Reviewer #2 (Public Review):

This manuscript by Marjaneh et al is an original research article that aimed to understand the genetic complexity of atrial septal defects by using QTL analysis in advanced intercross lines (AIL) QSi5 and 129T2/SvEms mouse strains, which represent mice with extremes of atrial septal phenotypes. This study is built on previous work by the authors (Biben. 2000), in which they developed three quantitative parameters of atrial septal morphology. These quantitative traits were previously proven by the authors to be associated with the prevalence of PFO across a variety of genetic backgrounds. Using an F2 design of the same strains they have previously identified 13 significant or suggestive QTL affecting these quantitative traits, (Kirk. 2006).

The current manuscript extends the previous analysis using the AIL approach at F14. This design, the fine mapping approach, and the rigorous downstream analysis allowed them to refine their previous findings. In addition, several new QTLs were discovered. Remarkably, the resolution was increased and the overlap between QTL for different traits was enhanced. Furthermore, they performed whole genome sequencing of the parental strains and identified high-confidence deleterious variants that are enriched in known human CHD genes as well as the genes within QTL regions that are expressed in the atrial septum, such as SMAD6. They also performed transcriptome analysis of septa at different developmental stages in parental strains and identified networks enriched in the ribosome, nucleosome, mitochondrial, and ECM biosynthesis underlying septal variation.

Overall, the manuscript was built on a clear rationale and employed a suitable genomics approach to address the topic. The results provide a substantial and important extension of the previous work at a larger scale and a higher level of resolution. The findings improve the status of current knowledge and provide valuable resources to unravel the genetic complexity of CHDs, with relevance to human PFO. The significance is deemed to be "Important" given the large-scale approach, the specificity of quantitative measures, and the resolution of the analysis pipeline. Analysis steps are well-designed providing potential candidate targets from their network analysis. Pending functional validation and confirmatory evidence of the causality in future mechanistic studies, the outcomes may lead to novel diagnostic and translational values.

Reviewer #3 (Public Review):

In previous studies, Harvey and colleagues described several genetically-influenced biometric parameters correlated with the patent foramen ovale (PFO) cardiac defect (Biben et al., 2000) and identified 13 quantitative trait loci (QTL) that affect these traits using a murine F2 intercross design with mouse strains demonstrating extreme septal phenotypes (Kirk et al., 2006). In the submitted manuscript, Marjaneh et al. follow up and refine these studies with a more in-depth QTL analysis utilizing an advanced intercross design (F14), combined with genome and transcriptome sequencing data supporting a role for the identified QTL in atrial septation. The paper is mostly genetic analysis with follow-up informatics and one example of a validated variant. The results are important, and implicate dozens of loci and hundreds of genes (including those in the BMP pathway, and others known to be essential for cardiac morphogenesis) in atrial septum formation, highlighting the complexity of the processes involved. This paper will be an important resource for the field and sets the stage for a follow-up to validate the many candidates identified that may impact cardiac morphogenesis and atrial septation, specifically. The manuscript is well-written and straightforward and does not suffer from major errors in logic or interpretation. The identification of implicated genetic variants will benefit the field of cardiac development and may inform the advancement of future therapeutics for human patients with PFO (for identified coding variants, in particular).

Reviewer #1 (Public Review):

In this study, single neurons were recorded, using tetrodes, from the parahippocampal cortex of 5 rats navigating a double-Y maze (in which each arm of a Y-maze forks again). The goal was located at any one of the 4 branch terminations, and rats were given partial information in the form of a light cue that indicated whether the reward was on the right or left side of the maze. The second decision point was uncued and the rat had no way of knowing which of the two branches was correct, so this phase of the task was more akin to foraging. Following the outbound journey, with or without reward, the rat had to return (inbound journey) to the maze and start to begin again.

Neuronal activity was assessed for correlations with multiple navigation-relevant variables including location, head direction, speed, reward side, and goal location. The main finding is that a high proportion of neurons showed an increase in firing rate when the animal made a wrong turn at the first branch point (the one in which the correct decision was signalled). This increase, which the authors call rate remapping, persisted throughout the inbound journey as well. It was also found that head direction neurons (assessed by recording in an open field arena) in the same location in the room were more likely to show the rate change. The overall conclusion is that "during goal-directed navigation, parahippocampal neurons encode error information reflective of an animal's behavioral performance" or are "nodes in the transmission of behaviorally relevant variables during goal-directed navigation."

Overall I think this is a well-conducted study investigating an important class of neural representation: namely, the substrate for spatial orientation and navigation. The analyses are very sophisticated - possibly a little too much so, as the basic findings are relatively straightforward and the analyses take quite a bit of work to understand. A difficulty with the study is that it was exploratory (observational) rather than hypothesis-driven. Thus, the findings reveal correlations in the data but do not allow us to infer causal relationships. That said, the observation of increased firing in a subset of neurons following an erroneous choice is potentially interesting. However, the effect seems small. What were the actual firing rate values in Hz, and what was the effect size?

I also feel we are lacking information about the underlying behavior that accompanies these firing rate effects. The authors say "one possibility is that the head-direction signal in the parahippocampal region reflects a behavioral state related to the navigational choice or the lack of commitment to a particular navigational route" which is a good thought and raises the possibility that on error trials, rats are more uncertain and turn their heads more (vicarious trial and error) and thus sample the preferred firing direction more thoroughly. Another possibility is that they run more slowly, which is associated with a higher firing rate in these cells. I think we, therefore, need a better understanding of how behavior differed between error trials in terms of running speed, directional sampling, etc. A few good, convincing raw-data plots showing a remapping neuron on an error trial and a correct trial on the same arm would also be helpful (the spike plots were too tiny to get a good sense of this: fewer, larger ones would be more helpful). It would be useful to know at what point the elevated response returned to baseline, how - was it when the next trial began, and was the drop gradual (suggesting perhaps a more neurohumoral response) or sudden.

Reviewer #2 (Public Review):

This work recorded neurons in the parahippocampal regions of the medial entorhinal cortex (MEC) and pre- and para-subiculum (PrS, PaS) during a visually guided navigation task on a 'tree maze'. They found that many of the neurons reflected in their firing the visual cue (or the associated correct behavioral choice of the animal) and also the absence of reward in inbound passes (with increased firing rate). Rate remapping explained best these firing rate changes in both conditions for those cells that exhibited place-related firing. This work used a novel task, and the increased firing rate at error trials in these regions is also novel. The limitation is that cells in these regions were analyzed together.

Reviewer #3 (Public Review):

The authors set out to explore how neurons in the rodent parahippocampal area code for environmental and behavioral variables in a complex goal-directed task. The task required animals to learn the association between a cue and a spatial response and to use this information to guide behavior flexibly on a trial-by-trial basis. The authors then used a series of sophisticated analytical techniques to examine how neurons in this area encode spatial location, task-relevant cues, and correct vs. incorrect responding. While these questions have been addressed in studies of hippocampal place cells, these questions have not been addressed in these upstream parahippocampal areas.

Strengths:

1) The study presents data from ensembles of simultaneously recorded neurons in the parahippocampal region. The authors use a sophisticated method for ensuring they are not recording from the same neurons in multiple sessions and yet still report impressive sample sizes.

2) The use of the complex behavioral task guards against stereotyped behavior as rats need to continually pay attention to the relevant cue to guide behavior. The task is also quite difficult ensuring rats do not reach a ceiling level of performance which allows the authors to examine correct and incorrect trials and how spatial representations differ between them.

3) The authors take the unusual approach of not pre-processing the data to group neurons into categories based on the type of spatial information that they represent. This guards against preconceived assumptions as to how certain populations of neurons encode information.

4) The sophisticated analytical tools used throughout the manuscript allow the authors to examine spatial representations relative to a series of models of information processing.

5) The most interesting finding is that neurons in this region respond to situations where rewards are not received by increasing their firing rates. This error or mismatch signal is most commonly associated with regions of the basal ganglia and so this finding will be of particular interest to the field.

Weaknesses:

1) The histological verification of electrode position is poor and while this is acknowledged by the authors it does limit the ability to interpret these data. Recent advances have enabled researchers to look at very specific classes of neurons within traditionally defined anatomical regions and examine their interactions with well-defined targets in other parts of the brain. The lack of specificity here means that the authors have had to group MEC, PaS, and PrS into a functional group; the parahippocampus. Their primary aim is then to examine these neurons as a functional group. Given that we know that neurons in these areas differ in significant ways, there is not a strong argument for doing this.

2) The analytical/statistical tools used are very impressive but beyond the understanding of many readers. This limits the reader's ability to understand these data in reference to the rest of the literature. There are lots of places where this applies but I will describe one specific example. As noted above the authors use a complex method to examine whether neurons are recorded on multiple consecutive occasions. This is commendable as many studies in the field do not address this issue at all and it can have a major impact as analyses of multiple samples of the same neurons are often treated as if they were independent. However, there is no illustration of the outputs of this method. It would be good to see some examples of recordings that this method classifies as clearly different across days and those which are not. Some reference to previously used methods would also help the reader understand how this new method relates to those used previously.

3) The effects reported are often subtle, especially at the level of the single neuron. Examples in the figures do not support the interpretations from the population-level analysis very convincingly.

The authors largely achieve their aims with an interesting behavioral task that rats perform well but not too well. This allows them to examine memory on a trial-by-trial basis and have sufficient numbers of error trials to examine how spatial representations support memory-guided behavior. They report ensemble recordings from the parahippocampus which allows them to make conclusions about information processing within this region. This aim is relatively weak though given that this collection of areas would not usually be grouped together and treated as a single unitary area. They largely achieve their aim of examining the mechanisms underlying how these neurons code task-relevant factors such as spatial location, cue, and presence of reward. The mismatch or error-induced rate remapping will be a particularly interesting target for future research. It is also likely that the analytical tools used in this study could be used in future studies.

Reviewer #1 (Public Review):

In this paper, the authors present evidence from studies of biopsies from human subject and muscles from young and older mice that the enzyme glutathione peroxidase 4 (GPx4) is expressed at reduced levels in older organisms associated with elevated levels of lipid peroxides. A series of studies in mice established that genetic reduction of GPx4 and hindlimb unloading each elevated lipid peroxide levels and reduced muscle contractility in young animals. Overexpression of GPx4 or N-acetylcarnosine blocked atrophy and loss of force generating capacity resulting from hindlimb unloading in young mice. Cell culture experiments in C2C12 myotubes were used to develop evidence linking elevated lipid peroxide levels to atrophy using genetic and pharmacologic approaches. Links between autophagy and atrophy were suggested.

Experiments on GPx4 expression levels, lipid peroxide levels, muscle mass and muscle force generating capacity were internally consistent and convincing. I thought the experiments supporting the view that autophagy contributed to atrophy were convincing. The hypothesis that altered lipidation of autophagy factors contributed was tested or supported in my view. Evidence for muscle atrophy in response to genetic or pharmacologic manipulations is a bit inconsistent throughout the paper, possibly because the small N of some experiments does not provide sufficient power to detect observed numeric differences in the means. The pattern of muscle fiber atrophy by fiber type is consistent throughout the paper but there is variability in which comparisons reached the threshold for significance, again, possibly because of the small N of the experiments. I agree with the authors that altered activity of enzymes in the contractile apparatus provides one explanation for the observed weakness but respectfully wish to point out there are others such as impaired excitation-contraction coupling which is well known to occur in aging.

Reviewer #2 (Public Review):

This is a well-written paper that reports that the accumulation of LOOH with age and disuse contributes to the loss of skeletal muscle mass and strength. Moreover, the authors report that LOOH neutralization attenuates muscle atrophy and weakness. The mechanism via which LOOH contributes to these phenotypes remains unclear but seems to be mediated by the autophagy-lysosomal axis. In addition, the paper also reports the efficacy of N-acetylcarnosine treatment in ameliorating muscle atrophy in mice.

The authors should consider the following points to improve the manuscript:

- The authors showed that inhibition of the autophagy-lysosome axis by ATG3 deletion or BafA1 was sufficient to reduce LOOH levels induced by GPx4 deletion, erastin, or RSL3. Moreover, they found that 4-HNE co-localizes with LAMP2. However, it remains unclear the precise mechanism via which LOOH contributes to muscle atrophy and how it is amplified by the autophagy-lysosomal axis. The authors could further test the functional interaction of 4-HNE with LAMP2 with additional experiments such as immunoprecipitation.

- A weak point of the paper is not having performed the experiments on 24-month-old-mice. At 20 months of age, the mice do not display any muscle wasting and myofiber atrophy compared to young mice that have completed postnatal muscle growth (=6-month-old-mice). It would be interesting to see the levels of 4-HNE in 24- or 30-month-old mice, and if N-acetylcarnosine treatment in older mice is able to rescue muscle atrophy induced by aging.

Previous studies have shown that inhibition of autophagy accelerates (rather than protect) from sarcopenia, and that autophagy is required to maintain muscle mass (Masiero 2009, PMID: 19945408; Castets 2013, PMID: 23602450; Carnio 2014, PMID: 25176656). On this basis, the authors should test whether their findings are valid only in the context of disuse atrophy or also in the context of sarcopenia (=24-30-month-old mice).

- In Fig.2 the authors report that GPx4 KD, erastin, and RSL3 reduce the diameter of myotubes. For how long and when was the treatment done? Looking at the images, it seems that there are some myoblasts in the cultures treated with GPx4 KD, erastin, and RSL3. Is it possible that these compounds reduce myotube size by inhibiting myoblast fusion rather than by inducing myotube atrophy?

- MDA quantification was done in the gastrocnemius although all the experiments in this paper were performed in the soleus and EDL. It would be good if the authors could explain the reason for this.

Reviewer #1 (Public Review):

In this study, the authors characterize the impact of histone deacetylation on spatial regulation of gene expression in the early gastrula embryo. They utilize Xenopus tropicalis as a vertebrate model embryo and focus on maternal HDAC1 and HDAC2 deacetylases to characterize the regulatory role of histone acetylation on zygotic transcription. In particular, they are interested in whether this epigenetic mark positively or negatively regulates gene expression for the presumptive germ layer and contributes spatially to cell lineage integrity in gastrulation.

Using gene expression analysis, they find that HDAC1 and HDAC2 are present maternally in the egg and throughout blastula and gastrula stages. By performing HDAC1 ChIP-Seq, they find that the deacetylase is already bound as early as the Stage 8 blastula - time of genome activation - and that HDAC1 peaks located within promoter regions generally increase over time from blastula to early gastrula, Stage 10.5. Interestingly, the binding of HDAC1 is not dependent on the zygotic transcript, as HDAC1 ChIP-seq peaks show little difference upon alpha-amanitin treatment. Many of the HDAC1 peaks correlate with peaks of both FoxH1 and Sox3, suggesting their role in its deacetylase recruitment to the genome. Examination of epigenetic signatures of HDAC1 bound regions using previously published datasets identifies distinct chromatin binding categories: authors find a strong correlation with H3K27-Ac and pan-H3Kac, and that HDAC1 generally binds to regions free of repressive marks such as H3K9-me3. The authors find that a majority of HDAC1 peaks contain H3K27Ac but not H3K37me3 peaks and approximately ten percent of HDAC1 loci have both activating and repressive marks.

The authors investigate a functional role for histone deacetylation by inhibiting it, using the broad inhibitor TSA, and HDAC1 specific inhibitor VPA. Importantly, they spatially characterize pan-H3K acetylation and gene expression changes in animal cag (AC) and vegetal mass (VG) regions on the embryo. These are very useful datasets that provide new insights into how histone acetylation is tied to the maintenance of lineage integrity. At a global level, they find that TSA inhibition leads to gastrulation arrest and leads to widespread upregulation of H3K acetylation (pan-H3Kac); suggesting that proper regulation of histone acetylation is required for development. Further, they find that previously repressed regions, marked by H3K27me3 show the most upregulation of pan-H3Kac upon TSA treatment. Regionally, they find a number of interesting results upon inhibition of histone acetylation. First, TSA treatment causes dysregulation - upregulation - of the animal cap (AC) pan-H3Kac peaks in vegetal mass (VG), and upregulation of VG peaks in the animal cap. This suggests that lineage specifically is likely maintained in part by HDAC-mediated de-acetylation of germ layer genes. Gene expression characterization in AC and VG explants +/- TSA treatment supports this conclusion as inappropriate upregulation of VG gene expression is found in AC and inappropriate upregulation of AC genes is found in VG. Somewhat surprisingly, HDACs also appears to play a positive regulatory role in germ layer expression. Focusing on genes near HDAC1 peaks containing H3K27Ac, the authors show that genes downregulated upon TSA treatment tend to be spatially restricted; downregulated genes in AC tended to be AC genes and downregulated genes in VG tended to be VG genes. This suggests that HDACs play both positive and negative roles in regulating germ layer expression in the gastrula.

Strengths of the work include the demonstration that histone deacetylase HDAC1 binds to the genome by the onset of genome activation, accumulates in promoters as the embryo develops through early gastrula, and that inhibition of histone deacetylation disrupts germ layer lineage integrity. New datasets include ChIP-seq of HDAC1 from blastula to gastrula, panH3Kac ChIP-seq within animal and vegetal regions of the embryo, and regional RNA-seq of embryos with and without TSA inhibition of histone acetylation. This study helps demonstrate and clarify that HDAC enzymes play both a positive and negative role in gene expression regulation, and that histone acetylation is required to maintain spatial specificity of germ layer expression in gastrula. Some of the weaknesses of the work include the correlative nature of the experiments and missing analysis. Overall, the research is interesting and impactful, contributing to a growing body of work about the role of histone acetylation in the spatial regulation of earliest cell fate decisions in the embryo.

Reviewer #2 (Public Review):

This manuscript dives deeply into the localized binding and potential function of the Histone deacetylase Hdac1, the major HDAC expressed in early frog development. The stage-specific binding of Hdac1 changes during early development, correlating with the binding due to maternal factors, then zygotically generally activated or generally repressed genes, and also genes that can be either activated or repressed depending on their context. The protein appears not to bind to constitutive heterochromatin.

The study pursues how the binding changes on Animal Cap versus Vegetal mass expressed genes, and studies how inhibition of Hdac1 with TSA or VPA affects the degree of acetylation and expression. Perhaps the most interesting finding is that inhibition of Hdac1 has large effects on the acetylation and expression of inactive, but facultatively expressed genes, while it has smaller hyperacetylation effects on already active facultatively expressed genes; despite a modest stimulation of the already stimulatory effects of acetylation, the additional acetylation correlates with inhibition of expression of this subset of genes. This result is clearly documented with embryonic region-specific effects on facultatively expressed genes. The effect on inactive genes fits with the general idea that Hdac1 is repressive, but the effect on already acetylated genes is not so easily explained, though some models are proposed.

The overall findings are important background for developmental and chromatin biologists because they add to the documentation of the correlations between acetylation, deacetylation, and expression of genes in development. The correlations allow the inference of potential functions, though these are not tested other than by pharmacological inhibition of Hdac1.

Reviewer #3 (Public Review):

This paper investigates how the epigenetic landscape is set up during early frog embryogenesis focusing on the role of the histone deacetylase, HDAC1, in the regulation of histone acetylation around the period of Zygotic gene activation. The authors document the progressive binding of HDAC1 to the embryonic genome around the time of ZGA and on genomic sites harbouring binding motifs for maternally provided transcription factors. The authors classify HDAC1 binding sites based on their association with different epigenetic markings on H3K27 (acetylation and/or methylation) in embryonic chromatin. They infer from the observed co-occurrence of "incompatible" acetylation and methylation marks on H3K27 residue on a subset of HDAC1 binding sites, that these H3K27 modifications occur in different parts of the embryos. Subsequently, they inhibit histone deacetylase activity by TSA and document its impact on the genomic distribution of acetylated histones as well as transcriptional deregulation in explant from different parts of the embryos. By cross comparing these data to the different classes of HDAC1-associated genomic regions, they conclude that HDAC1 is involved in the spatial regulation of embryonic gene expression. Altogether this work reveals how maternally provided transcription factors could direct chromatin modifiers to shape the epigenome of the developing embryos. The work however relies mostly on indirect evidence and it would be important in particular to confirm (i) that maternal factors are indeed required for HDAC1 targeting to chromatin and (ii) that the documented effect of TSA treatment is mediated through its inhibition of HDAC1.

Reviewer #1 (Public Review):

In this work, the authors investigated the mechanism by which ions are selected in ATP-gated P2X receptor channels using patch-clamp electrophysiology. P2X receptors are known to be cation-selective channels, but one of them (the P2X5) also displays anion permeability through a molecular mechanism that is unclear. Here, the authors identify in P2X2 a glutamate residue (E17) which plays a critical role in determining ion selectivity. This residue is localized in the intracellular side of the permeating pathway and is part of three large intracellular lateral fenestrations that are thought to be potential exit/entry pathways for ions. The authors elegantly show that when the side chain of E17 was substituted for cysteine, it became accessible to water-soluble, thiol-reactive methanethiosulfonate (MTS) derivatives that were applied from both sides of the membrane. By mutating E17 into lysine, which reverts the charge, they show that mutated channels displayed increased anion permeability, although channels still remained largely cation selective. However, reverting the charge in the mouse P2X5 (K17E and K17D), they provide evidence for a complete ion selectivity switch (that is mutated P2X5 became cation selective). Therefore, although the mechanism by which P2X2 selects cation versus anion still remains incompletely understood, it seems that K17 is a key determinant for P2X5 anion permeability.

The conclusions of this paper are well supported by data. The work should advance our understanding of ion selectivity in P2X receptors and will likely provide the foundation for further studies.

Reviewer #2 (Public Review):

The study by Tam and colleagues addresses the ion-conducting pathway and selectivity of P2X receptor channels. Recent structures of ATP-bound P2X receptors with the activation gate open revealed the presence of a cytoplasmic cap over the central ion permeation pathway. This prompts the authors to examine if lateral fenestrations are potential pathways for ions to permeate the intracellular end of the channel pore, even although they appear to be largely buried within the membrane. Based on sequence alignment, the authors identified a critical residue E17 within the intracellular lateral fenestrations and found that it is accessible to two thiol reactive reagents. Importantly, mutations of E17 also affect the relative permeability of the channels to cations and anions. The work thus solves an ion-conducting mystery of the physiologically important P2X receptor channels. It demonstrates that lateral fenestrations are part of the internal pore of P2X channels and play a critical role in determining ion selectivity.

The structural and sequence analysis is performed carefully, and the electrophysiological experiments are carried out beautifully. Although the data largely seem to support the conclusions, statistical analysis is required to strengthen the claims. Cysteine accessibility experiments may have alternative interpretations; thus, the rigor can be further improved to include the reversibility of the block by treating it with reducing agents.

Reviewer #3 (Public Review):

P2X channels are homomeric or heteromeric, non-selective cation channels that are gated by extracellular ATP. They are found in many tissues and are implicated in bodily functions including digestion and urination, and other processes such as pain and immune response. Recent atomic resolution structures of P2X3 and P2X7 have captured the principal gating states likely conserved within this channel family. Among novel structural features that were identified was a cytoplasmic cap that appears to stabilize the intracellular region of the pore in the open state. This cap is not present in the closed and inactivated states. From these data, it has been proposed that the intracellular side of a conductive P2X pore is formed by a cytoplasmic-exposed portion of a larger, membrane-embedded fenestration, a somewhat unusual characteristic for ion channels. In this manuscript, the authors delineated the region of the fenestration that is likely exposed to the cytoplasm and identify a residue that is negatively charged in P2X1-4 and P2X7 but positively charged in P2X5-6. They suggest that not only could this residue line the ion pore, but also it may contribute to differences in cation-to-anion permeability previously observed between these P2X subfamilies. They demonstrate by electrophysiology that E17 lines the ion pore through a series of classical MTS blocking experiments. They further demonstrate that the charge of this residue confers partial or strong cation to anion permeability in rP2X2 and mP2X5, respectively. This is an elegant investigation of the internal pore of P2X channels and the experiments presented in this work are of high quality.

Reviewer #1 (Public Review):

Authors aimed to decode signatures linked to tremor, slowness and effective motor control using different types of signals acquired from a group of Parkinson's disease patients during deep brain stimulation surgery. They were able to identify distinct frequency bands which corresponded to different symptoms and conclude that multi frequency band and cortical decoding surpass single frequency band and subthalamic nucleus-based decoding.

The main strength of the study is the recording types used to decode symptoms emerging during the same experimental task: authors leveraged micro and macro level recordings from the subthalamic nucleus and ECoG recordings from the motor cortex, enabling them to provide decoding performance across distinct recording scales and from two critical structures linked to Parkinson's pathophysiology. This allowed the authors to contrast rhythm-based signatures and timescales of Parkinson's disease motor symptoms.

The primary weakness is the level of description used to describe key methods which makes it difficult to unpack the results: authors should pay particular attention to validating and justifying metrics used for assessing behaviour (e.g., tremor, slowness, and effective motor control). Also, the relationship between behavioural measures and UPDRS scores should be further justified. For instance, (1) what is the definition of tremor amplitude probability density in the absence of tremor and what is its relationship to relevant subcategories of clinical tremor severity?; (2) why did the authors link tremor while performing a task to UPDRS rest tremor scores? ; (3) why did the authors opt for normalised cursor speed as a metric for slowness?; (4) Are there any implications of this normalisation when exploring slowness across participants? Authors consider cortical and subthalamic recordings separately: if these recordings were acquired simultaneously, analysing the relationship between the two signals (i.e., envelope, phase, phase-amplitude) would significantly improve the paper.

Authors aimed to decode signatures linked to different symptoms of Parkinson's disease. Results support their primary conclusions that cortical decoding performs better than subthalamic decoding and that using a multi-dimensional feature space improves the performance of the decoder. The paper and data generated will contribute to movement control, movement disorders, and brain stimulation fields.

Reviewer #2 (Public Review):

The present study aimed to demonstrate the utility of brain signal decoding for the differentiation of asynchronous motor signs in Parkinson's disease (PD). To this end, thirty-one PD patients undergoing deep brain stimulation electrode implantation were recruited to participate in an intraoperative motor task. Task performance was compared to extra-operative experiments in healthy subjects. Neural activity and movement traces were segmented into 7-second windows and attributed tremor and slowness measures. To integrate the two symptom domains an additional decoding state termed effective motor control was introduced, which represented the absence of symptoms. Support vector machine regression was used as the model of choice that was trained on individual recording sessions within subjects. All decoding targets from each neurophysiological modality reached significant prediction performances. This represents an important milestone in the current state of research towards machine learning-based intelligent adaptive deep brain stimulation.

Strengths

1. The present analysis is among the first to demonstrate the potential utility of brain signal decoding for the differentiation of asynchronous motor symptoms in Parkinson's disease. In the future, such approaches may be adopted in clinical brain-computer interfaces that can adapt stimulation in real time to concurrent therapeutic demand.

2. The effort from the research team and patients to acquire this important dataset is commendable. The time pressure in the operation room combined with the current trend of asleep surgery for deep brain stimulation makes such data very rare.

3. No relevant difference in decoding performance was found for subthalamic micro vs. macroelectrode recordings. This has practical significance because current sensing-enabled deep brain stimulation implants only allow for macro-recordings, which according to this study has no severe disadvantage over microelectrode recordings for movement decoding. Note that this question could only be answered in the intraoperative setting, which on the other hand can have disadvantages further described below.

4. Beyond the subthalamic nucleus, the authors corroborate the superiority of electrocorticography over subthalamic activity for movement and symptom decoding in Parkinson's disease. This provides further evidence that additional sensing electrodes may complement the subthalamic signals for adaptive deep brain stimulation.

5. Finally, the idea of decoding the presence of an effective motor state is creative and may inspire future developments in adaptive stimulation control algorithms.

Weaknesses

(Note that I take more words for weaknesses, not because they outweigh the strengths, but because I want to justify my criticism in more detail)

1. One inherent limitation of this study is the intraoperative setting, which demands the patients' skull be fixed to the stereotactic frame. This setting is not naturalistic per se and likely comes with additional perturbations in the brain states that are recorded. Thus, the generalization to real-world scenarios is limited. Given the unique opportunity to record invasive brain signals in humans, this limitation has to be accepted and should be taken into account for the interpretation of the results. As mentioned in the strengths, this is currently the only setting that allows for a comparison of micro- and macroelectrode recordings for brain signal decoding.

2. Similarly, the medication state is defined by the intraoperative scenario, as deep brain stimulation implantations are performed after the withdrawal of dopaminergic medication in the so-called dopaminergic OFF state. In this state, PD symptoms are aggravated, which is used clinically to provide a more reliable assessment of deep brain stimulation-induced symptom alleviation. This may also lead to an overestimation of decoding performances as the difference between the absence and presence of PD motor signs in the dopaminergic medication ON state during activities of daily living could be more nuanced.

3. The task design is very interesting as it allows for a continuous definition of symptom severity and motor performance. The comparison to healthy subjects demonstrated clearly higher tremor scores in PD but no significant differences in movement velocity (depicted as trending but p>0.2). This is somewhat unexpected as slowness of movement, also called bradykinesia, is a defining symptom of Parkinson's disease (PD). By definition, this symptom is present in all PD patients, also indicated in the clinical scores shown in the present study. Action tremor, i.e. the presence of tremulous muscle activity during motor performance, is comparatively rare. To support the clinical relevance of the movement tremor observed during the task, the authors show a correlation with the "resting tremor" score from the clinical assessment. It is unclear to me why resting, instead of action tremor scores are shown, as both are part of the clinical assessment (Unified Parkinson's disease rating scale - UPDRS part III). Ultimately, even though resting tremor is significantly more common in Parkinson's disease, not all patients of the current cohort had resting tremor (as indicated in the clinical score correlation). Thus, it remains somewhat puzzling how precise the 3-8 Hz activity actually captures tremor vs. motor noise or inaccuracy. A more fine-grained analysis comparing patients with clinically diagnosed action tremor (as defined by preoperative UPDRS assessment) and without tremor could have helped to support the clinical claims on symptom-specific decoding. On the other hand, the lack of a significant difference in the slowness of movement in the patient cohort relative to healthy controls questions the ability of the task to capture this symptom. Here, I am not sure whether the normalization procedure may have an influence on the comparability. Finally, movement velocity is an easy target that is distributed across a spectrum, so despite the lack of a significant difference in the healthy cohort, I am relatively confident that the decoding of movement slowness in the present cohort is clinically meaningful.

4. Overall, the pathophysiological framework is well placed in the current state of literature, while almost the entire field of brain signal decoding for adaptive deep brain stimulation was neglected. Successful decoding to address Parkinson's and essential tremor (another disorder with more common action tremor) was achieved by multiple groups in impactful studies representing more naturalistic extraoperative or fully embedded settings (Hirschmann et al., 2017, He et al., 2021, Opri et al., 2021). Additionally, other symptoms, like gait disturbances have been the target of machine learning analyses more recently (Louie et al., 2022 and Thenaisie et al., 2022). Here, the manuscript appears to avoid a discussion of the present endeavour in comparison to the current state of the field. One of our own studies has provided the first demonstration of the superiority of electrocorticography over subthalamic LFP for movement decoding, which I am happy to see replicated for the first time in the present manuscript. Importantly, the referenced study showed modality-dependent model performances, with gradient-boosted decision trees performing significantly better than linear models for electrocorticography, while Wiener filters have been repeatedly shown to perform well for subthalamic local field potentials (e.g. see Shah et al., 2018 IEEE Trans Neural Syst Rehabil Eng). The present study does not compare different machine learning architectures. Thus, decoding performances could potentially be further improved with more refined computational approaches. A more thorough overview of the literature from the many laboratories that are invested in this research across the globe would have improved the interpretation with respect to the broader impacts of the present manuscript.

5. The authors also present analyses of the spatial localization of relative decoding performances. They demonstrate higher tremor decoding performance in the dorsolateral subthalamic nucleus and higher decoding performance for the slowness of movement in the more central and ventral subthalamic regions. The authors interpret this as potential evidence to support clinical decision-making for optimized stimulation control of these symptoms at the respective locations. This is overly speculative and currently not backed by the data. First of all, the results only show the contrast of tremor vs. slowness of movement and not each individually. Thus, the spatial peak with each symptom domain could be very similar, e.g. in the dorsolateral STN, but a reversal of the difference only occurs at relatively low performances, e.g. in the ventral STN. Thus, showing both spatial distributions individually could be more informative. However, the claim that this could also be used to adjust stimulation location to alleviate the respective target symptoms is by no means backed by the data and remains an interesting speculation.

6. Finally, as in many brain signal decoding studies, the presented decoding performances are relatively low. The authors decided to present linear correlation metrics as Pearson's r values. These values are by definition higher than the commonly chosen Coefficient of determination or R² that provides a more interpretable performance metric. The amount of variance in the symptom scores that could be explained by the models ranged between 10% and 30% at a temporal resolution of 7 seconds. Moreover, the validity of the linear score is not entirely clear as Pearson's r can be heavily biased by non-normal distributions which were not assessed or at least not reported for the performance evaluation. These considerations do not severely limit the validity of the results themselves, as the authors have convincingly shown that significant decoding performances are possible and other studies in this field range in similar performance ranks. However, this point should remind us that a short-term clinical adoption of such methods is not yet in sight and further research is warranted. Before machine learning-based clinical computer interfaces can reach the clinical routine, the field has to work on more refined methods. In my opinion, the field will have to provide robust decoding performances with R² > 0.8 without patient-specific training to get into the realm of widespread clinical adoption.

Reviewer #3 (Public Review):

In this manuscript, the authors examine microelectrode and macroelectrode recordings from the human STN, as well as electrocorticography from the sensorimotor cortex in order to examine the neurophysiological biomarkers underlying tremor and bradykinesia. This is an important and timely topic, as the detection of such biomarkers can have implications for developing effective closed-loop DBS devices. Currently, there is some uncertainty as to which biomarkers may be relevant for which particular symptoms. Here the authors examine signals recorded from multiple depths within the STN and regress those signals onto behavioral measures of tremor and slowness as captured using a novel behavioral paradigm in which patients track movements on a screen in the intraoperative setting. This group has published on this paradigm previously, and here they now use support vector regressions to examine how the physiological data relates to these behavioral measures. In brief, they find that tremors and bradykinesia (slowness) correlate with different neural signatures from different locations. Overall, the results seem well supported, and the methods and statistical tests are sound.

Reviewer #1 (Public Review):

Temporal patterning allows a neural stem cell to generate different neural identities through the course of development. While this relationship has been demonstrated in many instances of stem cells and/or neurons, it is unclear how birth order translates to target specification. In this manuscript, the authors use live imaging and new tools generated from single-cell RNA sequencing data to address this issue.

They find that neurons born from a given time window (at the resolution of early>middle>late) innervate together - and distinctly from - those born at different temporal windows, though the specifics of the innervations differ between neural stem cell lineages. They also find that neurons achieve this by extending their dendrites in exploratory directions and selectively stabilising the ones in the appropriate direction. This process likely occurs at the sub-second timescales. Finally, they also demonstrate that embryonic-born (larval-specific) neurons that remodel to integrate into adulty-specific circuitry simultaneously perform pruning and dendrite extension to integrate into the circuitry at the appropriate time.

This is a valuable description of how developmental programmes imparted to neurons at the time of their birth might translate to their targetting and connectivity. It lays down a framework for understanding the defects in these processes.

Reviewer #2 (Public Review):

Wong et al. studied how dendrites find specific targets during the wiring process. They used the well-established Drosophila olfactory system to address the question. Specifically, they asked how dendrites of monoglomerulous projection neuron (PN) ensemble form a stereotyped topographic map in antennal lobes. They traced the developmental history of each individual projection neuron from anterodorsal (ad) or lateral (l) lineages and found that birth origin and birth order together specify the initial exploration territory and the terminal target. They then took a step further to ask how about the embryonic-born PNs most of which undergo remodeling during metamorphosis: do they maintain their dendritic target through metamorphosis or do they integrate re-extended dendrites into the adult-specific antennal lobes? They showed that ecdysone signaling simultaneously triggers pruning of the dendrites that formed larval antennal lobes and induces the outgrowth of new dendrites to be integrated into the adult antennal lobes. The methodologies, especially ex vivo explant live imaging, established a powerful paradigm to investigate the dynamics of synapse formation during development.

Reviewer #3 (Public Review):<br /> <br /> In this study, Wong et al, generate tools to genetically follow many of the Drosophila olfactory projection neurons. The antennal lobe, where 50 projection neurons need to form a stereotypic map where information from 50 types of olfactory receptor neurons is relayed to higher brain regions, is an exquisite system to study principles of neural circuit wiring. As such, the Luo lab has led the field in uncovering the mechanisms also generating tools that are needed to describe the system in unparalleled temporal and cell-type resolution. Here, they use cutting-edge genetic tools and imaging techniques to provide us with a better-than-ever understanding of the early phases of dendrite targeting and patterning of projection neurons.

Using these refined genetic tools, often allowing them to visualize two types of projection neurons at a time, they uncovered several important principles of dendrite targeting. They found that dendrite targeting is divided into two major steps - first, projection neurons target their dendrites to a few distinct locations, thereby forming a proto-map. This initial targeting is dictated by the combination of their birth time and lineage. As a second step, neurons pattern their dendrites into the adult-specific location by a dynamic process in which net growth is dictated by a balance between stabilization and retraction of dendritic processes. Finally, they found that the embryonic-born projection-neurons, which undergo developmental remodeling that include pruning of their connections to the larval antennal lobe (as it undergoes degeneration) and regrowth into the adult antennal lobe. Surprisingly, and in contrast to other remodeling neurons in Drosophila, pruning and regrowth occur simultaneously.

While the strong part of the paper is the cutting-edge tools, coupled with exceptional imaging strategies, its main weakness stems from the decision to remain in the descriptive realm.

Reviewer #1 (Public Review):

Previous studies from this group reported that PEG10 is increased in the spinal cord from Ubiquilin 2-/- mice as well as PEG10 being elevated in models of Ubiquilin 2-mediated ALS. In this study, the authors provide evidence supporting the concept that the proteasome factor Ubiquilin 2 regulates the activity of the Gag-pol retrotransposon gene (PEG10). Mutations in Ubiquilin 2 underlie a portion of the familial forms of ALS. It is found that in spinal cord tissue from sporadic ALS patients PEG10-pol levels are elevated, leading to the conclusion that altered regulation of PEG10 levels by Ubiquilin 2 and a subsequent alteration in genes regulating axon remodeling may contribute to all forms of ALS pathogenesis. Strengths of this work include the extensive analyses and direct data on the mechanism by which Ubiquilin 2 regulates PEG10 levels in human cells resulting in less PEG10. They further show that peptides generated by the self-cleavage of PEG10 alter the expression of genes involved in axon function. The major weakness of this study is the complete absence of data that directly show that an alteration of PEG10 by Ubiquilin 2 is critical for ALS pathogenesis. As noted by the authors, multiple pathways have been broadly implicated in genetics and sporadic forms of ALS. Thus while the study provides interesting data on the regulation of PEG10 by Ubiquilin 2, the extent to which this pathway underlies ALS pathogenesis and/or progression is speculative.

Reviewer #2 (Public Review):

This is a follow-up study by the senior author, who previously showed in a 2021 JBC paper that levels of Paternally Expressed Gene 10 (PEG10) protein, among many other protein changes, are increased in the spinal cord of Ubqln2 knockout (KO) animals (JBC 2021). In this report, they provide more direct evidence that PEG10 levels are regulated by ubqln2 and that PEG10 can be proteolytically cleaved generating fragments, which when overexpressed, induce alterations in gene expression. Through proteomic analysis of spinal cord tissue from control and ALS patients, they found that PEG10 levels and the signature of genes regulated by its products are increased in ALS, proposing that elevation in PEG10 is a novel marker and driver of ALS.

PEG10 resembles a retrotransposon, encoding virus-like gag-pol products. It is only found in eutherian mammals. Although it has lost its ability to transpose, it still retains the retroviral-like translation frameshifting property generating two main products, gag (reading frame 1, RF1) and gag-pol (RF1/2). PEG10 is essential for survival. It plays an important role in RNA-binding and trophoblast stem cell specification, being required for placental development. It is also expressed in several adult tissues, but its function in them is obscure. A recent study showed PEG10 RF1 and RF1/2 bind the deubiquiting enzyme USP9X, and that loss of USP9X destabilizes RF1 but not RF1/2, suggesting USP9X regulates ubiquitination and proteasomal degradation of PEG10 (Abed et al. PLOS One 2021). Additionally, Abed et al. showed PEG10 products support virus-like particle (VLP) assembly and that both RF1 and RF1/2 localize to the cytoplasm, whereas a portion of RF1/2 is found in the nucleus of some cells. They further showed PEG10 binds and regulates RNA expression, most probably through interaction with the 3'-ends of the RNAs but found no common binding motif suggesting interaction could be with the secondary structure.

As mentioned, the senior author previously reported in a JBC article in 2021 that PEG10 levels are elevated in ubqln2 knock out (KO) mice, but that its levels were slightly decreased in the P497S mouse model of ALS. They validated PEG10 as an interactor of ubqln2 by proximity-dependent biotin labeling. A review of the current manuscript follows.

1. Evidence that ubqln2 regulates PEG10 accumulation (Fig 1). The authors use human embryonic stem cells to investigate how knockout (KO) of different ubqln isoforms (1, 2, and 4) affects PEG10 accumulation, showing that only KO of ubqln2 increases the RF1/2 product.

a) There is considerable variation in PEG10 expression in the duplicate sample sets provided, but this is not reflected by the error bars (fig 1 A and B). For example, RF1/2 is quite different in the two ubqln4 KO lysates, yet the error bars do not capture the variation. Better loading and quantification is needed. Also, in the KO cells, gag levels are slightly increased, which is consistent with alterations in proteasomal degradation. Alternatively, the changes in RF1/2 could also result from changes in read-through translation, but this is not investigated. Also, it would be helpful to include blots showing the lower Mol weight PEG10 products, to see how they change relative to Fig 3.

Fig 1G. The authors examined if removal of the poly proline rich region (PPR) from PEG10 affects RF1/2 regulation by ubqln, confirming its requirement.

b) The mechanism why deletion of the PPR abolished RF1/2 regulation by ubqlns was not examined. Is it from accelerated degradation? Also, it is not clear why the authors use the triple ubqln KO cells and did not perform that tests in the different ubqln KO cells. The latter comment applies for several of their investigations, leading to uncertainty regarding the specificity of ubqln2 in PEG10 regulation. It is possible that removal of most ubqlns stalls protein degradation affecting PEG10 turnover?

2. The authors investigated the phylogenetic relationship between PEG10 and ubqln2 demonstrating that PEG10 levels from marsupials that lack a PPR can be increased by appending a PPR from human PEG10. They used triple ubqln KO cells for these investigations.

a) The change they describe is not obvious in Fig2C and E as they appear quite small. They also conclude that ubqln2 regulates PEG10 by these studies, but really the experiments show it is from loss of all ubqlns, not ubqln2 specifically.

3. The authors show PEG10 is capable of self-cleavage of the RF1 product, generating 2 detectable N-terminal products, and several other fragments, including a C-terminal nuclear capsid (NC) fragment (Fig3). They show expression of HA-tagged NC fragment localizes to mainly the nucleus, whereas several other PEG10 products and fragments localize to the cytoplasm. They provide strong support that PEG10 is capable of self-cleavage by mutation of an aspartate residue (D) in a DSG motif in the protein to alanine (A to → ASG), which abolished cleavage. They also conducted a nice experiment showing the ASG mutant can be cleaved in trans by introduction of WT PEG10.<br /> a) The authors never show evidence for liberation and accumulation of the NC fragment, only for an artificially tagged protein by immunofluorescence. Use of a tag to study its localization and affects is problematic as the could influence its properties. They need to show that the fragment is detectable because of their central claim that it is responsible for inducing changes in genes. Biochemical fractionation studies could also reveal the extent of the partitioning of the fragment in the nucleus and cytoplasm. The mechanism by which the NC fragment induces changes in gene expression is not clear.

4. The authors show differences in gene expression upon transfection of HEK293 cells with PEG10 RF1, RF1/2, and NC expression constructs. They show that two PEG10-regulated genes, DCLK1 and TXNIP, are both increased in the spinal cord in sporadic ALS cases compared to controls.<br /> a) It is not clear from the studies whether the changes found in ALS are related to changes in PEG10 specifically, or for other reasons. Additionally, more rigorous comparison in many more ALS and controls is needed. PEG10 levels increase upon cell differentiation (Abed et al.) so the changes in ALS may reflect a compensatory and protective response.

5. To investigate if PEG10 RF1/1 levels are altered by ALS mutations in ubqln2 they transfected ubqln TKO cells with either wt ubqln2, or with mutants carrying either the P497H or P506T ALS mutations. They show PEG10 RF1/2 levels are reduced by overexpression of both the wt and P497H mutant, but not by the P506T mutant. They claim that P497H expression did not affect RF1/2 levels. The authors conducted a proteomic comparison of extracts from the spinal cord of two controls, one P497H ubqln2 case, and six sporadic ALS cases. They found increased levels of RF1/2 in the ALS cases. They also found neurofilament medium and neurogranin were both reduced in the ALS cases. Based on these changes they speculate that PEG10 is a novel marker for ALS.<br /> a) The conclusion that the P497S mutant did not affect RF1/2 is incorrect. It reduced RF1/2 slightly more than wt ubqln2. In fact, it appears that expression of all three (wt and the 2 ALS mutants) ubqln2 proteins reduce RF1/2 significantly, compared to the TKO cells.<br /> b) The changes in PEG10 found in the ALS cases are difficult to evaluate because too few controls and ALS cases were used for the comparison. Huge variations in the levels of PEG10 and of the other proteins graphed In Fug 6B-F were seen in the two controls. The comparison needs to be done with many more samples for sound statistical comparison. Were the samples compared from the same region of the spinal cord?

