Reviewer #3 (Public review):

This study presents a detailed examination of the molecular and cellular organization of the mouse VNO, unveiling new cell types, receptor co-expression patterns, lineage specification regulation, and potential associations between transcription factors, guidance molecules, and receptor types crucial for vomeronasal circuitry wiring specificity. The study identifies a novel type of VSN molecularly different from classic VSNs, which may serve as accessory to other VSNs by secreting olfactory binding proteins and mucins in response to VNO activation. They also describe a previously undetected co-expression of multiple VRs in individual VSNs, providing an interesting view to the ongoing discussion on how receptor choice occurs in VSNs, either stochastic or deterministic. Finally, the study correlates the expression of axon guidance molecules associated with individual VRs, providing a putative molecular mechanism that specifies VSN axon projections and their connection with postsynaptic cells in the accessory olfactory bulb.

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

(1) The authors claim that they have identified two new classes of sensory neurons, one being a class of canonical olfactory sensory neurons (OSNs) within the VNO. This classification as canonical OSNs is based on expression data of neurons lacking the V1R or V2R markers but instead expressing ORs and signal transduction molecules, such as Gnal and Cnga2. Since OR-expressing neurons in the VNO have been previously described in many studies, it remains unclear to me why these OR-expressing cells are considered here a "new class of OSNs." Moreover, morphological features, including the presence of cilia, and functional data demonstrating the recognition of chemosignals by these neurons, are still lacking to classify these cells as OSNs akin to those present in the MOE. While these cells do express canonical markers of OSNs, they also appear to express other VSN-typical markers, such as Gnao1 and Gnai2 (Fig 2B), which are less commonly expressed by OSNs in the MOE. Therefore, it would be more precise to characterize this population as atypical VSNs that express ORs, rather than canonical OSNs.

(2) The second new class of sensory neurons identified corresponds to a group of VSNs expressing prototypical VSN markers (including V1Rs, V2Rs, and ORs), but exhibiting lower ribosomal gene expression. Clustering analysis reveals that this cell group is relatively isolated from V1R- and V2R-expressing clusters, particularly those comprising immature VSNs. The question then arises: where do these cells originate? Considering their fewer overall genes and lower total counts compared to mature VSNs, I wonder if these cells might represent regular VSNs in a later developmental stage, i.e., senescent VSNs. While the secretory cell hypothesis is compelling and supported by solid data, it could also align with a late developmental stage scenario. Further data supporting or excluding these hypotheses would aid in understanding the nature of this new cell cluster, with a comparison between juvenile and adult subjects appearing particularly relevant in this context.

(3) The authors' decision not to segregate the samples according to sex is understandable, especially considering previous bulk transcriptomic and functional studies supporting this approach. However, many of the highly expressed VR genes identified have been implicated in detecting sex-specific pheromones and triggering dimorphic behavior. It would be intriguing to investigate whether this lack of sex differences in VR expression persists at the single-cell level. Regardless of the outcome, understanding the presence or absence of major dimorphic changes would hold broad interest in the chemosensory field, offering insights into the regulation of dimorphic pheromone-induced behavior. Additionally, it could provide further support for proposed mechanisms of VR receptor choice in VSNs.

(4) The expression analysis of VRs and ORs seems to have been restricted to the cell clusters associated to the neuronal lineage. Are VRs/ORs expressed in other cell types, i.e. sustentacular, HBC or other cells?

Review update:

I believe the novel discovery of two classes of sensory neurons within the VNO-canonical olfactory sensory neurons (OSNs) and secretory vomeronasal sensory neurons (sVSNs)-should be interpreted with caution. Firstly, these cell types are relatively rare, constituting less than 2% of total cells and only 2-6% of the neuronal population (according to Fig. S3). While the OSNs exhibit gene expression profiles consistent with canonical olfactory signal transduction and cilia-related gene ontology, key aspects such as their cell morphology (including the presence of cilia) and functional evidence for chemosignal detection have yet to be demonstrated. The neuronal lineage of sVSNs remains unclear to me. It is uncertain what developmental trajectories these cells follow: do they arise as a specialized subtype of V1R or V2R lineages, or do they have an independent lineage determination, similar to OSNs? At what stage does the commitment to the sVSN lineage begin-during the INP stage or the immature sensory neuron stage? A pseudotime inference analysis of sVSNs could help clarify these questions.

Reviewer #1 (Public review):

Summary:

The fungal cell wall is a very important structure for the physiology of a fungus but also for the interaction of pathogenic fungi with the host. Although a lot of knowledge on the fungal cell wall has been gained, there is lack of understanding of the meaning of ß-1,6-glucan in the cell wall. In the current manuscript, the authors studied in particular this carbohydrate in the important human-pathogenic fungus Candida albicans. The authors provide a comprehensive characterization of cell wall constituents under different environmental and physiological conditions, in particular of ß-1,6-glucan. Also, β-1,6-glucan biosynthesis was found to be likely a compensatory reaction when mannan elongation was defective. The absence of β-1,6-glucan resulted in a significantly sick growth phenotype and complete cell wall reorganization. The manuscript contains a detailed analysis of the genetic and biochemical basis of ß-1,6-glucan biosynthesis which is apparently in many aspects similar to yeast. Finally, the authors provide some initial studies on immune modulatory effects of ß-1,6-glucan.

Reviewer #2 (Public review):

Summary:

The authors provide the first (to my knowledge) detailed characterization of cell wall b-1,6 glucan in the pathogen Candida albicans. The approaches range from biochemistry to genetics to immunology. The study provides fundamental information and will be a resource of exceptional value to the field going forward. Highlights include the construction of a mutant that lacks all b-1,6 glucan and the characterization of its cell wall composition and structure. Figure 5a is a feast for the eyes, showing that b-1,6 glucan is vital for the outer fibrillar layer of the cell wall. Also much appreciated was the summary figure, Figure 7, that presents the main findings in digestible form.

Strengths:

The work is highly significant for the fungal pathogen field especially, and more broadly for anyone studying fungi, antifungal drugs, or antifungal immune responses.<br /> The manuscript is very readable, which is important because most readers will be cell wall nonspecialists.<br /> The authors construct a key quadruple mutant, which is not trivial even with CRISPR methods, and validate it with a complemented strain. This aspect of the study sets the bar high.<br /> The authors develop new and transferable methods for b-1,6 glucan analysis.

Weaknesses:

The one "famous" cell type that would have been interesting to include is the opaque cell. Please include it in the next paper!

Reviewer #3 (Public review):

Summary:

The cell wall of human fungal pathogens, such as Candida albicans, is crucial for structural support and modulating the host immune response. Although extensively studied in yeasts and molds, the structural composition has largely focused on the structural glucan b,1,3-glucan and the surface exposed mannans, while the fibrillar component β-1,6-glucan, a significant component of the well wall, has been largely overlooked. This comprehensive biochemical and immunological study by a highly experienced cell wall group provides a strong case for the importance of β-1,6-glucan contributing critically to cell wall integrity, filamentous growth, and cell wall stability resulting from defects in mannan elongation. Additionally, β-1,6-glucan responds to environmental stimuli and stresses, playing a key role in wall remodeling and immune response modulation, making it a potential critical factor for host-pathogen interactions.

Strengths:

Overall, this study is well designed and executed. It provides the first comprehensive assessment of β-1,6-glucan as a dynamic, albeit underappreciated, molecule. The role of β-1,6-glucan genetics and biochemistry has been explored in molds like Aspergillus fumigatus, but this work shines important light on its role in Candida albicans. This is important work that is of value to Medical Mycology, since β-1,6-glucan plays more than just a structural role in the wall. It may serve as a PAMP and a potential modulator of host-pathogen interactions.

Weaknesses:

In keeping with an important role in immune recognition, it was suggested that the manuscript rigor would benefit from a more physiological evaluation ex vivo and preferably in vivo, assessment on stimulating the immune system within in the cell wall and not just as a purified component. This is a critical outcome measure for this study and gets squarely at its importance for host-pathogen interactions, especially in response to environmental stimuli and drug exposure. The authors addressed this issue contextually and indicate that it will require a more detailed immunologic evaluation but is not in keeping with the intent of this foundational study.

Reviewer #1 (Public review):

This work presents CTFFIND5, a new version of the software for determination of the Contrast Transfer Function (CTF) that models the distortions introduced by the microscope in cryoEM images. CTFFIND5 can take acquisition geometry and sample thickness into consideration to improve CTF estimation.

To estimate tilt (tilt angle and tilt axis), the input image is split into tiles and correlation coefficients are computed between their power spectra and a local CTF model that includes the defocus variation according to a tilted plane. As a final step, by applying a rescaling factor to the power spectra of the tiles, an average tilt-corrected power spectrum is obtained used for diagnostic purposes and estimate the goodness of fit. This global procedure and the rescaling factor resemble those used in Bsoft, Warp, etc, with determination of the tilt parameters being a feature specific of CTFFIND5 (and formerly CTFTILT). The performance of the algorithm is evaluated with tilted 2D crystals and tilt-series, demonstrating accurate tilt estimation in general.

CTFFIND5 represents the first CTF determination tool that considers the thickness-related modulation envelope of the CTF firstly described by McMullan et al. (2015) and experimentally confirmed by Tichelaar et al. (2020). To this end, CTFFIND5 uses a new CTF model that takes the sample thickness into account. CTFFIND5 thus provides more accurate CTF estimation and, furthermore, gives an estimation of the sample thickness, which may be a valuable resource to judge the potential for high resolution. To evaluate the accuracy of thickness estimation in CTFFIND5, the authors use the Lambert-Beer law on energy-filtered data and also tomographic data, thus demonstrating that the estimates are reasonable for images with exposure around 30 e/A2. While consideration of sample thickness in CTF determination sounds ideally suited for cryoET, practical application under the standard acquisition protocols in cryoET (exposure of 3-5 e/A2 per image) is still limited. In this regard, the authors are precise in the conclusions and clearly identify the areas where thickness-aware CTF determination will be valuable at present: in situ single particle analysis and in vitro single particle cryoEM of large specimens (e.g. viral particles).

In conclusion, the manuscript introduces novel methods inside CTFFIND5 that improve CTF estimation, namely acquisition geometry and sample thickness. The evaluation demonstrates the performance of the new tool, with fairly accurate estimates of tilt axis, tilt angle and sample thickness and improved CTF estimation. The manuscript critically defines the current range of application of the new methods in cryoEM.

Reviewer #2 (Public review):

This paper describes the latest version of the most popular program for CTF estimation for cryo-EM images: CTFFIND5. New features in CTFFIND5 are the estimation of tilt geometry, including for samples, like FIB-milled lamellae, that are pre-tilted along a different axis than the tilt axis of the tomographic experiment, plus the estimation of sample thickness from the expanded CTF model described by McMullan et al (2015). The results convincingly show the added value of the program for thicker and tilted images, such as are common in modern cryo-ET experiments. The program will therefore have a considerable impact on the field.

Comments on revised version:

My comments have been addressed adequately.

Reviewer #1 (Public review):

Summary:

In this study from Zhou, Wang, and colleagues, the authors utilize biventricular electromechanical simulations to illustrate how different degrees of ionic remodeling can contribute to different ECG morphologies that are observed in either acute or chronic post-myocardial infarction (MI) patients. Interestingly, the simulations show that abnormal ECG phenotypes - associated with higher risk of sudden cardiac death - are predicted to have almost no correspondence with left ventricular ejection fraction, which is conventionally used as a risk factor for arrhythmia.

Strengths:

The numerical simulations are state-of-the-art, integrating detailed electrophysiology and mechanical contraction predictions, which are often modeled separately. The population of ventricular simulations provide mechanistic interpretation, down to the level of single cell ionic current remodeling, for different types of ECG morphologies observed in post-MI patients. Collectively, these results demonstrate compelling and significant evidence for the need of incorporating additional risk factors for assessing post-MI patients.

The authors have addressed all of my previous concerns in this updated version.

Reviewer #2 (Public review):

Summary:

The authors constructed a multi-scale modeling and simulation methods to investigate the electrical and mechanical properties under acute and chronic myocardial infarction (MI). The simulated three acute MI conditions and two chronic MI conditions. They showed that these conditions gave rise to distinct ECG characteristics that have seen in clinical settings. They showed that the post-MI remodeling reduced ejection fraction up to 10% due to weaker calcium current or SR calcium uptake, but the reduction of ejection fraction is not sensitive to remodeling of the repolarization heterogeneities.

Strengths:

The major strength of this study is the construction of the computer modeling that simulates both electrical behavior and mechanical behavior for post-MI remodeling. The links of different heterogeneities due to MI remodeling to different ECG characteristics provide some useful information for understanding the complex clinical problems.

Weaknesses:

The rationale (e.g., physiological or medical bases) for choosing the 3 acute MI and 2 chronic MI settings is not clear. Although the authors presented a huge number of simulation data, in particular in the supplemental materials, it is not clearly stated what novel findings or mechanistic insights that this study gained beyond the current understanding of the problem.

Reviewer #2 (Public review):

Summary:

The article by Ryu and colleagues describes the circadian control of astrocytic intracellular calcium levels in vitro.

Strengths:

The authors used a variety of technical approaches that are appropriate and considerably improved the manuscript with experiments and more solid data analysis compared to the first version

Weaknesses:

Some conceptual issues are still present. This is a mechanistic paper done completely in vitro, all references to the in vivo situation are speculative and should be absolutely avoided unless the authors are citing in vivo work.

Reviewer #3 (Public review):

This study provides significant insights into how the circadian clock influences astrocytic Ca2+ homeostasis. Astrocyte biology is an active area of research and this study is timely and adds to a growing body of literature in the field. This research highlights the potential importance of circadian rhythms in astrocytes, offering a new perspective on their role in central nervous system regulation.

Reviewer #1 (Public review):

Summary:

Madigan et al. assembled an interesting study investigating the role of the MuSK-BMP signaling pathway in maintaining adult mouse muscle stem cell (MuSC) quiescence and muscle function before and after trauma. Using a full body and MuSC-specific genetic knockout system, they demonstrate that MuSK is expressed on MuSCs and that eliminating the BMP binding domain from the MuSK gene (i.e., MuSK-IgG KO) in mice at homeostasis leads to reduced PAX7+ cells, increased myonuclear number, and increase myofiber size, which may be due to a deficit in maintaining quiescence. Additionally, after BaCl2 injury, MuSK-IgG KO mice display accelerated repair after 7 days post-injury (dpi) in males only. Finally, RNA profiling using nCounter technology showed that MuSK-IgG KO MuSCs express genes that may be associated with the activated state.

Strengths:

Overall, the biology regulating MuSC quiescence is still relatively unexplored, and thus, this work provides a new mechanism controlling this process. The experiments discussed in the paper are technically sound with great complementary mouse models (full body versus tissue-specific mouse KO) used to validate their hypothesis. Additionally, the paper is well written with all the necessary information in the legends, methods, and figures being reported.

Weaknesses:

While the data largely supports the author's conclusions, I do have a few points to consider when reading this paper.

(1) For Figure 1, while I appreciate the author's confirming MuSK RNA and protein in MuSCs, I do think they should (a) quantify the RNA using qPCR and (b) determine the percentage of MuSCs expressing MuSK protein in their single fiber system in multiple biological replicates. This information will help us understand if MuSK is expressed in 1/10 or 10/10 PAX7-expressing MuSCs. Also, it will help place their phenotypes into the right context, especially when considering how much of the PAX7-pool is expressing MuSK from the beginning.

(2) Throughout the paper the argument is made that MuSK-IgG KO (full body and MuSC-specific KOs) are more activated and/or break quiescence more readily, but there is no attempt to test directly. Therefore, the authors should consider measuring the activation dynamics (i.e., break from quiescence) of MuSCs directly (EdU assays or live-cell imaging) in culture and/or in muscle in vivo (EdU assays) using their various genetic mouse models.

