding. This does not necessarily tell us how much information the population of neurons is encoding, as the authors suggest. For instance, every neuron in the population could be extremely selective to a particular parameter (i.e. positive feedback), but if half the neurons encode this attribute by increasing their firing but the other half of neurons encode it by decreasing their firing, the effects will be lost in the authors' regression model (i.e. the beta coefficient would equal 0). I would suggest that the authors consider using an alternative analysis method (e.g. a percentage of explained variance or coefficient of partial determination statistic for each neuron) to quantify coding strength - then compare this metric between the high and low tau neurons.

4) Similarly, in Fig 4 the authors suggest that the information is coded differently in the short and long tau neurons. However, they do not perform any statistical test to directly compare these two populations. One option would be to perform a permutation test, where the neurons are randomly allocated into the 'High TAU' or 'Low TAU' group. A similar comment applies to the different groups of neurons qualitatively compared in panel Fig 4C.

5) The authors make an interesting and well-supported case for why changing the AHP and GABA-B parameters in their model may be one mechanism which is sufficient to explain the differences in temporal signatures they observed between MCC and LPFC experimentally. However, I think in places the conclusions they draw from this are overstated (e.g. "This suggests that GABA-B inhibitory - rather than excitatory - transmission is the causal determinant of longer spiking timescales, at least in the LPFC and MCC."). There are many other biophysical differences between different cortical regions - which are not explored in the authors' modelling - which could also account for the differences in their temporal signatures. These could include differences in extra-regional input, the position of the region in an anatomical hierarchy, proportion of excitatory to inhibitory neurons, neurotransmitter receptor/receptor subunit expression, connectivity architecture etc. I think the authors should tone down the conclusions a little, and address some more of these possibilities in more detail in their discussion.

6) For the Hidden Markov Model, I think there are a couple of really important limitations that the authors only touch upon very briefly. Firstly, the authors are performing a population-level analysis on neurons which were not simultaneously recorded during the experiment (only mentioned in the methods). This really affects the interpretation of their results, as presumably the number of states and their duration is greatly influenced by the overall pattern of population activity which the authors are not able to capture. At this stage of the study, I am not sure how the authors can address this point. Secondly, the experimental data is compared to the network model which is not performing any specific task (i.e. without temporal structure). The authors suggest this may be the reason why their predictions for the state durations (Fig 7B) are roughly an order of magnitude out. Presumably, the authors could consider designing a network model which could perform the same task (or a simplified version with a similar temporal structure) as the subjects perform. This would be very helpful in helping to relate the experimental data to the model, and may also provide a better understanding of the functional importance of the metastability they have identified in behaviour.

7) It is not clear to me how many neurons the authors included in their dataset, as there appear to be inconsistencies throughout the manuscript (Line 73, Fig 1A-B: MCC = 140, LPFC = 159; line 97-98: MCC = 294, LPFC=276; Fig2: MCC = 266, LPFC = 258; Methods section line 734-735 and Fig 2S2: MCC = 298, LPFC = 272). While this is likely a combination of typos and excluding some neurons from certain analyses, this will need to be resolved. It will be important for the authors to check their analyses, and also add a bit more clarity in the text as to which neurons are being included/excluded in each analysis, and justify this.

Reviewer #3:

In this study, Michaluk et al. explored the membrane dynamics of the main glial glutamate transporter GLT1 in hippocampal astrocytes, which was previously shown to shape synaptic transmission through regulating extracellular levels of glutamate and whose dysfunction may lead to pathologic conditions. Their results underscore the importance of the GLT1 C-terminus in the membrane turnover as well as in the activity-dependent lateral diffusion of the transporter at the plasma membrane.