General comments

1. In the Discussion the authors write that because ubqln2 is the only ubqln capable of regulating PEG10 RF1/2 levels, the PXX domain that is only present in ubqln2 is likely responsible for the regulation. There is no proof in support of this hypothesis. Only one ALS-causing mutation (P506T) in the PXX domain, but not the P497H mutation in the same PXX domain, affected RF1/2 accumulation, inconsistent with general involvement of the PXX domain in PEG10 regulation.

2. The authors claim that ubqln2 may have specifically evolved to restrain PEG10 expression, but don't mention USP9X as being another regulator. The common theme that emerges from these studies is that PEG10 levels are regulated by any mechanism that interferes with ubiquitination/proteasomal degradation. Indeed, immunoblots of the gag-pol (RF1/2) in the different ubqln KO cells show a smear at high molecular weight consistent with the accumulation of ubiquitinated PEG10. The authors imply that the transcriptional changes caused by the alteration in PEG10 levels by ubqln2 are responsible for ALS (title of the paper), but this is merely speculation as the effects of the changes are not known. The changes found could be protective. They also claim PEG10 may serve as a novel biomarker for ALS, but such a claim is not justified from the limited analysis conducted so far, which will require more extensive proof.

Reviewer #3 (Public Review):

Ubiquilin 2 (UBQLN2) encoded by a familial predisposition gene for Amyotrophic Lateral Sclerosis (ALS) is a proteosome shuttle protein shown to associate with Cxx2 and mammalian Ty3/gypsy-like retrotransposon PEG10 in previous work by this author. Other work has shown that PEG10 is expressed from an imprinted gene required for placental development and in adrenal and brain tissues. Increases in PEG10 expression are also implicated in some cancers. Building on previous work by the senior authors (Whitely et al., 2021) which showed that PEG10 interacts with components of the UBQLN machiner and is elevated in UBQUILN TKO human and mouse cells, this work focuses on the interaction of UBQLN proteins and PEG10 target. Using a HEK human kidney cell line, authors show first that targeting of PEG10 depends upon a proline-rich repeat at the carboxy terminus of Gag-Pol unique to eutherian animals; second, that the aspartyl protease previously implicated in gag-pol processing can release the gag carboxyl-terminal CHCC zinc knuckle nucleocapsid to concentrate in the nucleus and correlates with changes in expression of genes related to neuronal development. Finally, they show that PEG10 is elevated in human spinal cord neuron cell lines.

Strengths:

The primary strengths of this manuscript lie in the multiple experiments linking UBQLN2 activity to the target PEG10 PPR motif and in potentially linking Gag-Pol and NC production to changes in cellular gene expression. The authors knock out multiple UBQLN genes in various species and demonstrate the phylogenetic correspondence between UBQLN2 PEG10 levels.

Weaknesses:

Although this manuscript links elevated PEG10 protein levels to fALS mutated UBQLN2 and changes in neuronal gene expression, it does not as the title suggests demonstrate that UBQLN2 control of PEG10 is required for "neuronal health in ALS". This is an awkward title and the link between neuronal health and the ability to turnover PEG10 is not clearly established since most of the experiments were conducted in non-neuronal human cell lines.

Authors could more completely set the context for their work including their own work (Whiteley et al., 2021) and findings in Angelman (UBE3A, Pandya et al., 2021) and Parkinsons (Sakharkar et al., 2019) which, rather than detracting from their work, would confer greater interest. In addition, they mention in passing that in the absence of familial predisposition mutations, in ALS UBQLN2 can be inactivated by trapping in aggregates. This undermines their comparison of fALS and sALS cells.

The multiple western blots while consistent with authors conclusions, do not show greater than two-fold differences in PEG10 protein levels in the absence of UBQLN2 proteins so that there are likely other factors besides UBQLN2 influencing PEG10 levels. For example, the authors do not comment on PEG10 extracellular VLP production which occurs in some cells or that other proteins previously implicated as targets of PEG10 could be influencing the neuronal phenotypes of fALS. In addition, clarification of the different phenotypes of fALS mutations in the UBQLN2 hotspot would have addressed concerns that more than UBQLN2 is involved in the phenotype.

Reviewer #1 (Public Review):

In this study, Jigo et al. measured the entire contrast sensitivity function and manipulated eccentricity and stimulus size to assess changes in contrast sensitivity and acuity for different eccentricities and polar angles. They found that CSFs decreased with eccentricity, but to a lesser extent after M scaling while compensating for striate-cortical magnification around the polar angle of the visual field did not equate to contrast sensitivity.

In this article, the authors used classic psychophysical tests and a simple experimental design to answer the question of whether cortical magnification underlies polar angle asymmetries of contrast sensitivity. Contrast sensitivity is considered to be the most fundamental spatial vision and is important for both normal individuals and clinical patients in ophthalmology. The parametric contrast sensitivity model and the extraction of key CSF attributes help to compare the comparison of the effect of M scaling at different angles. This work can provide a new reference for the study of normal and abnormal space vision.

The conclusions of this paper are mostly well supported by data, but some aspects of data collection and analysis need to be clarified and extended. 1) In addition to the key CSF attributes used in this paper, the area under the CSF curve is a common, global parameter to figure out how contrast sensitivity changes under different conditions. An analysis of the area under the CSF curve is recommended. 2) In Figure 2, CRFs are given for several SFs, but were the CRFs at the cutof-sf well-fitted? The authors should have provided the CRF results and corresponding fits to make their results more solid. 3) The authors suggested that the apparent decrease in HVA extent at high SF may be due to the lower cutoff-SF of the perifoveal VM. Analysis of the correlation between the change in HVA and cutoff SF after M scaling may help to draw more comprehensive conclusions. 4) In Figure 6, it would be desirable to add panels of exact values of HVA and VMA effects for key CSF attributes at different eccentricities, as shown in Figures 4B, D, and F, to make the results more intuitive.

More discussions are needed to interpret the results. 1) Due to the different testing distances in VM and HM, their retinae will be in a different adaptation state, making any comparison between VM and HM tricky. The author should have added a discussion on this issue. 2) In Figure 4, the HVA extent appears to change after M-scaling, although the analysis shows that M-scaling only affects the HVA extent at high SF. In contrast, the range of VMA was almost unchanged. The authors could have discussed more how the HVA and VMA effects behave differently after M-scaling. 3) The results in Figure 4 also show that at 11.3 cpd, the measurement may be inaccurate. This might lead to an inaccurate estimate of the M scaling effect at 11.3 cpd. The authors should discuss this issue more. 4) The different neural image-processing capabilities among locations, which is referred to as the "Qualitative hypothesis", is the main hypothesis explaining the differences around the polar angle of the visual field. To help the reader better understand this concept, the author should provide further discussions.

The authors should also provide more details about their measures. For example, high grayscale is crucial in contrast sensitivity measurements, and the authors should clarify whether the monitor was calibrated with high grayscale or only with 8-bit. Since the main experiment was measuring CS at different locations, it should also be clarified whether the global uniformity of the display was calibrated. In addition, their method of data analysis relies on parametric contrast sensitivity model fitting. One of the concerns is whether there are enough trials for each SF to measure the threshold. The authors should have included in their method the number of trials corresponding to each SF in each CSF curve.

Reviewer #2 (Public Review):

This is an interesting manuscript that explores the hypothesis that inhomogeneities in visual sensitivity across the visual field are not solely driven by cortical magnification factors. Specifically, they examine the possibility that polar angle asymmetries are subserved by differences not necessarily related to the neural density of representation. Indeed, when stimuli were cortically magnified, pure eccentricity-related differences were minimized, whereas applying that same cortical magnification factor had less of an effect on mitigating polar angle visual field anisotropies. The authors interpret this as evidence for qualitatively distinct neural underpinnings. The question is interesting, the manuscript is well written, and the methods are well executed.

1) The crux of the manuscript appears to lean heavily on M-scaling constants, to determine how much to magnify the stimuli. While this does appear to do a modest job compensating for eccentricity effects across some spatial frequencies within their subject pool, it of course isn't perfect. But what I am concerned about is the degree to which the M-scaling that is then done to adjust for presumed cortical magnification across meridians is precise enough to rely on entirely to test their hypothesis. That is, do the authors know whether the measures of cortical magnification across a polar angle that are used to magnify these stimuli are as reliable across subjects as they tend to be for eccentricity alone? If not, then to what degree can we trust the M-scaled manipulation here? In an ideal world, the authors could have empirically measured cortical surface area for their participants, using a combination of retinotopy and surface-based measures, and precisely compensated for cortical magnification, per subject. It would be helpful if the authors better unpacked the stability across subjects for their cortical magnification regime across polar angles.

2) Related to this previous point, the description of the cortical magnification component of the methods, which is quite important, could be expanded on a bit more, or even placed in the body of the main text, given its importance. Incidentally, it was difficult to figure out what the references were in the Methods because they were indexed using a numbering system (formatted for perhaps a different journal), so I could only make best guesses as to what was being referred to in the Methods. This was particularly relevant for model assumptions and motivation.

3) Another methodological aspect of the study that was unclear was how the fitting worked. The authors do a commendably thorough job incorporating numerous candidate CSF models. However, my read on the methods description of the fitting procedure was that each participant was fitted with all the models, and the best model was then used to test the various anisotropy models afterwards. What was the motivation for letting each individual have their own qualitatively distinct CSF model? That seems rather unusual. Related to this, while the peak of the CSF is nicely sampled, there was a lack of much data in the cutoff at higher spatial frequencies, which at least in the single subject data that was shown made the cutoff frequency measure seem like it would be unreliable. Did the authors find that to be an issue in fitting the data?

4) The manuscript concludes that cortical magnification is insufficient to explain the polar angle inhomogeneities in perceptual sensitivity. However, there is little discussion of what the authors believe may actually underlie these effects then. It would be productive if they could offer some possible explanation.

Reviewer #3 (Public Review):

Jigo, Tavdy & Carrasco used visual psychophysics to measure contrast sensitivity functions across the visual field, varying not only the distance from fixation (eccentricity) but also the angular position (meridian). Both parameters have been shown to affect visual sensitivity: spatial visual acuities generally fall off with eccentricity, it is now widely accepted that it is superior along the horizontal than the vertical meridian, and there may also be differences between the upper and lower visual field, although this anisotropy is typically less pronounced. The eccentricity-dependent decrease in performance is thought to be due to reduced cortical magnification in peripheral compared to central vision; that is, the amount of brain tissue devoted to mapping a fixed amount of visual space. The authors, therefore, include a crucial experimental condition in which they scale the size of their stimuli to account for reduced cortical magnification. They find that while this corrects for reduced performance related to stimulus eccentricity, it does not fully explain the variation in performance at different visual field meridians. They argue that this suggests other neural mechanisms than cortical magnification alone underlie this intra-individual variability in visual perception.

The experiments are done to an extremely high technical standard, the analysis is sound, and the writing is very clear. The main weakness is that as it stands the argument against cortical magnification as the factor driving this meridional variability in visual performance is not entirely convincing. The scaling of stimulus size is based on estimates in previous studies. There are two issues with this: First, these studies are all quite old and therefore used methods that cannot be considered state-of-the-art anymore. In turn, the estimates of cortical magnification may be a poor approximation of actual differences in cortical magnification between meridians. Second, we now know that this intra-individual variability is rather idiosyncratic (and there could be a wider discussion of previous literature on this topic). Since these meridional differences, especially between upper and lower hemifields, are relatively weak compared to the variance, a scaling factor based on previous data may simply not adequately correct these differences. In fact, the difference in scaling used for the upper and lower vertical meridian is minute, 7.7 vs 7.68 degrees of visual angle, respectively. This raises the question of whether such a small difference could really have affected performance.

That said, there have been reports of meridional differences in the spatial selectivity of the human visual cortex (Moutsiana et al., 2016; Silva et al., 2017) that may not correspond one-to-one with cortical magnification. This could be a neural substrate for the differences reported here. This possibility could also be tested with their already existing neurophysiological data. Or perhaps, there could be as-yet undiscovered differences in the visual system, e.g., in terms of the distribution of cells between the ventral and dorsal retina. As such, the data shown here are undoubtedly significant and these possibilities are worth considering. If the authors can address this critique either by additional experiments, analyses, or by an explanation of why this cannot account for their results, this would strengthen their current claims; alternatively, the findings would underline the importance of these idiosyncrasies in the visual cortex.

Reviewer #1 (Public Review):

Pasquereau and Turner investigated the encoding of reward and delay information in subthalamic (STN) neurons in behaving macaques. They record during a forced-choice task with three levels of reward and two levels of delay, using rejection rates to model subjective value. Task-dependent neurons, those which encoded reward and/or delay, were identified with a sliding-window regression model. They then investigated the time course of reward and delay information using a principal component analysis approach. They find that the strength of the first and four principal components varies systematically along the anteroposterior axis of the STN, suggesting a spatial distribution of value coding. These data, recorded in a controlled task, add to the understanding of STN function.

The data, collected from a well-defined brain area and with appropriate motor and oculomotor controls included during a straight-forward task, are a good foundation for investigating STN function. However, the statistical procedures used are not completely described and may not be appropriate, particularly in the sliding window analysis. Given this analysis underlies some of the further analyses, it must be clarified or corrected for the conclusions to stand. Further, the analysis only explores the encoding of delay at the time of a cue and does not consider how the value of delay may change over time.

The sliding window analysis, a common approach in investigating time-course data, necessitates multiple comparisons (188 time-bins here) and so requires a controlling procedure to keep the family-wise error-rate low. The authors describe, not completely, how the pre-instruction period was used to establish the boundaries for significance for each coefficient. The pre-instruction period, by the authors' own account, is a period of lower variance and so it would be expected that the boundaries for significance would be lower and the number of task-dependent neurons is therefore an overestimate. The shuffling process the authors use when they determine significance in their principal components analysis is a more appropriate method.

The task design and analysis provide a limited test of delay encoding. As only two levels of delay were tested, it is not possible to directly test whether the subjective discounting function is hyperbolic or exponential and hence whether the delay is encoded subjectively or objectively. Further, the task has several variable interval lengths (hold in: 1.2-2.8 s, short delay: 1.8-2.3 s, long delay: 3.5-4s) that frustrate interpretation. The distribution of these delays is not described, for example as it reads it seems possible that some long delay rewards are delivered with shorter latency between cue and reward than some short delay rewards (1.2 + 3.5 = 4.7s vs. 2.8+2.3 = 5.1 s). The authors have not considered that if the delay value is encoding, then the value, both objectively and subjectively, may be changing as the delay elapses. The variation of these task intervals may have an effect on the value of delay.

The principal components analysis is an interesting way to explore patterns of encoding and the spatial distribution of these patterns. In particular, the finding that Discounting- neurons, those whose firing rate increases with increasing reward cues and decreases with increasing delay cues, are preferentially found in the posterior STN, which the authors demonstrate with both the principal component analysis and the sliding-window classification analysis, challenges previous ideas of STN organization.

Reviewer #2 (Public Review):

The manuscript "Neural dynamics underlying self-control in the primate subthalamic (STN) nucleus" builds on a substantial literature indicating a role for the STN in impulsive actions, i.e. responding too early in tasks that require patience. The authors trained two monkeys to move a cursor to a target and then hold still, waiting for a reward. A visual cue indicated the reward magnitude and time interval that the monkeys were required to wait on each trial in order to get the reward. Understanding the mechanism by which the STN supports behavioral inhibition is important since the STN is a common target for deep brain stimulation for both neurological and psychiatric disorders. The authors claim that their results indicate that the STN integrates reward and delay information and that this representation is anatomically varied along the axis of the STN.

Plots of "rejection rate" (trials where the monkeys failed to wait until the rewards) as a function of delay and reward size seem to indicate that the monkeys understood the visual cue. The rejection rates were very low (less than 4% for almost all conditions) which indicates that the monkeys did not have a hard time inhibiting their behavior. It also meant that the authors could not compare trials where the monkeys successfully waited with trials where they failed to wait. This missing comparison weakens the link between the neurophysiological observations and the conclusions the authors made about the signals they observed.

The authors examined the STN activity aligned to the start of the delay and also aligned to the reward. Most of the "delay encoding" in the STN activity was observed near the end of the waiting period. The trouble with the analysis is that a neuron that responded with exactly the same response on short and long trials could appear to be modulated by delay. This is easiest to see with a diagram, but it should be easy to imagine a neural response that quickly rose at the time of instruction and then decayed slowly over the course of 2 seconds. For long trials, the neuron's activity would have returned to baseline, but for short trials, the activity would still be above baseline. As such, it is not clear how much the STN neurons were truly modulated by delay.

Another concern is the presence of eye movement variables in the regressions that determine whether a neuron is reward or delay encoding. If the task variables modulated eye movements (which would not be surprising) and if the STN activity also modulated eye movements, then, even if task variables did not directly modulate STN activity, the regression would indicate that it did. This is commonly known as "collider bias". This is, unfortunately, a common flaw in neuroscience papers.

Overall, while the work is potentially interesting, these methodological issues weaken the link between the data and the conclusions of the paper.

Reviewer #3 (Public Review):

The authors have been challenged to figure out the neural processing of delay discounting during waiting for upcoming reward outcomes after behavioral controls in the subthalamic nucleus, where unique brain regions as a part of the basal ganglia for cognitive and motor functions. They described the activity property of STN neurons for the delay gratification at the single neuron level and population level, using both conventional and recently developing approaches. The finding is novel, but the details of the analysis are sometimes inaccurate and needed to be improved. Their claims are now partially supported. If their analyses are improved, their findings have a significant impact on understanding the neural basis of delay discounting, which is one of the predominant behavioral characteristics among organisms.

Reviewer #1 (Public Review):

In this work, Pan et al. investigate the properties of the underexplored snake venom phosphodiesterase (svPDE) from a genomic, transcriptomic, and structural perspective. These analyses are complemented by comparisons with similar ENPP proteins to better understand the elements that may underline the specific role of svPDE in envenomation. The data support a role for svPDE that may be related to its interactions with partner proteins or due to its phosphodiesterase activity to enhance the cytotoxic effects of other venoms present in the environment.

Overall, the authors have done a good job of investigating the origins and function of svPDE. The evolutionary analyses are adequate and informative, which are expanded by further experiments to determine the structure and interactions of svPDE. The protein-protein interaction experiments and the svPDE activity experiments with different substrate types shed light on the possible role of the protein in the context of its cellular environment and point to the potential role of glycosylation as part of the mode of action of svPDE. These results will pose a good prelude for further research into the mechanism and interactions of svPDE from other species. Further, the mechanistic insights from this work may also help the development of antivenom compounds that target svPDE.

Reviewer #2 (Public Review):

The work integrated genomic and transcriptomic data to reconstruct the origin of the svPDE gene from the ancestral ENPP3 gene. The authors also analyzed the expression of svPDE along different snake lineages and different tissues in three species of venomous snakes. Finally, they purified an svPDE from the venom of Naja atra and analyzed its crystallographic structure and enzymatic function. The experiments are adequately designed and carefully planned and the conclusions made by the authors are well supported by evidence.

I have the following suggestions:

I could not find a section where the authors provided information regarding the origin of the analyzed venom and tissues. i.e. muscle tissue from Naja atra and venom for purification of svPDE. It is important to include this information.

The authors mention (Line 156) that "the genomic sequences of svPDE-E1a were present in all species of Serpentes but not in the species of Dactyloidae, Varanidae, and Typhlopidae.". As I understand it, the family Typhlopidae is included in the Suborder Serpentes. The conclusions stand of course, but I believe it is worth revising, for accuracy.

During the discussion (Line 315), it is stated that the expression of svPDE in Lamprophiidae is probably associated with the adaptation of prey selection as a dietary generalist compared to Viperidae and Elapidae. Provided that both of these clades have several species considered dietary generalists, I believe this statement is not strongly supported.

Also in the discussion (Line 320), the authors mention that Colubridae is traditionally regarded as a non-venomous clade. This statement is far from accurate given that Colubridae is a very diverse clade and several species within it have been shown to be at least moderately venomous. Various species have been shown to produce secretions comparable to those of front-fanged snakes.<br /> Furthermore, despite their difference in morphology, I believe there is little to no evidence that suggests Duvernoy's glands in colubrids have any functions differing from the venom glands of front-fanged snakes.

Reviewer #3 (Public Review):

The biochemical identity and the crystal structure of the snake venom phosphodiesterase (svPDE) were determined using protein purified from the crude venom of a snake (Naja atra) captured in Taiwan. The crystal structure was determined with and without AMP bound. The quality of the structure is excellent and the coordination of the bound AMP makes sense based on the coordination by side-chain residues and the known coordination of bound AMP to structural homologues (ENPP3). Naturally, it's interesting that snake venom produces a soluble variant of the membrane-anchored PDE found in humans.

Although the structure and the catalytic site seem overall similar, it is unclear what the role of the snake enzyme is in the host infection. Furthermore, there are a number of human ENPP enzymes and they have different substrate preferences and physiological roles. More detailed biochemistry would help to put the role of the svPDE into a physiological context.

Reviewer #1 (Public Review):

ARL15 forms a complex with the TRPM7 channel and CNNM transporters and is involved in the regulation of the TRPM7 function. To understand the regulatory mechanism, the authors performed biochemical and structural characterizations. In this work, they determined the crystal structure of ARL15 in complex with CNNM2 CBS domain, performed the mutational analysis based on the structure, and successfully revealed the binding mechanism between ARL15 and CNNM.

However, the detailed mechanism of TRPM7 inhibition by ARL15 remains unclear because the structure of TRPM7 in complex with ARL15 is still unknown. Furthermore, despite the structure determination of ARL15 in complex with CNNM, the effect of ARL15 on CNNM function is still unclear.

Nevertheless, the structural information on the ARL15-CNNM complex provided by the authors is valuable for the related research field, and the structure-based CNNM mutants specifically targeting disruption of binding to either ARL15 or PRL would also be useful.

Reviewer #2 (Public Review):

Mahbub et al further elucidate the structural and functional consequences of the ARL15-CNNM2 interaction for divalent cation transport. They show that ARL15 has low GTP binding affinity and could not detect GTPase activity, questioning whether ARL15 functions as a GTPase. Although the interaction of ARL15 and CNNMs has been demonstrated by multiple groups before, this study addresses some of the key questions that are central within the TRPM-CNNM-PRL-ARL15 field. Particularly, the authors have identified residues in both ARL15 and CNNM proteins which are required for their binding to one another. In addition, they have also illustrated how PRL proteins compete with ARL15 for their binding to CNNMs. Lastly, the functional consequences of ARL15 binding to CNNMs are shown by TRPM7-mediated Zn2+ transport assays.

However, the current dataset also comes with limitations. Previous studies demonstrated that PRLs interact with the CBS domains of CNNMs and lock them in their so-called "flat" confirmation. It remains unclear how ARL15 affects the structure of the CBS domains, especially in the presence of ATP. The subcellular localisation of these interactions has not been examined. Moreover, the consequences of ARL15 on TRPM7 activity are not completely elucidated. It remains unclear whether this functional effect is CNNM-dependent. Moreover, how the zinc uptakes translate to other divalent ion transport, such as magnesium, has not been examined. These questions should be answered to confirm the model as presented in Figure 7.

Reviewer #3 (Public Review):

The authors studied the interaction between Arl15 and CNNMs using various biochemical and biophysical approaches. Significantly, they solved the crystal structure of Arl15 and the CBS-pair domain of CNNM2 and demonstrated that PRLs and Arl15 could compete for binding to CNNMs. The study should advance our understanding of how cellular divalent ions are regulated via Arl15, CNNMs, and TRPM7, although some issues regarding the guanine nucleotide-binding of Arl15 need to be addressed.

Reviewer #1 (Public Review):

These authors use a mouse model of gestational intermittent hypoxia (GIH), a component of sleep apnea during pregnancy, to test the hypothesis that GIH induces inflammation in the central nervous system that impairs respiratory functions, in a sex-dependent manner. The major finding of this work is that spinal cord inflammation, mainly driven by activated microglia cells, impairs inactivity-induced inspiratory motor facilitation (iMF). The authors successfully test this hypothesis and their results support their conclusion.

Major strengths of this work include a robust study design, a well-defined translational model (GIH that sets on later in pregnancy), complementary biochemical and experimental methods such that correlated findings are followed up by interventional studies, and sufficient power to evaluate sex differences. In particular, the authors note the upregulation of several NF-kB regulates genes and increased concentration expression of inflammatory markers in the spinal cords of male mice. These mice also have deficits in the iMF response. By depleting microglia and blocking Ik-kinases, the authors convincingly demonstrate that the increased spinal inflammation is causative in the disruption of respiratory plasticity.

The major limitation as the manuscript is currently written is a clear rationale for evaluating the iMF response as a primary endpoint. One of the corresponding authors is an expert in iMF, but there is no rationale for why it is expected that this aspect of plasticity might be disrupted. The authors discuss breathing and respiratory function in the introduction, but these have not been measured here. It is not known whether GIH impacts respiratory response or baseline breathing in a spontaneous breathing model, including baseline frequency and tidal volume and the ventilatory responses to hypoxia and hypercapnia. Shortening the introduction to offer a clear rationale would be beneficial, given the wide audience of this journal. The limitations of this model, including vagotomy, mechanical ventilation, hyperoxic ventilation, and recording from the phrenic nerve in lieu of respiratory measures, should also be discussed in the discussion for readers not familiar with this model outside of the respiratory control field.

Reviewer #2 (Public Review):

Experiments were designed to determine if the adult offspring of mothers exposed to intermittent hypoxia (IH) during late gestation show reduced compensatory respiratory motor neuron plasticity, which is defined as an increase in respiratory motor system output that persists for a long-time following cessation of the perturbing stimulus. Here, the team uses a clever approach to evoke plasticity, which they term inactivity-induced respiratory motor facilitation. This approach has been shown to be repeatable and robust, and therefore useful for evaluating the impact of experimental interventions on compensatory respiratory motor system responses. The model is a paralyzed, mechanically ventilated, anesthetized rat in which the activity of a phrenic nerve is used as an index of excitability of the phrenic motor neuron population, which drives the diaphragm muscle in mammals. Importantly, the activity of the respiratory control system in the brainstem can be terminated by reducing the pH of the blood and cerebrospinal fluid (CSF) to a value that is unique to each animal. This value is called the central apneic threshold, and it occurs because pH-sensitive receptors in the brainstem provide critical excitatory synaptic input to the respiratory controller. Since the pH of the blood and CSF depends importantly on the corresponding levels of CO2 the pH can be adjusted up or down by manipulating the blood CO2. To evoke inactivity-induced respiratory motor facilitation, the group first sets the mechanical ventilator at a rate sufficient to reduce CO2 below the apneic threshold to stop phrenic motor output and then keeps the ventilator output at this level. Then, CO2 is added to the ventilator to raise the blood CO2 to levels just above the apneic threshold, which establishes the baseline level of phrenic motor neuron output. They then periodically stop adding CO2 to the inspired gas mixture, which allows CO2 to fall below the apneic threshold, which abolishes phrenic nerve activity. After 1 minute of apnea, the CO2 is reintroduced, blood CO2 levels rise and phrenic nerve activity resumes. This sequence of 1 minute of central apnea followed by 5 minutes of phrenic motor activity is repeated 5 times, and the recording continues for 60 minutes after the fifth apneic episode. As shown in figure 1, a progressive and long-lasting increase in phrenic nerve activity is observed in both male and female control animals, consistent with compensatory respiratory neuroplasticity. Interestingly, the neuroplastic response in the male offspring of animals exposed to intermittent hypoxia throughout gestation was abolished but was unchanged in the female offspring.

This striking, sex-dependent loss of respiratory motor neuroplasticity in the offspring of IH-exposed mothers was associated with increased inflammatory response in the cervical spinal cord, but not in the brainstem. In addition, the transcriptomes of both the spinal cord and brainstems from male offspring of IH-exposed mothers differed from control, with upregulation of genes targeting transcription factors involved in the inflammatory response, specifically the NF-kB/STAT pathways. Accordingly, additional experiments were done to demonstrate that blocking STAT transcription factor activation with intrathecally-delivered drugs restored the plastic response in the male offspring of IH-exposed mothers.

These are novel and interesting observations showing that GIH is associated with a strong, microglia-mediated inflammatory response in the spinal cord of adult males, but not female offspring. The inflammatory response was associated with a loss of compensatory neuroplasticity in phrenic motoneurons. The techniques employed include difficult and labor-intensive whole animal physiology experiments to RNA sequencing and microglial functional analyses. These data are thus important and of wide interest, as they link a gestational insult with spinal cord inflammation, microglial dysfunction, and a sex-dependent alteration in the ability to generate neuromotor plasticity that persists into adulthood. The main caveat is that IH does not model either obstructive or central apnea as both are associated with combined episodic hypoxia and hypercapnia. Moreover, whereas excitatory synaptic input to the phrenic motoneurons was periodically silenced to evoke "inactivity", patients with upper airway obstruction during sleep take great breathing efforts. The model used here seems more like central apnea; do pregnant humans typically have central or obstructive sleep apnea? Nonetheless, the experiments provide important insight into the impact of gestational hypoxia on the development of breathing control in male offspring.

Reviewer #3 (Public Review):

The role of maternal sleep apnea on neurological and physiological function in the offspring is of substantial interest and the investigators have contributed significantly to this emerging field via prior publications. Recent work has evidenced that recurrent bouts of gestational intermittent hypoxia (GIH) result in life-long changes in cardiovascular, cognitive, and metabolic function in the offspring. Recently, investigators have shown that GIH reprograms the neuroinflammatory response of neonates, such that the newborn offspring's normal immune response is attenuated following a Lipopolysaccharides (LPS) exposure and respiratory rhythm generation is considerably altered (Johnson et al. Respir Physiol Neurobiol. 2018). The present study by Mickelson et al. substantially extends these previous findings by showing that GIH results in region and sex-specific changes in the microglial activation of adult rats. In male rats, these changes are indicative of an increased pro-inflammatory profile and contribute to the blunted ability to elicit respiratory neuroplasticity following apneic challenge-induced breathing instability. While a robust attenuation of key inflammation-related genes was observed in spinal and brainstem regions of GIH-exposed female rats, these results were not pursued further and present another exciting area of investigation. Nonetheless, the primary goal of these studies was to elucidate the potential role of spinal microglial activation in decreasing respiratory neuroplasticity in adult rats, which has been investigated in-depth using clever and appropriate experimental approaches.

The respiratory motor system employs homeostatic neuroplastic mechanisms at the spinal level to increase phrenic motor output in response to reduced neural activation of respiratory pathways (also called inactivity-induced inspiratory motor facilitation (iMF)). Under carefully controlled conditions, lowering inspired CO2 levels causes cessation of phrenic inspiratory output (central apnea). The authors have previously utilized a protocol of recurrent central apneas to elicit iMF in phrenic motor output. In the present study, authors utilize this neurophysiological outcome to test the impact of GIH on altering the neuroplastic capacity of adult rats. A key finding of this study is that GIH attenuates iMF in male rats. This attenuation is not observed in female rats. To test the role of inflammation (in particular microglia-driven inflammation), the authors employ two approaches to inactivate spinal inflammatory pathways or deplete microglia in adult male rats. Building upon the 29 out of 12982 differentially expressed genes in cervical spinal cord microglia in GIH vs GNX (control exposure rats), the authors targeted the NF-κB pathway using intrathecally delivered TPCA-1 (NF-κB inhibitory subunit (IκB) inhibitor). Indeed, spinal TPCA-1 application restored iMF in GIH-exposed male rats. The second approach employed global microglial depletion using an orally delivered CSF1R inhibitor Pexidartinib (PLX3397) to show that iMF could be provoked in GIH-exposed male rats. It is important to note that although the authors do not report changes in microglial expression in GIH vs. GNX rats, they conclude that there are alterations in microglial activation that contribute to the GIH-induced attenuation of the neuroplastic capacity of respiratory motor networks.

A few questions emerge from this study. In the previous study by the group investigating changes in the inflammatory profile of newborns exposed to GIH, Cox-2 mRNA expression was shown to be elevated in the spinal cords of male rats. This is an interesting finding that has not been tested in GIH-exposed adult male rats in this study and it would be interesting to follow up on whether these changes in microglial profiles are conserved from newborn to adult stages. Indeed, the authors identify additional changes in hypoxia-responsive signaling pathways of GIH rats whose role in impaired respiratory plasticity would be an exciting follow-up to the current study.

The authors emphasize that the reduction in iMF capacity is due to changes in local spinal microglia activation. They do also report that 4 genes were upregulated in the brainstem region of GIH rats as compared to GNX rats. Without an appropriate anatomical control (such as hypoglossal motor output), it would not be appropriate to conclude that microglial activation resulting from GIH has no impact on respiratory networks. Further, the inclusion of bursting frequency data could provide some insight into neural drive originating in brainstem regions.

In summary, this study by Mickelson et al. provides a valuable framework for mechanisms imposing long-lasting changes in respiratory motor control following gestational exposure to episodes of sleep apnea. Furthermore, the work completed here may very well be relevant to other motor systems in which spinal microglia modulate the capacity to elicit homeostatic plastic changes. These changes are particularly important in the context of disease and injury and may impair the capacity of GIH-exposed individuals to elicit neuroplastic changes at the motor neuron level.

Reviewer #1 (Public Review):

The paper starts with a general explanation of the method behind temporal response functions (TRFs), an analysis technique for M/EEG data that has led to many new findings in the last few years. The authors touch upon convolution and show how a linear model can be used to model non-linear responses. The methods section provides a practical introduction to the TRF, in which advice on general analysis steps - such as EEG preprocessing and centering the predictor variables - is intertwined with explanations of the use of the toolbox Eelbrain. The results section outlines how to use the outcome of a TRF model to answer (cognitive) neuroscientific questions and provides a comparison between ERPs and TRFs. The discussion section touches upon a couple of considerations, the most important one being a discussion of the Sparsity prior/Boosting algorithm.

A first great merit of this paper is that it manages to clearly explain both the analysis and the important decisions a researcher needs to make in just a few pages. When following the steps outlined in (in particular) the methods section, the researcher will know how to implement a TRF model using Eelbrain, as well as have a general idea about the decisions that one needs to make in the process. Furthermore, the explicit comparison between ERPs and TRFs will help many understand what TRFs are, and in which ways they allow for more fine-grained analysis of the data than ERPs. For these reasons, this work is a suitable starting point for anyone who wants to get started with TRFs, and a good addition to the existing set of papers on this topic, such as Crosse, Di Liberto, Bednar, and Lalor (2016) and Sassenhagen (2019).

An important contribution of this work is the implementation of the Boosting algorithm. Although it is yet to be determined whether this algorithm creates better models of the neural data than previous implementations of the TRF, the authors provide good arguments for the suitability of this algorithm for the analysis of neural time-series data.

On the practical side, the tutorial analyses are well-designed for the target audience, with interpretable questions and contrast relevant to the field of cognitive neuroscience. The corresponding scripts are clear and well-commented. Finally, the implementation of this method in Python will be greatly appreciated - especially by those who do not have access to a MATLAB license.

All in all, this is a highly didactic paper that will help many researchers get started with temporal response functions both theoretically (to understand the method) and practically (to work with the toolbox). As such, this work has the potential to be of great importance in the field of cognitive neuroscience.

Reviewer #2 (Public Review):

The current manuscript presents a new toolbox to apply temporal response functions (TRFs) usable in python. TRFs are becoming more widely used and providing an accessible toolbox for a wider audience is very important and should be promoted. Overall, it also seems that the code accompanying the manuscript provides all the steps to do the analysis and could potentially be very useful. However, in the current version, the toolbox relies on one single way to solve the TRF estimation problem, which is the boosting algorithm. Providing a single algorithm makes it difficult to compare results from this toolbox with outcomes of other toolboxes which rely on different methods to solve the regression. The user is forced to work with this choice and is not provided other options (or easy ways to implement new options). Additionally, it seems unclear whether the toolbox is fully able to provide the means to generate predictors that are typically used in a TRF analysis. The github code provided for generating the predictors does not seem to be fully integrated with eelbrain and relies on code in the trftools toolbox, which contains code that the authors deem not yet stable enough to be released. Finally, the overall logic and idea behind the toolbox could have been explained better to make it more accessible to use.

Reviewer #1 (Public Review):

The authors propose that the ER-resident large GTPase Sey1, a homolog of mammalian atlastin, localizes to LDs and promotes their association with the Legionella-containing vacuole (LCV); They also propose that the effector LegG1 contributes to this process by activating the host GTPase RanA on the LCV surface. Once LDs associate with the LCV, the authors favor a model where LDs are taken up into the LCV lumen where they are consumed by L. pneumophila as a carbon source. They propose that the fatty acid transporter FadL, Lpg1810, is involved in the transport of palmitate across the bacterial membrane.

Strong points of this study are the use of Dictyostelium as a genetically tractable model system, the finding that FadL and the addition of exogenous palmitate positively affect intracellular bacterial growth, and the fact that LDs can be detected within LCVs which, if confirmed, would be of significant biological importance.

The main concern is that the molecular mechanism underlying LCV-LD dynamics and LD uptake have only been superficially described. It needs to be determined how exactly proteins like Sey1 or LegG1 promote LD recruitment to LCVs. Does this process really depend on Ran GTPases and if so, do constitutively inactive Ran mutants phenocopy the defects? And by what mechanism are LDs delivered across the LCV membrane into their lumen? The authors themselves raise that question in the discussion, but provide no explanation or supporting data. How commonly can LD uptake into LCVs be observed across a population of cells? And are the phenotypes observed upon deletion of Sey1 direct effects, or are global changes in the ER/host cell protein or lipid landscape indirectly causing those phenotypes? These are some of the questions that, once addressed, would improve the impact of this study.

Reviewer #2 (Public Review):

In this manuscript, Hüsler et al. aimed to evaluate the contribution of LDs, Sey1, and FadL to intracellular replication and palmitate catabolism of L. pneumophila in D. discoideum. The authors found that Sey1 regulates LD proteome composition and promotes Icm/Dot-dependent LCV-LD interactions as well as FadL-dependent fatty acid metabolism of intracellular L. pneumophila. The study is in general well-designed and performed. The data are clearly presented and valuable in enhancing awareness of the mechanisms of L. pneumophila infection. The evidence supporting the claims of the authors is solid, although the inclusion of additional controls and clarifications would have strengthened the study.

Reviewer #1 (Public Review):

In this manuscript, the authors were trying to achieve the generation of continuous cell lines after lineage-restricted mis-expression of RasV12 in vivo followed by primary cell culture. They used glia-, epithelia-, and muscle-specific Gal4s, to get glial, epithelial, and muscle cell lines, as well as the RU-inducible Gene Switch Gal4, to get neuronal and blood cell lines. They performed RNA-seq analysis of the cell lines and showed that they are most similar to each by principal component analysis. They compared their RNA-seq to the Fly Cell Atlas and showed that the cell lines were quite similar to their in vivo counterparts. They treated cell lines with the steroid hormone ecdysone and found that many of the cell lines differentiate. These cell lines also contain an attP site, allowing for CRISPR-based screens. These cell lines could be passaged for many generations, but robust effects were found in the early passages. These cell lines have been deposited at a public resource center (The Drosophila Genomics Resource Center, DGRC).

The major strengths of the paper include rigorous analysis of characteristics, gene expression, and differentiation potential of the cell lines. There were only a few minor weaknesses related to editorial changes in the manuscript.

The authors provide convincing results that support their conclusions and as such the authors achieved their aims.

This work is likely to have a positive impact on the Drosophila community. These cell lines will serve as a solid foundation for both low- and high-throughput screens.

Reviewer #2 (Public Review):

The authors describe the derivation of new and stable fly cell lines through a strategy of tissue-specific RasV12 expression and in some cases single cell cloning. Lines with molecular and, in some cases, phenotypic characteristics of the targeted tissue are identified: muscle, neural, glial, epithelial, and macrophage-like. These are (for the most part) karyotypically normal and amenable to genetic manipulation including transient and attP-mediated insertion. This paper reports a publicly available resource that will be of great use to many. The cell lines are ready for the well-established tools available for high-throughput screening using CRISPR, RNAi, and small molecules, and allow scalable biochemistry which has been a limitation of using Drosophila for some research questions. Moreover, the Ras-targeting approach is potentially a general way to make additional tissue-specific cells, and the authors describe several failures as well as successes in deriving tissue-specific lines. Overall it is a highly valuable piece of work. Ways that the paper reporting this work could be enhanced for the reader include 1) a more critical analysis of the limitations of these lines to represent their prospective in vivo tissues; 2) a more explicit comparison of these lines next to existing fly cell lines including but not limited to the workhorse S2, and 3) any information on the ease of use and behavior of these cells in the types of high-throughput/high-volume formats where they are likely to be most valuable.

Reviewer #3 (Public Review):

This is a clearly written, straightforward, resource paper describing the creation of several new cell lines that may prove useful to the Drosophila community. They are to be distributed through the Drosophila Genomics Resource Center and might be put to use at the Drosophila RNAi screening center.

Reviewer #1 (Public Review):

This study aimed to estimate contact parameters associated with the transmission of SARS-CoV-2 in unvaccinated South African households over one year. The authors found no correlation between the frequency or duration of contacts and infection risk. Similar parameters (e.g., sharing a room with the index patient) also failed to yield an association. Reassuringly, a robust association was found with the Ct of the index case; female sex and individuals aged 13-17 years were also associated with increased risk. In a more general analysis, obesity, age >5 and <60 y, and non-smoking status were associated with increased risk.

Strengths of the study are its relatively large size (131 households involving 497 people) with detailed proximity data; frequent testing to enable high ascertainment of infections; and ability to exclude individuals seropositive at baseline. Additionally, several outcomes were evaluated in the models, partly to accommodate uncertainty in the index case. Different model structures were evaluated to gauge robustness.

Limitations of the study include the fact that many index cases were likely enrolled after their infectious period, and it is possible that apparent secondary cases in the household arose from a shared exposure with the index case but had a longer latent period. Each of these factors could weaken the perceived effect of close contacts. Statistically, there is the vexing question of what age (gender, smoking, etc.) really represents mechanistically, and whether the models may be conditioning on a collider. Another statistical consideration is that many household contacts were excluded from the study because they were seropositive at baseline. In effect, their households may already have been "challenged" with the virus, and there may be heterogeneities in household susceptibility that are not fully considered by the simple exclusion of individuals with evidence of prior infection. Separating these household types in the analysis might have yielded different results.

All that said, it is telling that in these households, infection is not clearly linked to typically defined close contacts. This is an important result that complements other strong evidence that aerosols are the dominant route of transmission for SARS-CoV-2. This information is critical for the design of effective intervention strategies. Additionally, the authors outline how future studies can be designed to improve on this work.

Reviewer #2 (Public Review):

In this manuscript, the authors investigated the association of household close-range contact patterns with SARS-CoV-2 transmission in the household using proximity sensors deployed after the identification of SARS-CoV-2 in the household. They recruited participants in two urban communities in South Africa, Klerksdorp (North West Province) and Soweto (Gauteng Province) from October 2020 through September 2021. Their analysis suggests the lack of an association between close-range proximity events and SARS-CoV-2 household transmission.

Their study design looks reasonable, with useful household contacts data collected in the study. However, their regression analysis only considered a limited set of contact parameters (i.e., median measurements of duration, frequency, and average duration). It's not clear if this limitation will bias the conclusion regarding the lack of an association between close-range proximity events and SARS-CoV-2 household transmission.

Reviewer #3 (Public Review):

The manuscript by Kleynhans et al analyzes data from household contacts of SARS-CoV-2 cases at two sites in South Africa. Proximity sensors were distributed to household members following diagnosis of the "index case" and measured the frequency and duration of close contacts (defined as being face-to-face within 1.5 meters for at least 20 seconds). The authors then examined the association between the duration, frequency, and average duration of contacts and the risk of a diagnosis of SARS-CoV-2 among household members in the subsequent two weeks, for both contact with the index case and all cases within the household. The risk of infection among household members was high (~60%), but was not significantly associated with the contact metrics examined. The findings may indicate that aerosols may be the predominant mode of SARS-CoV-2 transmission within households; however, there are also a number of limitations associated with the design and analysis of the study, which the authors acknowledge and which may limit the interpretability of the conclusions of this study.

One important study limitation has to do with the design of the study: Sensors were not distributed to household members until a day or two after the diagnosis of the index case. Since individuals are most infectious with SARS-CoV-2 just prior to symptom onset, contact patterns were measured only after most transmission from the index case likely occurred. Furthermore, household members may have limited their contact with the index case, particularly if the index case attempted to isolate following their diagnosis, so the contact patterns measured are unlikely to be representative of typical mixing within the household.

Another important limitation has to do with the analytical approach: The logistic regression model assumes that the first person in the household to test positive for SARS-CoV-2 (i.e. the index case) infected all subsequent cases within the household. However, this approach does not account for chains of transmission within the household or transmission from outside the household (possibly from the same source that infected the index case). While this concern is partially addressed by also assessing the association between the risk of infection and contact with all infected household members, more sophisticated methods could be used to infer the most likely infector of each case. The possibility of multiple introductions of the virus from outside the household is also only partially addressed by excluding households in which more than one variant was detected. While these limitations (and others) are appropriately acknowledged by the authors in the Discussion, nevertheless they limit the conclusions that can be drawn from the study results.

It is also worth noting that the contact metrics as defined and analyzed in the model may not be the measures that are most relevant to transmission. The authors examined three different contact measures: the median daily duration of contact, the median daily frequency of contact, and the median daily average duration of contact (i.e. the ratio of the two previous measures). They chose to examine the median daily values because contact duration was heavily skewed and the number of days of follow-up varied after data cleaning, but it may be that longer-duration contacts important to transmission are not appropriately captured by these metrics. Indeed, the median daily duration of the contact is quite short (only ~18 minutes on average). It would be useful to also evaluate a measure such as the total cumulative duration of contact and frequency of contacts divided by the number of days of follow-up, which differs from the measures they calculate and would take into account more prolonged and frequent contacts.