(3) For Figure 2, given that mice are considered adults by 3 months, it is really surprising how just two months later they are starting to see a phenotype (i.e., reduced PAX7-cells, increased number of myonuclei, and increased myofiber size)-which correlates with getting older. Given that aged MuSCs have activation defects (i.e., stuck somewhere in the quiescence cycle), a pending question is whether their phenotype gets stronger in aged mice, like 18-24 months. If yes, the argument that this pathway should be used in a therapeutic sense would be strengthened.

(4) For Figure 4, the same question as in point (2), the increase in fiber sizes by 7dpi in MuSK-IgG KO males is minimal (going from ~23 to 27 by eye) and no difference at a later time point when compared to WT mice. However, if older mice are used (18-24 months old) - which are known to have repair deficits-will the regenerative phenotype in MuSK-IgG KO mice be more substantial and longer lasting?

(5) For Figure 6, this gene set is not glaringly obvious as being markers of MuSC activation (i.e., no MyoD), so it's hard for the readers to know if this gene set is truly an activation signature. Also, the Shcherbina et al. data presented as a column with * being up or down (i.e. differentially expressed) is not helpful, since you don't know whether those mRNAs in that dataset are going up with the activation process. Addressing this point as well as my point (1) will further strengthen the author's conclusions about the MuSK-IgG KO MuSCs not being able to maintain quiescence as effectively.

Reviewer #2 (Public review):

Summary:

The work by Madigan et al. provides evidence that the signaling of BMPs via the Ig3 domain of MuSK plays a role during muscle postnatal development and regeneration, ultimately resulting in enhanced contractile force generation in the absence of the MuSK Ig3 domain. They demonstrate that MuSK is expressed in satellite cells initially post-isolation of muscle single fibers both in WT and whole-body deletion of the BMP binding domain of MuSK (ΔIg3-MuSK). In mice, ΔIg3-MuSK results in increased muscle fiber size, a reduction in Pax7+ cells, and increased muscle contractile force in 5-month-old, but not 3-month-old, mice. These data are complemented by a model in which the kinetics of regeneration appear to be accelerated at early time points. Of note, the authors demonstrate muscle tibialis anterior (TA) weights and fiber feret are increased in a Pax7CreERT2;MuSK-Ig3loxp/loxp model in which satellite cells specifically lack the MuSK BMP binding domain. Finally, using Nanostring transcriptional the authors identified a short list of genes that differ between the WT and ΔIg3-MuSK SCs. These data provide the field with new evidence of signaling pathways that regulate satellite cell activation/quiescence in the context of skeletal muscle development and regeneration.

On the whole, the findings in this paper are well supported, however additional validation of key satellite cell markers and data analysis need to be conducted given the current claims.

(1) The Pax7CreERT2;MuSK-Ig3loxp/loxp model is the appropriate model to conduct studies to assess satellite cell involvement in MuSK/BMP regulation. Validation of changes to muscle force production is currently absent using this model, as is quantification of Pax7+ tdT+ cells in 5-month muscle. Given that MuSK is also expressed on mature myofibers at NMJs, these data would further inform the conclusions proposed in the paper.

(2) All Pax7 quantification in the paper would benefit from high magnification images including staining for laminin demonstrating the cells are under the basal lamina.

(3) The nanostring dataset could be further analyzed and clarified. In Figure 6b, it is not initially apparent what genes are upregulated or downregulated in young and aged SCs and how this compares with your data. Pathway analysis geared toward genes involved in the TGFb superfamily would be informative.

(4) Characterizing MuSK expression on perfusion-fixed EDL fibers would be more conclusive to determine if MuSK is expressed in quiescent SCs. Additional characterization using MyoD, MyoG, and Fos staining of SCs on EDL fibers would help inform on their state of activation/quiescent.

(5) Finally, the treatment of fibers in the presence or absence of recombinant BMP proteins would inform the claims of the paper.

Reviewer #3 (Public review):

Summary:

Understanding the molecular regulation of muscle stem cell quiescence. The authors evaluated the role of the MuSK-BMP pathway in regulating adult SC quiescence by the deletion of the BMP-binding MuSK Ig3 domain ('ΔIg3-MuSK').

Strengths:

A novel mouse model to interrogate muscle stem cell molecular regulators. The authors have developed a nice mouse model to interrogate the role of MuSK signaling in muscle stem cells and myofibers and have unique tools to do this.

Weaknesses:

Only minor technical questions remain and there is a need for additional data to support the conclusions.

(1) The authors claim that dIg3-MuSK satellite cells break quiescence and start fusing, based on the reduction of Pax7+ and increase of nuclei/fiber (Fig 2-3), and maybe the gene expression (Fig6). However, direct evidence is needed to support these findings such as quantifying quiescent (Pax7+Ki67-) or activated (Pax7+Ki67+) satellite cells (and maybe proliferating progenitors Pax7-Ki67+) in the dIg3-MuSK muscle.

(2) It is not clear if the MuSK-BMP pathway is required to maintain satellite cell quiescence, by the end of the regeneration (29dpi), how Pax7+ numbers are comparable to the WT (Fig4d). I would expect to have less Pax7+, as in uninjured muscle. Can the authors evaluate this in more detail?

(2) Figure 4 claims that regeneration is accelerated, but to claim this at a minimum they need to look at MYH3+ fibers, in addition to fiber size.

(3) The Pax7 specific dIg3-MuSK (Fig5) is very exciting. However, it will be important to quantify the Pax7+ number. Could the authors check the reduction of Pax7+ in this model since it would confirm the importance of MuSK in quiescence?

(3) Rescue of the BMP pathway in the model would be further supportive of the authors' findings.

(4) Is the stem cell pool maintained long term in the deleted dIg3-MuSK SCs? Or would they be lost with extended treatment since they are reduced at the 5-month experiments? This is an important point and should be considered/discussed relevant to thinking about these data therapeutically.

(5) Without the Pax7-specific targeting, when you target dIg3-MuSK in the entire muscle, what happens to the neuromuscular nuclei?

(6) Why were differences seen in males and not females? Is XIST downregulation occurring in both sexes? Could the authors explain these findings in more detail?

Reviewer #1 (Public review):

Summary:

The authors perform irCLIP of neuronal progenitor cells to profile eIF3-RNA interactions upon short-term neuronal differentiation. The data shows that eIF3 mostly interacts with 3'-UTRs - specifically, the poly-A signal. There appears to be a general correlation between eIF3 binding to 3'-UTRs and ribosome occupancy, which might suggest that eIF3 binding promotes protein synthesis, possibly through inducing mRNA closed-loop formation.

Strengths:

The study provides a wealth of new data on eIF3-mRNA interactions and points to the potential new concept that eIF3-mRNA interactions are polyadenylation-dependent and correlate with ribosome occupancy.

Weaknesses:

(1) A main limitation is the correlative nature of the study. Whereas the evidence that eIF3 interacts with 3-UTRs is solid, the biological role of the interactions remains entirely unknown. Similarly, the claim that eIF3 interactions with 3'-UTR termini require polyadenylation but are independent of poly(A) binding proteins lacks support as it solely relies on the absence of observable eIF3 binding to poly-A (-) histone mRNAs and a seeming failure to detect PABP binding to eIF3 by co-immunoprecipitation and Western blotting. In contrast, LC-MS data in Supplementary File 1 show ready co-purification of eIF3 with PABP.

(2) Another question concerns the relevance of the cellular model studied. irCLIP is performed on neuronal progenitor cells subjected to neuronal induction for 2 hours. This short-term induction leads to a very modest - perhaps 10% - and very transient 1-hour-long increase in translation, although this is not carefully quantified. The cellular phenotype also does not appear to change and calling the cells treated with differentiation media for 2 hours "differentiated NPCs" seems a bit misleading. Perhaps unsurprisingly, the minor "burst" of translation coincides with minor effects on eIF3-mRNA interactions most of which seem to be driven by mRNA levels. Based on the ~15-fold increase in ID2 mRNA coinciding with a ~5-fold increase in ribosome occupancy (RPF), ID2 TE actually goes down upon neuronal induction.

(3) The overlap in eIF3-mRNA interactions identified here and in the authors' previous reports is minimal. Some of the discrepancies may be related to the not well-justified approach for filtering data prior to assessing overlap. Still, the fundamentally different binding patterns - eIF3 mostly interacting with 5'-UTRs in the authors' previous report and other studies versus the strong preference for 3'-UTRs shown here - are striking. In the Discussion, it is speculated that the different methods used - PAR-CLIP versus irCLIP - lead to these fundamental differences. Unfortunately, this is not supported by any data, even though it would be very important for the translation field to learn whether different CLIP methodologies assess very different aspects of eIF3-mRNA interactions.

Reviewer #2 (Public review):

Summary:

The paper documents the role of eIF3 in translational control during neural progenitor cell (NPC) differentiation. eIF3 predominantly binds to the 3' UTR termini of mRNAs during NPC differentiation, adjacent to the poly(A) tails, and is associated with efficiently translated mRNAs, indicating a role for eIF3 in promoting translation.

Strengths:

The manuscript is strong in addressing molecular mechanisms by using a combination of next-generation sequencing and crosslinking techniques, thus providing a comprehensive dataset that supports the authors' claims. The manuscript is methodologically sound, with clear experimental designs.

Weaknesses:

(1) The study could benefit from further exploration into the molecular mechanisms by which eIF3 interacts with 3' UTR termini. While the correlation between eIF3 binding and high translation levels is established, the functionality of these interactions needs validation. The authors should consider including experiments that test whether eIF3 binding sites are necessary for increased translation efficiency using reporter constructs.

(2) The authors mention that the eIF3 3' UTR termini crosslinking pattern observed in their study was not reported in previous PAR-CLIP studies performed in HEK293T cells (Lee et al., 2015) and Jurkat cells (De Silva et al., 2021). They attribute this difference to the different UV wavelengths used in Quick-irCLIP (254 nm) and PAR-CLIP (365 nm with 4-thiouridine). While the explanation is plausible, it remains a caveat that different UV crosslinking methods may capture different eIF3 modules or binding sites, depending on the chemical propensities of the amino acid-nucleotide crosslinks at each wavelength. Without addressing this caveat in more detail, the authors cannot generalize their findings, and thus, the title of the paper, which suggests a broad role for eIF3, may be misleading. Previous studies have pointed to an enrichment of eIF3 binding at the 5' UTRs, and the divergence in results between studies needs to be more explicitly acknowledged.

(3) While the manuscript concludes that eIF3's interaction with 3' UTR termini is independent of poly(A)-binding proteins, transient or indirect interactions should be tested using assays such as PLA (Proximity Ligation Assay), which could provide more insights.

Reviewer #3 (Public review):

Summary:

In this manuscript by Mestre-Fos and colleagues, authors have analyzed the involvement of eIF3 binding to mRNA during differentiation of neural progenitor cells (NPC). The authors bring a lot of interesting observations leading to a novel function for eIF3 at the 3'UTR.

During the translational burst that occurs during NPC differentiation, analysis of eIF3-associated mRNA by Quick-irCLIP reveals the unexpected binding of this initiation factor at the 3'UTR of most mRNA. Further analysis of alternative polyadenylation by APAseq highlights the close proximity of the eIF3-crosslinking position and the poly(A) tail. Furthermore, this interaction is not detected in Poly(A)-less transcripts. Using Riboseq, the authors then attempted to correlate eIF3 binding with the translation efficacy of mRNA, which would suggest a common mechanism of translational control in these cells. These observations indicate that eIF3-binding at the 3'UTR of mRNA, near the poly(A) tail, may participate to the closed-loop model of mRNA translation, bridging 5' and 3', and allowing ribosomes recycling. However, authors failed to detect interactions of eIF3, with either PABP or Paip1 or 40S subunit proteins, which is quite unexpected.

Strength:

The well-written manuscript presents an attractive concept regarding the mechanism of eIF3 function at the 3'UTR. Most mRNA in NPC seems to have eIF3 binding at the 3'UTR and only a few at the 5'end where it's commonly thought to bind. In a previous study from the Cate lab, eIF3 was reported to bind to a small region of the 3'UTR of the TCRA and TCRB mRNA, which was responsible for their specific translational stimulation, during T cell activation. Surprisingly in this study, the eIF3 association with mRNA occurs near polyadenylation signals in NPC, independently of cell differentiation status. This compelling evidence suggests a general mechanism of translation control by eIF3 in NPC. This observation brings back the old concept of mRNA circularization with new arguments, independent of PABP and eIF4G interaction. Finally, the discussion adequately describes the potential technical limitations of the present study compared to previous ones by the same group, due to the use of Quick-irCLIP as opposed to the PAR-CLIP/thiouridine.

Weaknesses:

(1) These data were obtained from an unusual cell type, limiting the generalizability of the model.

(2) This study lacks a clear explanation for the increased translation associated with NPC differentiation, as eIF3 binding is observed in both differentiated and undifferentiated NPC. For example, I find a kind of inconsistency between changes in Riboseq density (Figure 3B) and changes in protein synthesis (Figure 1D). Thus, the title overstates a modest correlation between eIF3 binding and important changes in protein synthesis.

(3) This is illustrated by the candidate selection that supports this demonstration. Looking at Figure 3B, ID2, and SNAT2 mRNA are not part of the High TE transcripts (in red). In contrast, the increase in mRNA abundance could explain a proportionally increased association with eIF3 as well as with ribosomes. The example of increased protein abundance of these best candidates is overall weak and uncertain.

(4) Despite several attempts (chemical and UV cross-linking) to identify eIF3 partners in NPC such as PABP, PAIP1, or proteins from the 40S, the authors could not provide any evidence for such a mechanism consistent with the closed-loop model. Overall, this rather descriptive study lacks mechanistic insight (eIF3 binding partners).

(5) Finally, the authors suspect a potential impact of technical improvement provided by Quick-irCLIP, that could have been addressed rather than discussed.

Reviewer #1 (Public review):

Summary:

The manuscript by Bohra et al. describes the indirect effects of ligand-dependent gene activation on neighboring non-target genes. The authors utilized single-molecule RNA-FISH (targeting both mature and intronic regions), 4C-seq, and enhancer deletions to demonstrate that the non-enhancer-targeted gene TFF3, located in the same TAD as the target gene TFF1, alters its expression when TFF1 expression declines at the end of the estrogen signaling peak. Since the enhancer does not loop with TFF3, the authors conclude that mechanisms other than estrogen receptor or enhancer-driven induction are responsible for TFF3 expression. Moreover, ERα intensity correlations show that both high and low levels of ERα are unfavorable for TFF1 expression. The ERa level correlations are further supported by overexpression of GFP-ERa. The authors conclude that transcriptional machinery used by TFF1 for its acute activation can negatively impact the TFF3 at peak of signaling but once, the condensate dissolves, TFF3 benefits from it for its low expression.

Strengths:

The findings are indeed intriguing. The authors have maintained appropriate experimental controls, and their conclusions are well-supported by the data.

Weaknesses:

There are some major and minor concerns that related to approach, data presentation and discussion. But I think they can be fixed with more efforts.

Reviewer #2 (Public review):

Summary:

In this manuscript by Bohra et al., the authors use the well-established estrogen response in MCF7 cells to interrogate the role of genome architecture, enhancers, and estrogen receptor concentration in transcriptional regulation. They propose there is competition between the genes TFF1 and TFF3 which is mediated by transcriptional condensates. This reviewer does not find these claims persuasive as presented. Moreover, the results are not placed in the context of current knowledge.