To access GLT1 dynamics, the authors generated and imaged a pH-sensitive fluorescent analogue of the GLT1a isoform, namely GLT1-SEP, which fluoresces when exposed to the extracellular space but not in low pH intracellular compartments. By performing Fluorescent Recovery After Photobleaching (FRAP) in astrocytes from cell cultures, they show that about 75% of GLT1-SEP dwell at the cell membrane with a lifetime of about 22 s. Super-resolution dSTORM imaging further revealed that surface GLT1 distributes in clusters showing a spatial correlation with PSD-95 synaptic marker. In astrocytes from cell cultures or brain slices, the authors were able to monitor lateral diffusion of GLT1-SEP at the plasma membrane with FRAP; they recapitulated previous findings based on single molecule tracking experiments and showed that 25% of surface GLT1-SEP remains immobile (or slowly mobile) and that this immobile fraction decreases upon elevated network activity. Interestingly, deleting the C-terminus of GLT1-SEP does not alter much the intracellular fraction of GLT1-SEP, the fraction of immobile GLT1-SEP at the membrane or its ability to organize in clusters under basal conditions. However, GLT1-SEP lacking the C-terminus show a higher turnover at the membrane under basal conditions and surface GLT1-SEP clusters are not associated with synaptic markers anymore. Finally, removing the GLT1 C-terminus blocks the increase in the mobile fraction that is normally observed upon elevating neuronal activity.

Strengths:

While previous studies have unveiled a role for the lateral diffusion of GLT1 in controlling the recruitment of GLT1 near active synapses, the present study uses powerful optical approaches and analysis tools that allow for the monitoring of both lateral mobility and the exchange between membrane and intracellular fractions of GLT1. Furthermore, important and original information is provided about the nanoscale organization of GLT1 transporter at proximity of synapses and the fact that this organization depends on the C-terminal domain of GLT1. The results unveil an important role for membrane turnover as a possible 'redeployment' route for the immobile fraction of GLT1 at the plasma membrane.

Weaknesses:

1) Although overexpressed GLT1-SEP displays a similar expression pattern as endogenous GLT1 (assessed through dSTORM experiments), the expression level of GLT1-SEP relative to endogenous GLT1 has not been addressed by the authors. In particular, whether overexpressing GLT1-SEP impacts glutamate uptake currents and whether this could affect membrane turnover has not been measured.

2) The authors did not test the impact of neuronal activity on membrane turnover or surface distribution of GLT1-SEP, like they did for lateral mobility. This would be important to provide support for the 'redeployment route' hypothesis that the authors propose.

3) The FRAP data in organotypic slices looking at the effect of deleting GLT1 C-terminus, blocking mGluRs or buffering Ca2+ with BAPTA on GLT1 lateral mobility (Figure 5C-G) is not very convincing. The trend for lower immobile fraction upon 4AP compared to control is maintained across conditions. The lack of statistical difference between control and 4AP in Fig. 5D, 5E and 5F might come from the smaller n number (n = 30-48) compared to the control condition (n = 72) and/or higher variability.

4) The importance of GLT1 membrane turnover for controlling glutamate spillover ('extrasynaptic glutamate escape) and synaptic transmission/plasticity is missing.

5) While providing new information about the turnover of the GLT1a isoform, this study does not provide information about other GLT1 isoforms, in particular GLT1b, which contain unique C-terminal domains and which could thus display different membrane and lateral diffusion dynamics. The authors should justify why they focused on this specific isoform.

Reviewer #2:

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

Comments:

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

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

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

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

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

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

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

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

Reviewer #1:

Astrocyte glutamate transporter, GLT1, plays a crucial role in confining the levels of extrasynaptic glutamate, and therefore, understanding the cellular basis by which surface dynamics of GLT1 is regulated has implications in regulating glutamatergic transmission. Here, Michaluk et al. perform FRAP experiments using pHluorin (SEP)-tagged GLT1, and present a careful quantitative characterization of GLT1 surface dynamics that takes into account both lateral diffusion and exocytic delivery. The authors report that 25-30% of surface GLT1 represent immobile fraction which may be subject to slower exchange via exocytic delivery from intracellular compartments. In addition, the cytoplasmic domain of GLT1 plays a role in regulating GLT1 subcellular localization patterns and its activity-dependent dynamics. While the roles for mGluR and calcium-signaling mechanisms are explored, given the drugs have been applied under conditions in which neurons are equally affected, whether mGluR and calcium signaling involving calcineurin are engaged in astrocytes to impact GLT1 remains to be established. In addition, the super-resolution imaging, which does not discriminate between surface and intracellular pool of GLT1, is not well connected to the FRAP results, which is performed blind to the location of synapses.