Lastly, the measures of association reported in the manuscript are the odds ratios (ORs) associated with one additional second of contact per day. This is not a very biologically meaningful unit of measure, and when rounded to two significant digits, the ORs are not surprisingly 1.0 with 95% confidence intervals that also round to 1.0. It would be more interpretable to report the ORs associated with a 1-minute (rather than 1-second) increase in the duration of contact, and the biological interpretation of the ORs should be described in the text.

Reviewer #1 (Public Review):

FLC is a gene involved in cold-dependent induction of flowering, as prolonged cold exposure leads to a progressive decrease in the level of this floral repressor as a result of a digital switch from an ON to an OFF state occurring asynchronously in cell populations. In this work, the authors analyze the contribution of analog and digital regulation to FLC expression in the absence of cold exposure. To do so, they use a genetic trick to be able to perform this analysis in the wild-type Ler ecotype where the molecular tools are available to do such an analysis. In Ler, an activator of FLC is missing due to a natural mutation and FLC expression is repressed during vegetative development by a pathway called the autonomous repressive pathway, allowing for a rapid transition to flowering. The authors used two mutant allele in one component of the autonomous pathway, the FCA gene. In the strongest allele, FLC is highly expressed and the plant are late flowering while in the weaker allele FLC shows a weaker expression and the plant requires an intermediate time between Ler and the strong fca allele to flower.

The authors demonstrate that the expression levels of the FLC gene vary quantitatively in the three genetic background they use (Ler and two fca alleles), and that mutating FCA leads to an analog increase in FLC expression. The quantifications performed by the authors indicate that increased level of FLC correlate with a decrease in the proportion of cells that can switch OFF FLC, with the strong fca allele showing a negligible amount of cells that can switch OFF FLC. The authors further measure the half-life of FLC mRNA and FLC protein, and show that FLC expression switch from ON to OFF is a one-way-switching. They used these data to build a computational model of the regulation of FLC expression and show that the model can reproduce the dynamics of FLC protein level at the cell population level in a time-course with measurement at 7, 15 and 21 days after sowing. Taken together their work suggest that, at least in the weak fca mutant, a combination of analog and digital regulation of transcription explains the population-wide dynamics of FLC expression. The authors propose that this regulation could be explained by high level of transcription of FLC preventing the digital switch, as a result of the short half-lives of FLC mRNA and FLC protein.

The finding of this work are potentially of wide interest to understanding transcriptional regulation by providing a functional link between the digital and analog mode of regulation of gene expression. However, the evidence of a link between expression levels resulting from analog regulation and the digital regulation are only partly supported by correlations from cell population-wide analysis of FLC expression. The authors did not provide experiments to more directly test that higher level of transcription could indeed prevent the OFF switch of FLC. It is likely but not shown that the ON to OFF switch of FLC is regulated similarly in the absence of cold exposure (this study) and upon cold exposure. Also, in their model, the authors used the assumption that FLC switches off at division but they do not test this important assumption. Finally it is unclear whether this combination of analog and digital regulation is relevant to FLC regulation in wild-type plants or is only relevant to the laboratory-induced mutants studied in this work.

Reviewer #2 (Public Review):

In this manuscript, the authors proposed a mathematical model to describe analog and digital modes of gene regulation using FCA-mediated FLC regulation as a model. Previously, a similar approach revealed that the repression of FLC by vernalisation is digital. The authors utilized allelic variations of fca mutants (fca-1; a strong allele and fca-3; a weak allele), which resulted in the different levels of FLC de-repression. Unlike FLC in fca-1, where FLC is robustly ON or OFF states in the root cells, authors observed "intermediate" FLC-expressed cells (weak ON) in fca-3. The authors argued that these "intermediate" levels of FLC expression in root cells might indicate the presence of the analog mode of gene expression. In addition, the authors used the "age"-dependent FLC repression to validate whether digital mode can occur in fca-3 and concluded that it does happen. However, digital OFF does not occur in fca-1, and the authors speculated that this might be due to a "high" level of FLC transcription. Based on these observations, the authors developed a simple mathematical model to predict the transition from analog to digital gene regulation at the cell population level. It is an intriguing model/conclusion to show the "analog" mode of gene regulation, and FLC regulation has been an excellent model system for understanding various modes of gene regulation.

However, some significant issues need to be addressed.

1. Mechanistic details of how FCA regulates FLC have been extensively studied, and both transcriptional and co-transcriptional regulations occur. I understand that FCA affects the 3'end processing of antisense COOLAIR RNAs, which regulate FLC. FCA also physically interacts with COOLAIR RNAs and other proteins, including chromatin-modifying complexes, which establish epigenetic repression of FLC regardless of vernalisation. In addition, FCA appears to function to resolve R-loop at the 3' end FLC, and FLC preferentially interacts with m6A-modified COOLAIR by forming liquid condensates. FCA is also alternatively spliced in an autoregulatory manner, and fca-1 mutant was reported to be a null allele as fca-1 cannot produce the functional form of FCA transcripts (r-form).

However, I could not find any information on the fca-3 allele, which was reported to exhibit a weaker phenotype in terms of flowering time (Koornneef et al., 1991). In this manuscript, the authors showed that the level of FLC expression is lower than fca-1 and higher than Ler WT, but I could not find any other relevant information on the nature of the fca-3 allele. Given the known details on the function of FCA, the authors should explain how fca-3 shows an "intermediate" phenotype, which is highly relevant to the argument for an "analog" mode of regulation in fca-3. Therefore, the nature of the fca-3 mutant should be described in detail.

2. The authors used a transgene (FLC-venus) in which an FLC fragment from ColFRI was used. Both fca-1 and fca-3 is Ler background where FLC sequence variations are known. I understand that the authors introgressed the transgenic in Ler background to avoid the transgene effect, but it is not known whether fca-1 or fca-3 mutations have the same impact on Col- FLC.

3. Fig. 3A: I understand that Fig 3A is the qRT-PCR data using whole seedlings, and the gradual reduction of FLC from 7 DAG to 21 DAG was used to test the "analog" vs. "digital" mode of gene regulation in fca-1 and fca-3. I am not sure whether this is biologically relevant.

3-a. The authors wrote that "This experiment revealed a decreasing trend in fca-3 and Ler (Fig. 3A)". But, I do also see a "decreasing trend" in fca-1 as well (although I understand that they may not be statistically significant). I also noticed that the level of FLC in fca-1 at 7 day has a greater variation. Is there any explanation?

3-b. The decreasing trend observed in Ler (although the expression of FLC is already relatively low in Ler) may be the basis for the biological relevance. But Fig. 3D shows that the FLC-venus intensity in Ler root is not "decreasing".<br /> The authors interpreted that "root tip cells in Ler could switch off early, while ON cells still remain at the whole plant level that continue to switch off, thereby explaining the decrease in the qPCR experiment."<br /> Does this mean that the root tip system with FLC-venus cannot recapitulate other parts of plants (especially at the shoot tip where FLC function is more relevant)?

The authors utilize the root system with transgenes in mutant backgrounds to observe and model the gene repression (transgene repression, to be exact). If the root tip cells behave differently from other parts of plants, how could the authors use data obtained from the root tip system?

4. I do see both fca-1 and fca-3 can express FCA at a comparable level (Fig. 3B); thus, I guess that the authors are measuring total FCA transcripts and that fca-3 may result in different levels of "functional form" of FCA. But this is not clearly discussed.

5. Quantification based on image intensity needs to be carefully controlled. Ideally, a threshold to call "ON" or "OFF" state should be based on the comparison to internal control and it is not clear to me how the authors determined which cells are ON or OFF based on image intensity (especially in fca-3).

6. In many parts, I had to guess how the experiments were performed with what kind of tissues/samples. The methods section can benefit from a more thorough description.

Reviewer #3 (Public Review):

Gene regulation at the single cell level can appear in two fundamentally different modes: a digital mode, in which a certain gene is either ON or OFF, and an analog mode, where a gene can gradually modulate its expression in a range of values. Yet, it is unclear how such two modes might operate together. In the work by Antoniou-Kourounioti et al, the authors argue that the Arabidopsis floral repressor FLOWERING LOCUS C (FLC) exhibits such two regulatory modes in the Arabidopsis root before cold exposure, with analog preceding digital.

This work has the strength of performing an elegant combination of experimental and modelling approach to solve a non-trivial and fundamental question on gene regulation. At the experimental level, the authors are able to quantify the number of FLC transcripts as well as their protein levels at the single cell level in the studied Arabidopsis lines, and they elegantly recapitulate some of their experimental results with an in silico root model.

Although this work has a very high potential, I find there are several important aspects that require some attention.

I think further explanations and clarity are needed to help the readership understand the differences between digital and analog regulation, beyond the explanations illustrated by Fig 1. In my understanding, digital regulation will involve observing some kind of bimodality when quantifying expression levels at the single cell level (see Bintu et al 2016), but from the definitions of ON and OFF cells the authors did in this work (see below), and the modelling they propose, it seems not to be the case. Given the authors derive very strong conclusions from their quantifications on what is digital and what is analog, I think it is important to be clearer in this regard. Also, to clarify the possible scenarios of interplay between analog and digital, I believe it would help to emphasize and better connect the modelling part to the experimental part.

Another major concern to me is whether the extracted conclusions rely too much on certain choices the authors made when doing the quantifications from the experimental data. In particular,

1) The way the authors define ON and OFF cells sounds a bit arbitrary to me and, in my understanding, can affect a lot the outcomes and derived conclusions. The authors define ON cells to those cells having more than one transcript, or when they are above the value of 0.5 of the Venus intensity measure - what would it happen if the thresholds are slightly above these levels? And why such thresholds should be the same for the studied lines Ler, fca-3 and fca-1? By looking at the distributions of mRNAs and Venus intensities in Ler and fca-3 plants, one could argue that all cells are in an OFF, 'silent' state, and that what is measured is some 'leakage', noise or simply cell heterogeneity in the expression levels. If there is a digital regulation, I would expect to see this bimodality more clearly at some point, as it was captured in Berry et al (2015) - perhaps cells in fca-1 show at a certain level of bimodality? When seeing bimodality, one could separate ON and OFF states by unmixing gaussians, or something in these lines that makes the definition less arbitrary and more robust.

2) The authors use means in all their plots for histograms and data, and perform tests that rely on these means. However, many of these plots are skewed right distributions, meaning that mean is not a good measure of center. I think using median would be more appropriate, and statistical tests should be rather done on medians instead of means. If tests using medians were performed, I believe that some of the pointed results will be less significant, and this will affect the conclusions of this work.

3) Some data might require more repeats, together with its quantification. For instance, the expression levels for fca-1 in Fig 2E and Fig 3D at 7 days after sowing look qualitatively different to me - not just the mean looks different, but also the distribution; fca-1 in Fig 3D looks more monomodal, while in Fig 2E it looks it shows more a bimodal distribution. Having these two different behaviours in these two repeats indicates that, more ideally, three repeats might be needed, together with their quantification. Fig. 2C would also need some repeats. In Fig 1S1 C and D, it would be good to clarify in which cases there are 2 or more repeats -3 repeats might be needed for those cases in Fig 1S1 C-D that have large error bars.

Also, when doing the time courses, I find it would be very beneficial to capture an earlier time point for all the lines, to see whether it is easier to capture the digital nature of the regulation. Note that the authors have already pointed that 7 days after sowing might be too late for Ler line to capture the switch.

If the above comments are addressed and the authors manage to clarify how the digital and analog regulation are integrated in the chosen system, I believe this work would have a strong impact on a very wide scientific community, given it tackles a very fundamental question in gene regulation.

Reviewer #1 (Public Review):

The authors present a nice collection of analyses identifying the likely causal locus and pigmentation basis underlying color polymorphism in a model aposematic moth system. In general, the writing and presentation are very clear. There are several areas of the text, however, that could benefit from more clarity and attention to detail. Those changes should be very simple for the authors to make.

My primary concern however is the interpretation of their findings, in light of the lack of analysis of recombination, as well as the flanking region of their identified gene duplication. Specifically, while the authors do an OK job characterizing the genomic region 3' of their identified novel insertion/duplication associated with white coloration, I could not find an analysis of the 5' region, in which there could be other functional elements that could give rise to their "complex polymorphism". Additionally, the authors discuss their findings and the potential of their duplicated region to "provide a region of reduced recombination" (lines 249-251). However, they need to be much more clear with the reader that this is a hypothesis that they have not measured (even though they have WGS data from a sufficient number of individuals estimating LD, which I find strange).

Reviewer #2 (Public Review):

The authors set out to characterize the genetic architecture for aposematic color polymorphism in a species of tiger moths. It was previously known that the color polymorphism showed a non-sex-linked Mendelian inheritance pattern, and was thus likely controlled by an allelic change at a single autosomal locus. Based on observations in other species that traits with a similar simple inheritance pattern of polymorphic aposematic colors often involve supergenes, which refers to a tightly linked cluster of co-adapted loci, the authors tested the hypotheses that a supergene may be involved here in tiger moth polymorphism. To test this hypothesis, they used a combination of QTL mapping, GWAS, and RNA-seq approaches to identify regions of the genome that showed an association with the color pattern polymorphism. The genetic mapping approaches identified a candidate genomic region that contained >20 genes, including the genes yellow-e, and its paralog, valkea. The RNA-seq data showed these genes to be expressed differently in the developing wings of the different color morphs. The valkea paralog is associated with a duplicated chromosomal region that appears to only be present in the genomes of yellow-colored morphs. A phylogenetic-weighting approach was also used to attempt to distinguish the strength of associations of the yellow-e and valkea genes with the color polymorphism and found evidence suggesting valkea was the likely genetic switch for the color polymorphism. Lastly, the authors provide evidence that the differences in coloration involve a change in melanins, through chemical characterization of pigments extracts. Collectively, the authors provide a comprehensive examination of the color pattern genetics and compelling evidence that the polymorphism in pigmentation is controlled by an allelic change at a single autosomal locus that includes the yellow-e/valkea genes that show different expression patterns in the differently colored morphs.

Strengths:<br /> This study provides a comprehensive mapping effort to identify a locus responsible for modulating adaptive variation in natural populations of the tiger moth. This is an ideal trait and system to study the genetic basis of adaptive variation, as the trait variation has clear impacts on fitness and is under strong selection in natural populations. The genomes of Lepidoptera and their amenability for laboratory research and molecular methods make them well-suited for such mapping efforts. The authors used an impressive number of offspring from genetic crosses to conduct QTL mapping, which was nicely complemented with a population genomic GWAS approach to further narrow the candidate locus. The addition of the RNA-seq provides compelling evidence that genes at this locus are clearly involved in differences in wing pattern development.

The greatest strength of this study is perhaps its finding of "something new, using something old". I am referring to the finding of a novel duplication of the yellow gene being involved in pigment variation. Yellow is well-known to be involved in color pattern development in Drosophila and butterflies, but its role in the tiger moths is completely novel. A recent duplication of yellow being involved in adaptive variation is completely new and quite exciting. With other recent examples of gene duplications being involved in differences in butterfly color pattern development, there are now numerous cases of the rapid evolution of gene duplicates involved in generating wing pattern variation. Thus, the findings here should be of broad interest to those interested in the genetic changes involved in generating adaptive variation in natural populations.

Another strength of the study is the characterization of the melanic pigment changes involved in the polymorphism. Such detailed phenotypic analyses can offer critical insights into how the genetic differences found to be associated with color pattern variation, may function and influence wing pattern development.

Weaknesses:<br /> Despite narrowing the locus to a small number of genes through mapping efforts, the study falls short in identifying the genetic switch and sufficient evidence to confirm valkea's role in the color polymorphism.

The mapping efforts identified a narrow locus covering multiple genes from the yellow gene family and RNA-seq data clearly identified valkea and yellow-e as being differentially expressed between color morphs thereby implicating their involvement in differences in wing color pattern development. However, the type and number of genetic changes at this locus involved in generating the color polymorphism remain unresolved. Tree topology provides only suggestive evidence that genotypes at valkea show a stronger association with color pattern differences than at the other nearby yellow genes, and offers limited further resolution as the where the genetic switch may be (e.g. within coding or non-coding regions across the locus).

I am unconvinced that framing this study as a test for the role of a supergene, or "to test whether the polymorphism is associated with large structural rearrangements controlling multiple phenotypic elements, or the result of a single gene mutation" is most appropriate or strengthens the study. The alternative hypotheses of "large structural rearrangements" versus "single gene mutation" do not necessarily reflect the possible, or most likely hypotheses, and neither are not necessarily clearly supported by the results of the study. In other studies of wing color pattern polymorphisms in butterflies, the genetic changes controlling the variation have been non-coding mutations in putative cis-regulatory elements (CREs) that control the expression of a nearby gene involved in wing pattern development (see examples from Heliconius butterflies). These would be considered changes in CREs, not "single gene mutations". There are instances in which such changes impacting color pattern variation have been captured within structural rearrangements, such as polymorphic inversions of Heliconius numata, the single gene or CRE mutation and structural rearrangements both being involved are not mutually exclusive, thus it is difficult to frame this study as testing them as alternative hypotheses. The data presented in the study celery implicate a genomic region with multiple genes differentially expressed (DE) between color morphs, with one of the DE genes residing within a structural variation (insertion/deletion polymorphism). However, the study is unable to resolve if the large structural rearrangement is involved, or if a single versus multiple genes or CRE changes may be involved. Thus, I find it challenging and perhaps a weakness of the paper to frame the study as a test of these alternative hypotheses that are not necessarily mutually exclusive or able to be distinguished using the data in the study. I have similar concerns with the focus on supergenes (i.e. co-adapted gene complex) being a weakness for the paper, as the results of the study don't directly test for the presence or role of a co-adapted gene complex at the locus identified.

Reviewer #3 (Public Review):

This article aims at investigating the genetic and developmental basis underlying colour pattern polymorphism in the wood tiger moth. It combines GWAS and QTL data pointing at a candidate gene from the yellow gene family. The pattern of gene expression during wing disk development is then consistent with a potential role of this gene in the control of colour pattern variation but functional validation is lacking. The pigment analyses reveal the presence of pheomelanin on the wings, whose synthesis is known to be controlled by a pathway regulated by genes from the yellow family. The identification of these pigments suggests that variations in the colours of the wings in this species could indeed be caused by the regulation of the yellow pathway. Although functional validation establishing the exact role of the valkea gene is lacking, the data provided are in line with a pleiotropic effect of controlled by a small region of the genome enabling the series of phenotypic variations associated with the white coloration. The duplication event restricted to a single haplotype also provides a convincing mechanism for the restriction of recombination in this genomic region. However, the fact that the valkea gene is truncated questions its functionality. It remains possible that the developmental switch could be rather caused by the variations detected in the non-coding part of the duplicated region, causing differential patterns of expression in different genes, including yellow-e. Some deeper discussion is needed on the putative role of the valkea gene vs. of the regulatory regions in controlling the developmental switch between yellow and white morphs.

Altogether, this interesting study provides original and important results on the genetic architecture underlying balanced polymorphism in the wild.

Reviewer #1 (Public Review):

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

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

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

• The authors claimed in the introduction sections that CRISPR/Cas9-based methods are associated with off-target effects, however, the author's system requires the use CRISPR/Cas9 to knock out a given endogenous genes or to knock-in ARTi target sites to the 3' UTR of the gene of interest. Though the authors used a transient CRISPR/Cas9 system to minimize the potential off-target effects, the advantages of ARTi over CRISPR are likely less than claimed.

• Instead of generating gene-specific loss-of-function triggers for every new candidate gene, the authors identified a universal and potent ARTi to ensure standardized and controllable knockdown efficiency. It seems this would save time and effort in validating each lost-of-function siRNAs/sgRNAs for each gene. However, users will still have to design and validate the best sgRNA to knock out endogenous genes or to knock in ARTi target sites by CRISPR/Cas9. The latter is by no-means trivial. Users will need to design and clone an expression construct for their cDNA replacement construct of interest, which will still be challenging for big proteins.

• The approach of knocking-out an endogenous gene followed by replacement of a regulatable gene can also be achieved using regulated degrons, and by tet-regulated promoters included in the gene replacement cassette. The authors should include a discussion of the merits of these approaches compared with ARTi.

Reviewer #2 (Public Review):

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

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

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

Reviewer #1 (Public Review):

Van Dongen et al. investigated the methylation signature of smoking found in the blood among monozygotic twins ascertained from the Netherlands Twin Register. With their unique study design (which by design controls for the influence of age and sex), the authors shed light on DNA methylation levels that vary with smoking status, as well as with smoking cessation. The authors novel study design examined of twin pairs concordant or discordant for smoking status (current, former, never). The authors performed an epigenome-wide association study (EWAS) and identified 13 genome-wide significant CpGs that were differentially methylated between the discordant twin current-never smoking pairs. Another EWAS conducted by the authors found 5 additional genome-wide significant CpGs among current-former smoking discordant pairs. Each of the 13 identified CpG sites between current-former twins have been previously identified as associated with smoking. The authors found that 3 of these 13 CpGs are located within 1Mb of a single nucleotide polymorphism (SNP) previously associated with smoking initiation, suggesting a role for the SNP in both genetic susceptibility of smoking as well as methylation. The authors tested for enrichment of the 13 CpGs within traits and pathways and found enrichment among smoking related traits, as well as the dopaminergic synapse pathway. Interestingly, the authors found that twin pairs discordant for former smoking (former smoking-never pair) had methylation levels that nearly returned to baseline (never smoking) after smoking cessation. These data broaden our understanding of methylation signatures in the blood using a concordant/discordant smoking and twin study design. The authors evaluated within-twin pair methylation differences for the 13 significant CpGs and found twins concordant for smoking status had very little difference between their methylation levels, yet those discordant for smoking status had larger differences with the current-never smoking twins having the largest differences. Importantly, using a dataset with both methylation and RNA sequencing data, the authors found higher methylation at three CpGs was associated with lower gene expression providing functional context for their findings. The authors correctly acknowledge the limitations of only having blood to evaluate methylation signatures and using a methylation array rather than bisulfite sequencing.

There are a couple of aspects that would be useful to help with interpretation of their findings, such as whether presentation of a formal test for trend shows a linear relationship between overall DNA methylation and smoking pack-years and smoking quit time. It would help the reader if the authors could put their findings into context with what has been previously identified in studies such as the Framingham Heart Study or the prior twin study with concordant/discordant twins. While the findings are interesting, the moderate sample size and use of a methylation array rather than sequencing may ultimately lead this work to have only moderate impact on the field.

Reviewer #2 (Public Review):

The authors aimed to test the effects of smoking on the methylome while controlling for genetics to test for evidence of whether previous studies on genetically-unrelated individuals were confounded by genetics.

The strengths of this study of genetics-independent associations between smoking exposure and DNA methylation using an epigenome-scale approach are (1) its moderate sample size for a twin study (50-100 ) to detect some of the larger effects sizes (10-15%) found in this study; (2) the thorough EWAS methodology including adjusting for cellular heterogeneity and the use of Bonferroni correction; (3) the use of a within identical twin pair design; (4) the strong overlap between the results and those of previous similar studies in genetically unrelated individuals. Weaknesses include the use of methylation arrays that although targeted to putative regulatory regions, cover only around 2.5% of genomic CpGs, and the use of only a single tissue (blood). Both are acknowledged by the authors.

The authors achieved their aims and were able to test all their hypotheses. In general, the authors' claims were supported by their data, but they could empirically test for an association between methylation and expression at all top CpGs rather than just stating that a subset significantly associated.

This is an important set of findings for the field because genetic confounding has been levelled as a criticism of epigenomewide association studies. It therefore strengthens the evidence that environment (smoking) can change the methylome, assuming that the methylomes of each pair were similar prior to exposure.

Reviewer #3 (Public Review):

In order to address their study question of a potential shared genetic predisposition to both smoking and DNA methylation level, they indicate that a MZ discordant pair analysis would be very powerful.

The authors draw on the well-characterized and very large prospective study of twins and family members from the Netherlands, the Netherlands Twin Register (NTR). Over 3000 cohort members have DNA methylation assessed by arrays (450k and Epic). Monozygotic twin pairs discordant and concordant for smoking are included in epigenome-wide analyses, and followed-up using enrichment and gene expression studies.

The results demonstrate that the strongest associations that have been seen in unrelated individuals (such as for AHRR) are seen in the discordant pairs but do not have the statistical power to confirm or reject weaker (yet consistently seen) associations

Some mention of the effect of second-hand smoke (SHS) could be made as it is an exposure to smoke not due to one's own active smoking. As twin pairs often reside together or are in frequent contact/visiting - MZs more than DZ and females more than males, SHS may be attenuating differences between current and non-current smokers in discordant pairs rather than shared genetics. Likewise twin pairs often have the same or related occupation, and if smoking is common at their typical workplace (even if they work at different companies/employers), the non-smoking twin may be exposed to more tobacco than an unrelated never-smoker.

The study sample should be better described, especially with regard to how smoking behavior was assessed, and whether the twins in pairs discordant for smoking differ in characteristics that can affect DNA methylation. These details would be essential for understanding to what degree the observed findings are attributable to smoking.

The study provides important information on the smoking methylation relationship and supports the generally held view that smoking has a direct effect on methylation. Hence, methylation changes are a useful biomarker of current and past smoking. The current results indicate that confounding due to shared genetics is unlikely to be a major factor but some role cannot be excluded.

Reviewer #1 (Public Review):

The manuscript by Warren et al., presents evidence suggesting that aberrant Yap signaling plays a role in epithelial progenitor cell dysregulation in lung fibrosis. This work builds on a body of work in the literature that Hippo signaling is aberrantly regulated in idiopathic pulmonary fibrosis. They use a combination of single nuclear and spatial transcriptomics, together with in vivo conditional genetic perturbations of Hippo signaling in mice, to investigate roles for Yap/Taz signaling in alveolar epithelial homeostasis and remodeling associated with exposure to a fibrosing agent, bleomycin. They show that Taz and Tead1/4 are most abundantly expressed by alveolar type 1 (AT1) cells, but Nf2 immunoreactivity (upstream activator of Hippo) is observed predominantly within airway and AT2 cells. Bleomycin exposure was associated with reduced p-Mst in regenerating alveolar epithelium, that inactivation of Yap/Taz arrested AT2>AT1 differentiation, and inactivation of either Nf2 or Mst1/2 promoted AT1 differentiation after bleomycin exposure and reduced matrix deposition/fibrosis. They go on to show that compromised alveolar regeneration resulting from inactivation of Yap/Taz results in enhanced bronchiolization of injured alveoli. Experiments are well designed and include quantitative endpoints where appropriate, data of high quality, and results are generally supportive of conclusions. These studies provide valuable new data relating to roles for the Hippo pathway in regulation of alveolar homeostasis and epithelial regeneration/remodeling in injury/repair and fibrosis.

Reviewer #2 (Public Review):

The authors explored non-redundant, and potentially contrasting, roles of the Hippo effector transcription factors, YAP and TAZ, in the epithelial regenerative response to non-infectious lung injury. The strength of the work is the use of genetic mouse models that explored inducible loss of function of YAP and/or TAZ in an alveolar epithelial type 2 (AT2) specific manner. The main weakness of the work is that gene(s) inactivation was performed prior to lung injury and, therefore, does not take into account the contextual and dynamic nature of YAP/TAZ signaling; for example, work by other groups have shown that YAP/TAZ is activated early following injury followed by a decrease in activity, thus balancing proliferation and differentiation of AT2 cells (for review, see PMID: 34671628).

Reviewer #3 (Public Review):

The manuscript entitled "Hippo signaling impairs alveolar epithelial regeneration in pulmonary fibrosis" is a rigorous and timely report detailing the significance of Hippo signaling, Taz and Yap in AT2/AT1 differentiation and the subsequent impact on the progression of lung fibrosis versus repair/ regeneration. The authors experimental design and results support their conclusions. The identification of the distinct effects of Taz and Yap in these processes highlight the pathway and specific molecules as potential therapeutic targets.

The major strengths of these studies lie in the rigor of the elegant transgenic developmental/adult injury-repair mouse models combined with spatial transcriptomics and analyses. The weaknesses include a lack of detail presented in the methods, some legends and discussion.

Reviewer #1 (Public Review):

The authors of the current study investigated the effect of the suspension of the Australian breast, bowel and cervical cancer screening program for 3, 6, 9, or 12 months on cancer outcomes and cancer services.

The major strengths of the current study are the usage of the validated Policy1 modelling platform to estimate the effects of delays in the screening program on cancer outcomes. Furthermore, they described a wide range of different scenarios and looked at all three national screening programs together. A clear and detailed description of the screening programs was given. The results are well-described and detailed.

The authors reached their aim. They showed how a disruption of the breast cancer screening program of 12 months led to less screen-detected and interval invasive cancers, and to an increase in the percentage of tumours with a tumour size of more than 15mm or with nodal involvement. In addition, suspension of the bowel screening program for 12 months led to upstaging for 891 tumors. Suspension of the cervix screening program for 12 months let to 27 upstaged tumors, and to 69 extra tumors. On the contrary, suspension of the breast screening for 3 months did not lead to a higher percentage of tumours with a tumor size of more than 15 mm or to a higher percentage of tumors with nodal involvement. Suspension of the bowel screening program for 3 months led to upstaging of 261 tumors, and suspension of the cervical screening program for 6 months led to 21 extra tumors and to 9 upstaged tumors. The conclusion of the authors that 'maintaining screening participation is critical to reducing the burden of cancer at a population level' is therefore not completely correct, as suspension for 3 months might be needed in situations with limited resources and will not have a very large impact on the cancer burden.

This paper predicts upstaging due to the disruptions in the screening program. This information can be used by hospitals so they know what they can expect, and can be used in the future if decisions need to made about suspending the screening program.

Reviewer #2 (Public Review):

The report was based on three nation-wide cancer screening programs (breast, bowel, and cervix cancer). This paper attempts to simulate the potential impact of screening disruption on the cancer detection. The authors raised an important concern; that the screening disruption by COVID-19 pandemic would led to an increase in cervical cancer but a reduction in detection of breast and bowel cancer.

There are some issues that must be addressed to ensure the analysis and conclusions can be clearly studied. Importantly, it is not entirely clear if the simulation methodology applied to arrive at a scientific conclusion. The authors could provide more insights on how they will address not only the change of cancer detection but also the driving some improved methods for screening helping return to pre-pandemic levels.

1. A quasi-experimental before and after design as the methodological intention should be stated in the article. Although there are equally powerful alternatives with arguably less-stringent requirements that are appropriate and well-tested for natural experiments such as that intervened by the COVID-19 pandemic given the simulation methods, as of now obtaining the actual stage distribution of cancer and the cancer-specific mortality rates before and after the pandemic is possible for making scientifically valid conclusions based on observed data to support the simulation study.

2. The screening disruption is the only concerned parameter in modelling the change of cancer progression in this study. But delayed diagnosis after screening as another concern could be possibly affected by the pandemic. This should be taken into consideration in the simulation. The authors also claimed the cancer treatment could be also be affected by the pandemic, the evaluation on mortality is therefore not feasible. However, the impacts of COVID-19 pandemic on the delayed treatment and cancer treatment are important issues which should be covered by simulation study.

3. By simulations, the confident intervals for the outcomes should be provided as the requirement to determine the required reliability for the estimates.

Reviewer #3 (Public Review):

This is an interesting manuscript with an important subject pertaining to the impact of COVID-19 pandemic on various delayed schedules of population-based cancer screening, leading to the reduction of screen-detected cancers and the possible upstaging cancers. The results were assessed by simulation model (Policy I modelling) with the demonstration of Australia scenarios including three major cancers, including breast cancer, colorectal cancer, and cervical cancer.

Assess the impacts of COVID-19 disruption to population cancer screening for three major cancers on short-term and long-term outcomes for policy analysis.

The merit of this study is to provide a series of simulated results under disruption scenarios but the weakness are several-fold including lacking of mortality estimates, inadequate assessments and inaccurate reports on missed cancers (interval cancers) and upstaging.

Policy analysis based on disruption scenario through the simulation model would be very informative to guide policy-makers for designing a salvage program to minimize the impacts of COVID-19 disruptions.

Direct reporting data on the empirical disruption scenario instead of relying on the sensitivity analysis of disruption scenario is more transparent and convincing for the public.

Reviewer #1 (Public Review):

Overall, the paper by Dang and colleagues is an interesting addition to the field. This study investigates the relationship between socioeconomic status and lifetime obesity using group-based trajectory modeling. The authors identified three trajectories overall, the most prevalent being stable normal BMI. Overall, higher SES was associated with a greater risk of obesity, which is contradictory to studies that examine the relationship among developed countries. Their findings and conclusions are supported by their analysis/data, however, some consideration and additional details are needed to help understand and be more confident in the final results.

Strengths of this study include:

- The use of novel techniques to investigate the relationship between SES and lifetime obesity, which is important for understanding the life course of disease and for designing future public health interventions and strategies.<br /> - A large sample size.<br /> - The use of a population-based sampling strategy to recruit participants, which helps the generalizability of findings and limits volunteer bias.<br /> - The availability of data on SES and height/weight over a 20-year follow-up, including objectively measured weight and height.<br /> - The availability of important confounders (e.g., physical activity, energy intake).

While overall it is an interesting study, there are some considerations and unclarities that should be addressed.

Weaknesses of this study include:

- Lack of clarity on how the authors conceptualize and define socioeconomic status in some sections of the paper. A limitation is the definition of SES only encompasses educational attainment and occupation, and not other aspects (e.g. income, social class). However, most studies published to date also focus mostly on education and occupation.<br /> - A large majority (~90%) of participants were excluded from the analysis due to missing data on exposures and outcomes. This is a substantial proportion, and it is quite possible that this may have resulted in selection bias for those included vs. those not included, and may limit the generalizability of the findings.<br /> - As with all studies that use self-reported data, there is some potential for information bias. However, the authors do acknowledge this as a limitation in their study.<br /> - There is a lack of clarity with some of the methods (e.g. how multinomial logistic regression was used, latent classes, and how confounders were chosen). The paper would benefit from the inclusion of these details.

Reviewer #2 (Public Review):

The authors aimed to explore the relationship between life course SES and BMI trajectories. They achieve the aim partially, and they could present the results more clearly. The work is interesting and will inform China's obesity public health programs and policies, but it is also interesting for other countries and communities. The exploration of life course exposures is relevant in many ways, and the authors did a good job conceptualizing the BMI and SES trajectories. However, some issues need to be improved, such as the discussions about bias and improvements in the writing and presentation of results.

Reviewer #1 (Public Review)

There has been a lot of work showing that multi-peaked tuning curves contain more information than single peaked ones. If that's the case, why are single-peaked tuning curves ubiquitous in early sensory areas? The answer, as shown clearly in this paper, is that multi-peaked tuning curves are more likely to produce catastrophic errors.

This is an extremely important point, and one that should definitely be communicated to the broader community. And this paper does an OK job doing that. However, it suffers from two (relatively easily fixable) problems:

I. Unless one is an expert, it's very hard to extract why multi-peaked tuning curves lead to catastrophic errors.

II. It's difficult to figure out under what circumstances multi-peaked tuning curves are bad. This is important, because there are a lot of neurons in sensory cortex, and one would like to know whether multi-peaked tuning curves are really a bad idea there.

And here are the fixes:

I. Fig. 1c is a missed opportunity to explain what's really going on, which is that on any particular trial the positions of the peaks of the log likelihood can shift in both phase and amplitude (with phase being more important). However Fig. 1c shows the average log likelihood, which makes it hard to understands what goes wrong. It would really help if Fig. 1c were expanded into its own large figure, with sample log likelihoods showing catastrophic errors for multi-peaked tuning curves but not for single peaked ones. You could also indicate why, when multi-peaked tuning curves do give the right answer, the error tends to be small.

II. What the reader really wants to know is: would sensory processing in real brains be more efficient if multi-peaked tuning curves were used? That's certainly hard to answer in all generality, but you could make a comparison between a code with single peaked tuning curves and a _good_ code with multi-peaked tuning curves. My guess is that a good code would have lambda_1=1 and c around 0.5 (you could use the module ratio the grid cell people came up with -- I think 1/sqrt(2) -- although I doubt if it matters much). My guess is that it's the total number of spikes, rather than the number of neurons, that matters. Some metric of performance (see point 1 below) versus the contrast of the stimulus and the number of spikes would be invaluable.

Reviewer #2 (Public Review):

The authors try to introduce the encoding time factor into theories of optimal encoding of information in the nervous system

The major strength is in the rigorous analysis and in the simple yet important take home message.

The authors achieved their aim by proving their point with rigorous analyses and the results support their conclusions

The paper makes a simple yet important addition and will likely call for neuroscientists to include more carefully the importance of stimulus encoding time in their formulations of models of neural coding and in the interpretations of results.

Reviewer #1 (Public Review):

Buglak et al. describe a role for the nuclear envelope protein Sun1 in endothelial mechanotransduction and vascular development. The study provides a full mechanistic investigation of how Sun1 is achieving its function, which supports the concept that nuclear anchoring is important for proper mechanosensing and junctional organization. The experiments have been well designed and were quantified based on independent experiments. The experiments are convincing and of high quality and include Sun1 depletion in endothelial cell cultures, zebrafish, and in endothelial-specific inducible knockouts in mice.

Reviewer #2 (Public Review):

Endothelial cells mediate the growth of the vascular system but they also need to prevent vascular leakage, which involves interactions with neighboring endothelial cells (ECs) through junctional protein complexes. Buglak et al. report that the EC nucleus controls the function of cell-cell junctions through the nuclear envelope-associated proteins SUN1 and Nesprin-1. They argue that SUN1 controls microtubule dynamics and junctional stability through the RhoA activator GEF-H1.

In my view, this study is interesting and addresses an important but very little-studied question, namely the link between the EC nucleus and cell junctions in the periphery. The study has also made use of different model systems, i.e. genetically modified mice, zebrafish, and cultured endothelial cells, which confirms certain findings and utilizes the specific advantages of each model system. A weakness is that some important controls are missing. In addition, the evidence for the proposed molecular mechanism should be strengthened.

Specific comments:

1) Data showing the efficiency of Sun1 inactivation in the murine endothelial cells is lacking. It would be best to see what is happening on the protein level, but it would already help a great deal if the authors could show a reduction of the transcript in sorted ECs. The excision of a DNA fragment shown in the lung (Fig. 1-suppl. 1C) is not quantitative at all. In addition, the gel has been run way too short so it is impossible to even estimate the size of the DNA fragment.

2) The authors show an increase in vessel density in the periphery of the growing Sun1 mutant retinal vasculature. It would be important to add staining with a marker labelling EC nuclei (e.g. Erg) because higher vessel density might reflect changes in cell size/shape or number, which has also implications for the appearance of cell-cell junctions. More ECs crowded within a small area are likely to have more complicated junctions.<br /> Furthermore, it would be useful and straightforward to assess EC proliferation, which is mentioned later in the experiments with cultured ECs but has not been addressed in the in vivo part.

3) It appears that the loss of Sun1/sun1b in mice and zebrafish is compatible with major aspects of vascular growth and leads to changes in filopodia dynamics and vascular permeability (during development) without severe and lasting disruption of the EC network. It would be helpful to know whether the loss-of-function mutants can ultimately form a normal vascular network in the retina and trunk, respectively. It might be sufficient to mention this in the text.

4) The only readout after the rescue of the SUN1 knockdown by GEF-H1 depletion is the appearance of VE-cadherin+ junctions (Fig. 6G and H). This is insufficient evidence for a relatively strong conclusion. The authors should at least look at microtubules. They might also want to consider the activation status of RhoA as a good biochemical readout. It is argued that RhoA activity goes up (see Fig. 7C) but there is no data supporting this conclusion. It is also not clear whether "diffuse" GEF-H1 localization translates into increased Rho A activity, as is suggested by the Rho kinase inhibition experiment. GEF-H1 levels in the Western blot in (Fig. 6- supplement 2C) have not been quantitated.

5) The criticism raised for the GEF-H1 rescue also applies to the co-depletion of SUN1 and Nesprin-1. This mechanistic aspect is currently somewhat weak and should be strengthened. Again, Rho A activity might be a useful and quantitative biochemical readout.

Reviewer #3 (Public Review):

Here, Buglak and coauthors describe the effect of Sun1 deficiency on endothelial junctions. Sun1 is a component of the LINC complex, connecting the inner nuclear membrane with the cytoskeleton. The authors show that in the absence of Sun1, the morphology of the endothelial adherens junction protein VE-cadherin is altered, indicative of increased internalization of VE-cadherin. The change in VE-cadherin dynamics correlates with decreased angiogenic sprouting as shown using in vivo and in vitro models. The study would benefit from a stricter presentation of the data and needs additional controls in certain analyses.

1. The authors implicate the changes in VE-cadherin morphology to be of consequence for "barrier function" and mention barrier function frequently throughout the text, for example in the heading on page 12: "SUN1 stabilizes endothelial cell-cell junctions and regulates barrier function". The concept of "barrier" implies the ability of endothelial cells to restrict the passage of molecules and cells across the vessel wall. This is tested only marginally (Suppl Fig 1F) and these data are not quantified. Increased leakage of 10kDa dextran in a P6-7 Sun1-deficient retina as shown here probably reflects the increased immaturity of the Sun1-deficient retinal vasculature. From these data, the authors cannot state that Sun1 regulates the barrier or barrier function (unclear what exactly the authors refer to when they make a distinction between the barrier as such on the one hand and barrier function on the other). The authors can, if they do more experiments, state that loss of Sun1 leads to increased leakage in the early postnatal stages in the retina. However, if they wish to characterize the vascular barrier, there is a wide range of other tissue that should be tested, in the presence and absence of disease. Moreover, a regulatory role for Sun1 would imply that Sun1 normally, possibly through changes in its expression levels, would modulate the barrier properties to allow more or less leakage in different circumstances. However, no such data are shown. The authors would need to go through their paper and remove statements regarding the regulation of the barrier and barrier function since these are conclusions that lack foundation.<br /> 2. In Fig 6g, the authors show that "depletion of GEF-H1 in endothelial cells that were also depleted for SUN1 rescued the destabilized cell-cell junctions observed with SUN1 KD alone". However, it is quite clear that Sun1 depletion also affects cell shape and cell alignment and this is not rescued by GEF-H1 depletion (Fig 6g). This should be described and commented on. Moreover please show the effects of GEF-H1 alone.<br /> 3. In Fig. 6a, the authors show rescue of junction morphology in Sun1-depleted cells by deletion of Nesprin1. The effect of Nesprin1 KD alone is missing.

Reviewer #1 (Public Review):

In mammals, a small subset of genes undergoes canonical genomic imprinting, with highly biased expression in function of parent of origin allele. Recent studies, using polymorphic mouse embryos and tissues, have reevaluating the number of allele-specific expressed genes (ASE) to 3 times more than previously thought, however with most of these novel genes showing a very low ASE (50%-60% bias toward one parental allele). Here, the authors undergo a comparison of 4 datasets and complete bioinformatic reanalysis of 3 recent allele specific RNAseq to study potential novel imprinted genes, using recently released iSoLDE pipeline. Very few genes have been confirmed with true ASE in the different studies and/or validated by pyrosequencing analysis, However, the authors show that most of the newly discovered ASE genes are lying in close proximity of already known imprinted loci and could be co-regulated by these imprinted clusters. This is important to understand how and to which extent imprinted control regions control gene expression.

This manuscript highlights the number of potential false discovered imprinted genes in previous datasets that could result to either lack of replicates, weak allelic ratio or low gene expression and lack of read depth. But the lack of overlap in the ASE called genes (at the exception to the known imprinted genes) between the different datasets is worrying and important to discuss, as the authors did. I would have appreciated more details into the differences between the different datasets that could explain the lack of reproducibility : library preparation protocol, sequencer technology, SNP calling, number of reads per SNP, bioinformatics pipeline.

Studying allele specific expression of lowly expressed genes is difficult by technology based on PCR amplification (library preparation, pyrosequencing) and could result on a bias expression only due to the random amplification of a small pool of molecules. Could the author compare the level of expression of their different classes of genes? The more robust ASE genes in their study could be the more highly expressed? Several genes were identified only in one or two of the previous studies, were they expressed in the other studies when not define as ASE? This would also allow defining a threshold of expression to study allelic bias in the future. To conclude, this study is an important resource for the epigenetic field and better understand genomic imprinting.

Reviewer #2 (Public Review):

This work aims to understand genomic imprinting in the mouse and provide further insight to challenges and patterns identified in previous studies.

Firstly, genomic imprinting studies have been surrounded by controversy especially ~10 years ago when the explosion of sequencing data but immature methods to analyze it lead to highly exaggerated claims of widespread imprinting. While the methods have improved, clear standards are not set and results still have some inconsistencies between studies. The authors first do a meta-analysis of previous studies, comparing their results and doing a useful reanalysis of the data. This provides some valuable insights into the reasons for inconsistencies and guides towards better study designs. While this work does not exactly set a common standard for the field, or provide a full authoritative catalog of imprinted loci in mouse tissues, it provides a step in that direction. I find these analyses relatively simple and straightforward, but they seem solid.

Previous studies have described a relatively common pattern of subtle expression bias towards one parental allele, rather than the classical imprinting pattern of fully monoallelic expression. This work digs deeper into this phenomenon, using first the meta-analysis data and then also targeted pyrosequencing analysis of selected loci. The analysis is generally well done, although I did not understand why gDNA amplification bias was not systematically corrected in all cases but only if it was above a given (low) threshold. I doubt this would affect the results much though. To some extent the results confirm previously observed patterns (bimodal distribution of either subtle or full bias, and effect of distance from the core of the imprinted locus). The novel insights mostly concern individual loci, with discovery and validation of some novel genes, typically with a subtle or context-specific parental bias.