Strengths:

High level of ERalpha expression seems to diminish the transcriptional response. Thus, the results in Fig. 4 have potential insight into ER-mediated transcription. Yet, this observation is not pursued in great depth however, for example with mutagenesis of ERalpha. However, this phenomenon - which falls under the general description of non monotonic dose response - is treated at great depth in the literature (i.e. PMID: 22419778). For example, the result the authors describe in Fig. 4 has been reported and in fact mathematically modeled in PMID 23134774. One possible avenue for improving this paper would be to dig into this result at the single-cell level using deletion mutants of ERalpha or by perturbing co-activators.

Weaknesses:

There are concerns with the smRNA FISH experiments. It is highly unusual to see so much intronic signal away from the site of transcription (Fig. 2) (PMID: 27932455, 30554876) which suggests to me the authors are carrying out incorrect thresholding or have a substantial amount of labeling background. The Cote paper cited in the manuscript is likewise inconsistent with their findings and is cited in a misleading manner: they see splicing within a very small region away from the site of transcription.

One substantial way to improve the manuscript is to take a careful look at previous single cell analysis of the estrogen response, which in some cases has been done on the exact same genes (PMID: 29476006, 35081348, 30554876, 31930333). In some of these cases, the authors reach different conclusions than those presented in the present manuscript. Likewise, there have been more than a few studies which characterized these enhancers (the first one I know of is: PMID 18728018). Also, Oh et al. 2021 (cited in the manuscript) did show an interaction between TFF1e and TFF3, which seems to contradict the conclusion from Fig. 3. In summary, the results of this paper are not in dialog with the field, which is a major shortcoming.

In the opinion of this reviewer, there are few - if any - experiments to interrogate the existence of LLPS for diffraction limited spots such as those associated with transcription. This difficulty is a general problem with the field and not specific to the present manuscript. For example, transient binding will also appear as a dynamic 'spot' in the nucleus, independently of any higher order interactions. As for Fig. 5, I don't think treating cells with 1,6 hexanediol is any longer considered a credible experiment. For example, there are profound effects on chromatin independent of changes in LLPS (PMID: 33536240).

Summary:

In conclusion, I suggest that the authors look at alternative explanations and analyses -- many of which are experimentally and mathematically rigorous and pre-date the condensate model -- to explain their data.

Reviewer #1 (Public review):

Summary:

This work sets out to elucidate mechanistic intricacies in inflammatory responses in pneumonia in the context of aging process (Terc deficiency - telomerase functionality).

Strengths:

Very interesting, conceptually speaking, approach that is by all means worth pursuing. An overall proper approach to the posited aim.

Weaknesses:

The work is heavily underpowered and may have statistical deficits. This precludes at its current state drawing unequivocal conclusions.

I remain at my initial position regarding the weaknesses.

Reviewer #2 (Public review):

Summary

The authors demonstrate heightened susceptibility of Terc-KO mice to S. aureus-induced pneumonia, perform gene expression analysis from the infected lungs, find an elevated inflammatory (NLRP3) signature in some Terc-KO but not control mice, and some reduction in T cell signatures. Based on that, they conclude that dysregulated inflammation and T cell dysfunction play a major role in these phenomena.

The strengths of the work did not change, and include a problem not previously addressed (the role of Terc component of the telomerase complex) in certain aspects of resistance to bacterial infection and innate (and maybe adaptive) immune function.<br /> The weaknesses of this revised version still outweigh the strengths, because the authors did not substantially or experimentally answer the main criticism points, and have rather tried to argue away that which cannot be argued away. In summary, the most germane conclusions of this study remain plagued by flaws in experimental design, by lack of rigorous controls and by incomplete and inadequate approaches to testing of immune function.

I will devote the rest of the comments to the revised manuscript and its success or lack thereof in responding to prior criticisms. Prior criticisms are again listed below in italics, to provide context for the attempts of the investigators to respond.

(1) Reviewer 1 has justifiably criticized the exceptionally low power of the study, with 5 control and 3 experimental animals. The responding author has replied that the animal welfare laws preclude them from doing more experiments. That is unfortunate, and I sympathize with the authors. Nonetheless, in the absence of robust corroboration the rigor of the study remains severely compromised and the work is reduced to what I have pointed above - a preliminary and inconclusive study that is in need of deeper and more serious mechanistic investigation.

(2) Terc-KO mice are a genomic knockout model, and therefore the authors need to carefully consider the impact of this KO on a wide range of tissues. This, however, is not the case. There are no attempts to perform cell transfers, use irradiation chimera or crosses that would be informative.

In response to this criticism, the authors have quoted a whole bunch of papers characterizing different aspects of biology of these same mice. The most important paper in that regard would be the one by Matthe et al. on CD4 cells from these same mice. That study was limited and simply diagnosed in situ the changes in T cell pool, but did not decipher whether and to what extent such defects are cell-intrinsic or a byproduct of similarly altered microenvironments. Most importantly, none of that answers the original critique question of which cell types are truly the culprits in the Terc deletion phenotype presented here. As I indicated, one has to perform cell transfers, bone marrow irradiation chimera, additional genetic crosses and combinations thereof to substantiate whether the defects are ascribable to the lung tissue itself, the infiltrating myeloid cells, including macrophages, the T cells or a combination thereof. The authors provided none of this.

(3) Throughout the manuscript the authors invoke the role of telomere shortening in aging, and according to them their Terc-KO mice should be one potential model for aging. Yet the authors consistently describe major differences between young Terc-KO and naturally aging old mice, with no discussion of the implications. This further confuses the biological significance of this work as presented.

(4) Related to #2, group design for comparisons lacks a clear rationale. The authors stipulate that Terc-KO will mimic natural aging, but in fact, the only significant differences seen between groups in susceptibility to S. aureus are, contrary to the authors' expectation, between young Terc-KO and naturally old mice (Fig. 1A and B, no difference between young Terc-KO and young wt); or there are no significant differences at all between groups (Fig. 1, C, D,). I have also raised the issue of non-physiological nature of a germline Terc-KO, that does not mimic any known physiological or pathological state.<br /> The authors provided a non-response to this criticism. They argue in their response under (2) of their rebuttal that they included old mice as controls not for aging, because their experimental Terc-deletion mice were G3 and do not exhibit as much of a progeroid phenotype as G5 or G6 mice. But they still say in the revised formulation that these mice were infected "to explore the potential link to a fully developed aging phenotype". They just never conclude that no such link is substantiated by the vast majority of their data. Moreover, they come back to state in their response (4) that because the literature reported ".... reduction of Terc and Tert in tissues of old mice and rats. Therefore, as a potential immunomodulatory factor reduced Terc expression could be connected to age-related pathologies." So either they have used old mice here to compare aging phenotypes, and found that Terc-KO mice diverge massively from aging phenotypes, in which case they have to state so, or they are not using them as age comparators (in which case I am not sure what their purpose is).

(5) (originally part of criticism #4) I have criticized inadequate group design is when the authors begin dividing their Terc-KO groups by clinical score into animals with or without "systemic infection" (the condition where a bacterium spreads uncontrollably across the many organs and via blood, which should be properly called sepsis), and then compare this sepsis group to other groups (Suppl Fig. 1G; Fig. 2; lines 374-376 and 389-391). .... Most importantly, methodologically it is highly inappropriate to compare one mouse with sepsis to another one without. If Terc-KO mice with sepsis are a comparator group, then their controls have to be wild type mice with sepsis, who are dealing with the same high bacterial load across the body and are presumably forced to deploy the same set of immune defenses.<br /> The authors responded by making me aware of the 2016 JAMA definition of sepsis that invokes "a life-threatening organ dysfunction caused by a dysregulated host response to infection". I appreciate the correction, and note that in a human setting and globally, such a definition may make sense. The authors stated that bacteremia and not sepsis should be used as a criterion. I agree, and per my original criticism, believe it will be appropriate to compare bacteremic wt and KO mice.

(6) I am shortening my prior critique to make it more to the point that was not addressed: The authors conclude that disregulated inflammation and T cell dysfunction play a major role in S. aureus susceptibility. This may or may not be an important observation, because many KO mice are abnormal for a variety of reasons, and until such reasons are mechanistically dissected, the physiological importance of the observation will remain unclear. ....., the authors truly did not examine the key basic features of their model, including the features of basic and induced inflammatory and immune response. This analysis could be done either using model antigens in adjuvants, defined innate immune stimuli (e.g. TLR, RLR or NLR agonsists), or microbial challenge. The only data provided along these lines are the baseline frequencies of total T cells in the spleen of the three groups of mice examined (not statistically significant, Fig. 4B). We do not know if the composition of naïve to memory T cell subsets may have been different, and more importantly, we have no data to evaluate whether recruitment of the immune response (including T cells) to the lung upon microbial challenge is similar or different. So, what are the numbers and percentages of T cells and alveolar macrophages in the lung following S. aureus challenge and are they even comparable or are there issues in mobilizing the T cell response to the site of infection ? If, for example, Terc-KO mice do not mobilize enough T cells to the lung during infection, that would explain paucity in many T cell -associated genes in their transcriptomic set that they authors report. That in turn may not mean dysfunction of T cells but potentially a whole different set of defects in coordinating the response in Terc-KO mice.<br /> The authors did not respond to this criticism other than to provide more frequencies of different subsets. The key here are the NUMBERS of cells present at the peak of challenge, or better yet the kinetics of cell accumulation (again numbers), as well as transfer experiments to establish where the defect actually lies (mobilization, activation, proliferation, etc.).

(7) Related to that, immunological analysis is also inadequate. First, the authors pull signatures from the total lung tissue, which is both imprecise and potentially skewed by differences not in gene expression but in types of cells present and/or their abundance, a feature known to be affected by aging and perhaps by Terc deficiency during infection. Second, to draw any conclusions about immune responses, the authors would have to track antigen-specific T cells, which is possible for a wide range of microbial pathogens using peptide-MHC multimers. This would allow highly precise analysis of phenomena the authors are trying to conclude about. Moreover, it would allow them to confirm their gene expression data in populations of physiological interest.<br /> The authors agreed that this would be of interest but did nothing to provide it. They provided a sentence in the discussion stating that this (as well as many other experiments needed to interpret the results) would be of interest.

(8) Overall, the authors begun to address the role of Terc in bacterial susceptibility, but to what extent that specifically involves inflammation and macrophages, T cell immunity or aging remains unclear at the present.<br /> My conclusion from the prior review remains unchanged in the face of the revision that did not answer most of the previous criticism. The study as it stands is inconclusive and highly preliminary, with lack of clearly defined mechanistic underpinnings.

Reviewer #1 (Public review):

Summary:

The manuscript entitled "Staphylococcus aureus counters organic acid anion-mediated inhibition of peptidoglycan cross-linking through robust alanine racemase activity" by Panda, S et al. reports an extensive biochemical analysis of the result from a Tn screen that identified alr1 as being required for acetic acid tolerance. In the end, they demonstrate that reduced D-Ala pools in the ∆alr1 mutant lead to a drastic reduction in D-Ala-D-Ala dipeptide. They show that this is due to the ability of organic acid anions to limit the D-Ala-D-Ala ligase enzyme Ddl. They demonstrate that:

(1) Acetate exposure in the ∆alr1 results in reduced D-Ala-D-Ala dipeptide, but not the monomers.

(2) Acetate can bind to purified Ddl in vitro.

(3) This binding results in reduced enzyme activity.

(4) Other organic acid anions such as lactate, proprionate, and itaconitate can also inhibit Ddl.

The experiments are clearly described and logically laid out.

Comments on revised version:

Given that multiple reviewers noted that determining intracellular acetate levels would strengthen the impact of this manuscript, I still think the comment listed below should be dealt with. Radioactivity is not necessary for this. There are enzymatic kits that will allow for the accurate determination of acetate from a lysate of a known number of cells. This can be used to determine intracellular acetate levels.

(1) It is kind of tricky, but it is possible to measure intracellular acetate. That might be of interest to know where in the Ddl inhibition curve the cells actually are.

Reviewer #2 (Public review):

Summary:

In this manuscript, using Staphylococcus aureus as a model organism, Panda et al. aim to understand how organic acids inhibit bacterial growth. Through careful characterization and interdisciplinary collaboration, the authors present valuable evidence that acetic acid specifically inhibit the activity of Ddl enzyme that converts 2 D-alanine amino acids into D-ala-D-ala dipeptide, which is then used to generate the stem pentapeptide of peptidoglycan (PG) precursors in the cytoplasm. Thus, high concentration of acetic acid weakens the cell wall by limiting PG-crosslinking (which requires D-ala portion). However, S. aureus maintains a high intracellular D-ala concentration to circumvent acetate-mediated growth inhibition.

Strengths:

The authors utilized a well-established transposon mutant library to screen for mutants that struggle to grow in the presence of acetic acid. This screen allowed authors to identify that a strain lacking intact alr1, which encodes for alanine racemase (converts L-ala to D-ala), is unable to grow well in the presence of acetic acid. This phenotype is rescued by the addition of external D-ala. Next, the authors rule out the contribution of other pathways that could lead to the production of D-ala in the cell. Finally, by analyzing D-ala and D-ala-D-ala concentrations, as well as muropeptide intermediates accumulation in different mutants, the authors pinpoint Ddl as the specific target of acetic acid. In fact, synthetic overexpression of ddl alone overcomes the toxic effects of acetic acid. Using genetics, biochemistry, and structural biology, the authors show that Ddl activity is specifically inhibited by acetic acid and likely by other biologically relevant organic acids. Interestingly, this mechanism is different from what has been reported for other organisms such as Escherichia coli (where methionine synthesis is affected). It remains to be seen if this mechanism is conserved in other organisms that are more closely related to S. aureus, such as Clostridioides difficile and Enterococcus faecalis.

Weaknesses:

None noted. With new data the authors have satisfactorily addressed all the concerns of the previous version.

Reviewer #1 (Public review):

Summary:

The authors use fluorescence lifetime imaging (FLIM) and tmFRET to resolve resting vs. active conformational heterogeneity and free energy differences driven by cGMP and cAMP in a tetrameric arrangement of CNBDs from a prokaryotic CNG channel.

Strengths:

The data are excellent and provide detailed measures of the probability to adopt resting vs. activated conformations with and without bound ligands.

Weaknesses:

A limitation is that only the cytosolic fragments of the channel were studied.

Reviewer #2 (Public review):

The authors investigated the conformational dynamics and energetics of the SthK Clinker/CNBD fragment using both steady-state and time-resolved transition metal ion Förster resonance energy transfer (tmFRET) experiments. To do so, they engineered donor-acceptor pairs at specific sites of the CNBD (C-helix and β-roll) by incorporating a fluorescent noncanonical amino acid donor and metal ion acceptors. In particular, the authors employed two cysteine-reactive metal chelators (TETAC and phenM). This allowed to coordinate three transition metals (Cu2+, Fe2+, and Ru2+) to measure both short (10-20 Å, Cu2+) and long distances (25-50 Å, Fe2+, and Ru2+). By measuring tmFRET with fluorescence lifetimes, the authors determined intramolecular distance distributions in the absence and presence of the full agonist cAMP or the partial agonist cGMP. The probability distributions between conformational states without and with ligands were used to calculate the changes in free energy (ΔG) and differences in free energy change (ΔΔG) in the context of a simple four-state model.

Overall, the work is conducted in a rigorous manner, and it is well-written.

In terms of methodology, this work provides a further support to steady-state and time-resolved tmFRET approaches previously developed by the authors of the present work to probe conformational rearrangements by using a fluorescent noncanonical amino acid donor (Anap) and transition metal ion acceptor (Zagotta et al., eLife 2021; Gordon et al., Biohpysical Journal 2024; Zagotta et al., Biohpysical Journal 2024).