Reviewer #3:

One example of this problem is in the estimation of cancer risk. The risk is estimated on the basis of body size and lifespan. However, that lifespan is itself phylogenetically estimated from body size at least for the non-extant species. It is not clear to me from the manuscript whether all lifespans are so estimated, or whether observations are used for the lifespan of the extant species. If the latter, caution is indicated, because lifespan data are highly uneven and often given as observed maximal lifespans, which can be misleading if taken from, for instance, zoo specimens. In either case, the manuscript needs to more clearly emphasize that these are statistically-predicted risks, not measured risks.

At a larger scale, the authors have done their best with a dataset that suffers from a couple of problems. First, all of the extant very large-bodied animals form a single clade, with the hyrax as the sole small-bodied member of that clade. And since the titanohyrax is extinct, among the extant organisms (an available large-bodies species with genomes) there is then a true large-bodied clade of the sirenia and elephants and relatives. I understand that other evolutionary data make it clear that these represent two (three including titanohyrax) independent transitions to large-body sizes. But with only the modern or nearly modern genomes to work with, I am not sure that the duplication inference procedures and their coupling to the body size analysis statistically represents more than a single observation (e.g., a default of a single transition to large size along the tethytheria branch).

Similarly, the authors observe what appears to be a number of independent duplications of tumor suppressors in African and Asian elephants: duplications that are lacking in many of the ancient genomes considered. I know that the authors used rigorous statistical methods to correct for the fragmented nature of these ancient genomes, but it is very hard not to wonder if some of the data in Figure 4 is really not an artifact of using ancient genomes, where detecting recent gene duplications may be very difficult (several of the Asian and African elephant duplications in Figure 4 appear to be of the same genes). If these events are truly independent and not genome assembly/annotation artifacts, there is then an alternative hypothesis to propose. Thus, are the authors suggesting that there is a rapid turnover in the duplication of tumor suppressors, such that all elephants have such duplicates, but the particular duplications have short life spans and differ from species to species?

Finally, it would be nice to see a few more comments on the manatee genome and why it does (or doesn't) show the expected patterns for the genome evolution in the face of the evolution of larger body sizes.

I would also note that Figure 3 and 4 would benefit from greatly expanded captions: I do not fully understand what is being illustrated in, for instance, Figure 3B-why are certain dots connected with lines? Intersections between what in the y-axis label?

Reviewer #2:

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

Major comments:

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

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

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

Reviewer #1:

The strength of this paper is its coupling of careful phylogenetic work with genomics to demonstrate the take-home message: all afrotherians are equal, but some are more equal than others with respect to mechanisms that reduce cancer risk. This is a significant advance in our understanding of the evolution of cancer risk with body size, and in so doing it considerably lengthens the list of genes of interest. It also has interesting examples illustrating the logical criteria of consistency, necessity, and sufficiency that will make it quite useful in teaching critical thinking to students.

Reviewer #3:

Behaviours that are instrumental for producing reward can be either goal-directed or, after repeated practice, habitual. Tasks that dissociate these types of learning, notably outcome devaluation, are tricky to implement for studying intravenous drug delivery although there is great interest to understand the role of habits in controlling drug use and addiction and so this paper is important in that regard. This article takes a new approach analyzing response latencies to infer the types of decision-making process that underlies a reward-seeking behaviour. Goal-directed behaviours are argued to involve evaluation of the outcome of responding and/or deliberation between choices both of which should take time, and slow responding relative to an efficient but inflexible habit. So I think this approach is quite interesting. The paper is well written and the predictions are clear.