The study also provides some insights into mechanisms, especially by analysis of existing mouse models with a deletion of the ICR of specific loci. The change in the parental bias pattern was then used to infer potential methylation and chromatin-related mechanisms in these imprinted loci, including how the subtle bias further away is achieved. There are interesting novel findings here, as well as hypotheses for further research. However, this is an area where the conclusions rely quite heavily on published research especially as this study doesn't include single-cell resolution, and it's not entirely clear how much of e.g. the Figure 7 mechanisms part is based on discoveries of this study.

Imprinting is a fascinating phenomenon that can be informative of mechanisms of genome regulation and parental effects in general. It is a bit of a niche area though, and the target audience of this study is likely going to be limited to specialists doing research on this specific topic. As the authors point out, the functional importance of the findings is unknown.

Reviewer #3 (Public Review):

Genomic imprinting is a striking example of epigenetic inheritance in mammals with profound influence on growth and development. A powerful experimental approach to the study of imprinting involves reciprocal mouse F1 crosses; it allows direct assessment of the parent-of-origin effects in a genetically uniform setting that is also an order of magnitude richer in polymorphism than human samples. Use of RNA sequencing is a natural fit to systematic quantitative analysis of allele-specific expression; however, multiple RNA-seq studies of imprinting in F1 mouse tissues wildly disagree in the estimated numbers of novel imprinted genes and in the extent of allelic bias in these genes. In their study, Edwards et al. start with an observation that existing studies varied in their experimental design and data analysis procedures. To assess to what extent disagreements between findings are due to different data processing, they re-analyzed several published datasets using a single pipeline. Furthermore, they performed experimental validation of a number of the novel candidate imprinted genes using primer extension on RT-PCR products (pyrosequencing), to estimate the number of false positives.

Between re-analysis of RNA-seq datasets and the validation experiments, this study presents convincing evidence that most candidate novel imprinted genes are artefactual. The discordant predictions between studies remain even after processing all the data following ISoLDE protocol. Importantly, validated candidate genes tended to be on the periphery of known imprinted domains, suggesting that their boundaries are yet to be finalized.

This work brings into focus an important issue of reproducible analysis and interpretation of RNA sequencing data, especially the analysis of allele-specific expression, including in the specific case of imprinted genes. With novel molecular mechanisms described recently (such as H3K27me3-related parent-of origin gene regulation) and greater accuracy of measuring subtle allelic bias afforded by deep sequencing, the authors' suggested classification (canonical, weak canonical, non-canonical, and weakly biased) is a useful pragmatic step in dealing with the confusing terminology in different studies.

The authors make a strong case that the data analysis methods used in the analyzed studies are prone to false positives. However, the approaches they use are more of an invitation to further dialogue than a definitive recipe to follow. For example, the authors mention that combining the results of several analytical approaches should increase accuracy. However, if those approaches are erroneous, this could lead to two types of error: (1) tools might be erroneous in a similar way, then consistency of results might be taken as confirmation of correctness, (2) averaging results from tools with opposite biases would lead to loss of signal. In the long run, there is no substitute to developing statistically accurate tools and validating that they correctly deal with noise in the data. On the experimental side, Pyrosequencing also involves PCR. This does not change the main conclusions of this study but going forward, it is worth focusing on the methods less affected by amplification (such as allele-specific FISH, ddPCR, or direct RNA sequencing).

Reviewer #1 (Public Review):

In humans, mutations in specific ribosomal protein genes and ribosome assembly factors cause a group of diseases collectively known as ribosomopathies. Patients with these diseases typically display a number of remarkably similar tissue specific phenotypes including anemia and craniofacial abnormalities. The causes of the tissue specificity of these disorders have long remained an outstanding question in the field, and more recent evidence points to the induction of nucleolar stress which triggers a p53-dependent response and cell death. In previous work, the authors have shown that loss and gain of Drosophila Rps12 causes a number of unexpected phenotypes. This current paper seeks to investigate the function of Rps12 in mice.

The authors generate a conditional knockout allele within the mouse Rps12 locus and show that homozygous loss of Rps12 results in early embryonic lethality, while heterozygous mutants display a number of cell specific defects in the hematopoietic system. The authors provide evidence that haploinsufficiency of Rps12 results in erythropoiesis defects that worsen with age, a decrease in the number of hematopoietic progenitor cells, and disruption of hematopoietic stem cell (HSC) quiescence correlated with a failure of mutant HSCs to reconstitute peripheral blood. Strikingly, loss of Rps12 results in increased translation in HSCs and early progenitors, marked by activation of MEK/ERK and ARK/TOR signaling pathways.

Strengths<br /> The paper provides new evidence that loss of Rps12 results in a number of specific defects in the hematopoietic system. The phenotypic characterization is rigorous and clearly described in the text. The observations Rps12 heterozygotes exhibit increases in protein synthesis and loss of HSC quiescence are interesting and warrant further investigation. This paper will have broad appeal to those interested in development, stem cell maintenance, ribosome biology, and ribosomopathies.

Weaknesses<br /> The Rps12 gene has two embedded snoRNAs, the disruption of which could contribute to all of the described phenotypes. Additional work is needed to confirm that the mutant phenotypes are caused specifically by loss of Rps12

Reviewer #2 (Public Review):

Previous work from the authors' lab has shown that the classical 'Minute' phenotypes in Drosophila depend on the ribosomal protein Rps12, suggesting that Rps12 is a sensor of deficits in other ribosomal proteins (Rp). Increasing the dose of Rps12 enhances 'Minute' phenotypes, while loss of Rps12 suppresses them. However, Rps12+/- heterozygous flies do not display 'Minute' phenotypes.

In the current manuscript, the authors examine the consequences of deleting Rps12 in mice to explore its potential role in translational regulation and hematopoiesis. Homozygosity for an Rps12 null mutation is embryonic lethal, while heterozygous Rps12+/- mutant mice exhibit defects in growth, skeletal abnormalities, hydrocephalus and stroke. Consistent with other mouse Rp mutants, Rps12+/- mutant mice have a block in erythroid maturation and reduced spleen size. Hematopoietic stem and progenitor cell (HSPC) numbers are reduced in the bone marrow and are defective in repopulation transplant assays. Unexpectedly, Rps12+/- mutants show loss of HSC quiescence associated with AKT/MTOR and ERK pathway activation and increased global translation, a phenomenon that has not previously been reported in other Rp mutants. The authors conclude that Rps12 is critical for the maintenance of HSC quiescence and function.

Strengths<br /> The data reported in this manuscript nicely complement the existing literature on the functional effects of Rp mutations in mammalian hematopoiesis and development with loss of HSC quiescence and increased global translation in the Rps12 deficient mice. These unexpected findings will be of broad interest to scientists working in the field of ribosome assembly, ribosomopathies and hematopoiesis.

Weaknesses<br /> It remains unclear mechanistically how Rps12 haploinsufficiency activates the AKT/MTOR and ERK signaling pathways. It is also unclear to what extent the reported phenotypes might be indirect consequences of perturbing the expression of two small nucleolar RNA genes that are present in Rps12 introns 4 and 5 or a consequence of TP53 activation, which is known to influence the phenotype in other examples of Rp deletion mouse models. To fully justify the conclusions that the authors wish to draw, it would be important to assess the effect of the heterozygous Rps12+/- mutation on Rps12 protein expression, ribosomal subunit assembly and rRNA processing.

Reviewer #3 (Public Review):

In this manuscript, the authors studied the erythropoiesis and hematopoietic stem/progenitor cell (HSPC) phenotypes in a ribosome gene Rps12 mutant mouse model. They found that RpS12 is required for both steady and stress hematopoiesis. Mechanistically, RpS12+/- HSCs/MPPs exhibited increased cycling, loss of quiescence, protein translation rate, and apoptosis rates, which may be attributed to ERK and Akt/mTOR hyperactivation. Overall, this is a new mouse model that sheds light into our understanding of Rps gene function in murine hematopoiesis. The phenotypic and functional analysis of the mice are largely properly controlled, robust, and analyzed.

A major weakness of this work is its descriptive nature, without a clear mechanism that explains the phenotypes observed in RpS12+/- mice. It is possible that the counterintuitive activation of ERK/mTOR pathway and increased protein synthesis rate is a compensatory negative feedback. Direct mechanism of Rps12 loss could be studied by ths acute loss of Rps12, which is doable using their floxed mice. At the minimum, this can be done in mammalian hematopoietic cell lines.

Below are some specific concerns need to be addressed.

1. Line 226. The authors conclude that "Together, these results suggest that RpS12 plays an essential role in HSC function, including self-renewal and differentiation." The reviewer has three concerns regarding this conclusion and corresponding Figure3. 1) The data shows that RpS12+/- mice have decreased number of both total BM cells and multiple subpopulations of HSPCs. The frequency of HSPC subpopulations should also be shown to clarify if the decreased HSPC numbers arises from decreased total BM cellularity or proportionally decrease in frequency. 2) This figure characterizes phenotypic HSPC in BM by flow and lineage cells in PB by CBC. HSC function and differentiation are not really examined in this figure, except for the colony assay in Figure 3K. BMT data in Figure4 is actually for HSC function and differentiation. So the conclusion here should be rephrased. 3) Since all LT-, ST-HSCs, as well as all MPPs are decreased in number, how can the authors conclude that Rps12 is important for HSC differentiation? No experiments presented here were specifically designed to address HSC differentiation.

2. Figure 3A and 5E. The flow cytometry gating of HSC/MPP is not well performed or presented, especially HSC plot. Populations are not well separated by phenotypic markers. This concerns the validity of the quantification data.

3. It is very difficult to read bone marrow cytospin images in Fig 6F without annotation of cell types shown in the figure. It appears that WT and +/- looked remarkably different in terms of cell size and cell types. This mouse may have other profound phenotypes that need detailed examination, such as lineage cells in the BM and spleen, and colony assays for different types of progenitors, etc.

4. For all the intracellular phospho-flow shown in Fig7, both a negative control of a fluorescent 2nd antibody only and a positive stimulus should be included. It is very concerning that no significant changes of pAKT and pERK signaling (MFI) after SCF stimulation from the histogram in WT LSKs. There are no distinct peaks that indicate non-phospho-proteins and phospho-proteins. This casts doubt on the validity of results. It is possible though that Rsp12+/- have very high basal level of activation of pAKT/mTOR and pERK pathway. This again may point to a negative feedback mechanism of Rps12 haploinsufficiency.

5. The authors performed in vitro OP-Puro assay to assess the global protein translation in different HSPC subpopulations. 1) Can the authors provide more information about the incubation media, any cytokine or serum included? The incubation media with supplements may boost the overall translation status, although cells from WT and RpS12+/- are cultured side by side. Based on this, in vivo OP-Puro assay should be performed in both genotypes. 2) Polysome profiling assay should be performed in primary HSPCs, or at least in hematopoietic cell lines. It is plausible that RpS12 haploinsufficiency may affect the content of translational polysome fractions.

Reviewer #1 (Public Review):

This study aims to identify the existence of hedonic feeding and to distinguish it from homeostatic feeding, in Drosophila. The authors use direct observation of feeding events, a novel automated feeding event detector, inventive behavioral assays, and genetics to separate out the ways that Drosophila interacts with food. Using two choice assays, the authors find an increased duration of interactions with high-concentration sugars under conditions of expected satiety, which is considered to be hedonic feeding.

Strengths:

The technical advances in the measurement of animal interactions with food will help advance the understanding of feeding behavior and motivational states.

The correlation of specific types of food interactions across satiation state, sex, and circadian time will help drive forward the understanding of the scope of an animal's goals with feeding, and likely their relation between species and eating disorders.

The assessment of mushroom body circuitry in a type of food interaction is helpful for understanding the coding of feeding control in the brain.

Limitations:

All feeding data presented in the manuscript are from the interactions of individual flies with a source of liquid food, where interaction is defined as 'physical contact of a specific duration.' It would be helpful to approach the measurement of feeding from multiple angles to form the notion of hedonic feeding since the debate around hedonic feeding in Drosophila has been ongoing for some time and remains controversial. One possibility would be to measure food intake volumetrically in addition to food interaction patterns and durations (e.g. via the modified CAFE assay used by Ja).

Some of the statistical analyses were presented in a way that may make understanding the data unnecessarily difficult for readers. Examples include:

1) In Table I the authors present food interaction classifications based on direct observation. These are helpful. However, the classification system is updated or incompletely used as the manuscript progresses, most importantly changing from four categories with seven total subcategories to three categories and no subcategories. In subsequent data analyses, only one or two of these categories are assessed. It would be helpful, especially when moving from direct observation to automated categorization, to quantify the exact correspondences between all of the prior and new classifications, as well as elaborate on the types of data that are being excluded.

2) The authors switch between a variety of biological and physiological conditions with varying assays, which makes following the train of reasoning nearly impossible to follow. For example, the authors introduce us to circadian aspects of feeding behavior to introduce the concept of 'meal' and 'non-meal' periods of the day. It is then not clear in which of the subsequent experiments this paradigm is used to measure food interactions. Is it the majority of the subsequent figure panels? However, the authors also use starved flies for some assays, which would be incompatible with circadian-locked meals. The somewhat random and incompletely reported use of males and females, which the authors show behave differently, also makes the results more difficult to parse. Finally, the authors are comparing within-fly for the 'control environment' and between flies for their 'hedonic environment' (Figure 3A and subsequent panels), which I believe is not a good thing to do.

3) Statistical analyses are not always used consistently. For example, in Figures 3B and C, post hoc test results are shown for sucrose vs. yeast interactions, but no such statistics are given for 3E and 3F, preventing readers from assessing if the assay design is measuring what the authors tell us it is measuring.

Reviewer #2 (Public Review):

Weaver et al. used video analysis of flies that were feeding in their previously developed FLIC assay to begin to dissect the mechanisms of feeding. FLIC or Fly Liquid Interaction Counter records electrical signals that are generated when a fly touches a liquid food substrate with its legs or proboscis or both. Using video data of the liquid food interactions in the FLIC assay allowed the authors to precisely identify what a fly is doing in the feeding chamber and what the relationship is between the flies' behavior and the electrical signal recorded in the assay. This analysis produced the first detailed behavioral profile of feeding flies and allowed the authors to categorize different types of feeding in the FLIC assay, from tasting food (using their legs) to fast and long feeding bouts (using their proboscis).

After establishing what FLIC signals correspond to the different types of feeding, they used these signals to examine the food choices of starved and sated flies when presented with a sugar-rich (2% sucrose) or protein-rich (2% yeast + 1% sucrose) liquid food source. To represent hedonic feeding, they also presented flies with a choice between super sweet (20% sucrose) food or protein-rich (2% yeast + 1% sucrose) liquid food. Although fully fed flies show no difference in the number of times they visit either food choice, the flies spend more time feeding during their visits on 20% sucrose food than they do on regular sugar and on the yeast food source, suggesting that 20% sucrose is a more pleasurable food source. To make sure this was not due to the higher caloric content of 20% sucrose, they also offered flies food with the same sweetness as 20% sucrose (2% sucrose + 18% arabinose) but without caloric content and food with the same caloric content but the sweetness of 2% sucrose (2% sucrose + 18% sorbitol). This experiment showed that sweetness was the driver for the longer feeding bouts, confirming that sweeter food is apparently perceived as more pleasurable. They also looked at the effect of starving flies on the hedonic drive and found that starvation increases the time spent feeding on pleasurable food, consistent with findings in mammals that homeostatic feeding affects the hedonic drive.

To begin dissecting circuits underlying hedonic drive, the authors used CaMPARI expression in all neurons. CaMPARI is a green fluorescent reporter that turns red in the presence of Ca2+ (a measure of neuronal activity) and UV exposure. Fully fed flies in the super sweet food choice condition showed more red fluorescence in the mushroom bodies. Inhibiting a subset of these neurons acutely shows that horizontal lobes are required for the increased duration of feeding bouts on super sweet food. These lobes are innervated by a cluster of DA neurons and inhibiting them also blocks the increased super sweet feeding times.

The data in the paper largely support the conclusions. The application of this tool to distinguish between homeostatic and hedonic feeding is innovative and very compelling. As proof of principle of the strength of their paradigm, the authors identify a distinct brain circuit involved in hedonic feeding. The methods established in the paper make a deeper understanding of feeding mechanisms possible at both a genetic and brain circuit level.

Some of the data presentation is dense and could be improved to make this paper easier for readers to understand.

1) The dissection of feeding into distinct behavioral elements and its correlation with electrical FLIC signals that allow interpreting feeding types is a fundamental new method to dissect feeding in flies. However, the categories of micro-behaviors in Table 1 are not intuitive.

2) The details for the behavioral data analysis are not clear and should be made more obvious. For example, how many males and females were used in each experiment? Were any of the females mated or were they all virgins? If all virgins, why not use mated females? Mating status may have an effect on the feeding drive. If mated and virgin females were used, are there any differences between them? Similarly, for diurnal feeding experiments, it is not immediately clear from the graphs how many animals were used and how the frequencies were obtained (Fig. 1F, presumably averages for each category per fly but that is inconsistent with the legend in the supplement for this figure). Why does the transition heat map not include all micro-behaviors (Fig. 1E, no LQ data which are significant in diurnal feeding)?

3) The CaMPARI images do not look great, particularly in the pan-neuronal condition (Fig. 5A). It would be useful to include the movie of the stack. Did any other brain regions show activity differences, such as SEZ or PI? These regions are known to be involved in feeding so it seems surprising they show no effect.

Reviewer #1 (Public Review):

This work describes a novel high-throughput approach to diverse transgenesis which the authors have named TARDIS for Transgenic Arrays Resulting in Diversity of Integrated Sequences. The authors describe the general approach: the generation of a synthetic 'landing pad' for transgene insertion (as previously reported by this group) that has a split selection hygromycin resistance gene, meaning that only perfect integration with the insert confers resistance to the otherwise lethal hygromycin drug. The authors then demonstrate two possible applications of the technology: individually barcoded lineages for lineage tracing and transcriptional reporter lines generated by inserting multiple promoters. In both cases, the authors did a limited 'proof of concept' study including many important controls, showcasing the potential of the method. The conclusions for applications of this method in C. elegans are supported by the data and the authors discuss important observations and considerations. In the discussion, the authors expand the application of the method beyond C. elegans, which is speculative at this point given that a nontrivial aspect of the success of the method in worms is the self-assembly of DNA into heritable extrachromosomal arrays (a feature that is absent in most other systems).

Reviewer #2 (Public Review):

This paper explores the possibility of integrating diverse and multiple DNA fragments in the genome taking advantage of plasmids in arrays, and CRISPR-Cas.

Since the efficiency of integration in the genome is low, they, as others in the field, use selection markers to identify successful events of integration. The use of these selection markers is common and diverse, but they use a couple of distinct strategies of selection to:

– Introduce bar codes in the genome of individuals at one specific genomic site (gene for Hygromycin resistance with bar code in an intron with homology arms to complete a functional gene);

– Introduce promoters at two specific genomic landing pads downstream of fluorescent reporters.

The strengths of the study rely on the clever design of the selection markers, which enrich the collection of this type of markers. The weaknesses are the lack of novelty in the field in theoretical or practical terms. In fact, they do not show any innovative application of these approaches. Moreover, they show a limited number of experiments in the manuscript, or at least insufficient in my opinion for an article that is based on a methodology.

This work adds to other recent studies, e.g. from Nonet, Mouridi et al., and Malaiwong et al, that use the integration of single and multiple/diverse DNA sequences in the C. elegans genome, and thus is not as groundbreaking as claimed. The real test of this method will be its use to address biological questions.

Reviewer #1 (Public Review):

This work provides a comprehensive assessment of volumetric-MRI-based brain age estimates in relation to AD-related biomarkers and AD risk factors. Brain age modeling has been studied extensively in recent years. Brain age estimates are suggested surrogate markers for aging-associated changes in the brain. This paper provides findings on how brain age estimates are associated with AD-related amyloid and tau accumulation, cerebrovascular white matter disease, and unspecific neurodegeneration detected by plasma NfL and to some extent CSF NfL as well. The authors also provide important results on sex-specific differences in these associations.

Strengths:

Modeling and analyses were performed on different observational cohorts. Analysis was repeated for the cognitively unimpaired, and individuals with MCI separately.

Weaknesses:

Although the authors concluded that brain age prediction is a biomarker of AlD pathology, only associations were assessed in this study. Further analyses are required to truly assess the biomarker value of brain age prediction for AD pathology.

Reviewer #2 (Public Review):

In this work, the authors used machine learning techniques to predict chronological age in the large UK Biobank dataset using structural neuroimaging measures of regional brain volumes and cortical thickness in sex-stratified models. From these predictions, the authors calculated the brain-age delta, which is thought to reflect biological brain aging. The authors applied these models to four independent cohorts and calculated brain-age delta, which they then associated with several markers of Alzheimer's disease pathology, neurodegeneration, and cerebrovascular disease. The aim of these analyses was to validate brain-age delta as a clinically relevant marker of AD.

Strengths<br /> This is a well-written manuscript that explains a well-powered study of multiple deeply-phenotyped cohorts. An impressive amount of work went into this manuscript and that is evident from reading it. The manuscript was enjoyable to read and easy to follow, and the authors provided an informative summary figure visualizing the analysis plan of this work. More specifically there are five key strengths in this present work.<br /> First, instead of aiming for a brain-predicted age model with optimal predictive accuracy, as is typically the case in studies using brain-age delta measures, the authors used a model with a restricted feature set and a limited age range to allow for better neurobiological interpretability and to increase the relevance of this model to ageing cohorts.<br /> Second, the authors corrected for the proportional bias that is seen in brain age models and controlled subsequent analyses (i.e. associations between brain-age delta and markers of AD pathology, etc.) for chronological age. This is an important and necessary step when working with brain-age delta but is not always implemented across studies.<br /> Third, the authors computed Shapley Additive explanation values (SHAP) which quantified the contribution of different brain regions to the brain age prediction. This ensured that the model had neurobiological interpretability which is not always the case with brain-age prediction models. This was further improved by using a relatively restricted feature set that is often used in brain-age prediction studies as the most important regions could be easily visualized and therefore more readily interpreted. This is in contrast to other models that use a large number of smaller brain features, which are less easily vizualised and less interpretable.<br /> Fourth, importantly, the authors used sex-stratified models as they generated the brain-age delta measures separately in men and women. This allowed for sex-specific analyses of the associations between brain-age delta and markers of AD pathology, cerebrovascular disease, and neurodegeneration, which is important given evidence of sex differences in AD. These sex-stratified models also enabled the authors to compare the most relevant brain regions in the brain age prediction models. While previous work has reported sex differences in brain-age delta, the sex-specific contribution of specific brain features is important information that is not usually reported.<br /> Finally, in addition to investigating the association of brain-age delta with specific markers of AD pathology, cerebrovascular disease, and neurodegeneration, the authors also analyzed the association between brain-age delta and amyloid and tau status stages which provides important clinically relevant information. This information is important if future work aims to further investigate the use of brain-age delta in the field of AD.

Limitations<br /> There are three important weaknesses in this present work. First, the conclusion that "These results validate brain-age delta as a non-invasive marker of biological brain aging related to markers of AD and neurodegeneration" (from the Abstract) may be overstated. While we assume that brain-age delta reflects an accelerated ageing process, this is still a cross-sectional measure and the results show cross-sectional associations with markers of AD and neurodegeneration. For true validation of this measure as a non-invasive marker of biological brain aging with respect to markers of AD and neurodegeneration, we would need longitudinal data to show that changes in brain age are longitudinally associated with changes in markers of AD and neurodegeneration.<br /> Second, the authors reported that brain-age delta was not related to longitudinal brain change ('aging signature change'), which supports a recent finding that cross-sectional brain-age delta was not associated with longitudinal brain change but was associated with birthweight and polygenic risk scores for brain-age delta (Vidal-Pineiro et al., 2021 eLife). This previous finding led to the conclusion that brain-age delta may reflect early-life factors more so than longitudinal brain change or 'accelerated brain ageing'. This is a critical issue to contend with if we really wish to pursue further validation of the brain-age delta as a potential marker of aging<br /> Third, the analyses for the associations between brain-age delta and other variables are not corrected for multiple comparisons, even though a large number of comparisons are conducted. This means that some of the apparently significant results could be false positives. Appropriately correcting these analyses for multiple comparisons would strengthen the results, allowing for greater confidence in the significant results, and would avoid mistaken interpretations of false positive findings.

Appraisal<br /> The authors developed accurate and generalizable sex-specific measures of the brain-age delta. The authors demonstrated that brain-age delta was associated with measures of AD pathology and neurodegeneration. These have the potential to be useful findings that may promote the use of the brain-age delta in AD research. However, as these results are not corrected for multiple comparisons it is possible that some of these results may be false positives. Moreover, the finding that brain-age delta was not associated with longitudinal brain change may undermine the conclusions, as it could suggest that brain-age delta is not reflective of accelerated brain ageing.

Impact<br /> I believe that this work has two important impacts. First, the methods demonstrated in the present study highlight that sex-stratified models may be necessary for future brain-age delta studies, and given that the models were externally validated in four separate cohorts, a key impact is that future researchers will be able to apply the well-described brain-age models here in their own work. Second, the finding that brain-age delta was not related to longitudinal brain change or atrophy, supports previous similar findings and could suggest that brain-age delta does not, as previously assumed, reflect accelerated brain ageing. This may indicate that the brain-age delta is not a satisfactory marker of brain ageing and therefore could discourage future work with this metric that attempts to validate it is a clinical marker of brain ageing. If this issue could be alternatively explained or if brain-age delta is, in fact, shown to reflect brain ageing, then an additional potential impact is that it may support the future investigation into the use of brain-age delta in longitudinal studies of brain ageing and neurodegeneration.

Reviewer #3 (Public Review):

Cumplido-Mayoral and colleagues' study focused on the brain-age paradigm in the context of Alzheimer's disease risk. The goal was to valid brain-age 'deltas' by assessing how they relate to Alzheimer's biomarkers and related neurodegenerative measures. They did this by training a new brain-age model on FreeSurfer phenotypes (cortical and subcortical) using the UK Biobank dataset. They then tested multiple datasets including ALFA, ADNI, OASIS, and EPAD, focusing on cognitively unimpaired people and people with mild cognitive impairment. Using brain-age deltas calculated in the test sets, the authors then tested associations with a range of dementia-related measures, including the presence of MCI, APOE e4, amyloid and tau positivity, white matter hyperintensity volume and NfL levels from plasma or CSF.

Strengths include using multiple independent datasets from different sources. This provides large sample sizes and access to different data types. Another strength is the efforts to understand drivers of brain age prediction, by using the SHAP technique. The authors include a newly trained brain-age prediction model, which appears to work as well as existing alternative methods.

A weakness is the number of tests conducted and the absence of multiple comparison corrections. A problem with the SHAP analysis is that it does not account for the correlated nature of the input features.

Overall, the study met the stated aims, and I anticipate the results to make a positive contribution to the research field. The results tended to support the conclusions, particularly regarding the relationship between brain-age delta and the markers of neurodegeneration, AD risk, and cerebrovascular health. The only concern around this is whether the number of tests conducted has inflated the type I error rate and resulted in some false positives. This could have been explored further. The conclusions are sex differences are less well supported by the evidence. While some delta-by-sex interactions were significant, others were not (e.g., Figure 3), however, the interpretation focuses only on the significant ones to support blanket statements about the differences between males and females with regard to neurodegeneration. Given the issues about multiple comparisons, this seems premature and somewhat uneven.

Reviewer #1 (Public Review):

This study uses a rigorous methodological approach to chart thalamocortical tracts originating from distinct thalamic nuclei, coupled with a model to characterize relative tissue and fluid components along these tracts. This allows a precise description of changes specific to tracts between thalamic nuclei and distinct cortical projection areas. In conjunction with analyses of the microstructure at various distances along the tracts between the thalamus and cortex, these results demonstrate a remarkably consistent organization of thalamic projections as early as 23w, while also highlighting specific gestational-age (GA) dependent processes specific to each tract. This provides a strong step forward in characterizing the development of fetal white matter tracts from non-invasive diffusion MRI data.

Performing detailed neuroimaging analyses of fetal brain development incurs myriad technical challenges, and significant effort has been applied to overcome these. Nevertheless, several aspects of the approaches employed would benefit from better justification. For example, while acquisition parameters necessarily differ from those used in studies in post-natal developmental, or even adult, diffusion MRI studies, this raises several questions regarding the applicability of the modeling analyses employed (in particular, MSMT-CSD with low b-value dMRI data). Additionally, the normalization approach for assessing location-specific differences along each tract is complicated by the gross changes in brain size occurring during this period. Distinguishing the contribution of location-specific changes in microstructure from topographical change (e.g., terminal zones may constitute a smaller relative portion of the tract at later GAs), would enhance the inferences drawn from these results.

It's unclear from the methods how mothers were recruited to get the range of GAs represented, and whether this incurred any demographic correlations to GA. Some more description of recruitment, and a demographic comparison to GA, to clarify that there was not likely to be bias in who was scanned at different times (e.g., 2nd vs 3rd trimester) would strengthen the generalizability of these results.

The statistical basis for comparison among GA groups in the analysis of location-dependent changes in microstructure is not clear. E.g., the characterization of the depths at which GA-dependent differences in tissue fraction occur should be more clearly laid out, such that these observations can be demonstrated quantitatively, rather than reported descriptively.

Reviewer #2 (Public Review):

Wilson et al. investigated the development of thalamocortical tracts in the fetal brain using in vivo diffusion magnetic resonance imaging (dMRI). In their results, fiber tracts terminating in the prefrontal, superior parietal, and visual cortex connect to discrete areas of the thalamus in an anterior-to-posterior manner. The reported fetal thalamus parcellation is remarkably consistent with parcellation observed in adults, which has significant implications for the development of experience-expectant vs. experience-dependent neurocircuitry. Using along-tract analysis, the authors also identify distinct trajectories of tissue maturation along tracts connecting the thalamus to the medial prefrontal cortex, visual cortex, and superior parietal cortex. Next, these maturation maps were segmented using a histologically defined fetal atlas, which revealed unique maturation within fetal neural compartments across gestation. The study introduces an exciting analytical model for bridging the gap between histology and dMRI, enhancing both the interpretability of dMRI metrics in the fetal brain and validating dMRI as a sensitive tool that can reveal organizing principles of fetal brain development. The sample size is impressive for fetal imaging and analyses were completed in individual subject spaces, which helps to minimize the warping of dMRI data.

The conclusions of the paper are largely well-supported by the data, but some aspects of sample composition and data analysis require clarification and extension to ensure the generalizability of the results.

1. Sociodemographic makeup of the sample is insufficiently considered. The authors provide information about fetal gestational age and fetal sex, but no other information about the sample is provided. Readers familiar with the developing human connectome project will know the data was collected in the United Kingdom, but this is not stated explicitly in the manuscript. There is no other information provided about the sample, so it is unclear whether the included 140 maternal-fetal dyads are representative of the broader population. Complex social experiences that vary as a function of income, racial and ethnic identity, and education are potent influences on the developing brain, and there is notable meta-analytic work demonstrating the sociodemographic makeup of a sample alters trajectories of brain development. Brain development in utero has also been shown to vary among fetuses who are later born preterm, yet there is no information about pregnancy complications or delivery (e.g., gestational age at birth) reported in the manuscript. This lack of sociodemographic and health information significantly impedes inference regarding result generalizability.

2. Over half of the collected data were discarded because of failing data quality checks. This is common in fetal data, but it is unclear what thresholds were used to determine exclusion and whether the excluded cases fall evenly along the age spectrum. Typically, MRI data from younger fetuses show greater motion artifacts compared to data collected in older fetuses, which presents a significant confound for the present study that requires careful consideration. It is also unclear whether the motion correction strategies employed in the present study work equally well for all fetal ages. In short, additional analysis and information are required to ensure age-related motion is not unduly impacting the present results.

3. Given that the youngest age group was much smaller than the other groups (n=13), more data is also needed to assess the robustness of the tissue maturation trajectories reported for this young age group.

4. Sensitivity analyses that illustrate the findings are robust to different preprocessing choices would enhance analytic rigor.

Reviewer #3 (Public Review):

The period that is examined is in the range (21 to 37GW) and uses tractography to delineate five distinct thalamocortical pathways. The paper generates anatomically constrained whole-brain connectomes for each gestational week. The authors parcellate the thalamus according to to streamline connectivity that has been published about two decades ago. The authors delineate the developing thalamocortical pathways and parcellate the fetal thalamus according to its cortical connectivity using diffusion tractography. The study included the primary motor cortex, primary sensory cortex, posterior parietal cortex, dorsolateral prefrontal cortex, and primary visual cortex. With the limitations of the method, the authors delineated five major thalamocortical pathways in each gestational week.

The study finds consistent and distinct origins of different tracts, resembling the adult topology of thalamic nuclei as early as 23W gestation. The study monitors the transient compartment of the subplate and intermediate zone, internal capsule, and establishes references to complement histological knowledge.

The paper's hypothesis is straightforward: "the biological processes occurring in different fetal compartments leads to predictable changes in diffusion metrics along tracts, reflecting the appearance and resolution of these transient zones." Study transient structures, such as subcortical plate or subplate. The authors predict that as subplate neurons disappear the tissue fraction is becoming relatively higher in the deep grey matter and the cortical plate and lower in the subplate. The authors investigate this by characterising the entire trajectory of tissue composition changes between the thalamus and the cortex, to explore the role of transient fetal brain developmental structures on white matter maturational trajectories. The authors demonstrate that along-tract sampling of diffusion metrics can capture temporal and compartmental differences in the second to the third trimester, reflecting the maturing neurobiology of the fetal brain described in histology studies.

Reviewer #1 (Public Review):

In this manuscript, Harada et al. build upon prior studies in honeybees and mammalian cells that high levels of mannose impair proliferation, glucose entry into glycolysis. Here, they find that an inability to adequately metabolize mannose results in dNTP depletion and impaired DNA synthesis at replication forks, which sensitizes to chemotherapy. They provide solid evidence that dNTP depletion is sufficient to impair proliferation and increase chemosensitivity, although causality in the context of an inability to metabolize mannose is not established.

Strengths:<br /> This is a very rigorous, well-designed study and the findings are valuable and broadly interesting for the metabolism and cancer communities. The methods are comprehensive and the experimental details in the legends are complete.

Weaknesses:<br /> When giving context to their work, the authors focus heavily on what is known about mannose metabolism in honeybees and do not discuss thoroughly what is known in cancer cells, including prior work that performed very in-depth metabolic phenotyping of mannose phosphate isomerase low and high cells. The claim that the activity of the pentose phosphate pathway is not affected by mannose is not completely justified by the data presented, as pathway flux is not examined. Moreover, the mechanistic connection between mannose and dNTP depletion is not established. Finally, causality for dNTP depletion in cell cycle perturbation and chemosensitivity is not established.

Reviewer #2 (Public Review):

Harada et al. investigated the mechanism by which high mannose levels inhibit cellular proliferation and enhance chemotherapy. The authors used CRISPR-Cas9 to delete mannose phosphate isomerase (MPI), a key enzyme for metabolizing mannose, in human cancer cells. They found that MPI knockout leads to decreased proliferation of cancer cells when challenged with supraphysiologic concentrations of mannose. Mannose challenge increased sensitivity to both cisplatin and doxorubicin chemotherapy. It also induced slow cell-cycling with impaired entry into the S phase and progression to mitotic phase. Proteomic analysis revealed down-regulation of cell-cycle related proteins following mannose challenge. Specifically, MCM2-7 proteins are decreased, indicating a failure of replication fork progression. The authors show that high mannose conditions disengage dormant origin sites from DNA synthesis during replication stress induced by cisplatin, confirming relevance to induced chemotherapy sensitivity. Metabolic analysis revealed decreased glycolytic activity, increased oxidative phosphorylation, and depleted nucleotides. Finally, pharmacologic inhibition of de novo dNTP biosynthesis using hydroxyurea treatment produced similar effects on cell-cycle progression, chemotherapy sensitivity, and inhibition of DNA synthesis from dormant origins, indicating that high mannose induced depletion of dNTP pools may be the major mechanism behind the anti-cancer effects of mannose.

Strengths: Overall, the authors used a robust approach with several techniques showing consistent results. The use of multiple clones and cell lines increases confidence in the reported findings. Additionally, the re-expression of MPI in MPI-KO cells eliminated the sensitivity to high mannose conditions, increasing confidence that the findings are not due to off-target effects. The authors are thorough in characterizing the defects in cell-cycle progression and have robust molecular evidence to support the failure of DNA synthesis from dormant origins during chemotherapy-induced replication stress. The use of both proteomics and metabolomic techniques generates a robust picture of molecular effects of mannose challenge. Lastly, the demonstration of similar mechanistic effects by pharmacologic inhibition of de novo dNTP synthesis provides support that depletion of dNTPs is a major cause for the anti-cancer effects of high mannose.

Weaknesses: While the conclusions of this paper are supported by strong and consistent evidence, there are limitations in the relevance of the models used. The study was conducted using cancer cells genetically engineered to not express MPI. However, cancer cells ubiquitously express MPI. Drawing conclusions about metabolic remodeling based on metabolite pool sizes alone is not recommended, as pool sizes can increase or decrease due to changes in production or consumption. Isotope labeling studies would reconcile the reasons for accumulation or depletion of metabolite pool sizes. Lastly, in Figure 3, the authors show down regulation of cell cycle progression genes in response to mannose challenge. However, there is also upregulation of proteins related to various cell death mechanisms including ferroptosis and necrosis, suggesting there may be additional mechanisms to explain the effects of mannose challenge. It is unclear why the cell-cycle explanation was pursued without addressing other possibilities.

Reviewer #3 (Public Review):

The manuscript approaches an important problem associated with mannose challenge and subsequent changes in metabolism and DNA replication. The researchers employed MPI-KO human cancer cells to explore the key mechanism behind the anti-cancer activity of mannose, and demonstrated that the large influx of mannose exceeding the capacity to metabolize it, that is, the onset of 'honeybee syndrome', induced dramatic metabolic remodeling that led to dNTP loss.

• They established MPI-KO human cancer cells using the CRISPR-Cas9 system, and exploited the mannose auxotrophy and sensitivity observed in MPI-KO mouse embryonic fibroblasts (MPI- KO MEFs) (DeRossi et al., 2006). The addition of a physiological concentration of mannose (50 μM, unchallenged) to culture medium supported the proliferation of MPI-KO MEFs. However, mannose challenge increased the sensitivity of MPI-KO HT1080 cells to DNA replication inhibitors (i.e., cisplatin and doxorubicin) when the cells had been preconditioned with excess 5 mannose prior to the drug treatment.<br /> • Thus, induction of honeybee syndrome suppresses cell proliferation and increases chemosensitivity in MPI-KO human cancer cell models.<br /> • These results suggest that mannose challenge severely impairs the entry of the cells into S phase and its progression to mitotic phase. Strikingly, however, switching of the mannose-challenge medium to the mannose-unchallenged medium after long-term mannose challenge (6 days) resulted in robust cell proliferation.<br /> • The researchers observed downregulation of proteins related to the cell cycle and DNA replication in mannose-challenged cells (Fig. 3A), which were enriched with the mini-chromosome maintenance 2-7 (MCM2-7) complex.<br /> • Together, these results indicate that mannose challenge disengages dormant origins from DNA synthesis during replication stress, thus exacerbating DNA damage.<br /> • Our finding that DNA synthesis from dormant origins during replication stress is highly sensitive to the dNTP pool size is in good agreement with the therapeutic advantages of RNR inhibition in enhancing the efficacy of radiochemotherapy (Kunos and Ivy, 2018).<br /> The work is of potentially great importance in understanding the action of mannose on cancer cells and the resulting sensitization to anti-cancer agents.

Reviewer #1 (Public Review):

It is now widely accepted that the age of the brain can differ from the person's chronological age and neuroimaging methods are ideally suited to analyze the brain age and associated biomarkers. Preclinical studies of rodent models with appropriate neuroimaging do attest that lifestyle-related prevention approaches may help to slow down brain aging and the potential of BrainAGE as a predictor of age-related health outcomes. However, there is a paucity of data on this in humans. It is in this context the present manuscript receives its due attention.

Comments:

1) Lifestyle intervention benefits need to be analyzed using robust biomarkers which should be profiled non-invasively in a clinical setting. There is increasing evidence of the role of telomere length in brain aging. Gampawar et al (2020) have proposed a hypothesis on the effect of telomeres on brain structure and function over the life span and named it as the "Telomere Brain Axis". In this context, if the authors could measure telomere length before and after lifestyle intervention, this will give a strong biomarker utility and value addition for the lifestyle modification benefits.

2) Authors should also consider measuring BDNF levels before and after lifestyle intervention.

Reviewer #2 (Public Review):

In this study, Levakov et al. investigated brain age based on resting-state functional connectivity (RSFC) in a group of obese participants following an 18-month lifestyle intervention. The study benefits from various sophisticated measurements of overall health, including body MRI and blood biomarkers. Although the data is leveraged from a solid randomized control set-up, the lack of control groups in the current study means that the results cannot be attributed to the lifestyle intervention with certainty. However, the study does show a relationship between general weight loss and RSFC-based brain age estimations over the course of the intervention. While this may represent an important contribution to the literature, the RSFC-based brain age prediction shows low model performance, making it difficult to interpret the validity of the derived estimates and the scale of change. The study would benefit from more rigorous analyses and a more critical discussion of findings. If incorporated, the study contributes to the growing field of literature indicating that weight-reduction in obese subjects may attenuate the detrimental effect of obesity on the brain.

The following points may be addressed to improve the study:

Brain age / model performance:

1. Figure 2: In the test set, the correlation between true and predicted age is 0.244. The fitted slope looks like it would be approximately 0.11 (55-50)/(80-35); change in y divided by change in x. This means that for a chronological age change of 12 months, the brain age changes by 0.11*12 = 1.3 months. I.e., due to the relatively poor model performance, an 80-year-old participant in the plot (fig 2) has a predicted age of ~55. Hence, although the age prediction step can generate a summary score for all the RSFC data, it can be difficult to interpret the meaning of these brain age estimates and the 'expected change' since the scale is in years.

2. In Figure 2 it could also help to add the x = y line to get a better overview of the prediction variance. The estimates are likely clustered around the mean/median age of the training dataset, and age is overestimated in younger subs and overestimated in older subs (usually referred to as "age bias"). It is important to inspect the data points here to understand what the estimates represent, i.e., is variation in RSFC potentially lost by wrapping the data in this summary measure, since the age prediction is not particularly accurate, and should age bias in the predictions be accounted for by adjusting the test data for the bias observed in the training data?

3. In Figure 3, some of the changes observed between time points are very large. For example, one subject with a chronological age of 62 shows a ten-year increase in brain age over 18 months. This change is twice as large as the full range of age variation in the brain age estimates (average brain age increases from 50 to 55 across the full chronological age span). This makes it difficult to interpret RSFC change in units of brain age. E.g., is it reasonable that a person's brain ages by ten years, either up or down, in 18 months? The colour scale goes from -12 years to 14 years, so some of the observed changes are 14 / 1.5 = 9 times larger than the actual time from baseline to follow-up.

- The questions above should be investigated and addressed in the context of potential challenges with using brain age as a marker (see e.g., https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25837, https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26144).

RSFC for age prediction:

1. Several studies show better age prediction accuracy with structural MRI features compared to RSFC. If the focus of the study is to use an accurate estimate of brain ageing rather than specifically looking at changes in RSFC, adding structural MRI data could be helpful.

2. If changes in RSFC is the main focus, using brain age adds a complicated layer that is not necessarily helpful. It could be easier to simply assess RSFC change from baseline to follow up, and correlate potential changes with changes in e.g., BMI.

The lack of control groups

1. If no control group data is available, it is important to clarify this in the manuscript, and evaluate which conclusions can and cannot be drawn based on the data and study design.

Reviewer #3 (Public Review):

The authors report on an interesting study that addresses the effects of a physical and dietary intervention on accelerated/decelerated brain ageing in obese individuals. More specifically, the authors examined potential associations between reductions in Body-Mass-Index (BMI) and a decrease in relative brain-predicted age after an 18-months period in N = 102 individuals. Brain age models were based on resting-state functional connectivity data. In addition to change in BMI, the authors also tested for associations between change in relative brain age and change in waist circumference, six liver markers, three glycemic markers, four lipid markers, and four MRI fat deposition measures. Moreover, change in self-reported consumption of food, stratified by categories such as 'processed food' and 'sweets and beverages', was tested for an association with change in relative brain age. Their analysis revealed no evidence for a general reduction in relative brain age in the tested sample. However, changes in BMI, as well as changes in several liver, glycemic, lipid, and fat-deposition markers showed significant covariation with changes in relative brain age. Three markers remained significant after additionally controlling for BMI, indicating an incremental contribution of these markers to change in relative brain age. Further associations were found for variables of subjective food consumption. The authors conclude that lifestyle interventions may have beneficial effects on brain aging.

Overall, the writing is concise and straightforward, and the langue and style are appropriate. A strength of the study is the longitudinal design that allows for addressing individual accelerations or decelerations in brain aging. Research on biological aging parameters has often been limited to cross-sectional analyses so inferences about intra-individual variation have frequently been drawn from inter-individual variation. The presented study allows, in fact, investigating within-person differences. Moreover, I very much appreciate that the authors seek to publish their code and materials online, although the respective GitHub project page did not appear to be set to 'public' at the time (error 404). Another strength of the study is that brain age models have been trained and validated in external samples. One further strength of this study is that it is based on a registered trial, which allows for the evaluation of the aims and motivation of the investigators and provides further insights into the primary and secondary outcomes measures (see the clinical trial identification code).