For what concerns Cyclic nucleotide-binding domain (CNBD)-containing ion channels, the literature on this subject is vast and the authors of the present work have significantly contributed to the understanding of the allosteric mechanism governing the ligand-induced activation of CNBD-containing channels, including a detailed description of the energetic changes induced by ligand binding. Particularly relevant are their works based on DEER spectroscopy. In DeBerg et al., JBC 2016, the authors described, at atomic details, the conformational changes induced by different cyclic nucleotides on the HCN CNBD fragment and derived energetics associated with ligand binding to the CNBD (ΔΔG). In Collauto et al., Phys Chem Chem Phys. 2017, they further detailed the ligand-CNBD conformational changes by combining DEER spectroscopy with microfluidic rapid freeze quench to resolve these processes and obtain both equilibrium constants and reaction rates, thus demonstrating that DEER can quantitatively resolve both the thermodynamics and the kinetics of ligand binding and the associated conformational changes.<br /> In the revised manuscript the authors better framed their work in light of the literature by highlighting novelty and limitations, in particular the decision to work with the isolated Clinker/CNBD fragment and not with the full-length protein.

Reviewer #3 (Public review):

Summary:

The manuscript by Eggan et al provides insights into conformational transitions in the cyclic nucleotide binding domain of a cyclic nucleotide-gated (CNG) channel. The authors use transition metal FRET (tmFRET) which has been pioneered by this lab and previously led to detailed insights into ion channel conformational changes. Here, the authors not only use steady-state measurements but also time-resolved, fluorescence lifetime measurements to gain detailed insights into conformational transitions within a protein construct that contains the cytosolic C-linker and cyclic nucleotide binding domain (CNBD) of a bacterial CNG channel. The use of time-resolved tmFRET is a clear advancement of this technique and a strength of this manuscript.

In summary, the present work introduces time-resolved tmFRET as a novel tool to study conformational distributions in proteins. This is a clear technological advance. The limitations of the truncated construct used in this study and how they relate to the energetics in full-length CNG channels are discussed. It will be interesting to see in the future how results compare to similar measurements on full-length channels, for example, reconstituted into nanodiscs.

Strengths:

The results capture known differences in promoting the open state between different ligands (cAMP and cGMP) and are consistent across three donor-acceptor FRET pairs. The calculated distance distributions are further in agreement with predicted values based on available structures. The finding that the C-helix is conformationally more mobile in the closed state as compared to the open state quantitatively increases our understanding of conformational changes in these channels.

Weaknesses:

The results describe movements of the C-helix in CNBDs, but detailed energetics as calculated in this study, need to be limited to the truncated protein construct. This is a weakness that cannot be overcome easily as it will require future experiments using the full-length channel.

The data only describe movements of the C-helix. Upon ligand binding, the C-helix moves upwards to coordinate the ligand. Thus, the results are ligand-induced conformational changes (as the title states). Allosteric regulation usually involves remote locations in the protein, which is applicable only in a limited fashion here.

Reviewer #1 (Public review):

Summary:

In this study, the authors developed a novel radiotherapy sensitivity score (NPC-RSS) for nasopharyngeal carcinoma patients using machine learning algorithms. They identified 18 key genes associated with radiosensitivity and demonstrated that NPC-RSS could effectively predict radiotherapy response in both public and in-house datasets. Furthermore, they found that the key genes of NPC-RSS were closely related to immune characteristics, the expression of radiosensitivity-related genes, and signaling pathways involved in disease progression. The authors validated the consistency of expression of two key genes, SMARCA2 and CD9, with NPC-RSS in their own cell lines. They also showed that the radiosensitive group, classified by NPC-RSS, exhibited a more enriched and activated state of immune infiltration compared to the radioresistant group.

Strengths:

(1) The study employed a comprehensive approach by integrating multiple machine learning algorithms to develop a robust predictive model for radiotherapy sensitivity in nasopharyngeal carcinoma patients.<br /> (2) The predictive performance of NPC-RSS was validated using both public and in-house datasets, demonstrating its potential clinical applicability.<br /> (3) The authors conducted extensive analyses to investigate the biological mechanisms underlying the association between NPC-RSS and radiotherapy response, including immune characteristics, radiosensitivity-related gene expression, and relevant signaling pathways.<br /> (4) The consistency of key gene expression with NPC-RSS was validated in the authors' own cell lines, providing additional experimental evidence.

Weaknesses:

(1) The sample size of the in-house dataset used for training the model was relatively small (34 patients), which might limit the generalizability of the findings.<br /> (2) The authors did not perform functional experiments to directly validate the roles of the identified key genes in radiotherapy sensitivity, relying instead on associations with immune features and signaling pathways.<br /> (3) The study did not discuss the potential limitations of using machine learning algorithms, such as the risk of overfitting and the need for larger, diverse datasets for more robust model development and validation.

Reviewer #2 (Public review):

Summary:

This article utilizes machine learning methods and transcriptomic data from nasopharyngeal carcinoma (NPC) patients to construct a biomarker called NPC-RSS that can predict the radiosensitivity of NPC patients. The authors further explore the biological mechanisms underlying the relationship between NPC-RSS and radiotherapy response in NPC patients. The main objective of this study is to guide the selection of radiotherapy strategies for NPC patients, thereby improving their clinical outcomes and prognosis.

Strengths:

(1) The combination of multiple machine learning algorithms and cross-validation was used to select the best predictive model for radiotherapy sensitivity from 71 differentially expressed genes, enhancing the robustness and reliability of the predictions.<br /> (2) Functional enrichment analysis revealed close associations between NPC-RSS key genes and immune characteristics, expression of radiotherapy sensitivity-related genes, and signaling pathways related to disease progression, providing a biological basis for NPC-RSS in predicting radiotherapy sensitivity.<br /> (3) Grouping NPC samples according to NPC-RSS showed that the radiotherapy-sensitive group exhibited a more enriched and activated state of immune infiltration compared to the radioresistant group. In single-cell samples, NPC-RSS was higher in the radiotherapy-sensitive group, with immune cells playing a dominant role. These results clarify the mechanism of NPC-RSS in predicting radiotherapy sensitivity from an immunological perspective.<br /> (4) The study used public datasets and in-house cohort data for validation, confirming the good predictive performance of NPC-RSS and increasing the credibility of the results.

Limitation:

(1) The study focuses on a specific type of nasopharyngeal carcinoma (NPC) and may not be generalizable to other subtypes or related head and neck cancers. The applicability of NPC-RSS to a broader range of patients and tumor types remains to be determined.<br /> (2) The study does not account for potential differences in radiotherapy protocols, doses, and techniques between the training and validation cohorts, which could influence the performance of the predictive model. Standardization of treatment parameters would be important for future validation studies.<br /> (3) The binary classification of patients into radiotherapy-sensitive and resistant groups may oversimplify the complex spectrum of treatment responses. A more granular stratification system that captures intermediate responses could provide more nuanced predictions and better guide personalized treatment decisions.<br /> (4) The study does not address the potential impact of other relevant factors, such as tumor stage, histological subtype, and concurrent chemotherapy, on the predictive performance of NPC-RSS. Incorporating these clinical variables into the model could enhance its accuracy and clinical utility.

Reviewer #1 (Public review):

In their paper, Kang et al. investigate rigidity sensing in amoeboid cells, showing that, despite their lack of proper focal adhesions, amoeboid migration of single cells is impacted by substrate rigidity. In fact, many different amoeboid cell types can durotax, meaning that they preferentially move towards the stiffer side of a rigidity gradient.

The authors observed that NMIIA is required for durotaxis and, buiding on this observation, they generated a model to explain how durotaxis could be achieved in the absence of strong adhesions. According to the model, substrate stiffness alters the diffusion rate of NMAII, with softer substrates allowing for faster diffusion. This allows for NMAII accumulation at the back, which, in turn, results in durotaxis.

The evidence provided for durotaxis of non adherent (or low-adhering) cells is strong. I am particularly impressed by the fact that amoeboid cells can durotax even when not confined. I wish to congratulate the authors for the excellent work, which will fuel discussion in the field of cell adhesion and migration.

Reviewer #2 (Public review):

Summary:

The authors developed an imaging-based device, that provides both spatial confinement and stiffness gradient, to investigate if and how amoeboid cells, including T cells, neutrophils and Dictyostelium can durotax. Furthermore, the authors showed that the mechanism for the directional migration of T cells and neutrophils depends on non-muscle myosin IIA (NMIIA) polarized towards the soft-matrix-side. Finally, they developed a mathematical model of an active gel that captures the behavior of the cells described in vitro.

Strengths:

The topic is intriguing as durotaxis is essentially thought to be a direct consequence of mechanosensing at focal adhesions. To the best of my knowledge, this is the first report on amoeboid cells that are not dependent on FAs to exert durotaxis. The authors developed an imaging-based durotaxis device that provides both spatial confinement and stiffness gradient and they also utilized several techniques such as quantitative fluorescent speckle microscopy and expansion microscopy. The results of this study have well-designed control experiments and are therefore convincing.

Reviewer #1 (Public review):

Summary:

Intravital microscopy (IVM) is a powerful tool that facilitates live imaging of individual cells over time in vivo in their native 3D tissue environment. Extracting and analysing multi-parametric data from IVM images however is challenging, particularly for researchers with limited programming and image analysis skills. In this work, Rios-Jimenez and Zomer et al have developed a 'zero-code' accessible computational framework (BEHAV3D-Tumour Profiler) designed to facilitate unbiased analysis of IVM data to investigate tumour cell dynamics (via the tool's central 'heterogeneity module') and their interactions with the tumour microenvironment (via the 'large-scale phenotyping' and 'small-scale phenotyping' modules). It is designed as an open-source modular Jupyter Notebook with a user-friendly graphical user interface and can be implemented with Google Colab, facilitating efficient, cloud-based computational analysis at no cost.

To demonstrate the utility of BEHAV3D-TP, they apply the pipeline to timelapse IVM imaging datasets to investigate the in vivo migratory behaviour of fluorescently labelled DMG cells in tumour-bearing mice. Using the tool's 'heterogeneity module' they were able to identify distinct single-cell behavioural patterns (based on multiple parameters such as directionality, speed, displacement, and distance from tumour edge) which was used to group cells into distinct categories (e.g. retreating, invasive, static, erratic). They next applied the framework's 'large-scale phenotyping' and 'small-scale phenotyping' modules to investigate whether the tumour microenvironment (TME) may influence the distinct migratory behaviours identified. To achieve this, they combine TME visualisation in vivo during IVM (using fluorescent probes to label distinct TME components) or ex vivo after IVM (by large-scale imaging of harvested, immunostained tumours) to correlate different tumour behavioural patterns with the composition of the TME. They conclude that this tool has helped reveal links between TME composition (e.g. degree of vascularisation, presence of tumour-associated macrophages) and the invasiveness and directionality of tumour cells, which would have been challenging to identify when analysing single kinetic parameters in isolation.

A key limitation of the pipeline is that it does not overcome the main challenges and bottlenecks associated with processing and extracting quantitative cellular data from timelapse and longitudinal intravital images. This includes correcting breathing-induced movement artifacts, automated registration of longitudinal images taken over days/weeks, and accurate, automated segmentation and tracking of individual cells over time. Indeed, there are currently no standardised computational methods available for IVM data processing and analysis, with most laboratories relying on custom-built solutions or manual methods. This isn't made explicit in the manuscript early on (described below), and the researchers rely on expensive software packages such as IMARIS for image processing and data extraction to feed the required parameters into their pipeline. This limitation unfortunately reduces the likely impact of BEHAV3D-TP on the IVM field.

Nonetheless, this computational framework appears to represent a useful and comparatively user-friendly tool to analyse dynamic multi-parametric data to help identify patterns in cell migratory behaviours, and to assess whether these behaviours might be influenced by neighbouring cells and structures in their microenvironment. When combined with other methods, it, therefore, has the potential to be a valuable addition to a researcher's IVM analysis 'tool-box'.

Strengths:

(1) The figures are clearly presented, and the manuscript is easy to follow.

(2) The pipeline appears to be intuitive and user-friendly for researchers with limited computational expertise. A detailed step-by-step video is also included to support its uptake.

(3) The different computational modules have been tested using a relevant dataset.

(4) All code is open source, and the pipeline can be implemented with Google Colab.

(5) The tool combines multiple dynamic parameters extracted from time-lapse IVM images to identify single-cell behavioural patterns and to cluster cells into distinct groups sharing similar behaviours, and provides avenues to map these onto in vivo or ex vivo imaging data of the tumour microenvironment.

Weaknesses:

(1) As highlighted above, the tool does not facilitate the extraction of quantitative kinetic cellular parameters (e.g. speed, directionality, persistence, and displacement) from intravital images. Indeed, to use the tool researchers must first extract dynamic cellular parameters from their IVM datasets, requiring access to expensive software (e.g. IMARIS as used here) and/or above-average computational expertise to develop and use custom-made open-source solutions. This limitation is not made explicit or discussed in the text.

(2) The number of cells (e.g. per behavioural cluster), and the number of independent mice, represented in each result figure, is not included in the figure legends and are difficult to ascertain from the methods.

(3) The data used to test the pipeline in this manuscript is currently not available, making it difficult to assess its usability. It would be important to include this for researchers to use as a 'training dataset'.

(4) Precisely how the BEHAV3D-TP large-scale phenotyping module can map large-scale spatial phenotyping data generated using LSR-3D imaging data and Cytomap to 3D intravital imaging movies is unclear. Further details in the text and methods would be beneficial to aid understanding.

(5) The analysis provides only preliminary evidence in support of the authors' conclusions on DMG cell migratory behaviours and their relationship with components of the tumour microenvironment. Conclusions should therefore be tempered in the absence of additional experiments and controls.

Reviewer #2 (Public review):

Summary:<br /> The authors produce a new tool, BEHAV3D to analyse tracking data and to integrate these analyses with large and small-scale architectural features of the tissue. This is similar to several other published methods to analyse spatiotemporal data, however, the connection to tissue features is a nice addition, as is the lack of requirement for coding. The tool is then used to analyse tracking data of tumour cells in diffuse midline glioma. They suggest that 7 clusters exist within these tracks and that they differ spatially. They ultimately suggest that these behaviours occur in distinct spatial areas as determined by CytoMAP.

Strengths:

(1) The tool appears relatively user-friendly and is open source. The combination with CytoMAP represents a nice option for researchers.

- The identification of associations between cell track phenotype and spatial features is exciting and the diffuse midline glioma data nicely demonstrates how this could be used.

Weaknesses:

(1) The strength of democratizing this kind of analysis is undercut by the reliance upon Imaris for segmentation, so it would be nice if this was changed to an open-source option for track generation.

(2) The main issue is with the interpretation of the biological data in Figure 3 where ANOVA was used to analyse the proportional distribution of different clusters. Firstly the n is not listed so it is unclear if this represents an n of 3 where each mouse is an individual or whether each track is being treated as a test unit. If the latter this is seriously flawed as these tracks can't be treated as independent. Also, a more appropriate test would be something like a Chi-squared test or Fisher's exact test. Also, no error bars are included on the stacked bar graphs making interpretation impossible. Ultimately this is severely flawed and also appears to show very small differences which may be statistically different but may not represent biologically important findings. This would need further study.

(3) Figure 4 has similar statistical issues in that the n is not listed and, again, it is unclear whether they are treating each cell track as independent which, again, would be inappropriate. The best practice for this type of data would be the use of super plots as outlined in Lord et al. (2020) JCI - SuperPlots: Communicating reproducibility and variability in cell biology.

(4) The main issue that this raises is that the large-scale phenotyping module and the heterogeneity module appear designed to produce these statistical analyses that are used in these figures and, if they are based on the assumption that each track is independent, then this will produce inappropriate analyses as a default.

Reviewer #3 (Public review):

Summary:

The manuscript by Rios-Jimenez developed a computational tool, BEHAV3D Tumor Profiler, to analyze intravital imaging data and extract distinctive tumor cell migratory phenotypes based on the quantified 3D image data.