My main issue in evaluating the current article is that while different predictions are made about when response latency should be relatively fast or slow, since the article is framed in terms of dissociating goal-directed and habitual processes, I feel there should be some independent evaluation of whether the target behaviour is in fact goal-directed or habitual. The authors rely on the amount of training as extended training has been shown to promote habitual control. However, exactly how much training is needed and how other parameters (type of reward, schedules of reinforcement, choice or single outcome) affect when habitual control may emerge varies widely in the literature and I don't think we can take for granted that after a certain amount of training responding will be habitual without testing that.

It is also important to consider alternative explanations for differences in response latency. A behaviour that is well-practiced might well be expected to become more efficient and faster. This need not be due to habit formation. The authors acknowledge the possibility that responding could be at floor but don't really discuss it or whether it might apply more to the saccharin response.

Reviewer #2:

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

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

Reviewer #1:

Vandaele et al. probe the mechanisms of decision making in rats when making a forced choice between drug and non-drug reward. The authors have led the field in this domain. In this manuscript, a retrospective analysis of choice response times from many rats in their past work is used to tease out potential decision-making mechanisms. We know already from decades of work that choice response times are almost always log-normally distributed (humans, non-human primates, rodents). The question here is whether differences in the mean and dispersion of these distributions can be used to derive insights into nature of the decision-making mechanism - a deliberative comparison versus a race model - and how this may differ for rats that prefer cocaine over saccharin and how this might be altered by more extended training. These questions are framed in terms of the differences between goal-directed and habitual behavior which, to be frank, I found less compelling (these response time data are of significant interest in their own right). I enjoyed reading this manuscript. It was thoughtful and well presented. I have only two comments.

First, much, if not all, of the absolute differences between latencies in sample and choice phases appear to be carried by the sample rather than the choice phase. Choice latencies for cocaine preferring rats, saccharin preferring rats, and the indifferent rats are all very similar. In contrast, the sampling latencies for cocaine preferring rats and the indifferent rats are longer. I am not sure why this should be. My reading was that the authors were more concerned with the choice side of the experiment being different, not the sample phase. Is this predicted by the models being tested? I struggled to understand why an SCM-like model would predict the difference being in the sample phase. Either way, the authors could be clearer about where the difference is expected to lie and why the sample phase is so obviously different in some conditions and the choice phase so similar.

Second, the main and real issue for me is whether the differences between response latencies in the sample versus choice phases plausibly reflect operation of different decision making mechanisms (race model versus deliberative processing) or different operation of the same decision-making mechanism. I don't know the answer, but I could not really derive the answer from the data and modelling provided. The authors frame the differences in response time as being uniquely predicted or explained by different forms of choice. The models that the authors are using are closely linked to, and intellectually derived from, models of human choice reaction time. The most successful of these models are the diffusion model (DDM) (Ratcliff, R., Smith, P.L., Brown, S.D., and McKoon, G. (2016). Diffusion Decision Model: Current Issues and History. Trends in Cognitive Sciences 20, 260-281) and the linear ballistic accumulator (LBA) (Brown, S.D., and Heathcote, A. (2008). The simplest complete model of choice response time: linear ballistic accumulation. Cognitive Psychology 57, 153-178.2008).

Even though the DDM and LBA adopt different architectures to each other (but the same architectures as those in Supp Fig 1A), they are intended to explain the same data. Of relevance, the same model (a DDM or an LBA) can explain differences in both the response distribution and the mean response time via changes in the starting point of evidence accumulation, rate of evidence accumulation, and/or the boundary or threshold at which evidence is translated into choice behavior. So, for either a difference accumulator model (DDM) or a race model (LBA), the difference between sampling and choice performance could reflect changes in how the model is operating between these two phases, including a change in the starting point of the decision [bias], a change in rate of accumulation [evidence], a change in threshold [caution] or collapsing boundary scenario, rather than reflecting operation of a completely different decision-making mechanism.