One weakness of the study is that no comparison between the active control group and the two experimental groups has been carried out, which would have enabled causal inferences on the potential effects of different types of interventions on changes in relative brain age. In this regard, it should also be noted that all groups underwent a lifestyle intervention. Hence, from an experimenter's perspective, it is problematic to conclude that lifestyle interventions may modulate brain age, given the lack of a control group without lifestyle intervention. This issue is fueled by the study title, which suggests a strong focus on the effects of lifestyle intervention. Technically, however, this study rather constitutes an investigation of the effects of successful weight loss/body fat reduction on brain age among participants who have taken part in a lifestyle intervention. In keeping with this, the provided information on the main effect of time on brain age is scarce, essentially limited to a sign test comparing the proportions of participants with an increase vs. decrease in relative brain age. Interestingly, this analysis did not suggest that the proportion of participants who benefit from the intervention (regarding brain age) significantly exceeds the number of participants who do not benefit. So strictly speaking, the data rather indicates that it's not the lifestyle intervention per sé that contributes to changes in brain age, but successful weight loss/body fat reduction. In sum, I feel that the authors' claims on the effects of the intervention cannot be underscored very well given the lack of a control group without lifestyle intervention.

Another major weakness is that no rationale is provided for why the authors use functional connectivity data instead of structural scans for their age estimation models. This gets even more evident in view of the relatively low prediction accuracies achieved in both the validation and test sets. My notion of the literature is that the vast majority of studies in this field implicate brain age models that were trained on structural MRI data, and these models have achieved way higher prediction accuracies. Along with the missing rationale, I feel that the low model performances require some more elaboration in the discussion section. To be clear, low prediction accuracies may be seen as a study result and, as such, they should not be considered as a quality criterion of the study. Nevertheless, the choice of functional MRI data and the relevance of the achieved model performances for subsequent association analysis needs to be addressed more thoroughly.

Reviewer #1 (Public Review):

In the paper, the authors illustrated a novel method for Electrolytic Lesioning through a microelectronics array. This novel lesioning technique is able to perform long-term micro-scale local lesions with a fine spatial resolution (mm). In addition, it allows a direct comparison of population neural activity patterns before and after the lesions using electrophysiology. This new technique addresses a recent challenge in the field and provides a precious opportunity to study the natural reorganization/recovery at the neuronal population level after long-term lesions. It will help discover new causal insights investigating the neural circuits controlling behavior.

Several minor concerns are summarized below:

It was not always clear what the lesion size was. This information is important for future applications, for example, in the visual cortex, where neurons are organized in retinotopy patterns.

It would be helpful if the author could add some discussion about whether and how this method could be used in other types of array/multi-contact electrodes, such as passive neuropixels, S-probes, and so on. In addition, though an op-amp was used in the design, it would still be helpful if the author could provide a recommended range for the impedance of the electrodes.

Reviewer #2 (Public Review):

This work by Bray et al. presented a customized way to induce small electrolytic lesions in the brain using chronically implanted intracortical multielectrode arrays. This type of lesioning technique has the benefit of high spatial precision and low surgical complexity while allowing simultaneous electrophysiology recording before, during, and after the lesion induction. The authors have validated this lesioning method with a Utah array, both ex vivo and in vivo using pig models and awake-behaving rhesus macaques. Given its precision in controlling the lesion size, location, and compatibility with multiple animal models and cortical areas, the authors believe this method can be used to study cortical circuits in the presence of targeted neuronal inactivation or injury and to establish causal relationships before behavior and cortical activity.

Strengths:

Great presentation of design considerations that addressed the gaps of current lesioning and neuronal inactivation methods, especially the cross-compatibility that allows this method to be used across different cortical regions and in different animal models, making it easy to be adopted into a variety of electrophysiology studies.

This method can induce lesions that are highly precise and repeatable in size and location, allowing for robust investigation of neuronal circuit function. When combined with the ability to record without disruption both in the acute and chronic phase after lesioning, this would create a great tool to study neural adaptation and reorganization.

The customized current source is simple, low-cost, yet effective in delivering precise, controllable current for electrolytic lesioning, and thus easy to adopt for a range of neuroscience applications.

Extensive ex vivo testing and validation were performed before moving into in vivo and eventually nonhuman primate (NHP) experiments, successfully reducing animal use.

Weaknesses:

In many of the figures, it is not clear what is shown and the analysis techniques are not well described.

The flexibility of lesioning/termination location is limited to the implantation site of the multielectrode array, and thus less flexible compared to some of the other termination methods outlined in Appendix 2.

Although the extent of the damage created through the Utah array will vary based on anatomical structures, it is unclear what is the range of lesion volumes that can be created with this method, given a parameter set. It was also mentioned that they performed a non-exhaustive parameter search for the applied current amplitude and duration (Table S1/S2) to generate the most suitable lesion size but did not present the resulting lesion sizes from these parameter sets listed. Moreover, there's a lack of histological data suggesting that the lesion size is precise and repeatable given the same current duration/amplitude, at the same location.

It is unclear what type of behavioral deficits can result from an electrolytic lesion this size and type (~3 mm in diameter) in rhesus macaques, as the extent of the neuronal loss within the damaged parenchyma can be different from past lesioning studies.

The lesioning procedure was performed in Monkey F while sedated, but no data was presented for Monkey F in terms of lesioning parameters, lesion size, recorded electrophysiology, histological, or behavioral outcomes. It is also unclear if Monkey F was in a terminal study.

As an inactivation method, the electrophysiology recording in Figure 5 only showed a change in pairwise comparisons of clustered action potential waveforms at each electrode (%match) but not a direct measure of neuronal pre and post-lesioning. More evidence is needed to suggest robust neuronal inactivation or termination in rhesus macaques after electrolytic lesioning. Some examples of this can be showing the number of spike clusters identified each day, as well as analyzing local field potential and multi-unit activity.

The advantages over recently developed lesioning techniques are not clear and are not discussed.

There is a lack of quantitative histological analysis of the change in neuronal morphology and loss.

There is a lack of histology data across animals and on the reliability of their lesioning techniques across animals and experiments.

There is a lack of data on changes in cortical layers and structures across the lesioning and non-lesioning electrodes.

Reviewer #1 (Public Review):

This paper presents a highly compelling and novel hypothesis for how the brain could generate signals to guide navigation towards remembered goals. Under this hypothesis, which the authors call "Endotaxis", the brain co-opts its ancient ability to navigate up odor gradients (chemotaxis) by generating a "virtual odor" that grows stronger the closer the animal is to a goal location. This idea is compelling from an evolutionary perspective and a mechanistic perspective. The paper is well-written and delightful to read.

The authors develop a detailed model of how the brain may perform "Endotaxis", using a variety of interconnected cell types (point, map, and goal cells) to inform the chemotaxis system. They tested the ability of this model to navigate in several state spaces, representing both physical mazes and abstract cognitive tasks. The Endotaxis model performed reasonably well across different environments and different types of goals.

The authors further tested the model using parameter sweeps and discovered a critical level of network gain, beyond which task performance drops. This critical level approximately matched analytical derivations.

My main concern with this paper is that the analysis of the critical gain value (gamma_c) is incomplete, making the implications of these analyses unclear. There are several different reasonable ways in which the Endotaxis map cell representations might be normalized, which I suspect may lead to different results. Specifically, the recurrent connections between map cells may either be an adjacency matrix, or a normalized transition matrix. In the current submission, the recurrent connections are an un-normalized adjacency matrix. In a previous preprint version of the Endotaxis manuscript, the recurrent connections between the map cells were learned using Oja's rule, which results in a normalized state-transition matrix (see "Appendix 5: Endotaxis model and the successor representation" in "Neural learning rules for generating flexible predictions and computing the successor representation", your reference 17). The authors state "In summary, this sensitivity analysis shows that the optimal parameter set for endotaxis does depend on the environment". Is this statement, and the other conclusions of the sensitivity analysis, still true if the learned recurrent connections are a properly normalized state-transition matrix?

Overall, this paper provides a very compelling model for how neural circuits may have evolved the ability to navigate towards remembered goals, using ancient chemotaxis circuits.

This framework will likely be very important for understanding how the hippocampus (and other memory/navigation-related circuits) interfaces with other processes in the brain, giving rise to memory-guided behavior.

Reviewer #2 (Public Review):

The manuscript presents a computational model of how an organism might learn a map of the structure of its environment and the location of valuable resources through synaptic plasticity, and how this map could subsequently be used for goal-directed navigation.

The model is composed of 'map cells', which learn the structure of the environment in their recurrent connections, and 'goal-cell' which stores the location of valued resources with respect to the map cell population. Each map cell corresponds to a particular location in the environment due to receiving external excitatory input at this location. The synaptic plasticity rule between map cells potentiates synapses when activity above a specified threshold at the pre-synaptic neuron is followed by above-threshold activity at the post-synaptic neuron. The threshold is set such that map neurons are only driven above this plasticity threshold by the external excitatory input, causing synapses to only be potentiated between a pair of map neurons when the organism moves directly between the locations they represent. This causes the weight matrix between the map neurons to learn the adjacency for the graph of locations in the environment, i.e. after learning the synaptic weight matrix matches the environment's adjacency matrix. Recurrent activity in the map neuron population then causes a bump of activity centred on the current location, which drops off exponentially with the diffusion distance on the graph. Each goal cell receives input from the map cells, and also from a 'resource cell' whose activity indicates the presence or absence of a given values resource at the current location. Synaptic plasticity potentiates map-cell to goal-cell synapses in proportion to the activity of the map cells at time points when the resource cell is active. This causes goal cell activity to increase when the activity of the map cell population is similar to the activity where the resource was obtained. The upshot of all this is that after learning the activity of goal cells decreases exponentially with the diffusion distance from the corresponding goal location. The organism can therefore navigate to a given goal by doing gradient ascent on the activity of the corresponding goal cell. The process of evaluating these gradients and using them to select actions is not modelled explicitly, but the authors point to the similarity of this mechanism to chemotaxis (ascending a gradient of odour concentration to reach the odour source), and the widespread capacity for chemotaxis in the animal kingdom, to argue for its biological plausibility.

The ideas are interesting and the presentation in the manuscript is generally clear. The two principle limitations of the manuscript are: i) Many of the ideas that the model implements have been explored in previous work. ii) The mapping of the circuit model onto real biological systems is pretty speculative, particularly with respect to the cerebellum.

Regarding the novelty of the work, the idea of flexibly navigating to goals by descending distance gradients dates back to at least Kaelbling (Learning to achieve goals, IJCAI, 1993), and is closely related to both the successor representation (cited in manuscript) and Linear Markov Decision Processes (LMDPs) (Piray and Daw, 2021, https://doi.org/10.1038/s41467-021-25123-3, Todorov, 2009 https://doi.org/10.1073/pnas.0710743106). The specific proposal of navigating to goals by doing gradient descent on diffusion distances, computed as powers of the adjacency matrix, is explored in Baram et al. 2018 (https://doi.org/10.1101/421461), and the idea that recurrent neural networks whose weights are the adjacency matrix can compute diffusion distances are explored in Fang et al. 2022 (https://doi.org/10.1101/2022.05.18.492543). Similar ideas about route planning using the spread of recurrent activity are also explored in Corneil and Gerstner (2015, cited in manuscript). Further exploration of this space of ideas is no bad thing, but it is important to be clear where prior literature has proposed closely related ideas.

Regarding whether the proposed circuit model might plausibly map onto a real biological system, I will focus on the mammalian brain as I don't know the relevant insect literature. It was not completely clear to me how the authors think their model corresponds to mammalian brain circuits. When they initially discuss brain circuits they point to the cerebellum as a plausible candidate structure (lines 520-546). Though the correspondence between cerebellar and model cell types is not very clearly outlined, my understanding is they propose that cerebellar granule cells are the 'map-cells' and Purkinje cells are the 'goal-cells'. I'm no cerebellum expert, but my understanding is that the granule cells do not have recurrent excitatory connections needed by the map cells. I am also not aware of reports of place-field-like firing in these cell populations that would be predicted by this correspondence. If the authors think the cerebellum is the substrate for the proposed mechanism they should clearly outline the proposed correspondence between cerebellar and model cell types and support the argument with reference to the circuit architecture, firing properties, lesion studies, etc.

The authors also discuss the possibility that the hippocampal formation might implement the proposed model, though confusingly they state 'we do not presume that endotaxis is localized to that structure' (line 564). A correspondence with the hippocampus appears more plausible than the cerebellum, given the spatial tuning properties of hippocampal cells, and the profound effect of lesions on navigation behaviours. When discussing the possible relationship of the model to hippocampal circuits it would be useful to address internally generated sequential activity in the hippocampus. During active navigation, and when animals exhibit vicarious trial and error at decision points, internally generated sequential activity of hippocampal place cells appears to explore different possible routes ahead of the animal (Kay et al. 2020, https://doi.org/10.1016/j.cell.2020.01.014, Reddish 2016, https://doi.org/10.1038/nrn.2015.30). Given the emphasis the model places on sampling possible future locations to evaluate goal-distance gradients, this seems highly relevant. Also, given the strong emphasis the authors place on the relationship of their model to chemotaxis/odour-guided navigation, it would be useful to discuss brain circuits involved in chemotaxis, and whether/how these circuits relate to those involved in goal-directed navigation, and the proposed model.

Finally, it would be useful to clarify two aspects of the behaviour of the proposed algorithm:

1) When discussing the relationship of the model to the successor representation (lines 620-627), the authors emphasise that learning in the model is independent of the policy followed by the agent during learning, while the successor representation is policy dependent. The policy independence of the model is achieved by making the synapses between map cells binary (0 or 1 weight) and setting them to 1 following a single transition between two locations. This makes the model unsuitable for learning the structure of graphs with probabilistic transitions, e.g. it would not behave adaptively in the widely used two-step task (Daw et al. 2011, https://doi.org/10.1016/j.neuron.2011.02.027) as it would fail to differentiate between common and rare transitions. This limitation should be made clear and is particularly relevant to claims that the model can handle cognitive tasks in general. It is also worth noting that there are algorithms that are closely related to the successor representation, but which learn about the structure of the environment independent of the subjects policy, e.g. the work of Kaelbling which learns shortest path distances, and the default representation in the work of Piray and Daw (both referenced above). Both these approaches handle probabilistic transition structures.

2) As the model evaluates distances using powers of adjacency matrix, the resulting distances are diffusion distances not shortest path distances. Though diffusion and shortest path distances are usually closely correlated, they can differ systematically for some graphs (see Baram et al. cited above).

Reviewer #3 (Public Review):

This paper argues that it has developed an algorithm conceptually related to chemotaxis that provides a general mechanism for goal-directed behaviour in a biologically plausible neural form.

The method depends on substantial simplifying assumptions. The simulated animal effectively moves through an environment consisting of discrete locations and can reliably detect when it is in each location. Whenever it moves from one location to an adjacent location, it perfectly learns the connectivity between these two locations (changes the value in an adjacency matrix to 1). This creates a graph of connections that reflects the explored environment. In this graph, the current location gets input activation and this spreads to all connected nodes multiplied by a constant decay (adjusted to the branching number of the graph) so that as the number of connection steps increases the activation decreases. Some locations will be marked as goals through experiencing a resource of a specific identity there, and subsequently will be activated by an amount proportional to their distance in the graph from the current location, i.e., their activation will increase if the agent moves a step closer and decrease if it moves a step further away. Hence by making such exploratory movements, the animal can decide which way to move to obtain a specified goal.

I note here that it was not clear what purpose, other than increasing the effective range of activation, is served by having the goal input weights set based on the activation levels when the goal is obtained. As demonstrated in the homing behaviour, it is sufficient to just have a goal connected to a single location for the mechanism to work (i.e., the activation at that location increases if the animal takes a step closer to it); and as demonstrated by adding a new graph connection, goal activation is immediately altered in an appropriate way to exploit a new shortcut, without the goal weights corresponding to this graph change needing to be relearnt.

Given the abstractions introduced, it is clear that the biological task here has been reduced to the general problem of calculating the shortest path in a graph. That is, no real-world complications such as how to reliably recognise the same location when deciding that a new node should be introduced for a new location, or how to reliably execute movements between locations are addressed. Noise is only introduced as a 1% variability in the goal signal. It is therefore surprising that the main text provides almost no discussion of the conceptual relationship of this work to decades of previous work in calculating the shortest path in graphs, including a wide range of neural- and hardware-based algorithms, many of which have been presented in the context of brain circuits.

The connection to this work is briefly made in appendix A.1, where it is argued that the shortest path distance between two nodes in a directed graph can be calculated from equation 15, which depends only on the adjacency matrix and the decay parameter (provided the latter falls below a given value). It is not clear from the presentation whether this is a novel result. No direct reference is given for the derivation so I assume it is novel. But if this is a previously unknown solution to the general problem it deserves to be much more strongly featured and either way it needs to be appropriately set in the context of previous work.

Once this principle is grasped, the added value of the simulated results is somewhat limited. These show: 1) in practical terms, the spreading signal travels further for a smaller decay but becomes erratic as the decay parameter (map neuron gain) approaches its theoretical upper bound and decreases below noise levels beyond a certain distance. Both follow the theory. 2) that different graph structures can be acquired and used to approach goal locations (not surprising) .3) that simultaneous learning and exploitation of the graph only minimally affects the performance over starting with perfect knowledge of the graph. 4) that the parameters interact in expected ways. It might have been more impactful to explore whether the parameters could be dynamically tuned, based on the overall graph activity.

Perhaps the most biologically interesting aspect of the work is to demonstrate the effectiveness, for flexible behaviour, of keeping separate the latent learning of environmental structure and the association of specific environmental states to goals or values. This contrasts (as the authors discuss) with the standard reinforcement learning approach, for example, that tries to learn the value of states that lead to reward. Examples of flexibility include the homing behaviour (a goal state is learned before any of the map is learned) and the patrolling behaviour (a goal cell that monitors all states for how recently they were visited). It is also interesting to link the mechanism of exploration of neighbouring states to observed scanning behaviours in navigating animals.

The mapping to brain circuits is less convincing. Specifically, for the analogy to the mushroom body, it is not clear what connectivity (in the MB) is supposed to underlie the graph structure which is crucial to the whole concept. Is it assumed that Kenyon cell connections perform the activation spreading function and that these connections are sufficiently adaptable to rapidly learn the adjacency matrix? Is there any evidence for this? As discussed above, the possibility that an algorithm like 'endotaxis' could explain how the rodent place cell system could support trajectory planning has already been explored in previous work so it is not clear what additional insight is gained from the current model.

Reviewer #1 (Public Review):

stdpopsim is an existing, community-driven resource to support population genetics simulations across multiple species. This paper describes improvements and extensions to this resource and discusses various considerations of relevance to chromosome-scale evolutionary simulations. As such, the paper does not analyse data or present new results but rather serves as a general and useful guide for anyone interested in using the stdpopsim resource or in population genetics simulations in general.

Two new features in stdpopsim are described, which expand the types of evolutionary processes that can be simulated. First, the authors describe the addition of the ability to simulate non-crossover recombination events, i.e. gene conversion, in addition to standard crossover recombination. This will allow for simulations that come closer to the actual recombination processes occurring in many species. Second, the authors mention how genome annotations can now be incorporated into the simulations, to allow different processes to apply to different parts of the genome - however, the authors note that this addition will be further detailed in a separate, future publication. These additions to stdpopsim will certainly be useful to many users and represent a step forward in the degree of ambition for realistic population genetics simulations.

The paper also describes the expansion of the community-curated catalog of pre-defined, ready-to-use simulation set-ups for various species, from the previous 6 to 21 species (though not all new species have demographic models implemented, some have just population genetic parameters such as mutation rates and generation times). For each species, an attempt was made to implement parameters and simulations that are as realistic as possible with respect to what's known about the evolutionary history of that species, using only information that can be traced to the published literature. This process by which this was done appears quite rigorous and includes a quality-control process involving two people. Two examples are given, for Anopheles gambiae and Bos taurus. The detailed discussion of how various population genetic and demographic parameters were extracted from the literature for these two species usefully highlights the numerous non-trivial steps involved and showcases the great deal of care that underlies the stdpopsim resource.

The paper is clearly written and well-referenced, and I have no technical or conceptual concerns. The paper will be useful to anyone interested in population genetics simulations, and will hopefully serve as an inspiration for the broader effort of making simulations increasingly more realistic and flexible, while at the same time trying to make them accessible not just to a small number of experts.

Reviewer #2 (Public Review):

Lauterbur et al. present a description of recent additions to the stdpopsim simulation software for generating whole-genome sequences under population genetic models, as well as detailed general guidelines and best practices for implementing realistic simulations within stdpopsim and other simulation software. Such realistic simulations are critical for understanding patterns in genetic variation expected under diverse processes for study organisms, training simulation-intensive models (e.g., machine learning and approximate Bayesian computation) to make predictions about factors shaping observed genetic variation, and for generating null distributions for testing hypotheses about evolutionary phenomena. However, realistic population genomic simulations can be challenging for those who have never implemented such models, particularly when different evolutionary parameters are taken from a variety of literature sources. Importantly, the goal of the authors is to expand the inclusivity of the field of population genomic simulation, by empowering investigators, regardless of model or non-model study system, to ultimately be able to effectively test hypotheses, make predictions, and learn about processes from simulated genomic variation. Continued expansion of the stdpopsim software is likely to have a significant impact on the evolutionary genomics community.

Strengths:

This work details an expansion from 6 to 21 species to gain a greater breadth of simulation capacity across the tree of life. Due to the nature of some of the species added, the authors implemented finite-site substitution models allowing for more than two allelic states at loci, permitting proper simulations of organisms with fast mutation rates, small genomes, or large effect sizes. Moreover, related to some of the newly added species, the authors incorporated a mechanism for simulating non-crossover recombination, such as gene conversion and horizontal gene transfer between individuals. The authors also added the ability to annotate and model coding genomic regions.

In addition to these added software features, the authors detail guidelines and best practices for implementing realistic population genetic simulations at the genome-scale, including encouraging and discussing the importance of code review, as well as highlighting the sufficient parameters for simulation: chromosome level assembly, mean mutation rate, mean recombination rate or recombination map if available, effective size or more realistic demographic model if available, and mean generation time. Much of these best practices are commonly followed by population genetic modelers, but new researchers in the field seeking to simulate data under population genetic models may be unfamiliar with these practices, making their clear enumeration (as done in this work) highly valuable for a broad audience. Moreover, the mechanisms for dealing with issues of missing parameters discussed in this work are particularly useful, as more often than not, estimates of certain model parameters may not be readily available from the literature for a given study system.

Weaknesses:

An important update to the stdpopsim software is the capacity for researchers to annotate coding regions of the genome, permitting distributions of fitness effects and linked selection to be modeled. However, though this novel feature expands the breadth of processes that can be evaluated as well as is applicable to all species within the stdpopsim framework, the authors do not provide significant detail regarding this feature, stating that they will provide more details about it in a forthcoming publication. Compared to this feature, the additions of extra species, finite-site substitution models, and non-crossover recombination are more specialized updates to the software.

When it comes to simulating realistic genomic data, the authors clearly lay out that parameters obtained from the literature must be compatible, such as the same recombination and mutation rates used to infer a demographic history should also be used within stdpopsim if employing that demographic history for simulation. This is a highly important point, which is often overlooked. However, it is also important that readers understand that depending on the method used to estimate the demographic history, different demographic models within stdpopsim may not reproduce certain patterns of genetic variation well. The authors do touch on this a bit, providing the example that a constant size demographic history will be unable to capture variation expected from recent size changes (e.g., excess of low-frequency alleles). However, depending on the data used to estimate a demographic history, certain types of variation may be unreliably modeled (Biechman et al. 2017; G3, 7:3605-3620). For example, if a site frequency spectrum method was used to estimate a demographic history, then the simulations under this model from stdpopsim may not recapitulate the haplotype structure well in the observed species. Similarly, if a method such as PSMC applied to a single diploid genome was used to estimate a demographic history, then the simulations under this model from stdpopsim may not recapitulate the site frequency spectrum well in the observed species. Though the authors indicate that citations are given to each demographic model and model parameter for each species, this may not be sufficient for a novice researcher in this field to understand what forms of genomic variation the models may be capable of reliably producing. A potential worry is that the inclusion of a species within stdpopsim may serve as an endorsement to users regarding the available simulation models (though I understand this is not the case by the authors), and it would be helpful if users and readers were guided on the type of variation the models should be able to reliably reproduce for each species and demographic history available for each species.

Reviewer #3 (Public Review):

Lauterbur et al. present an expansion of the whole-genome evolution simulation software "stdpopsim", which includes new features of the simulator itself, and 15 new species in their catalog of demographic models and genetic parameters (which previously had 6 species). The list of new species includes mostly animals (12), but also one species of plant, one of algae, and one of bacteria. While only five of the new animal species (and none of the other organisms) have a demographic model described in the catalog, those species showcase a variety of demographic models (e.g. extreme inbreeding of cattle). The authors describe in detail how to go about gathering genetic and demographic parameters from the literature, which is helpful for others aiming to add new species and demographic models to the stdpopsim catalog. This part of the paper is the most widely relevant not only for stdpopsim users but for any researcher performing population genomics simulations. This work is a concrete contribution towards increasing the number of users of population genomic simulations and improving reproducibility in research that uses this type of simulations.

Reviewer #1 (Public Review):

Sun and colleagues outline structural and mechanistic studies of the bacterial adhesin PrgB, an atypical microbial cell surface-anchored polypeptide that binds DNA. The manuscript includes a crystal structure of the Ig-like domains of PrgB, cryo-EM structures of the majority of the intact polypeptide in DNA-bound and free forms, and an assessment of the phenotypes of E. faecalis strains expressing various PrgB mutants.

Generally, the study has been conducted with a good level of rigor, and there is consistency in the findings. However, I do have some specific technical concerns relating to the study. Although the PX work has been expertly undertaken, the Cryo-EM structures reported are both at ~10-angstrom resolution. Visual inspection indicates that the positioning of the PrgB domains into the EM envelopes is somewhat questionable and this needs to be addressed. The narrative of the manuscript very much hinges on this being correct. In addition, wrt the PrgB mutant studies, it could be that the loss of function observed in specific mutants is simply a consequence of mutation-induced misfolding of those polypeptides. Experimental evidence supporting the direct interaction between the PAD and the stalk domains in PrgB is also lacking.

Reviewer #2 (Public Review):

Having previously solved the X-ray crystallographic structure of the polymer adhesin domain (PAD) of PrgB from E. faecalis, the authors looked to build on that work by crystallizing a nearly full-length construct of PrgB. Though they were successful in their crystallization endeavors, the crystal contained only what was previously thought to be two domains with RGD motifs. The authors' high-resolution structure shows that in fact the C-terminal portion of PrgB is made up of four immunoglobulin-like domains. The authors then set out to collect single-particle cryoEM data in a bid to obtain a full-length structure of PrgB, both in the presence and absence of ssDNA. The authors were only able to obtain quite low-resolution data, which they fit their crystal structures into. The authors then used these structures to inform the design of novel deletion mutants and point mutations, as well as to rationalize years of phenotypic data from other published mutants.

The X-ray crystallographic structure is beautiful and in combination with their in vivo data allowed them to propose a model where PrgB positions cells at an appropriate distance for conjugation. The cryoEM data are not convincing in their current state, and I, therefore, don't believe that their model of the immunoglobulin domains acting to protect the PAD domain of PrgB from PrgA is well supported. Perhaps there are 2D classes or other data that make a case for the fit of the crystal structures into the cryoEM volumes, but without a PAD deletion or perhaps a dataset including a PAD-specific antibody, I don't feel the fit is supported.

The in vivo experiments appear to be done well and the authors' discovery that the Ser-Asn-Glu is not important for generalized aggregation but has an additional yet unknown role in conjugation and biofilm formation is exciting and well supported by their data.

Reviewer #1 (Public Review):

In this study, Dominici et. al. show that small molecule inhibition of Type I PRMTs in muscle stem cells (MSCs) can result in the expansion of this cell type in vitro, solving a major limitation in the field. Importantly, once the inhibitor is removed these stem cell differentiate "normally". This advance will likely facilitate CRISPR-based screening approaches and stem cell engraftment therapy. Furthermore, they show that when a mouse model of Duchenne muscular dystrophy is treated with these same inhibitors these mice rather rapidly gain grip strength, demonstrating the therapeutic value of these findings.

Strengths:

- Previous studies from the same group have shown that the conditional ablation of PRMT1 in MSCs results in the expansion of this cell type, but this expanded PRMT1-null MSC pool cannot terminate the myogenic differentiation program. This raises the question of whether PRMT1 small molecule inhibition of MSCs will also facilitate the expansion of these cells, and if the removal of the inhibitor after expansion will result in a large functional pool of MSCs, which could then be used for both in vitro and in vivo studies.

- Using a combination of muscle fiber culture, myoblast culture and single cell RNA-seq, this is indeed what they show.

- They also perform two types of in vivo experiments to validate their cell culture findings; 1) MSCs expanded under the treatment of MS023 were washed clean of the inhibitor and engrafted into the tibialis anterior muscle. These cells were marked with GFP to allow efficient tracking. Mice receiving the MS023-treated MSCs produced more than double the mature GFP+ muscle fibers than cells treated with DMSO. 2) A mouse model of Duchenne muscular dystrophy displayed grip strength improvement after just one treatment of MS023.

- MS023 is a Type I PRMT inhibitor and thus can also target CARM1. CARM1 has been implicated in MSC function by the Rudnicki group. Importantly, they exclude a role for CARM1 in the expansion of MSC cell number by treatment with a very specific CARM1 inhibitor, TP064. Thus, indicating that PRMT1 inhibition is likely the main driver of this expansion phenotype.

Weaknesses:

- Very few weaknesses.

- The in vivo efficacy of MS023 does not seem to be very great. The mice treated with MS023 display a very small reduction in ADMA levels and a small increase in SDMA levels (Fig S6A).

Reviewer #2 (Public Review):

In this manuscript, Dominici et al. aim to determine whether the reversible inhibition of the type I protein arginine methyltransferases (PRMT) would maintain the stemness of muscle stem cells in culture and enable subsequent regenerative capacities. They demonstrate that the type I PRMT inhibitor MS023 enhances self-renewal and in vitro expansion of muscle stemm cells isolated from mice. Using a very rigorous single cell RNA-sequencing approach, they further demonstrate that a distinct sub-populations of cells emerge under type I PRMT inhibition and that these cells entered the differentiation program more efficiently. Moreover, they revealed a shift in metabolism in these cells, which they confirmed in vitro. Finally, they demonstrate that MS023 enhances muscle stem cells engraftment in vivo and that the direct injection of MS023 increases muscle strength in a mice model of Duchenne muscular dystrophy.<br /> This study will have a great impact in the field of stem cells and offer potential therapeutic avenues for diseases such as Duchenne muscular dystrophy.

Two weaknesses are noted which lie in overstatements of the findings. There are six type I PRMTs (PRMT1, 2, 3, 6, 8, and CARM1), all of which are inhibited by MS023. While the authors demonstrate that their observations are not due to the inhibition of CARM1, they do not demonstrate that it is due to the inhibition of PRMT1, as they suggest.

Furthermore, this study suggests that the switch and elevated cellular metabolism in muscle stem cells due to MS023 enhanced self-renewal and engraftment capabilities but does not demonstrate this fact directly as stated.

Reviewer #3 (Public Review):

Dominici et al studied the effects of the type I PRMT inhibitor MS023 on skeletal muscle stem cells (MuSCs) and on muscle strength in dystrophin-deficient mdx mice. The authors observed an enhanced proliferative capacity of cultured MuSCs with an increase of Pax7+/MyoD- cells. The observations are more or less in line with previous studies of the same group, describing reduced differentiation but enhanced proliferation of MuSCs after genetic inactivation of Prmt1. scRNA-seq identified different subpopulations of MuSCs, showing a shift to increased Pax7 expression and elevated oxidative phosphorylation and glycolysis after treatment with MS023. Treatment of MuSC with MS023 during expansion in vitro enhanced engraftment of MuSCs and treatment of dystrophic mdx mice increased muscle strength.

Overall, the manuscript provides new insights into the beneficial effects of the type I PRMT inhibitor MS023 for skeletal muscle regeneration. The description of the MS023-induced transcriptional and metabolic changes in MuSC is interesting and the effects on MuSC transplantation and muscle strength are stunning. However, the proposed underlying mechanism, which is claimed to rely on the expansion of MuSC and 'reprograming' of MuSCs towards a "unique and previously uncharacterized identity" is not sufficiently supported. The extent of the description of scRNA-seq data is inappropriate. Some conclusions from the scRNA-seq data appear to be overinterpreted or are rather trivial. It remains completely unclear whether the MS023-stimulated increase of metabolic pathway activity (OXPHOS, glycolysis) plays any role for preserving stem cell properties of MuSC during expansion and improves engraftment. Additional functional and mechanistic studies are required to explore the underlying molecular processes. Furthermore, it remains completely unclear whether the acclaimed increase in grip and tetanic strength of mdx mice after MS023 treatment relies on enhanced expansion of MuSC mediated by PRMT1 inhibition.

Reviewer #1 (Public Review):

Using health insurance claims data (from 8M subjects), a retrospective propensity score matched cohort study was performed (450K in both groups) to quantify associations between biphosphonate (BP) use and COVID-19 related outcomes (COVID-19 diagnosis, testing and COVID-19 hospitalization. The observation periods were 1-1-2019 till 2-29-2020 for BP use and from 3-1-2020 and 6-30-2020 for the COVID endpoints. In primary and sensitivity analyses BP use was consistenyl associated with lower odds for COVID-19, testing and COVID-19 hospitalization.

The major strength of this study is the size of the study population, allowing a propensity-based matched-cohort study with 450K in both groups, with a sizeable number of COVID-19 related endpoints. Health insurance claims data were used with the intrinsic risk of some misclassification for exposure. In addition there probably is misclassification of endpoints as testing for COVID-19 was lmimited during the study period. Furthermore, the retrospective nature of the study includes the risk of residual confounding, which has been addressed - to some extent - by sensitivity analyses.

In all analyses there is a consistent finding that BP exposure is associated with reduced odds for COVID-19 related outcomes. The effect size is large, with high precision.

The authors extensively discuss the (many) potential limitations inherent to the study design and conclude that these findings warrant confirmation, preferably in intervention studies. If confirmed BP use could be a powerful adjunt in the prevention of infection and hospitalization due to COVID-19.

Reviewer #2 (Public Review):

The authors performed a retrospective cohort study using claims data to assess the causal relationship between bisphosphonate (BP) use and COVID-19 outcomes. They used propensity score matching to adjust for measured confounders. This is an interesting study and the authors performed several sensitivity analyses to assess the robustness of their findings. The authors are properly cautious in the interpretation of their results and justly call for randomized controlled trials to confirm a causal relationship. However, there are some methodological limitations that are not properly addressed yet.

Strengths of the paper include:<br /> - Availability of a large dataset.<br /> - Using propensity score matching to adjust for confounding.<br /> - Sensitivity analyses to challenge key assumptions (although not all of them add value in my opinion, see specific comments)<br /> - Cautious interpretation of results, the authors are aware of the limitations of the study design.

Limitation of the paper are:<br /> - This is an observational study using register data. Therefore, the study is prone to residual confounding and information bias. The authors are well aware of that.<br /> - The authors adjusted for Carlson comorbidity index whereas they had individual comorbidity data available and a dataset large enough to adjust for each comorbidity separately.<br /> - The primary analysis violates the positivity assumption (a substantial part of the population had no indication for bisphosphonates; see specific comments). I feel that one of the sensitivity analyses 1 or 2 would be more suited for a primary analysis.<br /> - Some of the other sensitivity analyses have underlying assumptions that are not discussed and do not necessarily hold (see specific comments).

In its current form the limitations hinder a good interpretation of the results and, therefore, in my opinion do not support the conclusion of the paper.

The finding of a substantial risk reduction of (severe) COVID-19 in bisphosphonate users compared to non-users in this observational study may be of interest to other researchers considering to set up randomized controlled trials for evaluation of repurpose drugs for prevention of (severe) COVID-19.

Specific comments (in order of manuscript):

Methods:<br /> - Line 158: it is unclear how the authors dealt with patients who died during the follow-up period. The wording suggests they were excluded which would be inappropriate.<br /> - Why did the authors use CCI for propensity matching rather than the individual comorbid conditions? I presume using separate variables will improve the comparability of the cohorts. The authors discuss imbalances in comorbidities as a limitation but should rather have avoided this.<br /> - Line 301-10: it seems unnecesary to me to adjust for the given covariates while these were already used for propensity score matching (except comorbidities, but see previous comment). The manuscript doesn't give a rationale why did the authors choose for this 'double correction'.<br /> - In causal research a very important assumption is the 'positivity assumption', which means that none of the individuals has a probability of zero or one to be exposed. Including everyone would therefore not be appropriate. My suggestion is to include either all patients with an indication (based on diagnosis) or all that use an anti-osteoporosis (AOP) drug (or one as the primary and the other as the sensitivity analysis) instead of using these cohorts as sensitivity analyses. The choice should in my opinion be based on two aspects: whether it is likely that other AOP drugs have an effect on the COVID-19 outcomes and whether BP users are deemed to be more similar (in their risk of COVID-19 outcomes) to non-users or to other AOP drug users. Or alternatively, the authors might have discussed the positivity assumption and argue why this is not applicable to their primary analysis.<br /> - Sensitivity Analysis 3: Association of BP-use with Exploratory Negative Control Outcomes: what is the implicit assumption in this analysis? I think the assumption here is that any residual confounding would be of the same magnitude for these outcomes. But that depends on the strength of the association between the confounder and the outcome which needs not be the same. Here, risk avoiding behavior (social distancing) is the most obvious unmeasured confounder, which may not have a strong effect on other health outcomes. Also it is unclear to me why acute cholecystitis and acute pancreatitis-related inpatient/emergency-room were selected as negative controls. Do the authors have convincing evidence that BPs have no effect on these outcomes? Yet, if the authors believe that this is indeed a valid approach to measure residual confounding, I think the authors might have taken a step further and present ORs for BP → COVID-19 outcomes that are corrected for the unmeasured confounding. (e.g. if OR BP → COVID-19 is ~ 0.2 and OR BP → acute cholecystitis is ~ 0.5, then 'corrected' OR of BP → COVID-19 would be ~ 0.4.<br /> - Sensitivity Analysis 4: Association of BP-use with Exploratory Positive Control Outcomes: this doesn't help me be convinced of the lack of bias. If previous researchers suffered from residual confounding, the same type of mechanisms apply here. (It might still be valuable to replicate the previous findings, but not as a sensitivity analysis of the current study.)<br /> - Sensitivity Analysis 5: Association of Other Preventive Drugs with COVID-19-Related Outcomes: Same here as for sensitivity analysis 3: the assumption that the association of unmeasured confounders with other drugs is equally strong as for BPs. Authors should explicitly state the assumptions of the sensitivity analyses and argue why they are reasonable.

Results:<br /> - The data are clearly presented.<br /> - The C-statistic / ROC-AUC of the propensity model is missing.

Discussion:<br /> - When discussing other studies the authors reduce these results to 'did' or 'did not find an association'. Although commonly practiced, it doesn't justify the statistical uncertainty of both positive and negative findings. Instead I encourage the authors to include effect estimates and confidence intervals. This is particularly relevant for studies that are inconclusive (i.e. lower bound of confidence interval not excluding a clinically relevant reduction while upper bound not excluding a NULL-effect).<br /> - Line 1145 "These retrospective findings strongly suggest that BPs should be considered for prophylactic and/or therapeutic use in individuals at risk of SARS-CoV-2 infection." I agree for prophylactic use but do not see how the study results suggest anything for therapeutic use.<br /> - The authors should discuss the acceptability of using BPs as preventive treatment (long-term use in persons without osteoporosis or other indication for BPs). This is not my expertise but I reckon there will be little experience with long-term inhibiting osteoblasts in people with healthy bones. The authors should also discuss what prospective study design would be suitable and what sample size would be needed to demonstrate a reasonable reduction. (Say 50% accounting for some residual confounding being present in the current study.)<br /> - The authors should discuss the fact that confounders were based on registry data which is prone to misclassification. This can result in residual confounding.

Reviewer #1 (Public Review):

This is a welcome contribution investigating proteomics in different physiological muscle types in a particular murine (DHT) Ryr1 abnormality. This recapitulates a particular human clinical condition. It emerges with a comparative analysis of the expression not only of RyR1 protein but also of other functional proteins. The work emerges with insights into pathological mechanism of congenital myopathies linked to mutations in a range of other genes related to excitation contraction coupling.

Reviewer #2 (Public Review):

This study used tandem mass isobaric tags (TMT) and LC-MS/MS analyses to complete proteomic analyses of whole extensor digitorum longus (EDL), soleus, and extraocular muscles (EOM) excised from 3 month old male WT (n=5) and dHT (n=5) mice. The major strengths of the work include the comprehensive nature of the unbiased muscle proteome studies, validation of the experimental approach by confirming several well-known differences between fast and slow twitch muscles in the WT EDL and soleus proteome data, and the identification of distinct proteome changes and alterations in core ECC and SOCE complex stoichiometry in the three different muscles from dHT mice. The main limitation of this study is that the results are primarily descriptive in nature, and thus, do not provide mechanistic insight into how Ryr1 disease mutations lead to the muscle-specific changes observed in the EDL, soleus and EOM proteomes.

Results comparing fast twitch (EDL) and slow twitch (soleus) muscles from WT mice confirmed several known differences between the two muscle types (e.g. elevated type I myosin, slow troponin I/T/C isoforms, SERCA2, calsequestrin-2, and carbonic anhydrase 3 in soleus; elevated type IIb myosin, SERCA1, calsequestrin-1, collagen I, and parvalbumin in EDL), as well as an overall decrease in oxidoreductase activity associated proteins and increase in extracellular matrix proteins in EDL muscle. Relative levels of select proteins involved in muscle contraction, ECC, extracellular matrix, heat shock response, ribosomes, FK 506 binding, and calcium dependent kinase activity were are compared. Similar analyses between EOM/EDL and EOM/soleus muscles from WT mice were not conducted.

The authors next assessed changes in the EDL, soleus and EOM proteomes in muscles excised from dHT mice, which were previously shown to exhibit an early myopathy characterized by reductions in Ryr1 expression, muscle mass and specific force production. This analysis revealed that in addition to the expected decrease in Ryr1 levels in all three muscles, a large number of additional proteins were significantly increase/decreased altered in EDL (848 proteins), soleus (509 proteins), and EOM (677 proteins). Data in Fig. 3 indicate that more proteins were significantly upregulated than downregulated in all three dHT muscle groups. While a reactome pathway analysis for proteins changes observed in EDL is shown in Supplemental Figure 1, the authors do not fully discuss the nature of the proteins and corresponding pathways impacted in the other two muscle groups analyzed.

The authors conducted a targeted analysis of proteins involved in several select pathways known to be important for skeletal muscle (e.g. ECC proteins, contractile proteins, heat shock proteins, ribosomal proteins, FK506 binding proteins, calcium dependent protein kinases). Increases in some FK506 binding proteins were seen in EDL and EOM muscles of dHT mice, while increases in calcium dependent proteins kinases were observed in all three muscle groups. Overall, fewer protein changes were observed in soleus muscles of dHT mice, with most alterations impacting ECC and ribosomal proteins. Beyond the EDL reactome pathway analysis and author-selected protein analyses shown in Tables 2-4, the nature of the totality of proteins altered in each muscle group, the corresponding pathways involved, and the relative degree to which changes are conserved or unique across all three muscle groups analyzed are not fully evaluated or discussed.

The final part of this study used spiked-in labeled peptides in combination with parallel reaction-monitoring and high resolution TMT mass spectrometry to quantify several key proteins involved in coordinating the ECC (Ryr1 and Cacna1s) and SOCE (Stim1 and Orai1) processes. These analyses provide the first mass spectrometry-based quantification of the concentration (mol/kg) and stoichiometry (e.g. Ryr1/cacna1s, Stim1/Orai1, etc) of these proteins across the three different muscles in both WT and dHT mice. The results indicate that while the stoichiometry of the core ECC complex (Ryr1/Cacna1s ~0.6-0.7) is similar across all muscles in WT, this ratio is reduced in EDL and EOM (but not soleus) of dHT mice. Moreover, the stoichiometry of the core SOCE complex indicates that Orai1 levels are limiting in EDL and EOM muscle (Stim1/Orai1 ~25-50), while Orai1 protein was below detectable levels in soleus. Unlike the core ECC complex, core SOCE complex stoichiometry was unaltered in muscles of dHT mice. These findings have important implications regarding ECC and SOCE function in the three different muscle groups under both normal conditions and a mouse model of RYR1-related myopathy.

Reviewer #3 (Public Review):

The strength of this article is that the experiment performed was successfully validated by previously published results. However, it would be useful to determine whether changes in protein levels correlated with changes in mRNA levels and whether or not the protein present was functional, and whether Stac3 was in fact stoichiometrically depleted in relation to Cacna1s. The authors suggest that the change in RyR1 protein levels may have a knock-on effect on the levels of other proteins, which is a reasonable claim, but no experiments (such as using RNAi) were performed to confirm this. The authors also claim that an adaptive response exists to compensate for deleterious mutations, which is indeed well-established (see dosage compensation in x-linked disorders between XX women and XY men, for example), and their experiment is consistent with this finding but does not itself show this on the level of cells, tissues, or the RyR itself.

Minor concerns.<br /> 1) In the abstract, the authors stated that skeletal muscle is responsible for voluntary movement. It is also responsible for non-voluntary. The abstract needs to be refocused on the mutation and on what we learn from this study. Please avoid vague statements like "we provide important insights to the pathophysiological mechanisms..." mainly when the study is descriptive and not mechanistic.<br /> 2) The author should bring up the mutation name, location and phenotype early in the introduction. This reviewer also suggests that the authors refocus the introduction on the mutation location in the 3D RyR1 structure (available cryo-EM structure), if there is any nearby ligand binding site, protomers junction or any other known interacting protein partners. This will help the reader to understand how this mutation could be important for the channel's function.