Weaknesses:

(1) The most challenging task of analyzing 3D time-lapse imaging data is to accurately segment and track the individual cells in 3D over a long time duration. BEHAV3D Tumor Profiler did not provide any new advancement in this regard, and instead relies on commercial software, Imaris, for this critical step. Imaris is known to have a very high error rate when used for analyzing 3D time-lapse data. In the Methods section, the authors themselves stated that "Tumor cell tracks were manually corrected to ensure accurate tracking". Based on our own experience of using Imaris, such manual correction is tedious and often required for every time step of the movie. Therefore, Imaris is not a satisfactory tool for analyzing 3D time-lapse data. Moreover, Imaris is expensive and many research labs probably can't afford to buy it. The fact that BEHAV3D Tumor Profiler critically depends on the faulty ImarisTrack module makes it unclear whether the BEHAV3D tool or the results are reliable.

(2) The authors developed a "Heterogeneity module" to extract distinctive tumor migratory phenotypes from the cell tracks quantified by Imaris. The cell tracks of the individual tumor cells are all quite short, indicating relatively low motility of the tumor cells. It's unclear whether such short migratory tracks are sufficient to warrant the PCA analysis to identify the 7 distinctive migratory phenotypes shown in Figure 2d. It's also unclear whether these 7 migratory phenotypes correspond to unique functional phenotypes.

(3) Using only motility to classify tumor cell behaviours in the tumor microenvironment (TME) is probably not sufficient to capture the tumor cell difference. There are also other non-tumor cell types in the TME. If the authors aim to develop a computational tool that can elucidate tumor cell behaviors in the TME, they should consider other tumor cell features, e.g., morphology, proliferation state, and tumor cell interaction with other cell types, e.g., fibroblasts and distinct immune cells.

(4) The authors have already published two papers on BEHAV3D [Alieva M et al. Nat Protoc. 2024 Jul;19(7): 2052-2084; Dekkers JF, et al. Nat Biotechnol. 2023 Jan;41(1):60-69]. Although the previous two papers used BEHAV3D to analyze T cells, the basic pipeline and computational steps are similar, in particular regarding cell segmentation and tracking. The addition of a "Heterogeneity module" based on PCA analysis does not make a significant advancement in terms of image analysis and quantification.

Reviewer #1 (Public review):

Summary:

This article identifies ADGR3 as a candidate GPCR for mediating beige fat development. The authors use human expression data from Human Protein Atlas and Gtex databases and combine this with experiments performed in mice and a murine cell line. They refer to a GPCR bioactivity screening tool PRESTO-Salsa, with which it was found that Hesperetin activates ADGR3. From their experiments, authors conclude that Hesperetin activates ADGR3, inducing a Gs-PKA-CREB axis resulting in adipose thermogenesis.

Strengths:

The authors analyze human data from public databases and perform functional studies in mouse models. They identify a new GPCR with a role in thermogenic activation of adipocytes.

Considerations:

Selection of ADGRA3 as a candidate GPCR relevant for mediating beiging in humans:

The authors identify GPCRs that are expressed more highly in murine iBAT compared to iWAT in response to cold and assess which of these GPCRs are expressed in human subcutaneous or visceral adipocytes. Although this strategy will identify GPCRs that are expressed at higher levels in brown fat compared to beige and thus possibly more active in thermogenic function, the relevance in choosing GPCRs that also are expressed in unstimulated human white adipocytes should be considered. Thermogenic activity is not normally present in human white adipocytes. It would have strengthened the GPCR selection if the authors instead had assessed the intersection with human brown adipocytes that were activated with norepinephrine.

Strategy to investigate the role of ADGRA3 in WAT beiging:

Having identified ADGRA3 as their candidate receptor, the authors investigated the receptor in mouse models, the murine inguinal adipocyte cell line 3T3 and in human subcutaneous adipose progenitors (HAdsc) differentiated in vitro. Calling the human cells "beige" is a stretch as these cells are derived from a white adipose depot. The authors do observe regulation in UCP1 and abundance of mitochondria following modification of ADGRA3 in the cells. However, in future studies, it should be considered if the receptor rather plays a role in differentiation per se, and perhaps not specifically in thermogenic differentiation/activity.

According to the Human Protein Atlas and Gtex databases, ADGRA3 is not only expressed in adipocytes, but also in other tissues and cell types. The authors address this by measuring the expression in a panel of these tissues, demonstrating a knockdown not only in the adipose tissue, but also in the liver and less pronounced in the muscle (Figure S2). It should thus be emphasized that the decreased TG levels in serum and liver in the mice might in fact depend on Adgra3 overexpression in the liver. Even though this might not have been the purpose of the experiment, it is important to highlight this as it could serve as hypothesis building for future studies of the function of this receptor.

Reviewer #2 (Public review):

Based on bioinformatics and expression analysis using mouse and human samples, the authors claim that the adhesion G-protein coupled receptor ADGRA3 may be a valuable target for increasing thermogenic activity and metabolic health. Genetic approaches to deplete ADGRA3 expression in vitro resulted in reduced expression of thermogenic genes including Ucp1, reduced basal respiration and metabolic activity as reflected by reduced glucose uptake and triglyceride accumulation. In line, nanoparticle delivery of shAdgra3 constructs is associated with increased body weight, reduced thermogenic gene expression in white and brown adipose tissue (WAT, BAT), and impaired glucose and insulin tolerance. On the other hand, ADGRA3 overexpression is associated with an improved metabolic profile in vitro and in vivo, which can be explained by increasing the activity of the well-established Gs-PKA-CREB axis. Notably, a computational screen suggested that ADGRA3 is activated by hesperetin. This metabolite is a derivative of the major citrus flavonoid hesperidin and has been described to promote metabolic health. Using appropriate in vitro and in vivo studies, the authors show that hesperitin supplementation is associated with increased thermogenesis, UCP1 levels in WAT and BAT, and improved glucose tolerance, an effect that was attenuated in the absence of ADGRA3 expression.

Comments on revised version:<br /> In my opinion, the critical points I raised were not adequately addressed, neither in the revision nor in the response to the reviewer. Therefore, my initial assessment has not changed, the main claims are only partially supported by the data presented.

Reviewer #3 (Public review):

Summary:

The manuscript by Zhao et al. explored the function of adhesion G protein-coupled receptor A3 (ADGRA3) in thermogenic fat biology.

Strengths:

Through both in vivo and in vitro studies, the authors found that the gain function of ADGRA3 leads to browning of white fat and ameliorates insulin resistance.

Comments on revised version:

The revised manuscript by Zhao et al. has limited improvement. The authors refused to perform revised experiments using primary cultures even though two reviewers pointed out the same weakness (3T3-L1 adipocytes are unsuitable). Using infrared thermography to measure body temperature is also problematic.

Reviewer #1 (Public review):

Summary:

The mammalian Shieldin complex consisting of REV7 (aka MAD2L2, MAD2B) and SHLD1-3 affects pathway usage in DSB repair favoring non-homologous endjoining (NHEJ) at the expense of homologous recombination (HR) by blocking resection and/or priming fill-in DNA synthesis to maintain or generate near blunt ends suitable for NHEJ. While the budding yeast Saccharomyces cerevisiae does not have homologs to SHLD1-3, it does have Rev7, which was identified to function in conjunction with Rev3 in the translesion DNA polymerase zeta. Testing the hypothesis that Rev7 also affect DSB resection in budding yeast, the work identified a direct interaction between Rev7 and the Rad50-Mre11-Xrs2 complex by two-hybrid and direct protein interaction experiments. Deletion analysis identified that the 42 amino acid C-terminal region was necessary and sufficient for the 2-hybrid interaction. Direct biochemical analysis of the 42 aa peptide was not possible. Rev7 deficient cells were found to be sensitive to HU only in synergy with G2 tetraplex forming DNA. Importantly, the 42 aa peptide alone suppressed this phenotype. Biochemical analysis with full-length Rev7 and a C-terminal truncation lacking the 42 aa region shows G4-specific DNA binding that is abolished in the C-terminal truncation and with a substrate containing mutations to prevent G4 formation. Rev7 lacks nuclease activity but inhibits the dsDNA exonuclease activity of Mre11. The C-terminal truncation protein lacking the 42 aa region also showed some inhibition suggesting the involvement of additional binding sites besides the 42 aa region. Also, the Mre11 ssDNA endonuclease activity is inhibited by Rev7 but not the degradation of linear ssDNA. Rev7 does not affect ATP binding by Rad50 but inhibits in a concentration-dependent manner the Rad50 ATPase activity. The C-terminal truncation protein lacking the 42 aa region also showed some inhibition but significantly less than the full-length protein. Using an established plasmid-based NHEJ assay, the authors provide strong evidence that Rev7 affects NEHJ, showing a four-fold reduction in this assay. The mutations in the other Pol zeta subunits, Rev3 and Rev1, show a significantly smaller effect (~25% reduction). A strain expressing only the Rev7 C-terminal 42 aa peptide showed no NHEJ defect, while the truncation protein lacking this region exhibited a smaller defect than the deletion of REV7. The conclusion that Rev7 supports NHEJ mainly through the 42 aa region was validated using a chromosomal NHEJ assay. The effect on HR was assessed using a plasmid:chromosome system containing G4 forming DNA. The rev7 deletion strain showed an increase in HR in this system in the presence and absence of HU. Cells expressing the 42 aa peptide were indistinguishable from wild type as were cells expressing the Rev7 truncation lacking the 42 aa region. The authors conclude that Rev7 suppresses HR, but the context appears to be system-specific and the conclusion that Rev7 abolished HR repair of DSBs is unwarranted and overly broad.

Strength:

This is a well-written manuscript with well-executed experiments which suggest that Rev7 inhibits MRX-mediated resection to favor NEHJ during DSB repair. This finding is novel and provides insight into the potential mechanism of how the human Shieldin complex might antagonize resection.

Weaknesses:

The nuclease experiments were conducted using manganese as a divalent cation, and it is unclear whether there is an effect with the more physiological magnesium cation. The data largely support the conclusions, although the effect of Rev7 on HR is less well documented, as only a highly specialized assay is used that does not warrant the broad conclusion drawn. Specifically, the results that the Rev7 c-terminal truncation lacking the 42 aa region still suppresses HR is unexpected and unexplained.

In this revision the authors addressed most of my concerns by text revisions and addition of new data.

The new two hybrid data showing that the 42 amino acid segment interacts with MRN are valuable. However, it may not be clear to which subunit the 42 aa segment binds, as in the yeast 2H system the chromosomally encoded subunits are present or were the 2H experiments conducted in an MRN deletion background?. This could be acknowledged.

The material and methods section was updated to indicate use of 5 mM MnCl2 and 5 mM MgCl2 in the exonuclease assay but not the endonuclease assay. Please check if this is correct. Why the difference between both assays? There is a concern that the absence of ATP and Mg affects the endonuclease assay.

The addition of Dmc1 as a specificity control for the ATPase inhibition is nice and shows a specific effect. The use of Sae2 associated nuclease activity as a specificity control for the nuclease inhibition is problematic. There has been considerable debate about the Sae2 associated nuclease activity, which seems to have been solved by the Cejka lab showing that Sae2 is a cofactor of MRN without intrinsic nuclease activity (e.g. https://pubmed.ncbi.nlm.nih.gov/25231868/). Or do the authors want to suggest that Sae2 has intrinsic nuclease activity? The control may still be useful mentioning that the nuclease is associated but not intrinsic and citing the relevant papers.

Reviewer #2 (Public review):

In this study, Badugu et al investigate the Rev7 roles in regulating the Mre11-Rad50-Xrs2 complex and in metabolism of G4 structures. The authors also try to make a conclusion that REV7 can regulate the DSB repair choice between homologous recombination and non-homologous end joining.<br /> The major observations of this study are:

(1) Rev7 interacts with the individual components of the MRX complex in a two-hybrid assay and in a protein-protein interaction assay (microscale thermophoresisi) in vitro.<br /> (2) Modeling using AlphaFold-Multimier also indicated that Rev7 can interact with Mre11 and Rad50.<br /> (3) Using a two-hybrid assay, a 42 C terminal domain in Rev7 responsible for the interaction with MRX was identified.<br /> (4) Rev7 inhibits Mre11 nuclease and Rad50 ATPase activities in vitro.<br /> (5) Rev 7 promotes NHEJ in plasmid cutting/relegation assay.<br /> (6) Rev7 inhibits recombination between chromosomal ura3-1 allele and plasmid ura3 allele containing G4 structure.<br /> (7) Using an assay developed in V. Zakian's lab, it was found that rev7 mutants grow poorly when both G4 is present in the genome and yeast are treated with HU.<br /> (8) In vitro, purified Rev7 binds to G4-containing substrates.

In general, a lot of experiments have been conducted, but the major conclusion about the role of Rev7 in regulating the choice between HR and NHEJ is not justified.

(1) Two stories that do not overlap (regulation of MRX by Rev7 and Rev7 role in G4 metabolism) are brought under one umbrella in this work. There is no connection unless the authors demonstrate that Rev7 inhibits the cleavage of G4 structures by the MRX complex.

(2) The authors cannot conclude based on the recombination assay between G4-containing 2-micron plasmid and chromosomal ura3-1 that Rev7" completely abolishes DSB-induced HR". First of all, there is no evidence that DSBs are formed at G4. Why is there no induction of recombination when cells are treated with HU? Second, as the authors showed, Rev7 binds to G4, therefore it is not clear if the observed effects are the result of Rev7 interaction with G4 or impact on HR. The established HO-based assays where the speed of resection can be monitored (e.g., Mimitou and Symington, 2010) have to be used to justify the conclusion that Rev7 inhibits MRX nuclease activity in vivo.

Comments on the revised version:

I am satisfied with the revision. Specifically, i) the elimination of the G4 part and ii) the implementation of the HO-endonuclease resection assay described in Mimiou and Symington, 2010 significantly improved the clarity of the work and strengthened the conclusion about the Rev7 interference with DNA resection.

Reviewer #3 (Public review):

Summary:

REV7 facilitates the recruitment of Shieldin complex and thereby inhibits end resection and controls DSB repair choice in metazoan cells. Puzzlingly, Shieldin is absent in many organisms, and it is unknown if and how Rev7 regulates DSB repair in these cells. The authors surmised that yeast Rev7 physically interacts with Mre11/Rad50/Xrs2 (MRX), the short-range resection nuclease complex and tested this premise using yeast two hybrid (Y2H) and microscale thermophoresis (MST). The results convincingly showed that the individual subunits of MRX interacts robustly with Rev7. By AlphaFold Multimer modelling followed by Y2H confirmed that the carboxy terminal 42 amino acid is essential for interaction with MR and G4 DNA binding by REV7. The mutant rev7 lacking the binding interface (Rev7-C1) to MR shows moderate inhibition to the nuclease and the ATPase activity of Mre11/Rad50 in biochemical assays. Deletion of REV7 also causes a mild reduction in NHEJ using both plasmid and chromosome-based assays and increases mitotic recombination between chromosomal ura3-01 and the plasmid ura3 allele interrupted by G4. The revision also showed that rev7 deleted cells exhibit mild hyper-resection phenotype at 0.7 and 3 kb from the DSB using qPCR assays. The authors concluded that Rev7 facilitates NHEJ and antagonises HR even in budding yeast, but it achieves this by blocking Mre11 nuclease and Rad50 ATPase.

Weaknesses:

There are several strengths to the studies and the broad types of well-established assays were used to deduce the conclusion. Nevertheless, there are notable discrepancies on the mutant phenotypes that were to test the functionality of Rev7-MRX interaction on the repair outcomes, raising concerns on the validity of the proposed model. The manuscript also needs a few additional functional assays to reach the accurate conclusions as proposed. The revision responded to several comments raised by the reviewers, but they are inadequate to address the key concerns and did not offer sufficient and compelling experimental support to the main premise that Rev7-Mre11/Rad50/Xrs2 interactions regulate MRX activities in cells and thereby modulates DSB repair choice in budding yeast.