In thinking of a way forward I readily concede I could be wrong and the authors may effectively rebut this point. Another option could be to acknowledge this possibility and discuss it. E.g., does it really matter if it is a qualitatively different decision-making process or different operation of the same decision-making mechanism? I don't really think the action-habit distinction lives or dies by reaction/response time data, this distinction is almost certainly far less absolute than often portrayed in the addiction literature, and it is generally intended as an account of what is learned rather than an account of how that learning is translated into behaviour (even if an S-R mechanism provides an account of both). Response time data tell me, at least, something different about how what has been learned is translated into behaviour. The third, marginally more difficult but more interesting option, would be to explore these issues formally and to move beyond simple descriptive or LDA analyses of response time distributions. The LBA has a full analytical solution and there are reasonable approximations for the DDM. Formal modelling of choice response times (e.g., Bayesian parameter estimation for a race model or DDM) could indicate whether a single decision-making mechanism (LBA or DDM or something else) can explain response times under both sample and choice conditions or not. This is a standard approach in cognitive modelling. This would be compelling if it showed the dissociation the authors argue - i.e. one model cannot be fit to both sample and choice datasets for all animals. However, if one model can be fit to both, then formal modelling would show which decision making parameters change between the sample and choice conditions for cocaine v saccharin v individual animals to putatively cause the differences in response times observed. Either way, more formal modelling would provide a platform towards identification of those specific features of the decision-making mechanisms that are being affected.

Reviewer #3:

Mathsyaraja and collaborators analyzed the role of the MAX-Gene associated protein, referred to as MAG, in mouse models and human cell lines and organoids of Non-Small Cell Lung Cancer. MAG is a repressor, a MYC antagonist that opposes its transcriptional activity. It has TBX and bHLH domains. They found that MGA loss by shRNA or CRIPSR accelerated tumor development in vivo in the KP mouse models. Using RNA-Seq, the authors showed that MGA loss leads to the de-repression of the atypical/non-canonical PRC1.6 polycomb complex, E2F and MYC targets as well as increased invasion. ChiP-Seq/cut and run as well as proteomics, revealed that MGA, E2F6 and L3MBTL2 co-occupy thousands of promoters and that MGA interacts with E2F6, and many core members of PRC1.6. Finally, they mapped the DUF domain as required to bind the PRC1.6 complex and bring it to promoters.

Overall, the experiments are well executed, the paper clearly written and the conclusions justified by the data.

The new data in the present report are the in vivo data in the mouse models, the role of MGA in repressing invasion, in increasing IFN signaling and the anti-tumor response, and the identification of the DUF domain required for binding to the PRC1.6 complex.

However, a lot of the data presented in the manuscript are not novel and were previously published. A recent Molecular Cancer Research paper by Llabata and collaborators published in April 2020 (referred to in the text) has already identified the same MGA interactors by Mass Spectrometry and the same binding sites by ChIP-Seq using human lung adenocarcinoma cell lines. Llabata et al. found that MGA interacts with the non-canonical PCGF6-PRC1 complex (named PRC1.6) that includes L3MBTL2 and that the complex also contains MAX and E2F6 but not MYC. They clearly show that MAG binds to and represses genes that are bound and activated by MYC convincingly showing that MYC and MGA have opposite functions. This unfortunately tempers the enthusiasm of the reviewer.

Reviewer #2:

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

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

Reviewer #1:

The authors report the analysis of a Mga deletion and provide convincing evidence that Mga functions as a tumor suppressor during lung carcinogenesis. The data shown are clear, the message is important and the discussion is very careful. There is a certain overlap with a recent study by Llabata et al., but there is sufficient novelty in the current study.

Comments:

It seems that the investigation of publicly available datasets is essentially identical to the Schaub et al . analysis and not new data. If the authors want to maintain this, they would need to better explain what is new. One important piece of information that seems to be missing is whether the mutations are homozygous or heterozygous. So data on MGA and MYC protein expression in human tumors would greatly strengthen this part.