Reviewer #1 (Public Review):

The authors present normative modeling results using both structural data and functional connectivity data to demonstrate the strength of normative modeling in investigations of group effects, classification tasks, and brain-behavioral modeling. The models are built across 3 large data sets and tested in a rigorous manner. The strengths of this work are in the clarity or presentation, the demonstration of the value of normative modeling, the availability of the models and code, and the statistical rigor supporting the results. The work will have a significant impact on the field in that such models (built in large data sets) can be applied to smaller studies of specific populations of interest, therefore, facilitating research on many populations in a statistically rigorous manner.

Reviewer #2 (Public Review):

This work provides a direct extension of the authors' previously published paper "Charting brain growth and aging at high spatial precision" (Rutherford et al. 2022), expanding their highly valuable existing repository of pre-trained normative models to now also include cortical thickness, surface area, and functional connectivity data.

Strengths<br /> Building on previously published and validated methodology, this work significantly expands an existing modelling toolbox with new data modalities, particularly functional connectivity measures.

Model comparisons show that deviation scores derived from normative models perform as well, or better than, raw data models across three different benchmarking tests (group differences, classification, regression). The authors clearly demonstrate the utility of deviation scores in the assessment of both group and individual differences.

All code, including pre-trained normative models, tutorials, and analysis scripts are available online and very well documented. In addition, the authors are promising to make an easy-to-use online portal available soon.

Weaknesses<br /> Although still an impressively large multi-site data set, the sample size of the functional data (N=22k) is considerably smaller than that of the structural data (N=58k) which implies higher uncertainty in the functional normative model estimates.

The scope of functional normative models computed and shared by the authors is limited to coarse parcellations (based on the Yeo-17 and Smith-10 atlases). High-dimensional functional normative models, for now, still belong to the realm of future work.

Interpretation of deviation scores in classification and prediction tasks is not straightforward. Unlike raw data models, these derived summary measures do not have biological or clinical meaning on their own and can only be interpreted with respect to a pre-defined set of reference data.

Reviewer #3 (Public Review):

This important study continues the development of normative models of neuroimaging-derived features initiated by themselves (Rutherford et al., 2022a) in two directions. First, the existing models - which were developed on structural imaging features - are complemented with features derived from functional networks. Second, these models are compared with the utilization of the features themselves in three different inference settings. Overall, the evaluation of the functional networks modeling yielded similar benchmarking metrics in agreement with their previous structural modeling. The study delivers strong evidence that normative models efficiently increased the statistical power in mass univariate group difference testing. The improvement in the other two inferential scenarios was less evident. However, normative modeling was not comparatively detrimental and should continue to be investigated.

The study showcases several major strengths:<br /> - The methodological approach is robustly supported by previous work and protocol definitions by the authors, mainly (Rutherford, 2022a; 2022b).<br /> - The intent of the manuscript is very clear, structured first with a confirmation of the soundness of their functional-networks model and second the "head-to-head" comparison (a term used in the abstract which effectively describes the aim) to alternative inference approaches.<br /> - The results of task 1 are very compelling. The other two tasks, while perhaps less robust, are definitely relevant to be part of the communication and help draw a more accurate picture of the role of normative models in years to come.<br /> - The manuscript is accompanied by a comprehensive set of tutorials, examples, documentation, and the sharing of code, models, and data. Sharing all these resources is a decisive effort toward research transparency that deserves full recognition as scientific scholarship.

As major weaknesses, I will speculate that some researchers could understand this work as incremental. Although there's continuity with the previous work of the authors (otherwise would be a weakness, in my opinion), my assessment is that the science in this manuscript should be considered new results and hence deserve independent communication.

Finally, I would like to highlight how normative modeling outperformed its "direct" (saving the removal of confounding factors) inference counterpart in task 1, providing solid evidence of the usefulness of normative models beyond the classical application in "easy" clinical decisions (I refer the readers to the manuscript, which elaborates on these aspects more appropriately and comprehensively).

Reviewer #1 (Public Review):

This manuscript describes a relatively novel approach to discovering combinations of herbal medications that may help modulate immune responses, and in turn help treat diseases such as cancer. The authors use breast plasma call mastitis as a disease in which they present results from a non-blinded clinical trial with modest results.

The main shortcomings are a lack of rigor around standardizing the control group given steroids versus the treatment group given the combinations of herbal medications. There needs to be a detailed statistical analysis of the comparison in tumor size, stage, invasiveness, etc. as well as consideration of confounding disease states (autoimmune disease, prior cancers, diabetes, etc.). While the results are interesting in that the use of herbal medications is often overlooked in Western medicine, the manuscript needs great detail in the clinical comparison in order to provide convincing evidence for an effect.

Reviewer #2 (Public Review):

The work is rather interesting and novel because for the first time, the authors employed knowledge graph, a cutting-edge technique in the domain of artificial intelligence, to identify a novel herbal drug combination for the treatment of PCM. The results of the clinical trial study clearly demonstrated that the drug combination is effective to ameliorate the symptoms of PCM patients and improve the general health status of the patients. Overall, the strategy of this manuscript may provide a paradigm for the design of drug combination towards many other human disorders.

Reviewer #3 (Public Review):

The manuscript presented the identification of an herbal drug combination via the approach of knowledge graph for the treatment of plasma cell mastitis (PCM), a breast inflammation with severe and intense clinical symptoms. The authors evaluated the efficacy of the herbal drug combination in clinical trial, which recruited 160 patients thus far (Trial number: NCT05530226). The clinical trial results showed that the herbal drug combination could significantly reduce the recurrence rate and reverse the clinical symptoms of PCM patients.

The manuscript provides strong evidence for the following,<br /> 1. The authors showed that, for the first time, knowledge graph is a useful approach for the identification of herbal drug combination towards plasma cell mastitis. This is novel because in the past, the design of formulae in TCM is solely based on the principle of 'syndrome differentiation'.<br /> 2. The herbal drug combination identified by knowledge graph can markedly suppress various inflammatory cytokines in serum and restore clinical symptoms of PCM patients.<br /> 3. The herbal drug combination could reduce the recurrence rate of PCM, a major obstacle for PCM treatment.

The major merit of the manuscript is that the authors introduced the concept of knowledge graph into the domain of herbal drugs or TCM. Namely, the authors designed a knowledge graph towards systematic immunity or immunotherapy based on massive data mining techniques. The authors successfully identified an herbal drug combination for PCM with the help of a scoring system. Moreover, the authors conducted a clinical trial study and the clinical data showed that the herbal drug combination holds great promise as an effective treatment for PCM. The weakness of the manuscript is that some details for the herbal drug combination and the clinical trial study are missing.

Reviewer #1 (Public Review):

In this work, the authors set out to use contact tracing and whole-genome sequencing to track the elimination of dog-mediated rabies in Pemba island, Tanzania. A major strength is the use of multiple data types in the analysis. A major limitation is the rudimentary health economics approach to make claims about the cost-effectiveness of different approaches. The work will likely have an impact on influencing the practical policies that can be implemented to target the elimination of dog-mediated rabies in other regions/contexts.

Reviewer #2 (Public Review):

In this paper, the authors illustrate how a One Health approach can strengthen our understanding of the dynamics of the spread and the control of rabies. This is done by analyzing multiple epidemiological and sequence data from both dogs and humans, on the island of Pemba. The joint analyses of these data make it possible to reconstruct the history of rabies introduction and circulation on the island and to quantify the impact of different control measures in particular the cost per death averted.

Data documenting rabies epidemics tend to be rare and of limited quality so the effort to collect these data and analyze them with state-of-the-art statistical techniques should be saluted.

Reviewer #3 (Public Review):

In the proposed paper, the authors use a combination of case data and genetic data to characterise the impact of a dog vaccine campaign on rabies transmission on Pemba island. This represents an impressive set of data to answer key questions linked to rabies control. It is rare to see a combination of detailed genetic and epidemiology data from the same disease system. Overall, I thought it was an impressive paper. My only major concerns were with the phylogenetic analyses.

The phylogenetic analyses were difficult to understand. The authors use a phylogenetic framework to estimate the underlying number of rabid dogs per outbreak (171 in the first outbreak and 140 in the second one), but it was unclear to me where the information was coming from. From the supplementary material, it seems the authors build transmission trees consistent with the phylogenies. However, these are reliant on (a) a serial interval and (b) a dispersal kernel. There is no reference as to what serial interval distribution was used and how it was calculated. Similarly, there is no information on the dispersal kernel, including what data was used to fit it. I suspect that the serial interval for rabies (and probably the dispersal kernel) has a long tail, which would lead to substantial uncertainty in the transmission chains, however, I could not see uncertainty in the outbreak sizes.

Reviewer #1 (Public Review):

The authors developed a new concept: Skeletal age, which is chronological age + years lost due to suffering a low-energy fracture.<br /> There seem to be conceptual problems with this concept: It is not known if the years lost are lost due to the fracture or co-morbidities. In addition, with the possible exception of zoledronate after hip fracture, we have no evidence that this increased risk of mortality can be changed with interventions. Furthermore, it is not clear why the authors think that patients and doctors will better understand the implications of older "skeletal age", on future fracture risk and the need for prevention, for example, the 10-year risk of MOF? Knowing that my bones are older than me, could make a patient feel even more fragile and afraid of being physically active. The treatment will reduce the risk of future fractures, but this study provides no information about the effect on mortality of preventing the subsequent fracture or the risk of mortality associated with recurrent fractures.

Introduction:<br /> The statement that treatment reduces the risk of dying, needs modification as the majority of clinical trials have not demonstrated reduced mortality with treatment.<br /> It is not clear how the skeletal age captures the risk of a future fracture. The other difference between the idea of "skeletal age" and for example "heart age" is that there are treatments available for heart disease that reduce the risk of mortality, as mentioned above this has not been shown consistently in clinical trials in osteoporosis.

Discussion:<br /> The prevalent comorbidities; cardiovascular diseases, cancer, and diabetes, suggest that fracture patients die from their comorbidities and not their fractures.<br /> The discussion should be more balanced as there is a number of clinical trials demonstrating reductions in vertebral and non-vertebral fractures without effect on mortality. There may be specific effects of zoledronate on mortality, but that has not been shown for the vast majority of treatments.<br /> It is not correct that FRAX does not take mortality into account? It does not tell you specifically how high the risk of dying and how high the risk of a fracture is but integrates the two. "Skeletal age" does not provide either information, it just tells you that your skeleton is older than your chronological age - most patients and doctors will not associate that with an increased risk of dying - only of frailty.<br /> The statement that zoledronate reduces the "skeletal age" by 3 years, has not been demonstrated and it is not clear how this can be demonstrated by the analysis reported here. As the reduced mortality has only been shown for the Horizon RFT, this cannot be inferred for other treatments and other fracture types.<br /> The information provided by the "skeletal age" is only that the fracture you already had took x years of your remaining lifetime. With the exception of perhaps zoledronate after hip fracture, we have no indication from clinical trials that the treatment of osteoporosis will change this.

Reviewer #2 (Public Review):

The paper of Tran et al. introduces the concept of 'skeletal age' as a means of conveying the combined risk of fracture and fracture-associated mortality for an individual. Skeletal age is defined as the sum of chronological age and the number of years of life lost associated with a fracture. Using the very comprehensive Danish national registry and employing Cox's proportional hazards model they estimated the hazard of mortality associated with a fracture. Skeletal age was estimated for each age and fracture site stratified by gender. The authors propose to replace the fracture probability with skeletal age for individualized fracture risk assessment.

Strengths of the study lie in the novelty of the concept of 'skeletal age' as an informative metric to internalize the combined risks of fracture and mortality, the very large and well-described Danish National Hospital Discharge Registry, the sophisticated statistical analysis and the clear messages presented in the manuscript. The limitations of the study are acknowledged by the authors.

Reviewer #1 (Public Review):

The authors demonstrate that modest oscillatory changes in the E-I ratio occur throughout the day and are linked to changes in both synaptic excitation and inhibition. These conclusions were based on adequately sampled electrophysiological data of stimulation-driven and non-evoked excitatory and inhibitory currents. For these studies, a fixed stimulation was not used across slice recordings but was limited to intensities where the E-I ratio was stable. Two points may need further clarification in the text. Firstly, authors might comment on whether current magnitudes plateau at these stimulation intensities. Secondly, make clear why the cause of E-I balance changes was not elucidated from convergent, evoked measurements in the same cell, but instead relied on non-evoked measures of spontaneous miniature excitatory postsynaptic currents and miniature inhibitory postsynaptic currents (mEPSCs and mIPSCs) that were recorded separately in different cell populations. mEPSCs and mIPSCs data analysis relied on statistical scrutiny within genotype and could gain additional rigor and benefits to study reproducibility by applying tests ( e.g. two- way repeated measures (RM) ANOVA) that consider the influence of both genotype and time of day. With this approach, the authors could determine in figures 3 and 4 whether control (B6) mice exhibit the predicted increase in mEPSCs and reduction in mIPSCs at ZT0 when compared to its ASD mouse model. In a noteworthy experiment, the authors connect abnormalities in inhibitory oscillations to altered endocannabinoid signaling using measurements of spontaneous (s) IPSCs, where changes in sIPSC charge were noted. The measurements used to make the paper's conclusion lacked consistency and the authors can bridge these differences by testing whether WIN agonist treatment can restore normal daily E/I oscillation in FMR1 KO and BTBR mice using the stimulation-evoked measurements from figure 1. The study used male and female BTBR mice and only male Fmr1 KO mice. Sex- effects in the study were not disclosed, so it is unclear whether daily E/I oscillation changes were similar in male and female BTBR mice or occur at all in female Fmr1 KO mice. Lastly, numerous studies have noted significant changes in the magnitude of the E-I ratio in an autism mouse model and causally linked these changes to alterations in disorder-related behavior or homeostatic regulation of circuit activity. However, in this current study, neither the loss nor reversal of daily E/I ratio oscillation were causally linked to alterations in sleep timing and architecture or any change in behavioral phenotype. On a promising note, the authors did find a slight decrease in NREM delta power in the Fmr1 KO and a larger decrease in the BTBR mice. Future mechanistic studies on this topic may aim to buttress support for E/I oscillations rather than alterations to the overall E/I level in causing autism-related phenotypes by providing supporting examples of biological significance.

Reviewer #2 (Public Review):

In Bridi et al the authors convincingly show alteration of the E/I ratio oscillation in two mouse models (Fmr1 and BTBR) of ASD. They go on to examine two possible mechanisms that may underlie these changes, 1) sleep/wake cycle and timing and 2) eCB signaling, both of which have been shown to change E/I ratio oscillations. They find that eCB signaling is altered in both models while sleep/wake timing and cycle are normal, concluding that dysfunctional eCB signaling is likely contributing to the changes in E/I oscillation. The experiments are extremely well done, and conclusions are mostly supported by the data, however, there are some concerns with the interpretation of their findings which I will detail below.

1) The authors describe the changes in E/I ratio that they observe in the BTBR mouse line as a "phase-shift". However, to actually show a true phase shift they should record at all of the same time points as they did in the Fmr1 model. Based on just two time points the authors have not shown a "Phase-shift" a phase shift would have to show that the other two time points (Z6 and Z18) follow the predicted (-6hr?) shift. These data would also help define the length of the shift.<br /> 2) Are the changes in E/I ratio presynaptic or postsynaptic? The authors seem to suggest that the synaptic changes they observe are a loss or gain in synapses. Mini-analysis alone is not sufficient for this conclusion. Even if the authors have shown in a previous paper that PPR is unchanged in control mice, presynaptic effects could be contributing to the observed changes in the mouse models studied here. As eCB signaling is thought to be primarily presynaptic this lends additional motivation to explore presynaptic contributions to the observed phenotypes.<br /> 3) The authors do not make any comparisons between control and ASD model mice at any of their time points. It would be helpful to have additional comparisons between ASD model and control at each time point tested in Fig 1 to relate back to previously published studies that mostly record in the animals' light phase. In other words, please clarify at which phases the ASD E/I ratio is different from the control.

Reviewer #3 (Public Review):

The authors previously reported a daily oscillation of the excitation/inhibition ratio occurs normally in layer 2/3 cortical neurons in wild-type mice. In this manuscript, they examined the E/I ratio in the primary visual cortex in two different autism mouse models and showed that the daily oscillation was disrupted in both, albeit in different ways. They further demonstrated that complementary changes in excitatory and inhibitory synaptic transmissions were underlying the disrupted E/I ratio, which is also accompanied by alterations in the endocannabinoid signaling but not sleep time in general.

Disruption of the E/I ratio (or balance) has been a major theme of proposed mechanisms underlying sensory and behavioral abnormalities observed in autism spectrum disorder patients and animal models. The demonstration and characterization of the shift/flattening of the daily oscillation of E/I in the two mouse models provide strong evidence for a disruption of the daily dynamic regulation of the E/I ratio instead of an overall change in the absolute level of E/I, at least in layer 2/3 pyramidal neurons in the visual cortex examined here. These results call for a re-visit of previous studies and offer a potential explanation to reconcile conflicting prior reports regarding the valence of E/I ratio changes in different autism models and brain areas, taking the recording time during the day into consideration. It also raises the question of how the dysregulated daily E/I oscillation affects brain functions. On the other hand, the dissociation of sleep and E/I oscillation observed in the autism models may also provide an opportunity to better understand the functional relevance of sleep-dependent E/I oscillation in a normal brain in the future.

Reviewer #1 (Public Review):

Pathological conformation and aggregation of tau protein are involved in several neurodegenerative diseases such as tauopathies and Alzheimer's disease. Identifying drug-candidates capable of interfering with pathological transformation of tau remains a challenge for which sensitive and specific assays are needed. This article describes the development and characterization of tau biosensors based on NanoBit technology (nanoluciferase complementation). It is a well-designed and precise study providing very interesting new tools.

Strengths<br /> 1/ The authors have developed a variety of tau biosensors: some that can be used for basic research to monitor pathological tau transformation and others with properties suitable for drug screening. All biosensors make it possible to evaluate the action of different agents (chemical products, purified proteins, cell or tissue extracts) in a living cell.

2/ The intermolecular biosensors developed from a shortened version of tau (K18), or the full-length tau, and carrying the P301L mutation possess sensibility and specificity allowing their further development to identify drug-candidates interfering with tau self-interaction.

3/ The characterization of the tau probes confirmed the physiological and pathological knowledge concerning the tau protein: - proximity of full-length tau when bound to microtubules, - conformational changes of tau during its phosphorylation and - tau-self interaction induced by pathological seeds.

Weaknesses<br /> 1/ The suitability of tau biosensors for high-throughput screening needs to be further developed as experiments were only performed in 96-well plates. No scaling up in 384- or 1536-well plates was attempted. Moreover, the transfection of the biosensors could be tedious.

2/ The models used are basic (HEK-293T cells associated with tau aggregates, Aβ oligomers or mouse brain lysates). The number of drugs tested is also quite low.

Collectively, the conclusions drawn by the authors are supported by the results. These new biosensors will be easily usable by the scientific community in fundamental research and could also be of interest to pharmaceutical laboratories wishing to carry out screenings of molecules capable of impacting the pathological transformation of tau.

Reviewer #2 (Public Review):

Cecon et al presented a series of tau biosensors using the NanoBiT complementation system to monitor tau intramolecular and intermolecular interactions. Three major findings shown in the paper are discussed below.

(1) The authors added two modifications to the existing NanoBiT complementation-based biosensors including K18(P301L) and TauP301L which have the capabilities of monitoring tau-tau interactions in response to phosphorylation and seeding. It is important to first have a thorough characterization of the biosensors such as the basal comparative signals among the different isoforms/mutations (the data in the paper are mostly normalized) and how these signals correspond to their functional units such as whether they are monomers, oligomers or fibrils as confirmed by other biochemistry assays e.g. ThS staining. The interpretation on the functional effect of these biosensors in response to stimulation such as addition of seeds have to be discussed. For example, K18(P301L) biosensor is responding to both mK18 and aggK18 as well as aggTau but not mTau or oAB. It appears that the biosensor is unable to differentiate monomeric and aggregated species of K18 tau. Also, beta-amyloid oligomers have been shown to seed tau aggregation, but this is not the case shown by the study which warrants some discussion. A more thorough characterization of the luciferase biosensors would be essential before moving into other assays and high-throughput screening as it is important to know exactly what kind of tau species are being targeted.

(2) The authors added colchicine, a MT destabilizing drug, to the luciferase biosensor systems and showed that phosphorylation of WT tau takes place when it is still bound to MTs, as colchicine prevented its phosphorylation and suggested that tau species comprising of K18 and full-length WT tau might represent an interesting new therapeutic target, as K18 tau and tau with P301L mutation renders full-length WT tau responsive to seeding. It is an interesting concept to study how tau aggregation changes with respect to MT destabilization. However, it is worth noting that treatment with chemical compounds may cause many other effects that need to be well controlled/eliminated before reaching a conclusion. The authors showed that treatment with colchicine reduces luciferase signals of the tau biosensors and suggested that the luciferase signals arise from MT bound tau which is interesting. While colchicine is a well-known MT destabilization drug, it is still important to test if colchicine itself is perturbing tau-tau interaction as other studies have shown that colchicine might promote tau aggregation and cause cognitive dysfunction. From a different perspective, one might consider that MT destabilization may result in more tau in the cytosols due to their detachment from MTs and hence resulting in enhanced tau-tau interactions which would be reflected by an increased in biosensor signals. Furthermore, if tau proteins are already interacting when they are on the MTs, a disruption in MTs may not disrupt tau-tau interactions and might lead to enhanced tau-tau interactions. However, this is not the case shown in this study and perhaps a discussion on this interpretation would help to clarify some questions. The luciferase signal for tau on MTs might be due to tau being near one another when they are residing on MTs which acts as a scaffold to hold them together and not exactly due to tau-tau interactions. Hence, upon MT destabilization, the tau proteins lost the scaffolds that hold them together and hence results in a reduction in the luciferase signals. In terms of the therapeutic targeting of K18-WT tau complex, it is important to note that K18 has increased the responsiveness of WT tau to seeding by 2-fold as compared to the 107-fold change upon seeding of K18-K18 tau biosensor. Although significant, it is a very small change as compared to the signal obtained from K18 biosensors.

(3) Finally, the authors conducted a proof-of-concept study to illustrate the potential of the luciferase biosensor to be used in high-throughput screening drug discovery. The authors used tau seeds (Tg brain lysates), and not small molecules, to show the increase in luciferase signals with Z-factors of >0.5, which indicates excellent assay condition. The authors then further showed that known compounds reduced tau aggregation in Tg brain lysates and reduced luciferase signals of the biosensors. High throughput screening capability typically refers to the perturbation of biosensors or tau-tau interactions directly by drug compounds. From the experimental setup, it seems like the authors will be using luciferase biosensor in the presence of Tg brain lysates (together as a system) to screen for drug candidates, instead of using the biosensor directly to screen for compounds that have a direct effect in perturbing the biosensor. In this case, the Z-factor should be calculated for positive-control compounds that are applied to the biosensor+Tg lysates system. The IC50 of the compounds tested in this system should be determined and compared with the known IC50 values of these compounds in the available literature. It appears that the compounds are only exhibiting good inhibition at very high concentrations, suggesting the need to test and eliminate any non-specific effect such as compound aggregation at a very high concentration.

Reviewer #3 (Public Review):

The paper by Cecon et al. presents a novel biosensor approach designed to study aspects of Tau aggregation that employ the luciferase-based NanoLuc Binary Technology (NanoBiT). The last decade has seen a rise in the number and variety of Tau biosensor systems, each with its own strengths and weaknesses to study various aspects of Tau aggregation. So far, these have proven to be extremely useful tools for the detection of proteopathic Tau molecules from different origins, by virtue of their capacity to induce easily detectable aggregation of the "endogenous" reporter Tau proteins in the intracellular environment, enabling for example to interrogate the structural features that render the protein pathogenic; in addition, they have been employed for screening of therapeutic candidates that can inhibit or slow down the aggregation process. As regards the study of the aggregation process itself, such systems encounter important limitations in that the modifications done to the protein likely impact reaction rates (both intramolecular and intermolecular interactions) and the aggregation mechanism itself. Additionally, the majority of them rely on overexpression systems, further altering the dynamics of physiological interactions. This paper implements a recently developed and commercially available technology based on nano-luciferase complementation, which has been used to study transient protein-protein interactions but not yet for Tau, and reports on its utility to study both inter- and intra-molecular interactions of Tau in live-cells and seeding activity of exogenously added Tau.

Strengths<br /> The field of Alzheimer's will benefit greatly from cellular models that enable faithful replication of aggregation mechanisms that occur intracellularly involving Tau. The elucidation of high-resolution molecular structures of Tau fibrils from cryo-electron microscopy and the realisation that fibrils from different tauopathies display characteristic folds point to altered cellular states that drive the intrinsically-disordered protein (IDP) Tau to adopt specific conformations that spur pathological aggregation processes. The aggregate burden is known now to correlate well with disease progression. Tau has otherwise been described as a highly soluble protein, yet under certain circumstances it adopts a misfolded conformation that in the proximity of other monomers can template further misfolding and spur aggregation. Several biosensor systems have been developed that detect proteopathic Tau with high sensitivity, most notably those that consist of cell lines expressing intracellular FRET pairs. These have been invaluable to the field and have served to demonstrate that seeding activity strongly correlates with disease aggressiveness in Alzheimer's patients (see Dujardin et al. Nat Med 2021), among other important contributions. There are however major limitations in using these models to study aggregation mechanisms in a cellular context in that they rely on significant structural modifications to the protein that alter the aggregation energy landscape, among other artefactual concerns (e.g., protein overexpression).<br /> This paper sets out to showcase the applicability of the NanoBiT technology on the strength of the considerably smaller size of the fusion proteins. which comprise one large BiT fragment of 17.6 kDa and a small complementation peptide of only 11 amino acids, compared to for instance the popular Tau RD P301S FRET biosensor line that relies on CFP and YFP (both ~27 kDa) Tau-fused constructs as FRET pairs. This is important for interrogating intracellular inter- and intra-molecular interactions as steric effects impact reaction rates and mechanisms. This, coupled with high sensitivity of the bioluminescence signal and amenability for high throughput, comprise the most important advantages of this approach.

Weaknesses<br /> Perhaps the most significant advantage (conceptually) of the NanoBiT technology in this context is the ability to create intramolecular interaction sensors by fusing the fragments to opposite termini. This is especially useful for the N- and C- termini of Tau which are known to be in proximity in certain conformations. The same can be achieved with fluorescence complementation yet with the caveat of introducing larger molecules. Nevertheless, regardless of the smaller dimensions of the fusion protein, the modifications are likely to still alter protein interaction dynamics - this is relevant to both intra- and inter-molecular sensors. While this may not always be a major concern when working with globular proteins, it should be a key consideration when studying Tau aggregation. The energy landscape of intrinsically disordered proteins is highly sensitive to even small structural changes, as exemplified by conformational changes in Tau that render this otherwise highly-soluble protein aggregation-prone. The interaction between the complementary small and large fragments of NanoBiT is reversible and weak (reported as 190 uM), but may still stabilise non-intrinsic conformations. Demonstrating that interaction and aggregation kinetics are not affected significantly compared to the native protein in vitro would be required to support the physiological relevance of the claims related to inter- and intra-molecular interactions.

An additional concern with the intramolecular sensor is the ability to discriminate whether interactions are indeed intramolecular and not intermolecular, this introduces a confound for instance in the interpretation that a reduction in signal with the WT Tau conformation sensor after treatment with colchicine suggest that microtubules stabilise Tau in a conformation where N- and C- termini of a Tau monomer are in proximity, when this could also well be due to intermolecular interactions, or a combination of both (see the continuous stretch of density of Tau along protofilaments in Kellogg et al. Science 2018). Furthermore, the colocalization data is not of high enough quality to support the claims regarding microtubule interactions, in fact there seems to be stronger colocalization with the intramolecular sensor than with the intermolecular one. Better quality images and co-localization analysis are needed to support these interpretations. The paper thus falls short of providing compelling data to regard this method as a physiologically-relevant approach to study Tau molecular interactions.

Artefactual problems stemming from the aforementioned alterations are likely not as important for their applicability as sensors, as other Tau biosensors have shown the ability to detect proteopathic forms in a way that reflects the severity of pathology in various contexts, regardless of whether the ensuing aggregates faithfully replicate those encountered in pathology. It would then be of interest to assess how the NanoBiT technology fares compared to alternative cell models in regard to sensitivity. The paper provides a response curve with tissue extracted from a mouse model of tauopathy. The extracts are not purified for tau which makes comparison with other data difficult given that the degree of tauopathy is model and mouse dependent. A more extensive evaluation of the sensing capacity would be needed to establish sensitivity in a meaningful way, for instance with Tau forms for which concentration can be more appropriately estimated, e.g., recombinant Tau and IP-purified extracts from mouse and human tissues, or a direct comparison with other methods.

Reviewer #1 (Public Review):

The authors use a newly developed object-space memory task comprising of a "Stable" version and "Overlapping" version where two objects are presented in two locations per trial in a square open field. Each version consists of 5 training trials of 5-min presentations of an object-space configuration, with both object locations staying constant across training trials in the Stable condition, and only one object location staying fixed in the Overlapping condition. Memory is tested in a test trial 24 hours later where the opposite configuration is presented - overlapping configuration presented for the Stable condition and stable configuration presented for the Overlapping condition - with the thesis that memory in this test trial for the Overlapping condition will depend on the accumulated memory of spatial patterns over the training trials, whereas memory for the test trial in the Stable condition can be due to episodic memory of last trial or accumulated memory. Memory is quantified using a Discrimination Index (DI), comparing the amount of time animals spend exploring the two object locations.

Here, animals in other groups are also presented with an interference trial equivalent to the test trial, to test if the memory of the Overlapping condition can be disrupted. The behavioral data show that for RGS14 over-expressing animals, memory in the Overlapping condition is diminished compared to controls with no interference or controls where over-expression is inhibited, whereas memory in the Stable condition is enhanced. This is interpreted as interference in semantic-like memory formation, whereas one-shot episodic memory is improved. The authors speculate that increased cortical plasticity should lead to increased and larger delta waves according to the sleep homeostasis hypothesis, and observe that instead increased cortical plasticity leads to less non-REM sleep and smaller delta waves, with more prefrontal neurons with slower firing rates (presumably more plastic neurons). They further report increased hippocampal-cortical theta coherence during task and REM sleep, increased NonREM oscillatory coupling, and changes in hippocampal ripples in RGS14 over-expressing animals.

While these results are interesting, there are several issues that need to be addressed, and the link between physiology and behavioral results is unclear.

1) The behavioral results rely on the interpretation that the Overlapping condition corresponds to semantic-like memory and the Stable condition corresponds to episodic-like memory. While the dissociation in memory performance due to interference seen in these two conditions is intriguing, the Stable condition can correspond not just to the memory of the previous trial but also accumulated memory of a stable spatial pattern over the 5 testing trials, similar to accumulated memory of a changing spatial pattern in the Overlapping pattern.

Here, it is puzzling that in the behavioral control with no interference (Figure 1D), memory in the Stable and Overlapping condition is unchanged in the test trial, with the DI statistically at 0 in the test trial. In the original description of the Object Space task by the authors in the referenced paper, the measure of memory was a Discrimination Index significantly higher than 0 in both the Stable and Overlapping conditions. This discrepancy needs to be reconciled. Is the DI for the interference trial shown in Fig. S1 significantly different than 0? No statistics or description is provided in the figure legend here.

2) The physiology experiments compare Home cage (HC) conditions to the Object Space task (OS) throughout the manuscript. While some differences are seen in the control and RGS14 over-expressing animals, there is no comparison of the Stable vs. Overlapping condition in the physiology experiments. This precludes making explicit links between physiological observations and behavioral effects.

3) The authors speculate that learning will result in larger and more delta waves as per the synaptic homeostasis hypothesis. It should be noted here that an alternative hypothesis is that there should also be a selective increase in synaptic plasticity for learning and consolidation. The authors do observe that control animals show more frequent and higher-amplitude delta waves, but rather than enhancing this process, RGS14 animals with increased plasticity show the opposite effect. How can this be reconciled and linked with the behavioral data in the Stable and Overlapping condition? Similarly, there is an increase in slower-firing neurons in RGS14 over-expressing animals. Slower-firing neurons have been proposed to be more plastic in the hippocampus based on their participation in learned hippocampal sequences, but appropriate references or data are needed to support the assertion that slower-firing neurons in the prefrontal cortex are more plastic.

4) It is noted that changing cortical plasticity influences hippocampal-cortical coupling and hippocampal ripples, suggesting a cortical influence on hippocampal physiological patterns. It has been previously shown that disrupting prefrontal cortical activity does alter hippocampal ripples and hippocampal theta sequences (Schmidt et al., 2019; Schmidt and Redish, 2021). The current results should be discussed in this context.

Reviewer #2 (Public Review):

In this paper, the authors provide evidence to support the longstanding proposition that a dual-learning system/systems-level consolidation (hippocampus attains memories at a fast pace which are eventually transmitted to the slow-learning neocortex) allows rapid acquisition of new memories while protecting pre-existing memories. The authors leverage many techniques (behavior, pharmacology, electrophysiology, modelling) and report a host of behavioral and electrophysiological changes on induction of increased medial prefrontal cortex (mPFC) plasticity which are interesting and will be of significant interest to the broad readership.

The experimental design and analyses are convincing (barring some instances which are discussed below). The following recommendations will bolster the strength/quality of the manuscript:

1. Certain concerns regarding the interpretation and analysis of the behavioral data remain. The authors need to clarify if increased mPFC plasticity leads to only an increase in one-shot memory or 'also' interference of previous information. It seems that the behavioral results could also be explained by the more parsimonious explanation that one-shot memory is improved. Do the current controls tease apart these two scenarios? Additionally, the authors need to clarify why the 'no trial' and 'anisomycin' controls for the stable task perform at chance levels on exposure to a new object-place association on test day (Fig 1D). Finally, further description of how the discrimination index (exploration time of novel-exploration time of familiar/sum of both) is recommended i.e., in the stable condition, which 'object' is chosen as 'novel' (as both are in the same locations) for computing the index (Fig 1). Do negative DI values imply a neophobia to novel objects (and thus are a form of memory; this is also crucial because the modelling results (Fig 1E) use both neophilia and neophobia while negative discrimination indexes are considered similar to 0 for interpreting the behavioral results, as stated on page 3, lines 84-86?

2. The authors report lower firing rates in RGS14414 animals during the task in Fig 2F. It is indeed remarkable how large the reported differences are. The authors need to rule out any differences in the behavioral state of the animals in the two groups during the task, i.e., rest vs. active exploration/movement dynamics. Are only epochs during the task while the animals interact with the objects used for computing the firing rates (same epochs as Fig 1)? If not, doing so will provide a useful comparison with Fig 1. Additionally, although the authors make the case for slow firing rate neurons being important for plasticity (based on Grosmark and Buzsaki, 2016), it is crucial to note that the firing rate dynamic (slow vs. fast) in that study for the hippocampus is defined based on the whole recorded session (predominated by sleep), indeed the firing rates of the two groups (slow vs. fast/plastic vs. rigid) during the task/maze-running do not differ in that study. Therefore, the results here seem incongruent with the Grosmark and Buzsaki paper. Since this finding is central to the main claim of the authors, it either warrants further investigation or a re-interpretation of their results.

3. A concern remains as to how many of the electrophysiological changes they observe (firing rate differences, LFP differences including coupling, sleep state differences, Figs. 2-4) support their main hypothesis or are a by-product of injection of RGS14414 (for instance, one might argue that an increased 'capability' to learn new information/more plasticity might lead to more NREM sleep for consolidation, etc.). The authors need to carefully interpret all their data in light of their main hypothesis, which will substantially improve the quality/strength of the manuscript.

Reviewer #3 (Public Review):

The authors set out to test the idea that memories involve a fast process (for the acquisition of new information) and a slow process (where these memories are progressively transferred/integrated into more-long term storage). The former process involves the hippocampus and the latter the cerebral cortex. This 'dual-learning' system theoretically allows for new learning without causing interference in the consolidation of older memories. They test this idea by artificially increasing plasticity in the pre-limbic cortex and measuring changes in different learning/memory tasks. They also examined electrophysiological changes in sleep, as sleep is linked to memory formation and synaptic plasticity.

The strengths of the study include a) meticulous analyses of a variety of electrophysiological measurements b) a combination of neurobiological and computational tools c) a largely comprehensive analysis of sleep-based changes. Some weaknesses include questions about the technique for increasing cortical plasticity (is this physiological?) and the absence of some additional experiments that would strengthen the conclusions. However, overall, the findings appear to support the general idea under examination.

This study is likely to be very impactful as it provides some really new information about these important neural processes, as well as data that challenges popular ideas about sleep and synaptic plasticity.

Reviewer #2 (Public Review):

The manuscript by Yildiz et al investigates the early response of BECs to high fatty acid treatment. To achieve this, they employ organoids derived from primary isolated BECs and treat them with a FA mix followed by viability studies and analysis of selected lipid metabolism genes, which are upregulated indicating an adjustment to lipid overload. Both organoids with lipid overload and BECs in mice exposed to a HFD show increased BEC proliferation, indicating BEC activation as seen in DR. Applying bulk RNA-sequencing analysis to sorted BECs from HFD mice identified four E2F transcription factors and target genes as upregulated. Functional analysis of knock-out mice showed a clear requirement for E2F1 in mediating HFD induced BEC proliferation. Given the known function of E2Fs the authors performed cell respiration and transcriptome analysis of organoids challenged with FA treatment and found a shift of BECs towards a glycolytic metabolism.

The study is overall well-constructed, including appropriate analysis. Likewise, the manuscript is written clearly and supported by high-quality figures. My major point is the lack of classification of the progression of DR, since the authors investigate the early stages of DR associated with lipid overload reminiscent of stages preceding late NAFLD fibrosis. How are early stages distinguished from later stages in this study? Molecularly and/or morphologically? While the presented data are very suggestive, a more substantial description would support the findings and resulting claims.

Reviewer #2 (Public Review):

It is believed that the reason why women generally have lower rates of atherosclerotic events than men until menopause is due to the beneficial effects of estrogen on the cardiovascular system. The paper attempts to explain why hormone replacement therapy with estrogen is not effective in preventing atherosclerosis in post-menopausal women. The authors posit that accumulation of iron after menopause inhibits estrogen receptor expression and makes estrogen ineffective. Using mouse model of atherosclerosis and iron overload, they demonstrate that 1)atherosclerosis is increased in overectomized mice 2) estrogen supplement seems to further exacerbate atherosclerosis and this is accompanied by increased total body iron; 3) iron itself causes a decrease in ERa via increased proteasome degradation of Era via E3 ligase MDM2 and 4) iron chelation rescues the protective effects of estrogen in overectomized mice on atherosclerosis progression.

While interesting in terms of hypothesis, I found the manuscript (not the overall themes) but the individual experimental logic difficult to follow with unclear rationale for many of the experiments and timepoints chosen. Moreover the human data supporting these claims are weak in terms of what is shown. The authors only partially achieve their aims as many of the experiments in mice appear incomplete in terms of data shown and transparency. Some important controls are also missing.

This work has important potential to understand the causes of accelerated atherosclerosis in women after menopause and how to better prevent atherosclerosis in women of this age group

Reviewer #2 (Public Review):

In this manuscript, Nguyen et al. make use of recently determined cryo-EM structures of Nav1.7 channels in complex with ProTX-II, a peptide spider toxin that binds to VSD2 and stabilizes the deactivated state of the channel in addition to reducing peak currents. Previous work on making modified spider toxin peptides as potent and selective Nav1.7 inhibitors by Merck, Amgen, and others was conducted in a structure-blind manner. This manuscript demonstrates that it is possible to use structure data and computational tools to identify modified spider toxin peptides that show even better potency and selectivity properties.

The authors did a very nice job presenting their detailed results. This detailed material should be very helpful to researchers wanting to expand on this work toward the development of peptide-based pain drugs that selectively target Nav1.7. Their in-vitro electrophysiological analysis is excellent, showing full selectivity profiles (including difficult to work with channels such as hNav1.8 and hNav1.9) from HEK293 cells and also showing inhibition of the TTX-S current with both mouse and human cultured DRG neurons. The in-vivo work shows very strong analgesia in the hotplate model as well as in a model of oxaliplatin-induced peripheral neuroparthy, showing that PTx2-3127 is a powerful analgesic in rats.

Overall, this is an excellent investigation into the feasibility of using structural information and computational tools to design potent and selective Nav1.7 inhibitors. Such peptide-based inhibitors might be developed in the future as novel pain drugs.

Reviewer #2 (Public Review):

Jelen et al. developed a new taste conditioning paradigm where they pair a tastant (CS) with optogenetic activation of either sensory neurons or dopamine neurons. Activation of different cell types in training led to decreased sugar attraction or decreased salt avoidance. Depending on the activated cell type, the authors could even induce LTM with optogenetic activation. They found that the neural requirement for aversive or appetitive taste learning widely overlaps with the requirement for learning with other modalities (olfaction). They focus also on appetitive taste LTM formation, which requires caloric food intake after training similar to olfactory LTM.

Strengths:

The newly developed operant paradigm has several advantages compared to previous taste learning paradigms. The flies are freely walking and can be monitored throughout training and test. This allowed the authors to describe the temporal dynamics of learning and learned behavior. They could show that a specific type of dopamine neuron enhances salt sipping during training but was not sufficient to induce learning. Furthermore, they could now investigate both, appetitive and aversive learning, which was not possible before in immobilized flies. Optogenetic activation as the US in training allowed the authors to disentangle the need for caloric value in short-term and long-term memory.

Weaknesses:

Artificial activation of neurons seems to be sufficient to induce different memories in the fly. However, as the flies do not receive actual food in the training, those results may not represent the naturally used neural circuits, or only partial circuits underlying the normal taste learning. Also, the new paradigm has operant training, which might change the requirement or recruitment of learning circuits. Thus, the authors find similar neurons involved as in classical conditioning, which is very interesting, but also some differences.

Reviewer #2 (Public Review):

Kankaanpää and colleagues studied how lifestyle factors in adolescence (e.g., smoking, BMI, alcohol and exercise) associate with advanced epigenetic age in early adulthood.

Strengths:

The manuscript is very well written. Although the analyses and results are complex, the authors manage very well to convey the key messages.<br /> The twin dataset is large and longitudinal, making this an excellent resource to assess the research questions.<br /> The analyses are advanced including LCA capitalizing on the strength of these data.<br /> The authors also include a wider range of epigenetic age measures (n=6) as well as a broader range of lifestyle habits. This provides a more comprehensive view that also acknowledges that associations were not uniform across all epigenetic age measures.

Weaknesses:

The accuracy of the epigenetic age predictions was moderate with quite large mean absolute errors (e.g., +7 years for Horvath and -9 years for PhenoAge). Also, no correlations with chronological age are presented. With these large errors it is difficult to tease apart meaningful deviations (between chronological and biological age) from prediction error.

The authors claim that 'the unhealthiest lifestyle class, in which smoking and alcohol use co-occurred, exhibited accelerated biological aging...'. However, this is only partially true. For example, PhenoAge was not accelerated in lifestyle class C5. Similarly, all classes showed some degree of deceleration (not acceleration) with respect to DunedinPACE (Figure 3D). The large degree of heterogeneity across different epigenetic age measures needs to be acknowledged.

The authors claim that 'Practically all variance of AAPheno and DunedinPACE common with adolescent lifestyle was explained by shared genetic factors'. However, Figure 4 suggest that most of the variation (up to 96%) remained unexplained and genetics only explained around 10-15% of total variation. The large amount of unexplained variation should be acknowledged.

Reviewer #2 (Public Review):

Little is known about how the circadian clock regulates the timing of anthesis. This manuscript shows that the circadian clock regulates the diurnal rhythms in floral development of the sunflower. The authors have developed a new method to characterize the timing of floral development under normal conditions as well as constant dark and light conditions. The results from the treatment of darkness during the subjective night and day suggest that the circadian clock regulates the growth of ovary, stamen, and style differently.

All clock papers claim that the circadian clock regulates the fitness of organisms, however, it is hard to evaluate how the circadian clock affects the fitness of organisms. The timing of pollen release and stigma maturity is directly related to plant fitness. That's why the authors suggest that the circadian clock in sunflowers increases plant fitness by regulating the timing of anthesis.

Although the authors manipulated the light and temperature to examine the role of the circadian clock in floral development, the weakness of this manuscript is that there is no molecular evidence to show how the clock regulates floral development.

Reviewer #1 (Public Review):

The authors push a fresh perspective with a sufficiently sophisticated and novel methodology. I have some remaining reservations that concern the actual make-up of the data basis and consistency of results between the two (N=16) samples, the statistical analysis, as well as the "travelling" part.

I previously commented on the fact that findings from both datasets were difficult to discern and more effort should be made to highlight these. Also, a major conclusion "the directionality effect [effect of attention on forward waves] only occurs for visual stimulation" only rested on a qualitative comparison between studies. The authors have improved on this here, e.g., by toning down this conclusion. One thing that is still missing is a graphical representation of the data from Foster et al. (the second dataset analysed here) that would support the statistical results and allow the reader a visual comparison between the sets of findings.

Also, for any naive reader, the concept of travelling waves may be hard to grasp in the way data are currently presented - only based on the results of the 2D-FFT. Can forward and backward-travelling waves be illustrated in a representative example to make this more intuitive?