(1) AlphaFold model predicts that Mre11-Rev7 and Rad50-Rev7 binding interfaces overlap and Rev7 might bind only to Mre11 or Rad50 at a time. Interestingly, however, Rev7 appears dimerized (Fig.1). Since MR complex also forms with 2M and 2R in the complex, it should still be possible if REV7 can interact both M and R in the MR complex. The author should perform MST using MR complex instead of individual MR components. The authors should also analyze if Rev7-C1 is indeed deficient in interaction with MR individually and with complex using MST assay.

(2) The nuclease and the ATPase assays require additional controls. Does Rev7 inhibit the other nuclease or ATPase non-specifically? Are these outcomes due to the non-specific or promiscuous activity of Rev7? In fig.6, the effect of REV7 on the ATP binding of Rad50 could be hard to assess because the maximum Rad50 level (1 uM) was used in the experiments. The author should use the suboptimal level of Rad50 to check if REV7 still does not influence ATP binding by Rad50.

(3) The moderate deficiency in NHEJ using plasmid based assay in REV7 deleted cells can be attributed to aberrant cell cycle or mating type in rev7 deleted cells. The authors should demonstrate that rev7 deleted cells retain largely normal cell cycle pattern and the mating type phenotypes. The author should also analyze the breakpoints in plasmid based NHEJ assays in all mutants especially from rev7 and rev7-C1 cells.

(4) It is puzzling why the authors did not analyze end resection defects in rev7 deleted cells after a DSB. The author should employ the widely used resection assay after a HO break in rev3, rev7 and mre11 rev7 cells as described previously.

(5) Is it possible that Rev7 also contributes to NHEJ as the part of TLS polymerase complex? Although NHEJ largely depends on Pol4, the authors should not rule out the possibility if the observed NHEJ defect in rev7 cells are due at least partially to its well-known TLS defect and not all due to their role in MRX activity regulation as the authors proposed. In fact, rev3 or rev1 cells are partially defective in NHEJ (Fig. 7). Rev7-C1 is less deficient in NHEJ than REV7 deletion. These results predict that rev7-C1 rev3 could be more deficient than rev3 or rev7-C1, and such results might indicate that Rev7 contributes to NHEJ by two ways; one by interacting (and modulating) MRX and the other as part of Rev3-Rev7 complex. Additionally, the authors should examine if Rev7-C1 might be deficient in TLS. In this regard, does rev7-C1 reduce TLS and TLS dependent mutagenesis? Is it dominant? The authors should also check if Rev3/Rev1 complexes are stable in Rev7 deleted or rev7-C1 cells by immunoblot assays.

(6) Due to the G4 DNA and G4 binding activity of REV7, it is not clear which class of events the authors are measuring in plasmid-chromosome recombination assay in Fig.9. Do they measure G4 instability or the integrity of recombination or both in rev7 deleted cells. Instead, the effect of rev7 deletion or rev7-C1 on recombination should be measured directly by more standard mitotic recombination assays like mating type switch or his3 repeat recombination. The revision did not address these concerns, which still makes the interpretation of the provided recombination results difficult.

Reviewer #1 (Public review):

Papalamprou et al. established a methodology to differentiate iPSCs to the syndetome stage and validated it by marker gene expression and scRNA-seq analysis. They further found that inhibition of WNT signaling enhanced the homogeneity of the cell population after identifying a group of branching-off cells that overexpressed WNT. Their results will be helpful in developing cell therapy systems for tendon injuries. However, there are several issues to improve the manuscript:

IPA analysis was performed after scRNA-seq. Although it is knowledge-based software with convenient graphic utilities, it is questionable whether an unbiased genome-level analysis was performed. Therefore, it is not convincing if WNT is the only and best signal for the branching-off marker. Perhaps independent approaches, such as GO, pathway, or module analyses, should be performed to validate the findings.

According to the method section, two iPSC lines were used for the study. However, throughout the manuscript, it is not clearly described which line was used for which experiment. Did they show similar efficiency in differentiation and in responses to WNTi? It is also worrisome if using only two lines is the norm in the stem cell field. Please provide a rationale for using only two lines, which will restrict the observation of individual-specific differential responses throughout the study.

How similar are syndetome cells with or without WNTi? It would be interesting to check if there are major DEGs that differentiate these two groups of cells.

Please discuss the improvement of the current study compared to previous ones (e.g., PMID 36203346, 35083031, 35372337).

Reviewer #2 (Public review):

Summary:

Dr. Sheyn and colleagues report the step-wise induction of syndetome-like cells from human induced pluripotent stem cells (iPSCs), following a previously published protocol which they adjusted. The progression of the cells through each stage, i.e. presomitic mesoderm (PSM), somitic mesoderm (SM), sclerotome (SCL), and syndetome (SYN)) is characterized using FACS, RT-qPCR and immunofluorescence staining (IF). The authors performed also single-cell RNA sequencing (scRNAseq) analysis of their step-wise induced cells and identify signaling pathways which are potentially involved in and possibly necessary for syndetome induction. They then optimized their protocol by simultaneous inhibition of BMP and Wnt signaling pathways, which lead to an increase in syndetome induction while inhibiting off target differentiation into neural lineages.

Strengths:

The authors conducted scRNAseq analysis of each step of their protocol from iPSCs to syndetome-like cells and employed pathway analysis to uncover further insights into somitic mesoderm (SM) and syndetome (SYN) differentiation. They found that BMP inhibition, in conjunction with the inhibition of WNT signaling, plays a role in driving syndetome differentiation. Analyzing their scRNAseq results, they could improve the syndetome induction efficiency of their protocol from 47.6% to 67%-78% while off-target differentiation into neural lineages could be reduced.

Weaknesses:

The authors demonstrated the efficiency of syndetome induction solely by scRNA-seq data analysis before and after pathway inhibition, without using e.g. FACS analysis or immunofluorescence (IF)-staining based assessment. A functional assessment and validation of the induced cells is also completely missing.

Reviewer #3 (Public review):

Papalamprou et al sought to fine tune existing tenogenic differentiation protocols to develop a robust multi-step differentiation protocol to induce tendon cells from human GMP-ready iPSCs. In so doing, they found that while existing protocols are capable of driving cells towards a syndetome-like fate, the resultant cultures contain highly heterogeneous cell populations with sub-optimal cell survival. Through single cell transcriptomic analysis they identify WNT signaling as a potential driver of an off-target neural population and show that inhibition of WNT signaling at the later 2 stages of differentiation can be used to promote higher efficiency of generation of syndetome-like cells.

This paper includes a useful paradigm for identifying transcriptional modulators of cell fate during differentiation and a clear example where transcriptional data can be used to guide the chemical modulation of a differentiation protocol to improve cell output. The paper's conclusions are mostly well supported by the data, but the image analysis and discussion need to be improved to strengthen the impact.

The data outlining the differences between the differentiation outcome of the two tested iPSCs is intriguing, but the authors fail to comment on potential differences between the two iPSC lines that could result in drastically different cell outputs from the same differentiation protocol. This is a critically important point, as the majority of the SCX+ cells generated from the 007i cells using their WNTi protocol were found in the FC subpopulation that failed to form from the 83i line under the same protocol. From the analysis of only these 2 cells lines in vitro, it is difficult to assess whether this WNTi protocol can be broadly used across multiple cell lines to generate tenogenic cells. The authors failed to update the text of the manuscript to reflect the potential differences in the two cell lines and the general applicability of their protocol, but rather just include the description of the proposed explanation in the response to reviewer comments. These critical differences in the response to their protocol and their implications for the applications of this proof-of-concept study should be included in the main text.

The authors make claims about changes in protein expression but fail to quantify either fluorescence intensity or percent cell expression from their immunofluorescence analyses to substantiate these claims. The authors state in their response to reviewers that immunofluorescence is qualitative but continue to make quantitative statements such as upregulated or downregulated in both the text and legend describing these images. The authors should either perform the quantification of the IFs, use Western blots for protein quantification of their cell cultures, use Flow Cytometry to count cell numbers, or remove these quantitative words from the description of the images. The image quality and staining specificity continue to be a limitation of this study. These claims are not fully supported by the data as presented as it is unclear whether there is increased expression of tendon markers at the protein level or more cells surviving the protocol.

Reviewer #1 (Public review):

Summary:

The authors want to elucidate which are the mechanisms that regulate the immune response in physiological conditions in cortical development. To achieve this goal, authors used a wide range of mutant mice to analyse the consequences of immune activation in the formation of cortical ectopia in mice.

Strengths:

The authors demonstrated that Abeta monomers are anti-inflammatory and inhibit microglial activation. This is a novel result that demonstrates the physiological role of APP in cortical development.

The current manuscript has been slightly improved by additional experiments and editing of the text (many of the suggestions of the reviewers have not been included). However, the evidence supporting the conclusions of the study is still very weak and inconsistent.

Remaining weaknesses:

-There is no evidence that microglia express Emx1. The paper they referred (Zhang et al., 2014) was performed in adult mice so it is not comparable. Moreover, many other papers are saying that Emx1 is not expressed in microglia. Line 175: change in cytokine expression is not a strong evidence to state that Emx1 is expressed in microglia. Fig. S8: It is not clear whether the staining was performed on neuronal primary culture or cortical section? It is also unclear why there is a partial reduction of Ric8a mRNA levels in Emx1-Ric8a cKO and not a completed deletion?

-NestinCre and Emx1Cre mouse models are targeting the same type of cells in the developing cortex (cortical progenitors, glutamatergic neurons and astrocytes), but with one day difference in expression (Emx1 E9.5 and Nestin E10.5). In fact, previous studies using the same approach (Nestin-Ric8a cKO) found ectopias in the cortex, it is more in line with the results of Emx1-Ric8a cKO shown in the current study. There is no evidence to assume that ric8a deficiency in neural cell lineages is not responsible for basement membrane degradation and ectopia formation in ric8a mutants.

-Additional experiments should be performed to demonstrate that ectopia formation in Emx1-ric8a cKO mutant mice is due to an increase in immune stimulation and not a cell-autonomous effect. Using double cx3cr1-cre and nestin-cre ric8a mutant mice is not an argument to say that elevated immune activation of ric8a deficient microglia during cortical development is responsible for ectopia formation (line 2012-2013)

-The similarities between Ric8a cKO and APP cKO mice are not enough evidence to claim that APP and Ric8a are involved in the same anti-inflammatory pathway in microglia.

-Gel zymography is not the same as Western blot. For the quantification of the relative amount of protein, authors should use western blot and not immunofluorescence intensity as shown in Fig. 5g, h. For western blot, you also load the same amount of protein but you have to normalize your samples with a control protein.

-The graph of BrdU cell distribution in the mutant mice (Fig. S1 F) shows that there are more BrdU cells in bins 5-7 and less in bin 9, indicating an impaired migration of upper cortical neurons in the mutant mice. The authors claimed there are no differences in migration in the result section but the figure showed significant differences. Panels E, F in Fig S2 show the density of Cux1 and Ctip2 cells per area indicating no changes in the generation of upper and lower cortical neurons, but no information about the migration as authors claimed (lines 117-118). (what is the field for Ctip2 counting?). These experiments cannot rule out the possibility of cell-autonomous effect of Ric8a deletion in glutamatergic neurons or radial glial cells.

Reviewer #2 (Public review):

Kwon et al. used several conditional KO mice for the deletion of ric8a or app in different cell types. Some of them exhibited pial basement membrane breaches leading to neuronal ectopia in the neocortex.

I am glad to see that the authors performed some of the requested controls.

However, a huge problem with this manuscript which has been highlighted in the reviewer's comments but not corrected by the authors, is the claim that "A novel monomeric amyloid beta-activated signaling pathway regulates brain development". They do not have any proof that Abeta is the activating signal in vivo. Whatever they showed in vitro should be confirmed in vivo to make such a strong claim. The authors even recognized it in their responses to reviewers: "we currently do not have evidence that in the developing cortex Abeta monomers play a role in inhibiting microglia". Therefore, their title is misleading, not supported by the data, and must be changed to reflect accurately the results. Maybe something like "Involvement of microglia in the formation of cortical ectopia".

The abstract is also misleading and must be changed. The abstract is mostly about Abeta, pretending that this is the key part of their findings while they only provide a few in vitro experiments but nothing in vivo.<br /> This is such a bad way to summarize their data. Most of their in vivo data is about Ric8a, then a smaller in vivo part about APP and nothing about Abeta in vivo. But the title "novel monomeric amyloid beta-activated signaling pathway regulates brain development via inhibition of microglia" only mention Abeta. And the Abstract 90% focuses on Abeta.<br /> The first half of the introduction is about Abeta. Why would they focus their paper about Abeta while they basically have only one figure with in vitro data !! This is so deceptive.<br /> It seems that these authors do not fully understand the importance of having their claims supported by solid data.

(1) The authors did not show in vivo data supporting that Abeta monomers are the key players here.<br /> (2) The authors did not show in vivo data supporting the cytokine secretion data provided in vitro in a model system. They claim that it is not technically feasible to extract the extracellular (secreted) fractions of cytokines from an embryonic brain without causing cell lysis and the release of the intracellular pool. But how about RT-qPCR? After all, they showed that the pathway affects the transcription of several cytokines in microglia in vitro.<br /> (3) The authors did not provide a control experiment to show that the insult induced by LPS injection does not induce the phenotype in the ric8a-foxg1-cre mice.<br /> (4) They did not agree to verify the monomer state of their Abeta monomer preparation, even after addition to the culture medium. Abeta have a strong tendency to polymerize. However, because the authors added the requested result with Ab polymers which gave a different outcome. It is OK with me if they don't do it.<br /> (5) The app-cx3cr1-cre +LPS animals show ectopia only in only subsets of mutants and in most cases only in one of the hemispheres. Experiments examining potential changes in MMP9 are therefore difficult and were not done.

I don't mind the inability to perform all the suggestions from the reviewers but it is then necessary to tone down or remove the claims that are not supported by the data.<br /> This kind of issue appears several times later in the text too:

(1) At the end of the introduction "we found that APP and Ric8a form a pathway in microglia that is specifically activated by the monomeric form of Abeta and that this pathway normally inhibits the transcriptional and post-transcriptional expression of immune cytokines by microglia". Data from Abeta and cytokines are only in vitro, so it has to be specified.<br /> (2) Line 282: "Thus, these results indicate that monomeric Abeta possesses a previously unreported anti-inflammatory activity against microglia that strongly inhibits microglial inflammatory activation". Specify in vitro!<br /> (3) Line 322: "We have shown that heightened microglial activation due to mutation in the Abeta monomer-activated APP/Ric8a pathway results in basement membrane degradation and ectopia during cortical development." This is an overstatement. They did not show that Abeta monomers activate the pathway in vivo.<br /> (4) Line 332: "Thus, these results indicate that excessive inflammatory activation of microglia is responsible for ectopia formation in ric8a mutants." This is incorrect. Inhibition of Akt or stat3 does much more than just being pro-inflammatory. This could affect directly migration. The data only show that Akt and/or Stat3 might be involved.<br /> (5) Line 355: "these results indicate this Abeta monomer-regulated anti-inflammatory pathway normally promotes cortical development through suppressing microglial activation and MMP induction.". Another overstatement. There is no proof that Abeta is involved in vivo.<br /> (6) Line 362: "In this article, we have identified a novel microglial anti-inflammatory pathway activated by monomeric Abeta that inhibits microglial cytokine expression and plays essential roles in the normal development of the cerebral cortex". Another overstatement. There is no proof that Abeta is involved in vivo.<br /> (7) Line 365: "this pathway is mediated by APP and the heterotrimeric G protein GEF and molecular chaperone Ric8a in microglia and its activation leads to..." They should mention that its activation was in vitro.<br /> (8) Line 387: "In this study, we have shown that immune over-activation of microglia deficient in a monomeric Ab-regulated pathway results in excessive cortical matrix proteinase activation, leading basement membrane degradation and neuronal ectopia." Another overstatement. There is no support to claim that Abeta is involved in vivo. The immune overactivation was not shown in vivo but only in vitro in a model system that does not even reflect correctly what is happening in vivo due to chronic immune stimulation during in vitro culture.<br /> (9) Line 396: "we have also shown that the anti-inflammatory regulation of microglia in corticogenesis depends on a pathway composed of APP and the heterotrimeric G protein regulator Ric8a." Overstatement. They only showed the anti-inflammatory regulation in vitro and not during corticogenesis.<br /> It is just a matter of rewriting the title, abstract and text in an honest way, in order to make sure that every claim is supported by the data and in some cases acknowledge the weakness of the provided data and describe the multiple interpretations than could be drawn out of them.