Conceptually, one would to know whether tumor development in an MGA-delete situation depends on MYC. One would also like to know whether the polycomb complex that is assembled by MGA is tumor-suppressive. Therefore,the authors should perform a similar analysis as they did for MGA (introduce sgRNAs into the lung models) and score the phenotypes they get. Both experiments could be done in cell lines established from this model and either in vitro (that would allow a mechanistic analysis, e.g. RNA seq) or upon re-transplantation. This would also prevent simply reporting negative results.

The interpretation of the VENN diagram and the heatmaps in Figure 5A,B is somewhat uncertain. If one plots these for MYC, occupancy often simply parallels occupancy by RNAPII, so essentially being bound by MYC simply says the promoter is open/active. Is this the case for MGA and its complex partners? Or is there a specificity in binding? The authors should do RNAPII ChipSeqs in these cells, preferentially +/- MGA, and then show these alongside (and plot a correlation between MYC, RNAPII and MGA occupancy).

Along these lines, it is hard to understand how one obtains the extreme p-values shown in figure 5E and 5H, I would challenge this. If the authors want to maintain this, they should not use ENCODe data, but simply determine what genes are active in the cells (e.g. what promoters are bound by RNAPII) and then use those as background list and calculate P-values for overlap between MYC, MAX and E2F6.

Based on the description, the ChIPSeq analyses are not spike-normalized and I could not find information about the number of repeats. If it is n=1, the authors need to find a way to exclude that the differences are due to experimental variation.

I think the Llabata reference is missing in the list.

Reviewer #3:

This manuscript presents its two main results in Figure 3:

In response to a non-hydrolysable glucose analogue, E. coli cells show...

(1) Increase in fluorescence intensity of motors with labelled stator proteins, (2) Increase in speed of motor rotation and swimming

Sufficient controls are described to rule out possible indirect explanations of this effect, via buffer refreshment, metabolism of glucose, proton motive force (Fig 3D) and rotation direction (Fig 4F), and by contrast the effect is demonstrated to depend upon the chemotaxis receptor for glucose (Fig 4B) and the phosphotransferase system (Fig 4D), which is supports the chemotaxis system. These results are interpreted as evidence for a direct effect of the chemotaxis system upon the number of independent stator units, and thereby upon motor and swimming speeds.

This is a novel finding, and with better statistics (more repeats of fluorescence experiments) and better presentation of the findings (see below), the paper would be an important contribution to the field of bacterial chemotaxis. However, especially without presenting nor postulating a mechanism for the proposed direct effect, the paper might be more suitable for a more specialist journal.

Reviewer #2:

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

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

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

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

Reviewer #1:

Bacterial chemotaxis is a well-studied process at many levels, from the chemical networks that control the rotation of the flagella to the fluid dynamics of the motility itself. In the present paper the authors address the widely held view that ligand sensing is responsible only for changing the rotational bias of the motor driving flagellar motion, and not its speed. Using a well-established method of quantifying motor activity by monitoring the rotation of the cell body when the flagella are stuck to a surface, a fluorescent labelling technique to determine the membrane potential, a mutant with fluorescently labelled stator units, and direct measurements of swimming speed, the authors show that the sensing of a non-metabolizable analogue of glucose leads to a momentary increase in motor speed and stator unit numbers. At the same time, control experiments make it clear that this is purely as a consequence of ligand sensing. This behaviour is indeed contrary to the accepted view, and although the fundamental mechanism is as yet unclear, this is an important result.

On the whole I am very supportive of this work, which has been done with great care and clear logic. My only suggestion for improvement would be to make quantitative the changes in chemotactic behaviour that would be expected as a consequence of the motor speed changes revealed in this research. That is, can the authors put some numbers into a standard analysis of run-and-tumble dynamics to quantify any improvement in chemotactic efficiency or speed under such changes?