Finally, the way Bayes Factors from the Bayesian ANOVA are presented, especially with those close to the 'meaningful boundaries' ⅓ and 3, as defined in the 'Statistical analysis' section, requires some unification/revision. For example, here: "We found a positive correlation between contra- and ipsi- lateral backward waves, and occipital (all Pearson's r~=0.4, all BFs 10 ~=3) and -to a smaller extent- frontal areas (all Pearson's r~=0.3, all BFs 10 ~=2).", where the second part should strictly be labelled as inconclusive evidence. In the same vein, there is occasional mention of "negative effects", where it should say that evidence favours the absence of an effect.

Reviewer #2 (Public Review):

The present manuscript takes a new perspective and investigates the functional relevance of traveling alpha waves' direction for visual spatial attention. While the modulation of alpha oscillatory power - and especially the lateralization of alpha power - has been associated with spatial attention in the literature, the present investigation offers a new perspective that helps understand and differentiate the functional roles of alpha oscillations in the ipsi- versus contralateral hemisphere for spatial attention.

The present study uses a straightforward approach and provides an analysis of two EEG datasets, which are convergingly in line with the authors' claim that two patterns of travelling alpha waves need to be differentiated in visual spatial attention. First, backward waves in the ipsilateral hemisphere, and second, forward waves in the contralateral hemisphere, which are only observed during visual stimulation. Importantly, the authors test the relation of these patterns of traveling waves to the overall power of alpha oscillations and to the hemispheric lateralization of alpha power. Furthermore, to test the functional significance, the authors demonstrate that the pattern of forward and backward waves around stimulus onset differentiates between hits and misses in task performance.

Although the results are in line with the conclusions drawn, some questions remain. The authors investigate the relationship between traveling alpha waves and the hemispheric lateralization of alpha power, which is a well-established neural signature of spatial attention. Surprisingly, the lateralization of alpha power shown in Figure 3B appears relatively weak in the present dataset (by visual inspection), which raises the question of whether the investigation of a relation between lateralized alpha power and alpha traveling waves is warranted in the first place.

Furthermore, the authors employ between-subject correlations (with N = 16) to test the relationship between alpha traveling waves and (lateralized) alpha power. However, as inter-individual differences in patterns of travelling waves are not the main focus here, within-subject analyses of the same relations would be able to test the authors' hypotheses much more directly.

It needs to be appreciated that the authors analyze two datasets in the present study. However, the question remains whether the absence of the forward waves effect in paradigms without visual stimulation is a general one and would replicate in other datasets. Moreover, the manuscript would benefit from a discussion of the potential implications of traveling waves for functional connectivity between posterior and anterior regions.

Reviewer #1 (Public Review):

The manuscript by Silva et al. "Evaluation of the highly conserved S2 hairpin hinge as a pan-coronavirus target" seeks to evaluate a new epitope target on the S2 domain of SARS-CoV2 Spike protein and evaluate its potential as a pan-coronavirus target. This is an impressive combination of extensive structural, HDXMS-based dynamics and antibody engineering approaches. What is missing is a detailed correlation of HDXMS with Spike dynamics. The authors have not examined the allosteric effects of 3A3 binding to the Spike trimer, specifically cooperativity in antibody binding. Does binding of one Fab positively or negatively impact the subsequent binding of antibody? In this regard, readers would benefit from HDXMS spectral envelopes in figures, at least for the epitope locus peptides. Further, what is the effect of the intrinsic ensemble behavior of the Spike protein on 3A3 interactions? In a broader sense antibody binding is assisted by intrinsic trimer ensemble behavior, as observed by the lowered binding to the omicron variant- but are there induced binding effects? It would help to better integrate HDXMS with cryo-EM and antibody engineering. It is a novel, less explored epitope target on the S2 domain. Overall, a more definitive mechanistic conclusion for how targeting the S2 hinge can advance future pan-coronavirus strategies is missing.

Major Comments:

1) Given that the authors have demonstrated ensemble switching behavior from 4 ℃ to 37 ℃ (Costello et al. (2021)) why is this not factored in how the HDXMS is carried out? The samples were stored, frozen at -80 ℃, thawed, and equilibrated for 20 min at 20 ℃ with or without antibody present and analyzed by HDXMS. However, the reported t1/2 for trimer tightening at 37 ℃ is t1/2 = 2.5 h (Supplementary Fig. 7). The samples should ideally be analyzed under standardized conditions with the stable conformer. Sample heterogeneity from HDXMS is likely due to any of the following contributing factors:<br /> i) Intrinsic ensemble heterogeneity (Costello et al. (2021)), Kinetics of RBD- up and down conformational switching<br /> ii) Cooperativity of Fab binding.<br /> iii) Partial occupancy of trimer epitopes with bivalent IgG.<br /> iv) Combination of cooperativity effects and partial binding effects

I would predict for any of the above reasons, it is intriguing why are there no bimodal kinetics of deuterium exchange reported. Partial occupancy should be evident from HDXMS paratope analysis.

2) Pan-coronavirus neutralization potential is clearly evident. It is intriguing that the antibodies were isolated after immunization with an authentic MERS S2 domain but showed better selectivity to full-length 6P-engineered Spike. How is cooperativity built into antibody binding, given that the epitope site is occluded to various extents by the S1 domain and access is contingent upon RBD up-down kinetics?

3) I am surprised that there is no allostery described for 3A3 (Supplementary figures 5, 6).

Reviewer #2 (Public Review):

The authors report a conserved spike S2 hinge epitopes and two conformationally selective antibodies that help elucidate spike behavior. This work defines a third class of S2 antibody and provides insights into the potency and limitations of targeting this S2 epitope for future pan-coronavirus strategies.

Reviewer #3 (Public Review):

The study by Silva et al details the discovery and evaluation of a third class of broadly cross-reactive anti-Spike antibody that binds a conserved hinge region in the S2 domain. After immunizing mice with a stabilized S2 protein from MERS and generating scFv phage libraries, the authors were able to identify antibody 3A3, which showed broad cross-reactivity with SARS2 (including Omicron BA.1), SARS1, MERS, and HKU1 spike proteins. Using a combination of a low-resolution cryo-EM structure and HDX mass spectrometry, the authors were able to map amino acids in the antibody paratope and spike epitope, the latter of which is the hinge region of the Spike S2 domain (residues 980-1005) that plays a critical role in pre- to -post-fusion conformational changes. Through well-executed and comprehensive mutagenesis, binding, and functional assays, the authors further validated critical residues that lead to antibody escape, which centered around the 2P residues and diminished viral entry. While 3A3 and an affinity-enhanced engineered version, RAY53, did not show potent in vitro neutralization against the authentic virus, the antibody was shown to recruit Fc effector functions for viral clearance, in vitro.

Overall, the conclusions of this paper are well supported by the data, but the usefulness of such antibodies is likely limited. The work can be strengthened by extending the analysis of 3A3-like antibodies in the context of human immune responses and in vivo effectiveness.

1. Isolation of 3A3 was achieved after the generation of scFv-phage libraries following immunization with a MERS S2-domain immunogen in a mouse model. The fact that 3A3 binds well to 2P-stabilized sequences and binding/neutralization is diminished upon reversion of 2P mutations back to the native spike sequence (Figures 3a, 4c, and 5b), suggest that such antibodies would likely not arise from natural infection. This contrasts the isolation of fusion peptide and stem helix-directed antibodies, which were isolated from both immunized animals and convalescent individuals. To make their results more solid regarding the use of such antibodies in future vaccine strategies, the authors should provide evidence that 3A3-like antibodies can be identified in human donors. For example, they could enrich donor-derived S2-specific antibodies that bind both MERS and SARS2 S2 domains and evaluate the fraction of antibodies that recognize the hinge-epitope using competition binding assays (either ELISA or BLI), which have commonly been used to map epitope-specific sera responses. This could also be achieved with nsEMPEM of polyclonal IgGs bound to S2 proteins.

2. The authors speculate in the discussion that strategies to enhance access to the hinge epitope, which may include ACE2-mimicking antibodies, could promote enhanced viral clearance. In addition to ACE2-mimicking antibodies, several antibodies have been described that bind the RBD and promote S1 shedding (see for instance mAb S2A4 - Piccoli et al, 2020, Cell). Several 2nd generation vaccine platforms utilize RBD-only immunogens that are likely to induce high titers of ACE2-mimicking and cross-reactive S1-shedding antibodies. Thus, adding in vitro neutralization and ADCC experiments to assess synergy between 3A3/RAY53 and such antibodies would booster this speculative claim and be of interest to many in the field developing strategies for pan-coronavirus therapies.

3. The authors provide in vitro evidence in Figure 5c,d for Fc-mediated viral clearance. While in vivo data to show effectiveness in animal models is ideal, additional in vitro data that utilize engineered constructs that modulate effector function (e.g., DLE (+) or LALA (-)) would boost the authors' claims regarding Fc-mediated viral clearance mechanisms by EA3/RAY53.

Reviewer #1 (Public Review):

Nephronophthisis (Nphp) is a multigenic, recessive disorder of the kidney presenting in childhood that is characterized by cysts predominantly at the cortico-medullary junction and progressive fibrosis. An infantile form of the disease presents earlier with more diffuse cystic change. The condition is considered a ciliopathy because most of the genes linked to the condition encode proteins involved in ciliary biogenesis or function. Germline mutations in NPHP2 are associated with a particularly severe, infantile form of the disease. Given that interstitial fibrosis is a more prominent feature of Nphp compared to many other forms of polycystic kidney disease, the authors sought to determine the mutant cell types responsible for the phenotype.

In the current study, the authors generated and characterized mouse lines with Nphp2 selectively inactivated in either renal epithelial cell or stromal cell lineages and found that inactivation in renal epithelial cells was both necessary and sufficient to cause disease. They further showed that markers of interstitial fibrosis and proliferation increase in mutants prior to the onset of histologically evident cystic disease, suggesting that aberrant epithelial-stromal cell signaling is an early and primary feature of the condition (Figures 1-4). The study design was straightforward and appropriate to address the question, and the results support their conclusions.

They next tested whether the cilia-dependent cyst-activating pathway (CDCA) that is "unmasked" by loss of other PKD-related genes is similarly active in Nphp2 mutants by generating Nphp2/Ift88 double mutants. Their studies found that the severity of cystic disease and markers of proliferation and fibrosis was attenuated in double-mutants (Fig 5, 6). These studies were also appropriate for testing the hypothesis and the results were similarly consistent with their interpretation.

In the last set of studies, they tested whether valproic acid (VPA), a drug that has multiple modes of action including acting as a broad inhibitor of HDACs and previously used by the investigators in other forms of polycystic kidney disease, would have similar effects in Nphp2 mutants. The authors tested daily injection from days P10 through P28 in both control and Nphp2 mutant mice with VPA or an appropriate vehicle control and found that VPA was beneficial (Fig 7). The study design was acceptable and the results generally support their conclusions. The one perplexing result is shown in Fig 7B. The Nphp2 mutants, regardless of treatment status, have body weights (BW) that are significantly lower than the controls, with treated mutants even trending lower than their untreated mutant counterparts. This is unexplained and should be addressed. In the mutants with more widespread epithelial cell knock-out of Nphp2 (Ksp-Cre, Fig 1), total body weight decreased as mice became more severely cystic with renal impairment. In the milder form of disease produced with the Pkhd1-Cre (Fig 7), total body weight is inexplicably approx. 2g lower on average despite having much more modestly elevated KBWs and BUNs. Moreover, one might have expected that mutants treated with VPA would have had BWs intermediate between untreated mutants and controls since the severity of the disease was moderately attenuated. These differences raise the question as to whether body weight differences are due to factors independent of disease status, the most likely of which would be that the controls were not littermates. This prompted a careful review of the text for descriptions of the control mice. Throughout the study, the investigators describe selecting animals from the same "cohort", but this term is imprecise. There is little information provided about background strains, whether any of the lines were congenic, or whether any of the studies were done using littermate controls. This must be addressed. It would help if the investigators identified the litter status in their plots. This would clearly show relationships between animals and the number of litters that had animals with these properties. If littermates were not used for each study, the authors must explain both why they didn't do so and how they then selected which animals to use. This information is especially important for interpreting the results of their genetic interaction and drug treatment studies.

Several other considerations. The authors state that the effects of VPA are mediated through the drug's inhibition of HDACs and suggest that future studies could be directed at refining the specific HDAC. While this is certainly possible, the authors should acknowledge that VPAs have been reported to have numerous pharmacologic effects and targets and which of these is mediating the effects in their model is unknown. They would need mechanistic studies to show this, though it doesn't discount their possible efficacy as a therapy for PKD. The authors also state in their abstract that their double knock-out studies "support a significant role of cilia in Nphp2 function in vivo." It is not clear to me how their studies show this nor how they can exclude that ciliary activity is operating in an Nphp2-independent, parallel fashion that modulates some common downstream pathways.

Reviewer #2 (Public Review):

The manuscript by Li et al demonstrates the role of Nphp2/Invs in renal epithelia in preventing NPHP-like phenotypes, such as epithelial/stromal proliferation and stromal fibrosis, in mice. Previously, mutants of the Nphp2 allele in mice, generated by insertional mutagenesis, showed severe cystic kidney disease and fibrosis in neonates.

The authors nicely show that the NPHP-like phenotypes in mutant kidneys arise from abnormal signaling specifically within and from renal epithelial cells. Furthermore, the fibrotic response and abnormal increase of cell proliferation precede cyst formation and could be initiated independently of cyst formation. The authors also show that the removal of cilia reduces the severity of Nphp2 phenotypes. The authors suggest that similar to polycystins, NPHP2 inhibits a cilia-dependent cyst and fibrosis-activating pathway. Finally, the histone deacetylase (HDAC) inhibitor valproic acid (VPA) reduces these phenotypes and preserves kidney function in Nphp2 mutant mice, supporting HDAC inhibitors as potential candidate drugs for treating NPHP.

Overall, understanding the mechanisms driving NPHP phenotypes is important and drugging relevant pathways in treating this disease is an important unmet need in patients. The authors have provided insights into both these aspects in this study. The manuscript is nicely written, and the assays shown are rigorous and insightful.

Reviewer #3 (Public Review):

In this manuscript, Li et. al, investigate whether epithelial or stromal Nphp2 loss, a gene causative of nephronophthisis (NPHP), drives polycystic kidney disease (PKD) and kidney fibrosis in a novel floxed model of Nphp2. The authors found that only epithelial and not stromal Nphp2 loss results in NPHP-like phenotypes in their mouse model. In addition, the authors show that concurrent cilia, via Ift88 loss, and Nphp2 loss within the kidney epithelium as well as HDAC inhibition results in less severe PKD/kidney fibrosis, as has been shown in mouse models of other non-syndromic forms of PKD, such as autosomal dominant PKD caused by mutations to Pkd1 or Pkd2.

The authors aimed to understand (1) whether the published NPHP phenotype (kidney cysts and fibrosis), known from the global Nphp2 knockout mouse, is driven by the function of NPHP2 in the kidney epithelium or stromal cells; (2) if kidney fibrosis in NPHP is linked to kidney damage caused by cysts, or independent and preceding of the PKD phenotype; (3) whether cilia are required, causative, or prohibitive of NPHP cystogenesis; and (4) if a broad spectrum HDAC inhibitor is a potential therapeutic approach for NPHP.

With the provided results, the authors established that epithelial Nphp2 loss is likely a predominant driver of PKD in their model; however, they cannot exclude that stromal NPHP2 does not play a role in cysts growth post-initiation because the authors failed to directly compare their cell type-specific models to a global cre knockout (e.g. Cagg-cre). In addition, it is possible that cyst initiation/growth upon stromal Nphp2 loss occurs substantially slower compared to epithelial Nphp2 loss. However, it seems the authors did not look at kidney phenotypes beyond 28 days of age. Publications from the ADPKD field suggest, that stromal Pkd1 loss initiates cystogenesis much slower than epithelial Pkd1 loss. Further, while the authors suggest that kidney fibrosis precedes cyst development, the results supporting this conclusion are limited to one time point, analyzing IF staining of a single marker that can be compared between non-cystic and cystic time points. These analyses need to be extended to make any firm conclusions.

The most interesting finding of the manuscript, and likely most impactful to the field, is, that loss of cilia within the setting of epithelial Nphp2 loss reduces PKD severity. This finding parallels published findings for Pkd1 and Pkd2 which are suggested to function in a cilia-dependent cyst-activation mechanism. Unfortunately, the here shown studies, do not add to the mechanistic insight beyond showing the descriptive finding. Most importantly, it remains unclear whether NPHP2 functions in the same pathway as polycystin-1 or -2 (the Pkd1, Pkd2 gene products) or in a separate independent pathway.

With respect to the HDAC preclinical studies, the authors show supporting data that a broad-spectrum HDAC inhibitor may be suitable for slowing cyst growth in their model of NPHP. Overall, these studies are not novel to the field, as HDAC inhibition has been shown to slow PKD progression in various models of PKD al while not in NPHP specifically. Further, the studies seem like an add-on, which does not directly link to the prior cell type-specific studies of NPHP2, and no mechanisms linking the two concepts are provided.

Reviewer #1 (Public Review):

The paper reports important work in which the Fub-1 boundary of the Drosophila bithorax complex is characterized in detail. Fub-1 separates the bxd/pbx regulatory domain, which is active in PS6/A1, from the abx/bx regulatory domain, which is active in PS5/T3. The work presented provides compelling evidence that Fub-1 consists of two key elements: an insulating boundary region called HS1, which is regulated by an adjacent region called HS2. HS2 contains a promoter that is activated in PS6/A1 by enhancers in the bxd/pbx region. Read-through of HS1 by transcripts from the HS2 promoter blocks the insulating activity of HS1, allowing the bxd/pbx regulatory regions to activate Ubx transcription in PS6/A1. It has long been appreciated that boundary elements within the BX-C are regulated in a segment-specific fashion. The work presented in the Ibragimov manuscript provides a very nice example of how this segment-specific regulation can take place. For the most part, the work is very thorough and the conclusions are well-supported. However, there are a few important issues that should be addressed.

First, throughout the manuscript, it is stated that the read-through transcription of HS1 eliminates its blocking activity. Missing, however, is a test of whether the direction of transcription of HS1 is important. That is, no construct is tested in which HS1 is inverted so that RNAs from the HS2 promoter are transcribed from the opposite strand of HS1. If read-through transcription of HS1 is all that is required to abrogate its blocking activity, such a construct should behave identically to constructs in which HS1 is not inverted. However, if the structure of the F1HS2 RNA is critical to preventing the blocking activity of HS1, inversion of HS1 relative to HS2 may render it immune to inactivation by transcripts initiated at HS2.

Second, the terminology used to designate the constructs tested is very hard to follow and needs simplification. Since the orientation of HS1 in isolation is unimportant, perhaps just HS1 HS2, HS1 Inv(HS2), HS2 HS1, and Inv(HS2) HS1 could be used.

Third, in many places in the manuscript genotypes are shown in which the HS1 insulator is placed into F7attP50. For these genotypes, H1 is said to block the interaction between iab-6 and iab-7, but not to support bypass activity. Readers need some help here, as they will not understand why A5 and A6 tergites are black in these genotypes, as this implies that iab-5 is able to act over the HS1 element to activate Abd-B. One explanation may be that iab-5 can promote pigmentation by acting on abd-A.

Fourth, a more complete description of the HS1248 HS2505R genotype is needed. In this genotype, the H1 insulator is constitutively active, as H2 is inverted. Do animals of this genotype show a bxd phenotype in the larval cuticle? Do adults show a transformation of the halteres like that shown by classical bxd mutations? Answers to these questions would shed light on when H1 is active as an insulator, and whether it is active throughout PS6/A1.

Reviewer #2 (Public Review):

The work presented in the manuscript addresses regulatory mechanisms in a complex genome locus, the Bithorax-Complex (BX-C) in Drosophila. Here three homeotic genes are controlled by multiple regulatory domains, each of which comprises distinct sets of cis-regulatory elements including insulators, enhancers, Polycomb responsive elements, and promoters for coding and non-coding transcripts. Despite such complexity, the authors have made good efforts to explain the context for the study and the question that they are interested in, what is the function of an evolutionarily conserved but newly defined cis-element, Fub-1?

Fub-1 localizes at the chromatin boundary between the homeotic gene Ubx and the bxd/pbx regulatory domain, which thus predicts it is a chromatin insulator. To dissect the function of Fub-1, the authors utilized powerful and versatile gene exchange cassettes (phiC31/attp; FRT/FLP; Cre/Loxp) to engineer both the endogenous locus of Fub-1 and another insulator site Fab-7 to introduce exogenous Fub-1. Using these transgenic tools, they tested the insulator activity of Fub-1. They first confirmed that deleting Fub-1 causes changes in chromosomal configuration in the flanking region using Micro-C. However, unexpectedly, they found that Fub-1 depletion does not cause homeotic transformation, a phenotype that is expected to occur when the expression of the homeotic gene is changed due to the loss of chromatin insulators. Instead, they observed that only a sub-element within Fub-1 has an insulator function while the other sub-element that contains an active promoter suppresses insulator activity. They further demonstrated that although there is no detectable phenotype when both sub-elements are deleted, changing the direction of the promoter or stopping transcription by adding an SV40 terminator in between the two sub-elements could relieve the suppression of insulator activity. From this evidence, the authors conclude that transcriptional read-through from the active promoter of a non-coding transcript regulates the insulator activity of Fub-1.

The finding provides a new angle to examine regulation by insulators and reveals a new function of active promoters of non-coding transcripts. The work also leaves further questions, for example, how general is such a mechanism in the genome organization of Drosophila and other organisms, and what is the significance of the mechanism given that deleting the Fub-1 insulator does not cause phenotypic outcomes in Drosophila? In the discussion, the authors elaborated on possibilities to discuss these questions.

Reviewer #1 (Public Review):

Maksim Kleverov et al. developed the tool called Phantasus, a web application for matrix visualization and analysis of gene expression data generated by either microarray or RNA-seq technologies. By Phantasus, the users can load, normalize, and plot their own data or those available in public databases and investigate the samples to remove outliers before the differential expression analysis.

Phantasus can be accessed on-line or can be installed locally from Bioconductor.<br /> One of the advantages of the web application is that it combines an interactive graphical user interface with access to various R-based analysis methods. For the methods that rely on functions that are already available in the existing R packages, for such practices, only wrapper R functions are implemented. The tool was developed focusing on being helpful to both expert and non-expert users in bioinformatic gene expression analysis.

Reviewer #2 (Public Review):

Maksim et al. present Phantasus, a web application for interactive gene expression analysis. The tool allows the user to load microarrays and RNA-Seq data from NCBI GEO.<br /> The user is able to explore, normalize, filter and perform differential expression analysis using limma or DESeq2 pipelines for microarray and RNA-Seq data, respectively. The web tool is capable of generating figures such as PCA and volcano plots and performing gene set enrichment analysis. Phantasus has some advantages when compared to the set of tools already available, showing a good trade-off between ease of use, access to data and different functions. Furthermore, the application is open source and the pre-processed cache files are provided by the authors. Thus, the more experienced user can install the tool on a local computer.

Finally, Phantasus is limited to standardized analyzes available in its internal methods and databases, which may not meet the needs of researchers who wish to apply different types of quantification and normalization. However, this is the ideal tool for the non-bioinformatics user who wants to reanalyze public data or perform simple differential expression analyzes on their own data.

Reviewer #3 (Public Review):

Software UX design is not a trivial task and a point-and-click interface may become difficult to use or misleading when such design is not very well crafted. While Phantasus is a laudable effort to bring some of the out-of-the box transcriptomics workflows closer to the broader community of point-and-click users, there are a number of shortcomings that the authors may want to consider improving. Here I list the ones I found running Phantasus locally through the available Bioconductor package:

1. The feature of loading in one click one of the thousands of available GEO datasets is great. However, one important use of any such interfaces is the possibility for the users to analyze his/her own data. One of the standard formats for storing tables of RNA-seq counts are CSV files. However, if we try to upload from the computer a CSV file with expression data, such as the counts stored in the file GSE120660_PCamerge_hg38.csv.gz from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120660, a first problem is that the system does not recognize that the CSV file is compressed. A second problem is that it does not recognize that values are separated by commas, the very original CSV format, giving a cryptic error "columnVector is undefined". If we transform the CSV format into tab-separated values (TSV) format, then it works, but this constitutes already a first barrier for the target user of Phantasus.

2. Many RNA-seq processing pipelines use Ensembl annotations, which for the purpose of downstream interpretation of the analysis, need to be translated into HUGO gene symbols. When I try to annotate the rows to translate the<br /> Ensembl gene identifiers, I get the error

"There is no AnnotationDB on server. Ask administrator to put AnnotationDB sqlite databases in cacheDir/annotationdb folder"

3. When trying to normalize the RNA-seq counts, there are no standard options such as within-library (RPKM, FPKM) or between-library (TMM) normalization procedures. If I take log2(1+x) a new tab is created with the normalized data, but it's not easy to realize what happened because the tab has the same name as the previous one and while the colors of the heatmap changed to reflect the new scale of the data, this is quite subtle. This may cause that an unexperienced user to apply the same normalization step again on the normalized data. Ideally, the interface should lead the user through a pipeline, reducing unnecessary degrees of freedom associated with each step.

4. 4. Phantasus allows one to filter out lowly-expressed genes by averaging expression of genes across samples and discarding/selecting genes using some cutoff value on that average. This strategy is fine, but to make an informed decision on that cutoff it would be useful to see a density plot of those averages that would allow one to identify the modes of low and high expression and decide the cutoff value that separates them. It would be also nice to have an interface to the filterByExpr() function from the edgeR package, which provides more control on how to filter out lowly-expressed genes.

5. When attempting a differential expression (DE) analysis, a popup window appears saying:

"Your dataset is filtered. Limma will apply to unfiltered dataset. Consider using New Heat Map tool."

One of the main purposes of filtering lowly-expressed genes is mainly to conduct a DE analysis afterwards, so it does not make sense that the tool says that such an analysis will be done on the unfiltered dataset. The reference to the "New Heat Map tool" is vague and unclear where should the user look for that other tool, without any further information or link.

6. The DE analysis only allows for a two-sample group comparison, which is an important limitation in the question we may want to address. The construction of more complex designs could be graphically aided by using the ExploreModelMatrix Bioconductor package (Soneson et al, F1000Research, 2020).

7. When trying to perform a pathway analysis with FGSEA, I get the following error:

"Couldn't load FGSEA meta information. Please try again in a moment. Error: cannot open the connection In call: file(file, "rt")

Finally, there have been already some efforts to approach R and Bioconductor transcriptomics pipelines to point-and-click users, such as iSEE (Rue-Albrecht et al, 2018) and GeneTonic (Marini et al, 2021) but they are not compared or at least cited in the present work. One nice features of these two tools that I missed in Phantasus is the possibility of generating the R code that produces the analysis performed through the interface. This is important to provide a way to ensure the reproducibility of the analyses performed.

Reviewer #1 (Public Review):

In this work, the authors propose a "transfer learning" approach for modeling the properties of sequences that are selected from larger sequence pools on the basis of biophysical or functional properties, where the source populations may themselves be biased in composition. Examples include the set of immunogenic peptides, considered as a subset of all HLA-presented peptides, or the set of TCRs that are specific for a given peptide epitope, as selected from within the much larger pool of all peripheral TCRs. The motivation for transfer learning is that there may only be small numbers of selected sequences available for training and many more examples of the background sequences. Rather than directly fitting a single model on the selected sequences, the idea is to first fit a background model that captures the properties of the source/background population of sequences, using the many examples available for training, and then train a "differential" model that specifically seeks to capture the differences between the selected and background populations. This differential model is trained using the subset of selected sequences, by optimizing their likelihood under a composite model that combines the background model (whose parameters are frozen) and the differential model. The specific architecture used here is the "restricted Boltzmann machine" (RBM), which can be thought of as a generalization of the position-weight matrix approach that can capture pairwise and higher-order interactions between positions. The applications are the two mentioned above, prediction of immunogenic peptides and prediction of TCRs specific for a given peptide-MHC epitope. This work builds on previous work by the authors applying the RBM architecture to peptide-MHC binding [Bravi et al., 2021b] and T-cell responses [Bravi et al., 2021a]. The advance here is in formalizing the "differential" framework and testing immunogenicity prediction and epitope specificity. Considering the field and the current state of the art, the main contributions of the manuscript appear to be theoretical/conceptual, in introducing the "diffRBM" method and providing a range of evaluations of its performance, for example, the use of contact prediction to assess the model. For TCR-epitope prediction, it does not look like the method improves over methods like TCRex or TCRdist, though an advantage is that the parameters may be more interpretable than some black box machine learning approaches. Also for epitope prediction, as noted by the authors, the model may be learning features that differentiate TCRs expressed by CD8+ T cells from the background of all TCRs (which is probably weighted toward CD4+ T cells). This would explain the poorer performance discriminating TCRs specific for one MHC class I epitope from those specified for a different class I epitope. For immunogenicity prediction, evaluations are so dependent on the specifics of the datasets, and the feature itself is so murky, that it's hard to say whether there is a performance advance here.

One nice feature of the diffRBM model is that scores ("single-site factors") can be assigned to individual amino acids in a peptide (or TCR) sequence that captures the contribution of that amino acid at that position to the overall score of the sequence, taking into account the sequence context. The authors show that these single-site factors, for the diffRBM model trained on immunogenic peptides, highlight positions that tend to be involved in TCR contacts as well as specific amino acids, such as "W at position 5", that have been found in previous studies to enhance TCR recognition. The single-site factors for a diffRBM model trained on epitope-specific TCRs appear to do a reasonable job of predicting CDR3 positions that contact the peptide.

Overall, the conclusions of the study are well-supported and the descriptions of the method's performance are balanced. The manuscript is well-written, and the supporting information nicely addresses minor questions that come up in reading the main text. One minor quibble I have is with the description of the method as "unsupervised", especially in the TCR-epitope prediction setting, since the sequences provided to the diffRBM for training, and which the model is tasked with learning differences between, is exactly the positive and negative sequences for the AUROC calculations (up to train/test sampling). It is also confusing to me that the overall selection factors for TCR-epitope binding are so very modest (0.19 for Flu M158, for example; Figure S20D, this is the "effective fraction of sequences retained in selected data compared to background ones"). This doesn't seem like it can be correct, given how focused some of these epitope-specific repertoires are. Overall, though, the study and associated software tools are likely to be useful contributions to the field.

Reviewer #2 (Public Review):

The work by Bravi et al. introduces a learning technique based on Restricted Boltzmann machines, that uses analog to differential learning to model two distinct datasets being part of a common biophysical framework but that behave differently depending on a set of parameters with "background" and "select" features. The biological problem tackled by the authors is the prediction of immunogenetic peptides versus non-immunogenetic ones, as well as determining the sequence features related to binding recognition.

My assessment of the strengths and weaknesses of this work is the following:

Strengths

The authors propose a novel and technically robust solution to a significant and currently unsolved problem in molecular immunology. They are detailed and exhaustive in the description of the formulation of their model as well as in the assessment analysis. Being this a hard problem, the results presented seem a very important step forward not only to solve some of these problems but also to provide convincing arguments that this methodology is more general than other previous approaches; that it can be applied to both immunogenicity prediction as well as binding specificity and is of generative nature. This can have a significant use in therapeutic applications. Another strength of this work is that their methodology could be easily applicable to other biological problems that deal with general versus selected features. For instance, specificity in recognition of other protein-protein interactions, protein-RNA recognition as well as the analysis of ever-growing SELEX and in vitro evolution datasets. Finally, I thought that the efforts of this work to provide "interpretable" learning models are important and definitely a strength of this work.

Weaknesses

As stated before, this work is detailed in nature and contains technical details to make it reproducible. However, in the attempt of the authors to compare against the large number of alternative approaches to this model, I felt that the readability of the article is affected. If this article is meant to be read by broader audiences that might utilize this framework in immunology research, at points the manuscript feels lost in comparison and descriptions of other methods. This is due to the fact that every time a new technical method is introduced, readers want to know about a comparison with other methods, but I feel that the manuscript can be rewritten in such a way that those technical comparisons don't become the major point of the paper and focuses more on how the predictive results of the model can be then applied in immunology. A similar point can also be raised about the methods section, although it has the advantage of being exhaustive and detailed, it also makes it hard for the reader to focus on the most important parts of the work. Perhaps, a better distribution of the methods and SI methods could help streamline the readability of this interesting work.

Reviewer #3 (Public Review):

The authors present in great detail a novel transfer of learning AI model architecture called diffRBM, which is based on the original RBM papers [Hinton, 2002, Hinton and Salakhutdinov, 2006]. They further show how this tool can be used to assess the immunogenicity of TCR positions and the importance of different by-position amino acid usages in creating this immunogenicity. They show that this novel method identifies all known important positions at least as well as existing analytical and structural methods, potentially in a more explanatory way.

Reviewer #1 (Public Review):

CD73 is a promising biomarker in cancer and has been characterized as having an immunosuppressive role in the tumor microenvironment. However, many cancer cell-intrinsic roles of CD73 are still under investigation. In this work, the authors explore the immune-independent roles of CD73 in cancer and demonstrate a function in maintaining metabolic fitness in cancer cells. The authors utilize genetic and pharmacological inhibition of CD73 to characterize metabolic changes in a panel of cancer cell lines and assess tumor growth in vivo. Furthermore, the authors demonstrate that the impaired metabolic fitness due to CD73 inhibition rendered cancer cells more susceptible to DNA-damaging agents. Overall, this work demonstrates the new roles of CD73 in cancer and provides a rationale for combination therapies including CD73 inhibition.

Reviewer #2 (Public Review):

This manuscript describes the involvement of CD73 in tumor cell metabolism by inhibiting CD73 expression in a CD73-positive tumor cell line. The authors demonstrated that CD73 deletion decreases aspartate synthesis via the alteration of mitochondrial respiration. The study is well-designed and the data are convincing.

Reviewer #1 (Public Review):

This important study by Bonnet et al addresses the question of how AMPA receptor numbers at the synapse are regulated during basal conditions and during chemically induced Long Term Potentiation (cLTP). Specifically, the study aims to determine which molecular mechanisms contribute to export from Golgi/the ER, intracellular trafficking of AMPA receptors, and insertion into the synaptic plasma membrane, respectively. The authors had previously established an approach to separately measure these distinct events: to enable a high-fidelity measurement of the Golgi/ER release and subsequent speed of GluA1-containing vesicles, the release of vesicles is synchronized. Finally, the insertion into the plasma membrane is measured by immunolabelling.<br /> The authors set out to specifically understand the contributions of two auxiliary proteins in AMPA receptor expression: 4.1N and SAP97. Namely, the authors find that under basal conditions, binding of SAP97 to GluA1 is necessary for the GluA1 release from the Golgi/ER and intracellular trafficking. In turn, binding of 4.1N to GluA1 is necessary for the exocytosis of the receptor at the plasma membrane at basal conditions. Following induction of cLTP, the authors find that the role of SAP97 remains similar to that observed under basal conditions but, interestingly, 4.1N significantly grows in influence and is required for all stages of GluA1 expression - from release from the Golgi/ER to exocytosis and insertion into the plasma membrane.

In summary, using convincing methodology, the authors are able to dissect the distinct roles of two proteins that bind to the C-terminal domain of the AMPA receptor subunit GluA1: 4.1N and SAP97.<br /> The scientific rigor is high in this work. For example, the question of whether the expression of GluA1 depends on physical interaction with 4.1N and/or SAP97 is nicely addressed by several, well-considered experiments. Overall, the authors' claims are well justified by the data presented.

I did not find any major scientific weaknesses in this manuscript. The approach developed by the group appears to be a good tool for studying the molecular choreography at the synapse under different conditions and the results will be of interest to a wide range of neuroscientists.

Reviewer #2 (Public Review):

The study of Bonnet et al. focuses on how proteins 4.1N and SAP97 affect intracellular trafficking (IT) and externalisation of AMPA receptors (AMPARs) in cultured rat hippocampal neurons. To specifically look at IT, the authors combine the so-called Ariad approach with confocal spinning disc microscopy and photobleaching of dendritic regions, developed in their previous paper (Hangen et al., 2018). This allowed them to synchronously release newly synthesized AMPARs from the ER (upon addition of a synthetic ligand) and measure the number of vesicles carrying AMPARs, their velocity as well as time spent moving and pausing. To detect the insertion of AMPARs at the plasma membrane, live immunolabelling was used. Using RNA-based knock-outs of 4.1N and SAP97 proteins as well as mutants of the AMPAR C-terminus which mediates interactions with these two proteins, in basal conditions and during chemically induced long-term potentiation (cLTP), they could show that the two proteins play different roles in AMPAR trafficking, with SAP97 more profoundly affecting IT compared to 4.1N in basal conditions.

The unique approach allowing observation of IT of AMPARs and a series of tested mutants in basal and cLTP conditions are the main strengths of the paper and also result in the main new finding which is differential regulation of AMPAR IT by 4.1N and SAP97. The measurements of IT parameters and externalisation of unmodified AMPARs across different conditions (and the previous publication) are very reproducible and that makes the whole approach very reassuring.

However, a few points regarding the methodology and analysis remained after reading the manuscript:<br /> Due to the tested mutants, I find the data for the 4.1N-AMPAR interaction particularly strong, but less so for SAP97. For SAP97, sh-RNA experiments are performed and the delta7 mutant is tested. In the case of 4.1N, sh-RNA knockouts were found to be affected by interactions other than AMPAR-4.1N, so the same might be the case for SAP97. Delta4.1N mutant was found to be less reliable than the S816A S818A mutant, in which the AMPAR C-terminus length was retained and 4.1N binding abolished via two mutations. Although only 4 amino acids were removed in the delta7 mutant, this still changes the length of the AMPAR C-terminus. It would be good to acknowledge these aspects of SAP97 experiments.

As there is a number of conditions tested in the paper and to make the conclusions clearer, it might be useful to provide a summary table. It seems to me there are conditions where IT parameters remain unchanged, but no condition where externalisation is not reduced compared to the relevant control condition. Hence, I would agree that 4.1N might be less relevant than SAP97 for IT, but I am not sure it is clear that 4.1N plays a bigger role in externalisation than SAP97, which is what the conclusion figure (Fig. 7) seems to be implying.

Reviewer #3 (Public Review):

This manuscript uses novel techniques to examine the intracellular trafficking and membrane insertion of AMPA receptors to dissect the molecular mechanism involved in regulating these processes in neuronal cultures under basal conditions and during the induction of a chemical form of long-term potentiation (LTP). Specifically, they examine the role of the interaction of the GluA1 subunit with two neuronal proteins SAP97 and 4.1N. The manuscript uses a novel approach to synchronize and temporally control the release of GluA1-containing receptors from the ER and examine its trafficking through the Golgi and dendrites to the plasma membrane. This assay can measure the number of GluA1-containing intracellular vesicles, their speed of trafficking, and the delivery of newly synthesized GluA1 to the surface.

First, the authors use shRNA knockdown (KD) techniques to decrease the expression of SAP97 and 4.1 and found dramatic effects on the number of GluA1-containing vesicles and plasma membrane insertion of GluA1. SAP97 had a larger effect on trafficking while 4.1N had a larger effect on plasma membrane insertion. The authors then went on to use mutants of GluA1 that lack the whole C-terminal domain or mutations in the SAP97 and 4.1N biding sites in GluA1 C-termini and examine the trafficking of these mutants. These mutations decreased the intracellular trafficking and the membrane insertion of GluA1. In addition, the authors mutated phosphorylation sites that have been reported to regulate the interaction of GluA1 with 4.1N. Mutations in these sites that eliminated phosphorylation inhibits membrane insertion while the phosphomimetic mutations did not affect membrane insertion. Finally, mutations in the SAP97 and 4.1N binding sites including mutations in the phosphorylation sites also inhibited chemical-induced LTP increases in the regulation of GluA1 ER-Golgi exit, intracellular transport, and membrane insertion.

These studies are well done and novel and provide support for the role of the GluA1 C-termini and its protein interactors in the trafficking of the AMPA receptor under basal and plasticity conditions. This contributes new data using a novel approach to the controversy over the role of the C-termini of AMPA receptors in the regulation of AMPA receptor function. It supports the role of these interactions in AMPA receptor function.

Reviewer #1 (Public Review):

The authors used viral replication assays to select for and define the resistance pathways against ten developmental Protease Inhibitors (PIs) and their parent drug, Darunavir (DRV), which is one of the leading antiretrovirals used to treat people living with HIV/AIDS. There are two specific regions of the small molecule inhibitors that are actively being modified to increase potency against drug-resistant mutants, the P1' region, and the P2' region, which protrude into pockets of PR occupied by I84 / I50, and a neighboring region containing D29-D30, respectively. Selections using drugs containing small modifications of the P1' region led to primary mutations at PR position I184V, but not I150V. In contrast, selections using drugs containing larger modifications at the P1' region led to primary mutations at PR position I150V, a pathway that is less fit. Furthermore, having modifications at the P2' position added additional potency to the inhibitors, most evident within the I184V pathway. The authors rationalize their findings using previously published structural biology data. These results provide the first evidence for de novo pathway selection using state-of-the-art drugs based on the DRV scaffold and provide an atomic basis for designing compounds that are highly active against DRMs. The comprehensive nature of the analysis of drug resistance to the latest generation PIs, and the insights gained that can be rationalized based on atomic structure, are the major strengths of the paper. The weakness is the lack of commentary on the accessory mutations, which frequently arise in the selections but are not well-explained. It would also be useful to provide some concrete suggestions for minimizing drug resistance using 5th generation PIs, as part of a discussion.

Reviewer #2 (Public Review):

Spielvogel and colleagues report in vitro studies investigating the development of de novo resistance of HIV to Darunavir. Darunavir is one of the most widely used protease inhibitors worldwide, but pathways for the development of de novo resistance are uncertain, as many individuals have had prior protease inhibitor experience prior to treatment with darunavir. As such studies of the kind reported here are essential. The authors have performed foundational studies using compelling and complementary approaches to characterize the emergence of protease drug resistance. They have investigated darunavir, as well as a series of 10 structurally related compounds to provide a clear picture of the role of side chains in the development of resistance. They have complemented these studies with precise structural studies of the interactions of drug with WT and mutant viruses. These data are relevant to the understanding of clinical responses to darunavir and are important in developing new protease inhibitors.

Reviewer #3 (Public Review):

Darunavir (DRV) has been shown to be a potent HIV-1 protease inhibitor in individuals, has pM binding to the protease active site, has activity to protease inhibitor resistant HIV-1s, and has been reported to be difficult to develop resistance to individuals and in tissue culture. The authors argue that given published studies of generating HIV-1 resistance to DRV in tissue culture was not accomplished and all published studies started with either a drug-resistant virus or a combination of drug-resistant viruses for selection, new information can be gleaned as to the viral mutational pathways leading to drug-resistant viruses from HIV-1 wild type (no pre-existing drug mutations) NL4-3.

To better understand the development of HIV-1 wild-type DRV resistance, Spielvogel and colleagues detail their studies on characterizing HIV-1 protease genomic and structural alterations and viral fitness before and during the development of tissue culture resistance to DRV, as well as 10 new compounds (UMass compound series) based on DRV. The UMass compounds have distinct R1 and R2 groups as compared to DRV, which provides for a comprehensive chemical toolset to probe protease genetics and structural changes and alterations in viral fitness resulting during HIV protease drug resistance development in tissue culture. Differences in HIV protease resistance patterns developing over time combined with the potency of the protease inhibitors to HIV mutants resulting from inhibitor selections provide insights as to how DRV chemical groups impact resistance development. The manuscript is comprehensive, well-written, and informative, yet dense and with some figures that readers may not find informative.

Protease inhibitor tissue culture selection of wild-type NL4-3 was based on increasing protease inhibitor concentrations over time. Generally, the DRV resistance mutations that came up early de novo from wild-type NL4-3 virus were, 84V, followed by the acquisition of accessory mutations, predominately 54L and 82I, with 84V, 85V, 46I, 47V, 63P, and others as well, which became entrenched over time. The 84V mutational series have been reported for DRV as the authors noted. To determine the DRV selection pattern from pre-existing HIV single drug-resistant population a pool of 26 single mutant viruses was used for selection. Similar patterns were seen as for wild-type viruses, starting with 84V.

Interestingly, when the UMass compound series was used to select wild-type NL4-3 in tissue culture, 3 mutational series resulted, a protease mutational pattern similar to DRV (UMass 1, and 4, a protease mutational pattern starting with 50V, and followed by the predominate accessory mutations 10F, 13V, 33F, 46I, 63P, and 71V, but not 84V (UMass 3,6,7,8,9, and 10) and a mixture of both populations (UMass 2 and 5). When the HIV single drug-resistant population pool was used, which didn't contain 50V, was used for selection, UMass 2,4,7, and 8 retained the same mutational patterns as the original wild-type HIV selection, where, interestingly, UMass 6 utilized the 84V mutational pathway, rather than 50V, when the 84V mutation was pre-existing.

The results pointed out that modification of the DRV R2 and R1 groups alters selection patterns. It appears that a smaller hydrophobic side chain at the P1' position appears to drive towards 84V selection, whereas a larger side chain selects for the 50V pathway. UMass compounds 2, 5, 7, and 10 demonstrate the highest potency to both 50V/71V and 84V mutant viruses. Interestingly, UMass 2 and 5 were selected for both 50V/71V and 84V resistance mutational pathways, whereas 7 and 10 were selected for 50V/71V pathways.

Based on entry/replication studies, the authors argue that pushing viruses to select 50V/71V mutational pathways in protease, vs 84V mutational pathways in protease, promotes a higher genetic barrier to overcome resistance. This would be due to the reduction in fitness for the 50V/71V protease mutant and the large number of accessory mutants required to regain fitness. However, more in-depth analyses of the various mutants are warranted to support this point, such as head-to-head viral replication studies. A further limitation to the general conclusions is whether mutations in Gag provide for compensatory mutations to augment protease (and viral) fitness for the UMass compound findings.