Reviewer #1 (Public review):

The authors describe the dynamic distribution of laminin γ1 in the olfactory system and forebrain. Using immunohistochemistry and transgenic lines, they found that the olfactory system and adjacent brain tissues are enveloped by basement membrane (BMs) from the earliest stages of olfactory system assembly. They also found that laminin deposits follow the axonal trajectory of axons. They performed a functional analysis of the sly mutant to analyse the function of laminin γ1 in the development of the zebrafish olfactory system. Their study revealed that laminin enables the shape and position of olfactory placodes to be maintained late in the face of major morphogenetic movements in the brain, and its absence promotes the local entry of sensory axons into the brain and their navigation towards the olfactory bulb.

They showed that in the laminin γ1 mutants no BM staining of laminin could be detected around the OP and the brain. The authors then elegantly used electron microscopy to analyse the ultrastructure of the border between the OP and the brain.<br /> The authors performed a quantitative analysis of the loss of function of Laminin γ1 (sly mutants).<br /> Olfactory axon migration is drastically impaired in sly mutants, demonstrating that Laminin γ1-dependent BMs are essential for the growth and navigation of axons from the OP to the olfactory bulb. They propose that the BM of the OP prevents its deformation in response to mechanical forces generated by morphogenetic movements of the neighbouring brain.<br /> Although the results are expected, the experiments carried out and the results are robust and elegant.

Reviewer #2 (Public review):

Summary:

This manuscript addresses the role of extracellular matrix in olfactory development. Despite the importance of these extracellular structures, the specific roles and activities of matrix molecules are still poorly understood. Here, the authors combine live imaging and genetics to examine the role of the laminin gamma 1 in multiple steps of olfactory development. The work comprises a descriptive but carefully executed, quantitative assessment of the olfactory phenotypes resulting from loss of laminin gamma 1. Overall, this is a constructive advance in our understanding of extracellular matrix contributions to olfactory development, with a well-written Discussion with relevance to many other systems.

Strengths:

The strengths of the manuscript are in the approaches: the authors have combined live imaging, careful quantitative analyses, and molecular genetics. The work presented takes advantage of many zebrafish tools including mutants and transgenics to directly visualize the laminin extracellular matrix in living embryos during the developmental process.

Weaknesses:

Weaknesses in the first round of critique were addressed in the revision, and a minor caveat is regarding interpretation of differences in tissue size and shape in fixed samples (comparing mutants and controls); the fixation process can alter these properties and may do so differently between genotypes.

Reviewer #4 (Public review):

Summary:

In this elegant study XX and colleagues use a combination of fixed tissue analyses and live imaging to characterise the role of Laminin in olfactory placode development and neuronal pathfinding in the zebrafish embryo. They describe Laminin dynamics in the developing olfactory placode and adjacent brain structures and identify potential roles for Laminin in facilitating neuronal pathfinding from the olfactory placode to the brain. To test whether Laminin is required for olfactory placode neuronal pathfinding they analyse olfactory system development in a well-established laminin-gamma-1 mutant, in which the laminin-rich basement membrane is disrupted. They show that while the OP still coalesces in the absence of Laminin, Laminin is required to contain OP cells during forebrain flexure during development and maintain separation of the OP and adjacent brain region. They further demonstrate that Laminin is required for growth of OP neurons from the OP-brain interface towards the olfactory bulb. The authors also present data describing that while the Laminin mutant has partial defects in neural crest cell migration towards the developing OP, these NCC defects are unlikely to be the cause of the neuronal pathfinding defects upon loss of Laminin. Altogether the study is extremely well carried out, with careful analysis of high-quality data. Their findings are likely to be of interest to those working on olfactory system development, or with an interest in extracellular matrix in organ morphogenesis, cell migration, and axonal pathfinding.

Strengths:

The authors describe for the first time Laminin dynamics during the early development of the olfactory placode and olfactory axon extension. They use an appropriate model to perturb the system (lamc1 zebrafish mutant), and demonstrate novel requirements for Laminin in pathfinding of OP neurons towards the olfactory bulb.<br /> The study utilises careful and impressive live imaging to draw most of its conclusions, really drawing upon the strengths of the zebrafish model to investigate the role of laminin in OP pathfinding. This imaging is combined with deep learning methodology to characterise and describe phenotypes in their Laminin-perturbed models, along with detailed quantifications of cell behaviours, together providing a relatively complete picture of the impact of loss of Laminin on OP development.

Weaknesses:

Some of the statistical tests are performed on experiments where n=2 for each condition (for example the measurements in Figure S2) - in places the data is non-significant, but clear trends are observed, and one wonders whether some experiments are under-powered.

Reviewer #1 (Public review):

Summary:

Jirouskova and colleagues in their study have carried out an in-depth proteomic characterization of the dynamics of the liver fibrotic response and the resulting resolution in two distinct models of liver injury: CCl4-induced model of hepatotoxicity and pericentral/bridging liver fibrosis and the DDC feeding model of obstructive cholestasis and periportal fibrosis. They focussed on both the insoluble extracellular matrix (ECM) components as well as the soluble secreted factors produced by hepatic stellate cells (HSCs) and/or portal fibroblasts (PFs). They identified compartment- and time-resolved proteomic signatures in the two models with disease-specific factors or matrisomes. Their study also identified phenotypic differences between the models such as that while the CCl4-induced model induced profound hepatotoxicity followed by resolution, the DDC model induced more lasting liver damage and proteomic changes that resembled advanced human liver fibrosis favouring hepatocarcinogenesis.

Overall, this comprehensive and very well-conducted study is rigorous and well-planned. The conclusions are supported by compelling studies and analyses. One caveat is the lack of mechanistic experiments to prove causality, but this can be carried out in follow-up studies.

Strengths:

(1) A major strength of the study is that the experiments are rigorous and very well conducted. For instance, the authors utilized two models of liver fibrosis to study different aspects of the pathology - hepatotoxicity vs cholestasis. In addition, 4 time points for each model were investigated - 2 for fibrosis development and 2 for fibrosis resolution. They have taken 3 components for proteomic analyses - total lysates, insoluble ECM components as well as the soluble secreted factors. Thus, the authors provide a comprehensive overview of the fibrosis and resolution process in these models.

(2) Another great strength of the study is that the methodology utilized was able to dissect unique pathways relevant to each model as well as common targets. For example, the authors identified known pathways such as mTOR signalling to be differentially regulated in the CCl4 vs DDC model. mTOR signalling was increased in the DDC model which is associated with hyperproliferation. Thus showing that the approach taken is specific enough to distinguish between the two similar (both induce fibrosis) but distinct mechanisms (hepatotoxicity vs cholestasis) is a strong point of the study.

Weaknesses:

(1) The authors themselves propose in their Introduction that the "ECM-associated changes are increasingly perceived as causative, rather than consequential"; however, they have not conducted mechanistic (gain of function/loss of function) studies either in vitro or in vivo from any of their identified targets to truly prove causality. This remains one of the limitations of this study. Thus, future studies should investigate this point in detail. For instance, it would have been intriguing to dissect if knocking out specific genes involved in one specific model or genes common to both would yield distinct phenotypic outcomes.

(2) The majority of the conclusions are derived primarily from the proteomic analyses. Although well conducted, it would strengthen the study to corroborate some of the major findings by other means such as IHC/IF with the corresponding quantifications and not only representative images.

Reviewer #2 (Public review):

Summary:

The authors suggest that ECM abundance and composition change depending on the aetiology of liver fibrosis. To understand this they have investigated the proteome in two models of animal fibrosis and resolution. They suggest their findings could provide a foundation for future anti-fibrotic therapies.

Strengths:

The animal models used are widely studied models of liver fibrosis from both parenchymal and biliary damage aspects. Both would allow analysis of resolution. The CCl4 model in particular fully reverts to a 'healthy' liver following cessation of the insult. I am less clear whether/how quickly the ductal plugs clear in DDC models and thus this may not provide the response they are looking for in terms of reversibility. I believe there have been several extensive studies using a transcriptomics approach in assessing genes and cells involved in the CCl4 model of resolution. Even more mutliomic models of general fibrosis progression in many of the mouse models of fibrosis. However, the proteomic approach they have used is robust and they have made some attempts to integrate with cell-type specific signatures from previously published data.

Although there is minimal data, hepatocyte elasticity is a very interesting part of their study. Additional data and focussed attention on the mechanisms underpinning this would be very insightful.

Weaknesses:

As it currently stands, the data, whilst extensive, is primarily focussed on the proteomic data which is fairly descriptive and I am not clear on the additional insight gained in their approach that is not already detailed from the extensive transcriptomic studies. The manuscript overall would benefit from some mechanistic functional insight to provide new additional modes of action relevant to fibrosis progression. Whilst there is some human data presented it is a minimal analysis without quantification that would imply relevance to disease state.

Although studying disease progression in animals is a fundamental aspect of understanding the full physiological response of fibrotic disease, without more human insight makes any analysis difficult to fulfil their suggestion that these targets identified will be of use to treat human disease.

Some of the terminology is incorrect while discussing these models of injury used and care should be taken. For example - both models are toxin-induced and I do not think these data have any support that the DDC model has a higher carcinogenic risk. An investigation into the tumour-induced risk would require significant additional models. These types of statements are incorrect and not supported by this study.

Reviewer #1 (Public review):

The manuscript consists of two separate but interlinked investigations: genomic epidemiology and virulence assessment of Salmonella Dublin. ST10 dominates the epidemiological landscape of S. Dublin, while ST74 was uncommonly isolated. Detailed genomic epidemiology of ST10 unfolded the evolutionary history of this common genotype, highlighting clonal expansions linked to each distinct geography. Notably, North American ST10 was associated with more antimicrobial resistance compared to others. The authors also performed long-read sequencing on a subset of isolates (ST10 and ST74) and uncovered a novel recombinant virulence plasmid in ST10 (IncX1/IncFII/IncN). Separately, the authors performed cell invasion and cytotoxicity assays on the two S. Dublin genotypes, showing differential responses between the two STs. ST74 replicates better intracellularly in macrophages compared to ST10, but both STs induced comparable cytotoxicity levels. Comparative genomic analyses between the two genotypes showed certain genetic content unique to each genotype, but no further analyses were conducted to investigate which genetic factors were likely associated with the observed differences. The study provides a comprehensive and novel understanding of the evolution and adaptation of two S. Dublin genotypes, which can inform public health measures.

The methodology included in both approaches was sound and written in sufficient detail, and data analysis was performed with rigour. Source data were fully presented and accessible to readers. Certain aspects of the manuscript could be clarified and extended to improve the manuscript.

(1) For epidemiology purposes, it is not clear which human diseases were associated with the genomes included in this manuscript. This is important since S. Dublin can cause invasive bloodstream infections in humans. While such information may be unavailable for public sequences, this should be detailed for the 53 isolates sequenced for this study, especially for isolates selected to perform experiments in vitro.

(2) The major AMR plasmid in described S. Dublin was the IncC associated with clonal expansion in North America. While this plasmid is not found in the Australian isolates sequenced in this study, the reviewer finds that it is still important to include its characterization, since it carries blaCMY-2 and was sustainedly inherited in ST10 clade 5. If the plasmid structure is already published, the authors should include the accession number in the Main Results.

(3) The reviewer is concerned that the multiple annotations missing in<br /> (a) plasmid structures in Supplementary Figures 5 & 6, and<br /> (b) genetic content unique to ST10 and ST74 was due to insufficient annotation by Prokka. I would recommend the authors use another annotation tool, such as Bakta (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8743544/) for plasmid annotation, and reconstruction of the pangenome described in Supplementary Figure 10. Since the recombinant virulence plasmid in ST10 is a novel one, I would recommend putting Supplementary Figure 5 as a main figure, with better annotations to show the virulence region, plasmid maintenance/replication, and possible conjugation cluster.

(4) The authors are lauded for the use of multiple strains of ST10 and ST74 in the in vitro experiment. While results for ST74 were more consistent, readouts from ST10 were more heterogenous (Figure 5, 6). This is interesting as the tested ST10 were mostly clade 1, so ST10 was, as expected, of lower genetic diversity compared to tested ST74 (partly shown in Figure 1D. Could the authors confirm this by constructing an SNP table separately for tested ST10 and ST74? Additionally, the tested ST10 did not represent the phylogenetic diversity of the global epidemiology, and this limitation should be reflected in the Discussion.

(5) The comparative genomics between ST10 and ST74 can be further improved to allow more interpretation of the experiments. Why were only SPI-1, 2, 6, and 19 included in the search for virulome, how about other SPIs? ST74 lacks SPI-19 and has truncated SPI-6, so what would explain the larger genome size of ST74? Have the authors screened for other SPIs using more well-annotated databases or references (S. Typhi CT18 or S. Typhimurium ST313)? The mismatching between in silico prediction of invasiveness and phenotypes also warrants a brief discussion, perhaps linked to bigger ST74 genome size (as intracellular lifestyle is usually linked with genome degradation).

(6) On the epidemiology scale, ST10 is more successful, perhaps due to its ongoing adaptation to replication inside GI epithelial cells, favouring shedding. ST74 may tend to cause more invasive disease and less transmission via fecal shedding. The presence of T6SS in ST10 also can benefit its competition with other gut commensals, overcoming gut colonization resistance. The reviewer thinks that these details should be more clearly rephrased in the Discussion, as the results highly suggested different adaptations of two genotypes of the same serovar, leading to different epidemiological success.

Reviewer #2 (Public review):

This is a comprehensive analysis of Salmonella Dublin genomes that offers insights into the global spread of this pathogen and region-specific traits that are important to understanding its evolution. The phenotyping of isolates of ST10 and ST74 also offers insights into the variability that can be seen in S. Dublin, which is also seen in other Salmonella serovars, and reminds the field that it is important to look beyond lab-adapted strains to truly understand these pathogens. This is a valuable contribution to the field. The only limitation, which the authors also acknowledge, is the bias towards S. Dublin genomes from high-income settings. However, there is no selection bias; this is simply a consequence of publically available sequences.

Joint Public Review:

Following up on their previous work, the authors investigated whether cell-to-cell transmission of HIV-1 activates the CARD8 inflammasome in macrophages, an important question given that inflammasome activation in myeloid cells triggers proinflammatory cytokine release. The data support the idea that CARD8 is activated by the viral protease and promotes inflammation. However, time-course analyses in primary T cells and macrophages and further information on the specific inflammasome involved would further increase the significance of the study.