Reviewer #3:

This study by Pipitone et al. combines SBF-SEM microscopy with quantitative proteomics and lipidomics to explore chloroplast differentiation. Authors describe that chloroplast biogenesis occurs in a first phase of structure establishment with thylakoid biogenesis, followed by a second phase of chloroplast division. The images and 3D reconstructions are beautiful, the quantitative data are novel, and their integration offers a new perspective into the seedling de-etiolation process, a model system for physiological and molecular studies. However, in my opinion some aspects need to be better explained and significantly improved.

• In lines 276-282, the authors write: "After 8h of illumination (T8), we observed decreased abundance of only one protein (the photoreceptor cryptochrome 2, consistent with its photolabile property) and increased levels of only three proteins, which belonged to the chlorophyll a/b binding proteins category involved in photoprotection (AT1G44575 = PsbS; AT4G10340= Lhcb5; AT1G15820= Lhcb6". This is striking, as many well studied proteins change in abundance during the first hours of de-etiolation. Actually, looking into the data set with the quantification data for the ~5,000 proteins, it appears that many proteins do show significant changes between T0 and T8. For example PORA and ELIP, changes that are also reflected in figure 6A.

• Related to the above, well known proteins for example phyA and HY5, that undergo drastic changes in abundance when etiolated seedlings are first exposed to light, do not show changes in T4,T8 and T12 relative to T0 in the proteomics data set. This raises questions about the proteomic approach (sensitivity of the method?) or the experimental setup. Could authors please comment on this? I feel that validation of the proteomics approach is critical, especially taking into account the central conclusion that "the first 12h of illumination saw very few significant changes in protein abundance".

• Lines 570-572: A reference is needed. Also, it is mentioned that PSII appears later than PSI, which does not seem to match the observation that PSII proteins appear earlier than PSI, or that the surface area occupied at early time points by PSII is greater than the one occupied by PSI. Please check.

• Are the calculations of thylakoid surface expansion over time consistent with previous available data using tomography? Please include.

• In the introduction, authors could include mention of the massive transcriptional reprogramming that takes place during de-etiolation. In addition, I think that comparison of the proteomics data with the transcriptomic changes during de-etiolation (well described in the literature) would allow further understanding of the distinct phases proposed. For the chloroplast proteins already present in the dark, how does this correlate with expression of the corresponding genes?

Reviewer #2:

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

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

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

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

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

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

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

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

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

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

Reviewer #3:

The present manuscript focuses on a subpopulation of layer 5 neurons in medial and lateral entorhinal cortex and its functional connections to target neurons in layers 2, 3 and 5. The authors show a difference in LVb-to-LVa connectivity between MEC and LEC. The results suggest that the entorhinal output circuit via LVb-to-LVa is present primarily in LEC.

The work relies on and is made possible by a newly described transgenic mouse (TG) where LVb neurons can be labeled and stimulated with light. The authors showed that these neurons are largely co-labeled with PCP4, a marker for LVb. They compared the apical dendritic extent from TG labeled cells (LVb) and Nac retrogradely labeled cells (LVa) in medial and lateral EC. The intrinsic electrophysiological properties of LVa and LVb neurons were measured and used for PCA showing segregation according to sublayer and region. The axonal distribution and translaminar local connections of LVb neurons form the TG mice were then examined. Cells were recorded in vitro and filled with biocytin, both from MEC and LEC, with multiple cells in the same slice, documented with high quality images. The study of the LVb translaminar connectivity via a direct comparison of postsynaptic responses in neurons in different layers in the same slice is the gold standard for this type of functional connectivity analysis. There is also an investigation of mixed excitatory-inhibitory postsynaptic response sequences, and evidence for a dorso-ventral gradient in LVb-to-LVa connectivity in MEC is given.

The study combines TG mice, immunolabeling, retrograde labeling, morphological analysis and in vitro electrophysiology with optogenetic photo-stimulation. While it builds on already published work by the same group and others, by comparing the local target neurons of LVb in MEC and in LEC, the manuscript provides a unique contribution to the literature on the laminar circuit organization in the Entorhinal Cortex. In view of the central position of this area in the hippocampal memory systems of the rodent brain, these results are of interest to a broader neuroscience audience. It is also a nice example of a bottom-up approach, where data on the entorhinal translaminar connectivity may influence and constrain theories of hippocampal-cortical processing.