Reviewer #1 (Public Review):

Thakkar et al describe the immune effects of 3rd and 4th doses of COVID-19 monovalent vaccines in a diverse cohort of immunocompromised cancer patients. They describe augmentation of anti-Spike antibodies after dose 3, especially seroconversion in 57% of patients, followed by a durable response over six months. The fourth dose was associated with increased anti-Spike antibodies in 67% of patients. T-cell responses were seen in 74% and 94% of patients after the third and fourth doses respectively. Strikingly, neutralization of Omicron was absent in all patients after the third dose but increased to 33% after the fourth dose.

Strengths:<br /> Diverse cohort (34% Caucasian, 31% AA, 25% Hispanic 8% Asian) including 106 cancer patients after dose 3, of which 47 patients were longitudinally assessed for six months, as well as eighteen patients assessed after the fourth dose.<br /> Seronegative as well as seropositive patients benefit from a third dose of vaccination.<br /> Assessment of cellular (T cell) immune responses and viral neutralization against wild-type as well as Omicron variant is commendable.

Weaknesses:<br /> The efficacy of the bivalent vaccine (Omicron specific) is not studied here, since the fourth dose of vaccine was a monovalent vaccine. This should be clarified in the discussion.<br /> The authors describe an increase in anti-S titers after monoclonal antibodies. Were any of the patients receiving IVIG, and what was the effect, if any on Anti-S antibodies?<br /> Characteristics of breakthrough infections, particularly if they had prolonged duration, would be important to include.

Reviewer #2 (Public Review):

In this manuscript, Thakkar and colleagues evaluate the immunogenicity of 3rd and 4th doses of SARS-CoV2 vaccinations in patients with cancer. The authors find that additional vaccine doses are able to seroconvert a subset of patients and that antibody levels correlate with T-cell responses and viral neutralization.

The main strengths of this manuscript are:<br /> 1) The authors systemically performed a broad array of immunological assessments, including assessments of antibody levels, T cell activity, and neutralization assays, in a large cohort of patients with cancer receiving 3rd and 4th doses of COVID vaccines.<br /> 2) The authors recruited an ethnically diverse cohort of patients with diverse cancer types, though enrolled participants were enriched for hematological malignancies.<br /> 3) Prior to FDA/CDC guidance supporting a 4th vaccine dose, the authors recruited participants with no or inadequate responses into a prospective clinical trial of a 4th dose, the results of which are outlined here.<br /> 4) The authors' findings that patients with hematologic malignancies and those receiving anti-CD20/BTK inhibitors have lower immunological responses to SARS-CoV-2 vaccines are consistent with multiple prior studies, including prior studies from these authors.<br /> 5) The authors also find that 3rd and 4th COVID vaccine doses are able to seroconvert a subset of patients with no or "inadequate" responses, though it's unclear whether seroconversion is enough for true protection from SARS-CoV-2 infection.

The main weaknesses of the manuscript include:<br /> 1) The study cohorts disproportionately enrolled patients with hematological malignancies who have been previously shown to mount lower immunological responses to COVID-19 vaccines; thus, the findings may not be representative of a typical oncology patient population.<br /> 2) The subgroup analyses were relatively small.<br /> 3) The nomenclature used in the manuscript was confusing when it came to "baseline" assessments and boosters versus additional doses of vaccines.<br /> 4) Ultimately, the major limitation of this manuscript is that antibody levels/T-cell responses/neutralization are surrogates for immune protection against SARS-CoV-2, but it's unclear what defines the ideal cutoffs for protection. Simply seroconverting may still be insufficient. The authors don't provide data showing antibody levels as relates to breakthrough infection, likely because they are underpowered for this analysis.

Reviewer #1 (Public Review):

In this work, Diekmann and Cheng have proposed a new computational model for hippocampal replay. The new model is based on the linear RL work by Piray and Daw 2021, and addresses a fundamental problem in the seminal replay model of Mattar and Daw 2018 (M&D). The new model is based on the default representation, which is a realistic account for state closeness in model-based RL.

This study addresses an important problem in neuroscience at the computational level. The proposed theory is a significant normative computational model that captures important aspects of experimental data in the replay literature. The paper is very well-written (a difficult task for a pure computational work) and figures illustrate the main concepts very well. I have only one question/suggestion:

I believe that there is important data in the literature that cannot be explained by the current model, especially regarding representation of the goal. That is fine; no model is complete, but it is important that authors discuss those caveats in the discussion.

Reviewer #2 (Public Review):

In their paper, Diekmann and Cheng describe a model for the generation of so-called hippocampal replay sequences - a process thought to play a central role in planning, decision making and the consolidation of new memories. Given the diversity of functions replay has been purported to support coming up with a single mechanism for it has remained a challenge. Diekmann and Cheng are able to achieve this with a relatively simple and intuitive model. Specifically, in their model replay is determined based on a finite number of factors; namely, the likelihood and reward-association of an experience, how similar an experience is with an agent's/animal's current state and whether an experience matches *too* much the current state (so to avoid replaying persistently the same state). With these few ingredients the authors are able to replicate important replay findings. Further, the authors emphasise that their model has the significant advantage of being more biologically feasible than other contemporary models in the field.

The model achieves its objectives broadly however the authors have not sufficiently explained the advantage of their model over other models - i.e. how they address the limitations of previous models - nor have they attempted to replicate multiple important features of replay - such as that it can often be non-local. Finally, the details of the biological implementation of their model, particularly with regard to the two modes it can operate in, have not been fleshed out. These points limit the potential impact of the model.

Reviewer #3 (Public Review):

This manuscript provides a remarkably simple, yet effective, model of hippocampal replay. A replay event is stitched together as a chain of reactivated experiences. Individual experiences are prioritized for reactivation according to three intuitive measures: the spatial proximity of an experience to that previously reactivated, the frequency of and reward associated with an experience, and an inhibitory term that propagates the replay across space. Under certain conditions, their model can produce replays that are nearly as optimal--in terms of teaching a reinforcement learning agent to successfully navigate to a reward--as those produced by Mattar and Daw's 2018 model which, by design, generates the most behaviorally useful replays.

The authors assert that their model can recapitulate the replay statistics observed in a subset of experimental works, including the ability of replay to generate novel 'short cuts' from segments of past experience, the resemblance of replay to Brownian diffusion following random exploration, the ability of replay to steer around environmental barriers, and the observation of pre-play. These claims are generally well supported by the data presented (in particular, the model seems to be quite robust to different parameters).

One important caveat is that the proposed model requires two modes ('default' and 'reverse') to simultaneously account for empirical findings and provide behavioral utility (the performance of the agent is poor when using the default mode, but comparable with that of Mattar and Daw in the reverse mode). The authors suggest that the brain could dynamically switch between modes (dubbed the 'dynamic' mode). I feel that the paper would be strengthened by focusing on this dynamic mode throughout and demonstrating that it produces replays with statistics matching empirical data. For example, what is the distribution of forward and reverse replays produced by the default model (figure 3D)? Since neither mode by itself is adequately consistent with experimental findings, showing that the model appropriately switches between modes would strengthen its plausibility.

The authors state that their model is able to recapitulate the finding that replay in sleep following random exploration can be described by Brownian diffusion. A key point in that paper was that the preceding behavior was not diffusive. The authors go some way to address this point by showing that their model produces diffusive replays even if the strength of experience across space is not uniform. However, it isn't clear to me that modeling non-uniform experience strength is equivalent to modeling non-diffusive behaviorally trajectories. A more convincing test would have been to simulate realistic behavioral trajectories and show that subsequent replay events are still diffusive.

In my view, the fact that the model can generate 'pre-play' (in this case, replay of a visually cued, but unvisited arm of the maze) is not particularly informative. In order to generate pre-play, the authors allow the agent to 'visually explore' the cued arm. The implementation of this visual exploration is equivalent to allowing the agent a limited amount of real physical experience on the cued arm. Thus, the finding of replay for the cued arm is unsurprising. It would have been more useful to show that the model over-represents the rewarded arm on a T-maze, given equal exploration of the arms (as in Mattar and Daw).

Also debatable is the authors' assertion that their model is biologically plausible, while that of Mattar and Daw is not. While the former model is certainly computationally less expensive, little experimental data exists that could definitively point to the biological plausibility or implausibility of either model.

Overall, this model is impressive in its ability to generate replay events with realistic and varied statistics, using only a few simple rules. It will be a welcomed addition to the fields of replay, learning and memory, and reinforcement learning.

Reviewer #1 (Public Review):

The manuscript by Zhang et al. titled "Retinal microvascular and neuronal pathologies probed in vivo by adaptive optical two-photon fluorescence microscopy" reports a custom-designed two-photon fluorescence microscope coupled with adaptive optics (AO-2PFM) that allows in vivo imaging of mouse retinal structures at a lateral resolution of ~0.8 μm and axial resolution of ~6.7 μm. The authors provided two examples of applications for in vivo imaging of mouse retinal structure and function. In the first example, AO-2PFM has been used to visualize capillary lesions in a mouse model of retinal angiomatous proliferation (RAP), a form of age-related macular degeneration characterized by capillary proliferation and focal vascular leakage. Using AO-2PFM, the authors observed capillary disruption, with which dye leakage was associated. In the second example, the authors performed in vivo functional imaging of Ca2+ signals in RGCs of the rd1 mouse - a model of retinal degeneration with a mutation in the Pde6B gene. They interpreted the elevated Ca2+ signals in RGCs of rd1 mouse as an indication of RGC hyperactivity that has been reported in ex vivo electrophysiological recordings. They further observed dampened Ca2+ signals in RGCs of rd1 mouse upon retro-orbital injection of lidocaine.

The authors carefully documented the technical features of this state-of-the-art in vivo mouse retina imaging system. The manuscript is very well written and, needless to say, the images presented are of superb quality. There is no doubt that the system will be of great value to many retinal researchers studying the normal structure and function of the retina as well as tracking the pathophysiology of retinal disease models longitudinally.

Reviewer #2 (Public Review):

This is a technical study by Ji and colleagues that uses adaptive optics to correct for the intrinsic aberrations of the mouse eye to improve the quality of in vivo two-photon retinal imaging. Currently, the most common approach to retinal imaging is to use isolated ex vivo retina preparations for direct access to the tissue. However, in vivo retinal imaging offers the unique advantage of tracking long-term changes in vascular/cellular structure and function in disease or development. The authors describe an optimized adaptive optical two-photon microscope setup for imaging fluorescent markers through the mouse eye and evaluate the effect of the wavefront sensing area on the imaging quality. They further demonstrate the power of this setup by monitoring the focal vascular leakage in a mouse model of proliferative vascular retinopathy and by monitoring drug-induced population activity changes using GCaMP6s in a mouse model of photoreceptor degeneration. Together, these results provide a valuable, enabling technical resource for applying AO-two-photo imaging to study outstanding questions in retinal biology that require long-term in vivo imaging. Overall, this is an important development with a broad impact on the investigation of neuronal and vascular functions in the retina.

Reviewer #3 (Public Review):

Zhang, Q. et al. developed a two-photon fluorescence microscope (2PFM) by incorporating direct wavefront sensing adaptive optics (AO), which is optimized for mouse in vivo retinal imaging. By using the same 2PFM with the option of using or not using the incorporated AO system, this team compared the in vivo retinal images and convincingly demonstrated that AO correction acquired brighter and higher resolution images of retinal ganglion cells (RGCs) and their axons in both densely and sparse labeled transgenic mouse lines, normal and defected capillary vasculatures, and RGC spontaneous activities detected by genetic Ca2+ sensor. Interestingly and importantly, this team found that a global correction by removing the common aberration from the entire FOV enhances imaging signals throughout the entire large FOV, indicating a preferable AO imaging strategy for large FOVs. The potential applications of the in vivo retinal imaging techniques and strategies developed by this study will certainly inspire further investigation of the dynamic morphological and functional changes of retinal vasculatures and neurons during disease progression and before and after treatments.

It would be beneficial to the manuscript and the readers if the authors can elaborate on optic design a little bit more. For example, whether the incorporation of AO adversely affects the 2PFM optic design? If the 2PFM can be further optimized by uncompromised optic design without incorporating AO, the quality of in vivo images will comparable to the AO-2PFM or not?

Reviewer #1 (Public Review):

This manuscript investigates the question of how polylysogeny impacts competition with a sensitive non-lysogen, and how this is shaped by phage resistance. This is an important and timely question, as lysogeny can be a strategy to invade new niches, and prophages are important vehicles for the acquisition of a range of virulence factors by pathogens including Klebsiella. The authors use a polylysogenic Klebsiella clone in competition with a non-lysogen that is sensitive to at least some of the prophages produced by the polylysogen. They compete these strains over a 30-day period and measure host population dynamics and evolution of phage resistance and lysogenic conversion in the (initially) sensitive competitor. Overall, the experiment shows that lysogen formation is relatively rare and short-lived. Instead, phage resistance through complete loss of the capsule is the primary mechanism evolving, but other resistant capsule mutants, with more subtle mutations affecting capsule expression, emerge as well. The authors have collected a very impressive amount of data and made some very interesting observations.

My main problem with this paper is that the manuscript lacks a clear narrative, making it very hard to extract the key message this paper wants to convey. Related to this, (some of) the conclusions that the authors make do not appear to be well supported by the data. For example, the authors conclude that selection favours more subtle capsule mutations because they are less costly than capsule-loss mutants (lines 497-500). However, there are no data to support this conclusion, as fitness costs of the various resistance phenotypes analysed were not measured. Apart from the genotypes, the data that are presented in this show that these subtle mutants have more subtle decreases in capsule production compared to the mutants that show a complete loss of capsule. But this does not tell us their relative cost. It also doesn't tell us how the emergence of these different mutants relates to phage pressure, because whilst bacterial population dynamics data are monitored meticulously, phage dynamics data are missing (I have not found them in the supplemental information either). This makes it impossible to directly relate the emergence of the various resistance mechanisms to phage infection pressure during the coevolution experiment, even though this appears to be a hypothesis the authors wish to test.

Overall I think the overarching question of the manuscript is important and the model system is a very relevant one to study this question, but in my view, the current data don't support the conclusions of the paper. Apart from these criticisms, the manuscript is very well written and the figures are overall easy to interpret.

Reviewer #2 (Public Review):

This manuscript presents data on multiple experiments regarding the co-evolution of poly-lysogenic and phage-susceptible Klebsiella pneumoniae strains. In particular, the manuscript aimed to determine the mechanisms of resistance that would shape bacterial competition over co-evolutionary timescales. The major finding is that the potential for lysogenization as a phage resistance mechanism is narrow and only likely to occur given certain circumstances. Moreover, the manuscript again reinforces the importance of receptor changes -initially loss, but modification in structure or expression over longer time scales- as a major mechanism of phage resistance that influences bacterial competition.

Strengths<br /> A major strength of this manuscript is the care in designing experiments and conducting follow-up experiments to isolate the essential elements to support each of the conclusions. This includes using orthogonal methods such as sequencing and modeling to support or expand the findings from culturing and experimental evolution. The study features results that were beautifully replicated (e.g. Figure 3) lending confidence to the findings.

Weaknesses<br /> Two weaknesses of the manuscript in its current form are: 1) a need to discuss other studies that also have found context-dependent results and 2) more focus on delivering the key overall "message" of the paper to the reader. Finally, not a weakness, but a (necessary) limitation is the study system, but this manuscript sets a bar for other groups to test in their systems to probe the generality of the findings.

The support for the conclusions is compelling. The findings were counter to the initial expectation (lysogenization as a major feature) and the manuscript does an admirable job of supporting the unexpected conclusion with thorough experimental work, supplemented with modeling.

This manuscript will be of great significance in microbial evolution, both for its implications in limiting the scope of lysogenization as a viable phage resistance mechanism in the long term and for its significant experimental rigor, particularly with regard to the co-evolutionary timescale studied. The study has very important implications for the evolution of antimicrobial resistance and phage therapy.

Reviewer #1 (Public Review):

Rab27 is a major regulator of insulin granule exocytosis from beta cells, and it acts via (at least) three distinct effector proteins; Granuphilin, Melanophilin and Exophilin-8. Although the role of each of these three Rab-effectors in the regulation of insulin secretion is fairly well-established from studies of KO mice, the functional hierarchy between the effectors remains largely unknown. This study by Zhao et al addresses this question by investigating how simultaneous loss of two these effectors influence insulin granule exocytosis and also provide an explanation for their differential regulation of this process. They propose that Exophilin-8 acts upstream of Melanophilin, which in turn is involved in crash-fusion of granules with the plasma membrane, and that the interaction between these two effectors require the exocyst complex. This mode of exocytosis is relatively rare and only accounts for around 20% of all fusion events. The majority of fusion events instead involves exocytosis of granules stably docked at the plasma membrane. The authors propose that this mode of exocytosis also depends on Exophilin-8, now acting by removal of a Granuphilin-mediated exocytic clamp.

Technically, this is a superb study where the authors use primary mouse islets isolated from both single and double KO mice and perform both bulk secretion assays and single-cell granule imaging to elucidate the role of Rab27 effectors in glucose-stimulated insulin secretion. Unfortunately, while visualization of granule dynamics is performed in living cells, visualization of the Rab27 effectors and the ecoxyst components is restricted to static immunofluorescence imaging. It is therefore difficult to reconcile granule dynamics with effector action. While the results are clearly presented and largely consistent with previous work, I feel that many of the conclusions are based on over-interpretation of data and that important control experiments are missing. The authors are able to confirm their and others' previous observations that each of the three Rab27 effectors have distinct functions during insulin secretion. A connection between insulin granule exocytosis and the Exocyst complex has also been established in previous studies. The most intriguing finding in this study is that the Exocyst complex function in cooperation with Rab27 and its effectors, thus connecting these two pathways, and that there appears to be a functional hierarchy amongst the Rab27 effectors where Exophilin-8 act upstream of the other two. What remains unclear to me is how this entire process is regulated and how it relates to prevailing models of insulin granule pools and modes of exocytosis.

Understanding the mechanism that regulate insulin secretion is imperative for understanding how this process fails in certain types of diabetes. This study reinforces the concept that the secretion of insulin granules is a very heterogenous process that involves multiple pools of granules and modes of exocytosis and provides important new information on how cross-talk between these pathways help to shape the secretory response and give it robustness.

Reviewer #2 (Public Review):

Insulin exocytosis is a tightly orchestrated process that involves proteins acting in complexes near the plasma membrane. The authors have contributed much of the field's knowledge on how exophilin anchors insulin granules in cortical actin and works with other effectors to prepare granules for exocytosis. Here they find that, while both exophilin and melanophilin localize to the exocyst, functionally they are not equivalent. TIRF imaging of monolayer dispersed beta cells, although a non-physiologic model to study islet cell secretion (which requires homotypic and heterotypic cell coupling), is nonetheless an established method that the authors have used with expert proficiency. The imaging and quantitative methods here should be broadly applicable to those studying secretory events at cellular resolution, and practical details e.g. the need for double transfection in RNAi experiments, are helpful and appreciated.

Reviewer #3 (Public Review):

In this manuscript Zhao et al investigated how multiple Rab27 effectors work to regulate insulin secretion by murine pancreatic b-cells. They do this by comparing the phenotypes of b-cells/islets lacking effectors doubly or singly. Their main findings/contributions are that:

Mlph works downstream of Myrip/exophilin-8 to mobilise granules for fusion from the actin network to the plasma membrane after stimulation.

Mlph and exophilin-8 interact via the exocyst

Down-regulation of exocyst affects exocytosis in cells expressing exophilin-8

Exophilin-8 promotes fusion of granules docked by granuphilin at the membrane

Exophilin-8 not required for Grph related granule docking at the plasma membrane

A model for how the three effectors coordinate ISG secretion. According to this model there are 2 insulin secretion pathways in b-cells; a) where Exo8 acts upstream of Mlph and with actin/Myosin Va/VIIa, exocyst and syntaxin 4 to move dock granules in actin and promote exocytosis, and b) where Exo8 works in an antagonistic manner with Grph promoting secretion of granules docked at the membrane by Grph.

This is an interesting/important question and the authors make important contributions (above). In general experiments are well designed and controlled but there are some questions that remain open that could have been included to make the study a more comprehensive analysis of Rab27 effectors in insulin secretion.

Reviewer #1 (Public Review):

Decidualization, denoting the transformation of endometrial stromal cells into specialized decidual cells, is a prerequisite for normal embryo implantation and successful pregnancy in humans. Abnormal cytokine-associated inflammation during decidualization can alter the endometrium's receptivity to healthy embryo implantation. Jiang and colleagues present an important analysis of the role and function of the Gaq axis on the inflammatory response during decidualization essential for early pregnancy, and present preliminary data on its clinical relevance.

The data narrative provides solid evidence of the mechanisms suggested by Jiang and colleagues. The study is highlighted both by the in vitro analysis and also by the study of human samples and subjects impacted by Recurrent Pregnancy Loss (RPL). Overall, the data seems to justify the conclusions taken, although some of the methodology and data interpretation require further clarification and justification.

Reviewer #2 (Public Review):

This manuscript provided evidence that Gaq is a key regulator of the expression of inflammatory cytokines to maintain the proper progress of decidualization of human endometrial stromal cells for successful implantation and pregnancy. The major strength of the manuscript is the experimental design to answer sequential scientific questions regarding the function of Gaq during decidualization in the human endometrium using various molecular and pharmacologic tools. A weak point of this manuscript is that the author did not provide a reason to focus on HDAC5 among various downstream targets for the study of Gaq. In addition, if the authors make a knockout mouse of Gaq and characterize its phenotypes to support what they found in human stromal cells, the findings in this manuscript could become a piece of compelling evidence for the importance of Gaq during decidualization in the human endometrium for a successful pregnancy. This could be the next scientific topic for the authors to pursue this project.

Reviewer #1 (Public Review):

The expression and localization of Foxc2 strongly suggest that its role is mainly confined to As undifferentiated spermatogonia (uSPGs). Lineage tracing demonstrated that all germ cells were derived from the FOXC2+ uSPGs. Specific ablation of the FOXC2+ uSPGs led to the depletion of all uSPG populations. Full spermatogenesis can be achieved through the transplantation of Foxc2+ uSPGs. Male germ cell-specific ablation of Foxc2 caused Sertoli-only testes in mice. CUT&Tag sequencing revealed that FOXC2 regulates the factors that inhibit the mitotic cell cycle, consistent with its potential role in maintaining a quiescent state in As spermatogonia. These data made the authors conclude that the FOXC2+ uSPG may be the true SSCs, essential for maintaining spermatogenesis. The conclusion is largely supported by the data presented, but two concerns should be addressed: 1) terminology used is confusing: primitive SSCs, primitive uSPGs, transit amplifying SSCs... 2) the GFP+ cells used for germ cell transplantation should be better controlled using THY1+ cells.

Reviewer #2 (Public Review):

The authors found FOXC2 is mainly expressed in As of mouse undifferentiated spermatogonia (uSPG). About 60% of As uSPG were FOXC2+ MKI67-, indicating that FOXC2 uSPG were quiescent. Similar spermatogonia (ZBTB16+ FOXC2+ MKI67-) were also found in human testis.

The lineage tracing experiment using Foxc2CRE/+;R26T/Gf/f mice demonstrated that all germ cells were derived from the FOXC2+ uSPG. Furthermore, specific ablation of the FOXC2+ uSPGs using Foxc2Cre/+;R26DTA/+ mice resulted in the depletion of all uSPG population. In the regenerative condition created by busulfan injection, all FOXC2+ uSPG survived and began to proliferate at around 30 days after busulfan injection. The survived FOXC2+ uSPGs generated all germ cells eventually. To examine the role of FOXC2 in the adult testis, spermatogenesis of Foxc2f/-;Ddx4-cre mice was analyzed. From a 2-month-old, the degenerative seminiferous tubules were increased and became Sertoli cell-only seminiferous tubules, indicating FOXC2 is required to maintain normal spermatogenesis in adult testes. To get insight into the role of FOXC2 in the uSPG, CUT&Tag sequencing was performed in sorted FOXC2+ uSPG from Foxc2CRE/+;R26T/Gf/f mice 3 days after TAM diet feeding. The results showed some unique biological processes, including negative regulation of the mitotic cell cycle, were enriched, suggesting the FOXC2 maintains a quiescent state in spermatogonia.

Lineage tracing experiments using transgenic mice of the TAM-inducing system was well-designed and demonstrated interesting results. Based on all data presented, the authors concluded that the FOXC2+ uSPG are primitive SSCs, an indispensable subpopulation to maintain adult spermatogenesis.

The conclusion of the mouse study is mostly supported by the data presented, but to accept some of the authors' claims needs additional information and explanation. Several terminologies define cell populations used in the paper may mislead readers.

1) "primitive spermatogonial stem cell (SSC)" is confusing. SSCs are considered the most immature subpopulation of uSPG. Thus, primitive uSPGs are likely SSCs. The naming, primitive SSCs, and transit-amplifying SSCs (Fig. 7K) are weird. In general, the transit-amplifying cell is progenitor, not stem cell. In human and even mouse, there are several models for the classification of uSPG and SSCs, such as reserved stem cells and active stem cells. The area is highly controversial. The authors' definition of stem cells and progenitor cells should be clarified rigorously and should compare to existing models.

2) scRNA seq data analysis and an image of FOXC2+ ZBTB16+ MKI67- cells by fluorescent immunohistochemistry are not sufficient to conclude that they are human primitive SSCs as described in the Abstract. The identity of human SSCs is controversial. Although Adark spermatogonia are a candidate population of human SSCs, the molecular profile of the Adark spermatogonia seems to be heterogeneous. None of the molecular profiles was defined by a specific cell cycle phase. Thus, more rigorous analysis is required to demonstrate the identity of FOXC2+ ZBTB16+ MKI67- cells and Adark spermatogonia.

3) FACS-sorted GFP+ cells and MACS-THY1 cells were used for functional transplantation assay to evaluate SSC activity. In general, the purity of MACS is significantly lower than that of FACS. Therefore, FACS-sorted THY1 cells must be used for the comparative analysis. As uSPGs in adult testes express THY1, the percentage of GFP+ cells in THY1+ cells determined by flow cytometry is important information to support the transplantation data.

4) The lineage tracing experiments of FOXC2+-SSCs in Foxc2CRE/+;R26T/Gf/f showed ~95% of spermatogenic cells and 100% progeny were derived from the FOXC2+ (GFP+) spermatogonia (Fig. 2I, J) at month 4 post-TAM induction, although FOXC2+ uSPG were quiescent and a very small subpopulation (~ 60% of As, ~0.03% in all cells). This means that 40% of As spermatogonia and most of Apr/Aal spermatogonia, which were FOXC2 negative, did not contribute to spermatogenesis at all eventually. This is a striking result. There is a possibility that FOXC2CRE expresses more widely in the uSPG population although immunohistochemistry could not detect them.

5) The CUT&Tag_FOXC2 analysis on the FACS-sorted FOXC2+ showed functional enrichment in biological processes such as DNA repair and mitotic cell cycle regulation (Fig.7D). The cells sorted were induced Cre recombinase expression by TAM diet and cut the tdTomato cassette out. DNA repair process and negative regulation of the mitotic cell cycle could be induced by the Cre/lox recombination process. The cells analyzed were not FOXC2+ uSPG in a normal physiological state.

6) Wei et al (Stem Cells Dev 27, 624-636) have published that FOXC2 is expressed predominately in As and Apr spermatogonia and requires self-renewal of mouse SSCs; however, the authors did not mention this study in Introduction, but referred shortly this at the end of Discussion. Their finding should be referred to and evaluated in advance in the Introduction.

Reviewer #3 (Public Review):

By popular single-cell RNA-seq, the authors identified FOXC2 as an undifferentiated spermatogonia-specific expressed gene. The FOXC2+-SSCs can sufficiently initiate and sustain spermatogenesis, the ablation of this subgroup results in the depletion of the uSPG pool. The authors provide further evidence to show that this gene is essential for SSCs maintenance by negatively regulating the cell cycle in adult mice, thus well-established FOXC2 as a key regulator of SSCs quiescent state.

The experiments are well-designed and conducted, the overall conclusions are convincing. This work will be of interest to stem cell and reproductive biologists.

Reviewer #1 (Public Review):

The fields of ancient and environmental DNA have many similarities. Practitioners are constantly tinkering with methods to extract as much information from biological samples as possible. Both fields of research also have to deal with the fact that only a tiny fraction of the DNA is 'on target' and that the background DNA (largely bacterial) is often immense.

In this research Urban et al tackle the question of individual identification of a flightless New Zealand parrot (the kakapo) using shotgun eDNA (from soil) within a study system where reference genomes exist for most of the animals within a population. Most eDNA studies stay in the relative safety of metabarcoding (typically on mitochondrial DNA) thus Urban et al are breaking new ground.

In this small-scale (and highly controlled) study, Urban et al. use shotgun eDNA from a gram of soil and then match kakapo reads to reference genomes. Using some innovative Bayesian inference the researchers are able to identify individuals within the populations.

There are a number of innovations in this study that have relevance to the conservation sector. The idea that we can identify individuals in a population in a non-invasive manner is an exciting prospect. It immediately conjures up the possibility of genetic mark-recapture applications. In the case of highly endangered populations, the work shows the value of building reference genomes for the whole population.

At its core, this is a proof-of-principal study that arguably leaves the reader with more questions than answers. I was left wondering (i) why didn't nanopore's adaptive sampling function enrich targets? (ii) how would short-read platforms compare (iii) could genomic signatures of other taxa (e.g. bats) identified by metabarcoding be detected in shotgun data? And (iv) is sediment the best substrate for this work?

Sedimentary DNA methods have been around for ~20 years and it is exciting to see the field continue to innovate. The speed and portability of nanopore devices may, with time, see real-time genotyping become a reality in conservation biology. I welcome these innovations as, on the global stage, we need all the tools we can get to battle the biodiversity crisis.

Reviewer #2 (Public Review):

This study uses DNA metabarcoding to identify vertebrates and kākāpō DNA in soils from sites where they are known to occur and from control sites housing related birds. The authors then attempt to identify individual kākāpō birds that have contributed DNA into just three samples with high kākāpō DNA content. For this, they use Oxford Nanopore adaptive sequencing, haplotype identification, and two statistical approaches to determine the number of individuals that contributed to a sample and which specific individuals contributed. This study builds on recent developments in the field that move eDNA into population genomics and individual surveillance.

The manuscript introduction does a satisfactory job of contextualizing the need for this study and the state of the field. It does not detail the challenges of applying adaptive ONT to eDNA samples and the kinds of choices such as selective assays available. I think the authors are using confusing language in the abstract and throughout that is not clear enough to be useful to a reader community that is interested in adopting ONT but not already using it.

As for the methods chosen for this study, I found it peculiar that the authors did not use qPCR specific to kākāpō to estimate the relative proportion of kākāpō eDNA to other vertebrate DNA in the total sample. A fair comparison of methods would make this study more useful to guide the field forward. qPCR should be more sensitive than metabarcoding and is the standard approach for the relative abundance that the terrestrial eDNA community uses for targeted studies.

There is a lot of work done in this study that would be useful to the eDNA community if it were presented clearly. Paragraphs are written often without topic sentences, headings are vague, specific objectives are not clearly outlined, and too many questions remain about why certain approaches were used. For example, there is a selective and non-selective approach used for ONT sequencing. In some places, is not clear what exactly the authors did, and it's not clear why the non-selective approach was preferred by the authors (as they describe in the discussion). The ONT portion of the methods seems written out of order and with frivolous choices about what details to include and omit. No mention of the pore destruction of selective/adaptive sequencing is described, so this study creates hyperbole about the promise of ONT unblocking pores for future research. There are drawbacks! Further, there surely is going to be a lot of interest in the statistical approaches to infer individuals and the number of individuals that shed DNA into a sample but this is not clearly explained. An effort to improve the writing quality throughout is needed prior to publication.

The study fails to describe the scale of the sites and how they are managed. As such, we cannot assess the distance from the site and why kākāpō DNA was found at an abandoned nest site. Maybe it was clear but the names of the sites are inconsistent throughout the ms, and there are assumptions that readers know about this field setting already, which is not a good assumption to make.

The discussion cites nobody and does not put the results back into the broader context of where the science is today. It is a weak discussion that just reiterates the results, but then boasts about the significance of the results when those results referred to were insufficiently described in the manuscript.

Altogether, I think this study has potential if the paper can be improved in clarity and quality. The science is solid and the topic is of great interest to a broad community.

Reviewer #1 (Public Review):

Medwig-Kinney et al perform the latest in a series of studies unraveling the genetic and physical mechanisms involved in the formation of C. elegans gonad. They have paid particular attention to how two different cell fates are specified, the ventral uterine (VU) or anchor cell (AC), and the behaviors of these two cell types. This cell fate choice is interesting because the anchor cell performs an invasive migration through a basement membrane. A process that is required for correct C. elegans gonad formation and that can act as a model for other invasive processes, such as malignant cancer progression. The authors have identified a range of genes that are involved in the AC/VC fate choice, and that imparts the AC cell with its ability to arrest the cell cycle and perform an invasive migration. Taking advantage of a range of genetic tools, the authors show that the transcription factor NHR-63 is strongly expressed in the AC cell. The authors also present evidence that NHR-63 is could function as a transcriptional repressor through interactions with a Groucho and also a TCF homolog, and they also suggest that these proteins are forming repressive condensates through phase separation.

The authors have produced an extensive dataset to support their two primary claims: that NHR-67 expression levels determine whether a cell is invasive or proliferative, and also that NHR-67 forms a repressive complex through interactions with other proteins. The authors should be commended for clearly and honestly conveying what is already known in this area of study with exhaustive references. But absent data unambiguously linking the formation and dissolution of NHR-67 condensates with the activation of downstream genes that NHR-67 is actively repressing, the novelty of these findings is limited.

Reviewer #2 (Public Review):

Medwig-Kinney et al. explore the role of the transcription factor NHR-67 in distinguishing between AC and VU cell identity in the C. elegans gonad. NHR-67 is expressed at high levels in AC cells where it induces G1 arrest, a requirement for the AC fate invasion program (Matus et al., 2015). NHR-67 is also present at low levels in the non-invasive VU cells and, in this new study, the authors suggest a role for this residual NHR-67 in maintaining VU cell fate. What this new role entails, however, is not clear. The model in Figure 7E shows NHR-67 switching from a transcriptional activator in ACs to a transcriptional repressor in VUs by virtue of recruiting translational repressors. In this model, NHR-67 actively suppresses AC differentiation in VU cells by binding to its normal targets and acting as a repressor rather than an activator. Elsewhere in the text, however, the authors suggest that NHR-67 is "post-translationally sequestered" (line 450) in nuclear condensates in VU cells. In that model, the low levels of NHR-67 in VU cells are not functional because inactivated by sequestration in condensates away from DNA. Neither model is fully supported by the data, which may explain why the authors seem to imply both possibilities. This uncertainty is confusing and prevents the paper from arriving at a compelling conclusion. What is the function, if any, of NHR-67 and so-called "repressive condensates" in VU cells?

Below we list problems with data interpretation and key missing experiments:

1) The authors report that NHR-67 forms "repressive condensates" (aka. puncta) in the nuclei of VU cells and imply that these condensates prevent VU cells from becoming ACs. Fig. 3A, however, shows an example of an AC that also assemble NHR-67 puncta (these are less obvious simply due to the higher levels of NHR-67 in ACs). The presence of NHR-67 puncta in the AC seems to directly contradict the author's assumption that the puncta repress the AC fate program. Similarly, Figure 5-figure supplement 1A shows that UNC-37 and LSY-22 also form puncta in ACs. The authors need to analyze both AC and VU cells to demonstrate that NHR-67 puncta only form in VUs, as implied by their model.

2) While a pool of NHR-67 localizes to "repressive condensates", it appears that a substantial portion of NHR-67 also exists diffusively in the nucleoplasm. This would appear to contradict a "sequestration model" since, for such a model to work, a majority of NHR-67 should be in puncta. What proportion of NHR-67 is in puncta? Is the concentration of NHR-67 in the nucleoplasm lower in VUs compared to ACs and does this depend on the puncta?

3) The authors do not report whether NHR-67, UNC-37, LSY-22, or POP-1 localization to puncta is interdependent, as implied in the model shown in Fig. 7.

4) The evidence that the "repressor condensates" suppress AC fate in VUs is presented in Fig. 4D where the authors deplete the presumed repressor LSY-22. First, the authors do not examine whether NHR-67 forms puncta under these conditions. Second, the authors rely on a single marker (cdh-3p::mCherry::moeABD) to score AC fate: this marker shows weak expression in cells flanking one bright cell (presumably the AC) which the authors interpret as a VU AC transformation. The authors, however, do not identify the cells that express the marker by lineage analyses and dismiss the possibility that the marker-positive cells could arise from the division of an AC-committed cell. Finally, the authors did not test whether marker expression was dependent on NHR-67, as predicted by the model shown in Fig. 7.

5) Interaction between NHR-67 and UNC-37 is shown using Y2H, but not verified in vivo. Furthermore, the functional significance of the NHR-67/UNC-37 interaction is not tested.

6) Throughout the manuscript, the authors do not use lineage analysis to confirm fate transformation as is the standard in the field. There are 4 multipotential gonadal cells with the potential to differentiate into VUs or ACs. Which ones contribute to the extra ACs in the different genetic backgrounds examined was not determined, which complicates interpretation. The authors should consider and test the following possibilities: disruption of NHR-67 regulation causes 1) extra pluripotent cells to directly become ACs early in development, 2) causes VU cells to gradually trans-fate to an AC-like fate after VU fate specification (as implied by the authors), or 3) causes an AC to undergo extra cell division(s)?? In Fig. 1F, 5 cells are designated as ACs, which is one more that the 4 precursors depicted in Fig. 1A, implying that some of the "ACs" were derived from progenitors that divided.

In conclusion, while the authors report on interesting observations, in particular the co-localization of NHR-67 with UNC-37/Groucho and POP-1 in nuclear puncta, the functional significance of these observations remains unclear. The authors have not demonstrated that the "repressive condensates" are functional and play a role in the suppression of AC fate in VU cells as claimed. The colocalization data suggest that NHR-67 interacts with repressors, but additional experiments are needed to demonstrate that these interactions are specific to VUs, impact VU fate, and sequester NHR-67 from its targets or transform NHR-67 into a transcriptional repressor.

Reviewer #1 (Public Review):

Vaparanta et al propose a new bioinformatic algorithm for pathway discovery from multi-omics data sources at one time point, and validate some of their algorithm's predictions using functional experiments. The authors should be commended for their detailed experimental work and comprehensive data collection around TYRO3 signaling in melanoma, which will likely be of value to that field. They also provide a mature software package that is well documented for implementing their bioinformatic methods. The reviewer's experience with the software was that it is computationally efficient/fast with well written code. The biological data (both multiomics and functional validation studies) will be of interest to melanoma research as well as scientists interested in TYRO3 signaling.

At this time, however, the bioinformatics algorithm proposed is of unclear utility to the broader multiomics community for the following reasons:

First, the algorithm itself has numerous hyperparameters, which can make it challenging to use and potentially highly sensitive to these user inputs. Just the regulatory complex inference step has 10 hyperparameters/settings required to be selected.

Second, the algorithm is presented in an ad hoc manner without mathematical/statistical justifications of the many design decisions and steps in the analysis. For example, the authors write "The inference of regulatory complexes from the combined score follows the nearest neighbor principle, assuming that while a single high combined score can be random chance, the combination of combined scores between 3 cell signaling molecules would be predictive". It is mathematically unclear that this is true, and thus this reviewer attempted to test the algorithm using simulated uncorrelated Gaussian noise (see code/outputs at end of the review) in 10K genes and 10 samples using a best attempt at hyperparameter selection per the code comments and documentation. It appears that nearly 1/3 of all genes (i.e., 3205 of 10K) were erroneously grouped into complexes (assuming no mistakes in reviewer's usage of the code). In general, "unbiased" pathway analysis in multiomics that is not relying on prior knowledge will require solving the extraordinarily challenging task of estimating a very large covariance matrix from statistically small sample sizes. This puts the method at high risk of producing spurious results.

Third, pathway analysis has long been a bioinformatic goal in the literature, with the authors citing a landmark paper for the WGCNA method from 2008. As such, there are numerous and long-standing discussions in the literature regarding challenges of pathway analysis (i.e., omics data often has dimensionality D far larger than sample size N, and correlation matrix estimation requires D^2 >> N parameters to be estimated) and its potential for spurious correlations. Some authors use sophisticated statistical tools (e.g., "Biological network inference using low order partial correlation" 2014, "Learning Large‐Scale Graphical Gaussian Models from Genomic Data" 2005, "Incorporating prior knowledge into Gene Network Study" 2013) to attempt to deal with this issue. Furthermore, the authors indicate that their approach is the first to attempt pathway analysis in multi-omics setting, stating "Integrative approaches combining more than one robust molecular association measure, however, have not been explored", but one can find attempts such as "An Integrative Transcriptomic and Metabolomic Study of Lung Function in Children With Asthma" to build on WGCNA for work in multiomics datasets. The 2020 review paper "Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources" seems to identify multiple published methods dealing with pathway estimation in multiomics datasets. As the paper stands, this reviewer cannot adequately assess the impact of the proposed bioinformatic algorithm and its results against the existing body of literature for pathway inference.

Reviewer #2 (Public Review):

The authors describe a bioinformatic platform that allows for unbiased pathway analysis from multiomics data. The concept is based on correlation, stoichiometry scores and their combination to evidence interaction between two proteins, transcripts or phosphosites in an omic dataset. This platform was developed and validated on both previously published and in house omics data. I really appreciate that the paper is well written and clear, and I would like to acknowledge the amount of work generated to produce the in-house dataset.

Reviewer #1 (Public Review):

Siegfried et al. study a very interesting and timely topic in cell biology: the connection between ER-PM membrane contact sites (MCS) and cell migration. In brief, the authors use the polarized epithelial model cell line (CACO-2) to study this process. They routinely compare parental cells (Control) with a clonal CACO-2 cell line knocked out (KO) for the ER tether protein VAP-A. They convincingly show that KO cells move faster but in a less directional manner, leading to slower monolayer migration. Interestingly, they showed that KO cells have larger focal adhesions (FAs), a phenotype that was reverted upon expression of the wild-type VAP-A but not of a VAP-A mutant (VAP-A-KDMD, mutation in the MSP domain) defective in binding to FFAT-containing partner proteins. Some observations regarding the role of VAP-A's MSP domain on the regulation of the actin cytoskeleton, although the evidence for this was incomplete. Furthermore, VAP-A depletion was shown to have an impact on PI(4,5)P2 levels at the PM (but not on PI(4)P levels at the Golgi membranes), to stabilize the dynamics of ER-PM MCS, and to increase FA lifetime by decreasing FA disassembly rate. Finally, they showed that there is a correlation between the appearance of ER-PM MCS at FAs with FA disassembly, however, how VAP-A plays a role in this effect is unclear. The authors put their findings in the context of the literature in the field to propose a working hypothesis by which VAP-A at ER-PM MCS could impact FA dynamics and cell motility.

Reviewer #2 (Public Review):

In this study, the authors assessed the role of the ER protein VAPA in cell migration and regulation of focal adhesions dynamics. The authors used CRISPR/Cas9 knock-out of VAPA in Caco-2 cells. They demonstrate that VAPA KO cells have slower migration capacity which is linked to a slower FA disassembly rate. Interestingly, the VAPA KO cells don't show any defects of PI4P level at endosomes nor at the Golgi complex but have a decreased PI4,5P2 level, probably linked to the redundant function of VAPB at endosomes and Golgi while VAPA might be solely responsible for effects on migration.

The results provided by the authors support their conclusions. The experiments performed are well carried out. The VAPA KO cells used in this study are originating from a clonal population but the authors used rescue experiments expressing the VAPA wild-type of the KDMD mutant to demonstrate the role of VAPA in the phenotype. In addition, appropriate and careful quantifications are provided with the different experiments, strengthening the conclusions. The data provided in this manuscript suggest a role for the ER-resident membrane contact protein VAPA in cell migration potentially independent of lipid homeostasis.

Reviewer #3 (Public Review):

In this manuscript, the authors examine the role of VAPA in focal adhesion (FA) turnover and cell motility via effects on ER-PM contact site functions. The authors show that VAPA KO CaCo2 cells form larger FA and have aberrant migration behavior and spreading. Those cells show lower levels of PI(4,5)P2 at PM, but no change in PI(4)P at Golgi and endosomes. PI(4)P is not tested at the PM. The authors show that VAPA KO cells have a similar number but less stable GFP-MAPPER positive ER-PM contact sites as compared to control cells. In contrast, FA are more stable over time in VAPA KO. The authors also aimed to evaluate GFP-MAPPER proximity with vinculin spots and concluded that ER-PM contacts partially overlap with FA, whereas they are more distant in VAPA KO. Thus, a correlation between stable contact sites near FA and FA disassembly likely exists. From this set of data, the authors suggest that VAPA has a key role at ER-PM contacts near FA by mediating lipid transfer, which ultimately enables internalization of integrins and FA disassembly.

The approach in the paper is innovative and interesting because VAPA is a major tether at contact sites and the link between contact sites and cytoskeleton dynamics and cell motility remains little explored. This can potentially lead to significant advances in the field. The experiments presented are technically well executed, but most of the results and hypotheses arising from VAPA KO cells are not tested by rescue experiments with exogenous VAPA and VAPA mutants. Although the proposed role for VAPA might fit with the data, the final model is not experimentally tested and is thus highly speculative. The role of VAPA at ER-PM contact sites near FA, and the direct link between VAPA, PI(4,5P)2, and FA disassembly, are not established. VAPA is not shown at ER-PM contacts in the manuscript. Some controls are missing and statistics must be improved. In summary, this work seems to be on the right track, but looks quite preliminary.