Major Comments:

1) Almost all TG labelled neurons are positive for PCP4 but not so vice versa, only 45.9 and 30.P% of PCP4 + neurons in LEC and MEC are labeled in the TG mouse (page 5) leaving open the possibility that the TG mouse labels a (specific?) subset of LVb neurons. Did you test whether TG labeled LVb cells co-localize with Ctip2 ?

2) The direct comparison of translaminar connectivity of LVb neurons is very convincing. But if your main conclusion (title) concerns the difference of LVb-to-LVa connectivity between MEC and LEC, it would have been more appropriate to test that in the same slice. While the data strongly support conclusions on the laminar differences of LVb connectivity, the evidence for differences in LVb-to-LVa connectivity between MEC and LEC is a bit weaker and more indirect.

3) Postsynaptic responses (in mV) in LEC are about twice as high in amplitude as in MEC (Fig. 4E vs Fig 5E), across all layers. Please discuss possible reasons, and possible impact on the circuit function. Is the probability to initiate action potentials higher in LEC ?

4) Give the onset latencies of postsynaptic excitatory potentials induced by LVb photostimulation. Are latencies monosynaptic? Or also polysynaptic? Ideally this could be tested by applying a cocktail of TTX-4-AP.

5) Figure 4 S3, Fig 5 S2. Analysis of inhibition. What is the cut-off criteria to say inhibition is present or not? It might be more appropriate to give the I/E ratio.

Reviewer #2:

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

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

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

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

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

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

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

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

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

Reviewer #1:

The current study by Ohara et al. describes differences in the connectivity patterns between LVb to LVa. The study builds on the authors previous study (Ohara et al., 2018) where they showed the intrinsic connectivity of LVb neurons in the MEC and LEC. The focus of the current study is the difference the authors observed in the strengths of connectivity between LVb and LVa in the MEC and LEC. The authors suggest that the in MEC Vb neurons do not provide substantial direct input to LVa neurons. The manuscript emphasizes the functional importance of difference as the authors suggest that "...hippocampal -cortex output circuit is present only in LEC, suggesting that episodic systems consolidation predominantly uses LEC-derived information and not allocentric spatial information from MEC." The study uses a newly developed mouse line to investigate connectivity differences, this is a nice technical approach and the experimental data is of high quality. While the data is solid, the authors tend to over-interpret their findings from the functional point of view. While the observed difference is quite interesting, it is unclear what the impact is on information flow in the MEC and LEC and to which degree they differ functionally. The authors assume major differences and their work is framed based on these expected differences, but the manuscript does not provide data that would demonstrate functionally distinct features.

Major Comments:

1) Throughout the text the authors treat their findings as if it was 'all-or-none' i.e the LEC has a direct connection between LVb and LVa while the MEC does not. This does not seem to be the case based on their data, the data shows that connectivity in the MEC is less robust but it is definitely there. The difference seems to be quantitative and not qualitative.

2) Due to this problem, the authors seem to be over-interpreting their data by suggesting that the information flow must be significantly different conceptually in the LEC and the MEC and this would have important implications for memory consolidation. It is impossible to draw these conclusions based on the data presented, as there are no experiments investigating the functional, network level consequences of these connectivity differences.

3) The electrophysiology experiments provide information about the basic parameters of the investigated cells, but these lack a physiological context that would allow the authors to evaluate the consequences of these differences on information flow and/or processing in the MEC and the LEC.

4) The study is using a novel transgenic mouse line to differentiate between LVb and LVa neurons, while this is definitely a strength of the study, this strategy allows the authors to visualize ~50% of LEC and ~30% MEC neurons. Since the authors aim to prove a negative (MEC does not have direct connection) the fact that ~70% of the neurons are not labelled could be problematic.