Strengths:

The manuscript is well-written and the data is of good quality. The evidence that CARD8 senses the HIV-1 protease in the context of cell-to-cell transmission is important since cell-to-cell transmission is thought to play a key role in viral spread in vivo, and inflammation is a major driver of disease progression. Clean knockout experiments in primary macrophages are a notable strength and the results clearly support the role of CARD8 in protease-dependent sensing of viral spread and the induction of IL1β release and cell death. The finding that HIV-1 strains are resistant to protease inhibitors differ in CARD8 activation and IL1β production is interesting and underscores the potential clinical relevance of these results.

Weaknesses:

One weakness is that the authors used T cell lines which might not faithfully reflect the efficiency of HIV-1 production and cell-cell transfer by primary T cells. To assess whether CARD8 is also activated by protease from incoming viral particles earlier time points should be analyzed. Finally, while the authors exclude the role of NLRP3 in IL-1b and the death of macrophages it would be interesting to know whether the effect is still Gasdermin D dependent.

Reviewer #1 (Public review):

Summary:

The authors test whether the archerfish can modulate the fast response to a falling target. By manipulating the trajectory of the target, they claim that the fish can modulate the fast response. While it is clear from the result that the fish can modulate the fast response, the experimental support for the argument that the fish can do it for a reflex-like behavior is inadequate.

Strengths:

Overall, the question that the authors raised in the manuscript is interesting.

Weaknesses:

(1) The argument that the fish can modulate reflex-like behavior relies on the claim that the archerfish makes the decision in 40 ms. There is little support for the 40 ms reaction time. The reaction time for the same behavior in Schlegel 2008, is 60-70 ms, and in Tsvilling 2012 about 75 ms, if we take the half height of the maximum as the estimated reaction time in both cases. If we take the peak (or average) of the distribution as an estimation of reaction time, the reaction time is even longer. This number is critical for the analysis the authors perform since if the reaction time is longer, maybe this is not a reflex as claimed. In addition, mentioning the 40 ms in the abstract is overselling the result. The title is also not supported by the results.

(2) A critical technical issue of the stimulus delivery is not clear. The frame rate is 120 FPS and the target horizontal speed can be up to 1.775 m/s. This produces a target jumping on the screen 15 mm in each frame. This is not a continuous motion. Thus, the similarity between the natural system where the target experiences ballistic trajectory and the experiment here is not clear. Ideally, another type of stimulus delivery system is needed for a project of this kind that requires fast-moving targets (e.g. Reiser, J. Neurosci.Meth. 2008). In addition, the screen is rectangular and not circular, so in some directions, the target vanishes earlier than others. It must produce a bias in the fish response but there is no analysis of this type.

(3) The results here rely on the ability to measure the error of response in the case of a virtual experiment. It is not clear how this is done since the virtual target does not fall. How do the authors validate that the fish indeed perceives the virtual target as the falling target? Since the deflection is at a later stage of the virtual trajectory, it is not clear what is the actual physics that governs the world of the experiment. Overall, the experimental setup is not well designed.

Reviewer #2 (Public review):

Summary:

This manuscript studies prey capture by archer fish, which observe the initial values of motion of aerial prey they made fall by spitting on them, and then rapidly turn to reach the ballistic landing point on the water surface. The question raised by the article is whether this incredibly fast decision-making process is hardwired and thus unmodifiable or can be adjusted by experience to follow a new rule, namely that the landing point is deflected from a certain amount of the expected ballistic landing point. The results show that the fish learn the new rule and use it afterward in a variety of novel situations that include height, side, and speed of the prey, and which preserve the speed of the fish's decision. Moreover, a remarkable finding presented in this work is the fact that fish that have learned to use the new rule can relearn to use the ballistic landing point for an object based on its shape (a triangle) while keeping simultaneously the 'deflected rule' for an object differing in shape (a disc); in other words, fish can master simultaneously two decision-making rules based on the different shape of objects.

Strengths:

The manuscript relies on a sophisticated and clever experimental design that allows changing the apparent landing point of a virtual prey using a virtual reality system. Several robust controls are provided to demonstrate the reliability and usefulness of the experimental setup.

Overall, I very much like the idea conveyed by the authors that even stimuli triggering apparently hardwired responses can be relearned in order to be associated with a different response, thus showing the impressive flexibility of circuits that are sometimes considered mediating pure reflexive responses. This is the case - as an additional example - of the main component of the Nasanov pheromone of bees (geraniol), which triggers immediate reflexive attraction and appetitive responses, and which can, nevertheless, be learned by bees in association with an electric shock so that bees end up exhibiting avoidance and the aversive response of sting extension to this odorant (1), which is a fully unnatural situation, and which shows that associative aversive learning is strong enough to override preprogrammed responding, thus reflecting an impressive behavioral flexibility.

Weaknesses:

As a general remark, there is some information that I missed and that is mandatory in the analysis of behavioral changes.

Firstly, the variability in the performances displayed. The authors mentioned that the results reported come from 6 fish (which is a low sample size). How were the individual performances in terms of consistency? Were all fish equally good in adjusting/learning the new rule? How did errors vary according to individual identity? It seems to me that this kind of information should be available as the authors reported that individual fish could be recognized and tracked (see lines 620-635) and is essential for appreciating the flexibility of the system under study.

Secondly, the speed of the learning process is not properly explained. Admittedly, fish learn in an impressive way the new rule and even two rules simultaneously; yet, how long did they need to achieve this? In the article, Figure 2 mentions that at least 6 training stages (each defined as a block of 60 evaluated turn decisions, which actually shows that the standard term 'Training Block' would be more appropriate) were required for the fish to learn the 'deflected rule'. While this means 360 trials (turning starts), I was left with the question of how long this process lasted. How many hours, days, and weeks were needed for the fish to learn? And as mentioned above, were all fish equally fast in learning? I would appreciate explaining this very important point because learning dynamics is relevant to understanding the flexibility of the system.

Reference:

(1) Roussel, E., Padie, S. & Giurfa, M. Aversive learning overcomes appetitive innate responding in honeybees. Anim Cogn 15, 135-141, doi:10.1007/s10071-011-0426-1 (2012).

Reviewer #1 (Public review):

This is an interesting manuscript tackling the issue of whether subcircuits of the cerebellum are differentially involved in processes of motor performance, learning, or learning consolidation. The authors focus on cerebellar outputs to the ventrolateral thalamus (VL) and to the centrolateral thalamus (CL), since these thalamic nuclei project to the motor cortex and striatum respectively, and thus might be expected to participate in diverse components of motor control and learning. In mice challenged with an accelerating rotarod, the investigators reduce cerebellar output either broadly, or in projection-specific populations, with CNO targeting DREADD-expressing neurons. They first establish that there are not major control deficits with the treatment regime, finding no differences in basic locomotor behavior, grid test, and fixed-speed rotarod. This is interpreted to allow them to differentiate control from learning, and their inter-relationships. These manipulations are coupled with chronic electrophysiological recordings targeted to the cerebellar nuclei (CN) to control for the efficacy of the CNO manipulation. I found the manuscript intriguing, offering much food for thought, and am confident that it will influence further work on motor learning consolidation. The issue of motor consolidation supported by the cerebellum is timely and interesting, and the claims are novel. There are some limitations to the data presentation and claims, highlighted below, which, if amended, would improve the manuscript.

(1) Statistical analyses: There is too little information provided about how the Deming regressions, mean points, slopes, and intercepts were compared across conditions. This is important since in the heart of the study when the effects of inactivating CL- vs VL- projecting neurons are being compared to control performance, these statistical methods become paramount. Details of these comparisons and their assumptions should be added to the Methods section. As it stands I barely see information about these tests, and only in the figure legends. I would also like the authors to describe whether there is a criterion for significance in a given correlation to be then compared to another. If I have a weak correlation for a regression model that is non-significant, I would not want to 'compare' that regression to another one since it is already a weak model. The authors should comment on the inclusion criteria for using statistics on regression models.

(2) The introduction makes the claim that the cerebellar feedback to the forebrain and cortex are functionally segregated. I interpreted this to mean that the cerebellar output neurons are known to project to either VL or CL exclusively (i.e. they do not collateralize). I was unaware of this knowledge and could find no support for the claim in the references provided (Proville 2014; Hintzer 2018; Bosan 2013). Either I am confused as to the authors' meaning or the claim is inaccurate. This point is broader however than some confusion about citation. The study assumes that the CN-CL population and CN-VL population are distinct cells, but to my knowledge, this has not been established. It is difficult to make sense of the data if they are entirely the same populations, unless projection topography differs, but in any event, it is critical to clarify this point: are these different cell types from the nuclei?; how has that been rigorously established?; is there overlap? No overlap? Etc. Results should be interpreted in light of the level of this knowledge of the anatomy in the mouse or rat.

(3) It is commendable that the authors perform electrophysiology to validate DREADD/CNO. So many investigators don't bother and I really appreciate these data. Would the authors please show the 'wash' in Figure 1a, so that we can see the recovery of the spiking hash after CNO is cleared from the system? This would provide confidence that the signal is not disappearing for reasons of electrode instability or tissue damage/ other.

(4) I don't think that the "Learning" and "Maintenance" terminology is very helpful and in fact may sow confusion. I would recommend that the authors use a day range " Days 1-3 vs 4-7" or similar, to refer to these epochs. The terminology chosen begs for careful validation, definitions, etc, and seems like it is unlikely uniform across all animals, thus it seems more appropriate to just report it straight, defining the epochs by day. Such original terminology could still be used in the Discussion, with appropriate caveats.

(5) Minor, but, on the top of page 14 in the Results, the text states, "Suggesting the presence of a 'critical period' in the consolidation of the task". I think this is a non-standard use of 'critical period' and should be removed. If kept, the authors must define what they mean specifically and provide sufficient additional analyses to support the idea. As it stands, the point will sow confusion.

Reviewer #2 (Public review):

Summary:

This study examines the contribution of cerebello-thalamic pathways to motor skill learning and consolidation in an accelerating rotarod task. The authors use chemogenetic silencing to manipulate the activity of cerebellar nuclei neurons projecting to two thalamic subregions that target the motor cortex and striatum. By silencing these pathways during different phases of task acquisition (during the task vs after the task), the authors report valuable findings of the involvement of these cerebellar pathways in learning and consolidation.

Strengths:

The experiments are well-executed. The authors perform multiple controls and careful analysis to solidly rule out any gross motor deficits caused by their cerebellar nuclei manipulation. The finding that cerebellar projections to the thalamus are required for learning and execution of the accelerating rotarod task adds to a growing body of literature on the interactions between the cerebellum, motor cortex, and basal ganglia during motor learning. The finding that silencing the cerebellar nuclei after a task impairs the consolidation of the learned skill is interesting.

Weaknesses:

While the controls for a lack of gross motor deficit are solid, the data seem to show some motor execution deficit when cerebellar nuclei are silenced during task performance. This deficit could potentially impact learning when cerebellar nuclei are silenced during task acquisition. Separately, I find the support for two separate cerebello-thalamic pathways incomplete. The data presented do not clearly show the two pathways are anatomically parallel. The difference in behavioral deficits caused by manipulating these pathways also appears subtle.

Reviewer #3 (Public review):

Summary:

Varani et al present important findings regarding the role of distinct cerebellothalamic connections in motor learning and performance. Their key findings are that:<br /> (1) cerebellothalamic connections are important for learning motor skills<br /> (2) cerebellar efferents specifically to the central lateral (CL) thalamus are important for short-term learning<br /> (3) cerebellar efferents specifically to the ventral anterior lateral (VAL) complex are important for offline consolidation of learned skills, and<br /> (4) that once a skill is acquired, cerebellothalamic connections become important for online task performance.

The authors went to great lengths to separate effects on motor performance from learning, for the most part successfully. While one could argue about some of the specifics, there is little doubt that the CN-CL and CN-VAL pathways play distinct roles in motor learning and performance. An important next step will be to dissect the downstream mechanisms by which these cerebellothalamic pathways mediate motor learning and adaptation.

Strengths:

(1) The dissociation between online learning through CN-CL and offline consolidation through CN-VAL is convincing.

(2) The ability to tease learning apart from performance using their titrated chemogenetic approach is impressive. In particular, their use of multiple motor assays to demonstrate preserved motor function and balance is an important control.

(3) The evidence supporting the main claims is convincing, with multiple replications of the findings and appropriate controls.

Weaknesses:

(1) Despite the care the authors took to demonstrate that their chemogenetic approach does not impair online performance, there is a trend towards impaired rotarod performance at higher speeds in Supplementary Figure 4f, suggesting that there could be subtle changes in motor performance below the level of detection of their assays.

(2) There is likely some overlap between CN neurons projecting to VAL and CL, somewhat limiting the specificity of their conclusions.

Reply to @Edmund https://forum.zettelkasten.de/discussion/comment/21907/#Comment_21907

Doto's book is the best and tightest yet for explaining both how to implement a Luhmann-artig zettelkasten as well as why along with the affordances certain elements provide. He does a particularly good job of providing clear and straightforward definitions which have a muddy nature in some of the online spaces, which tends to cause issues for people new to the practice. Sadly, for me, there isn't much new insight due to the amount of experience and research I bring to the enterprise.

I do like that Doto puts at least some emphasis on why one might want to use alphanumerics even in digital spaces, an idea which has broadly been sidelined in most contexts for lack of experience or concrete affordances for why one might do it.

The other area he addresses, which most elide and the balance gloss over at best, is that of the discussion of using the zettelkasten for output. Though he touches on some particular methods and scaffolding, most of it is limited to suggestions based on his own experience rather than a broader set of structures and practices. This is probably the biggest area for potential expansion and examples I'd like to see, especially as I'm reading through Eustace Miles' How to Prepare Essays, Lectures, Articles, Books, Speeches and Letters, with Hints on Writing for the Press (London: Rivingtons, 1905).

I could have had some more material in chapter 3 which has some fascinating, but still evolving work. Ideas like interstitial journaling and some of the related productivity methods are interesting, but Doto only barely scratches the surface on some of these techniques and methods which go beyond the traditional "zettelkasten space", but which certainly fall in his broader framing of "system for writing" promise.

Doto's "triangle of creativity", a discussion of proximal feedback, has close parallels of Adler and Hutchins' idea of "The Great Conversation" (1952), which many are likely to miss.

For those who missed out, Dan Allosso has posted video from the sessions at https://lifelonglearn.substack.com/ Sadly missing, unless you're in the book club, are some generally lively side chat discussions as the primary video discussion was proceeding. The sessions had a breadth of experiences from the new to the old hands as well as from students to teachers and everywhere in between.

Reviewer #1 (Public review):

Summary:

The authors successfully detected distinct mechanisms signalling prediction violations in the auditory cortex of mice. For this purpose, an auditory pure-tone local-global paradigm was presented to awake and anaesthetised mice. In awake rodents, the authors also evaluated interneuron cell types involved in responses to the interruption of the regularity imposed by local-global sequences. By performing two-photon calcium imaging and single-unit electrophysiology, the authors disentangled three phenomena underlying responses to violations of the distinct local-global regularity levels: Stimulus-specific adaptation, surprise and surprise adaptation. Both stimulus-specific adaptation and surprise-or deviant-evoked responses are observable<br /> under anaesthesia. Altogether, this work advances our understanding of distinct predictive processes computing prediction violations upon the complexity of the regularity imposed by the auditory sequence.

Strengths:

it is an elegant study beautifully executed.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #2 (Public review):

Summary:

Oddball responses are increases in sensory responses when a stimulus is encountered in an unexpected location in a sequence of predictable stimuli. There are two computational interpretations for these responses: stimulus-specific adaptation and prediction errors. In recent years, evidence has accumulated that a significant part of these sequence violation responses cannot be explained simply by stimulus-specific adaptation. The current work elegantly adds to this evidence by using a sequence paradigm based on two levels of sequence violations: "Local" sequence violations of repetitions of identical stimuli, and "global" sequence violations of stimulus sequence patterns. The authors demonstrate that both local and global sequence violation responses are found in L2/3 neurons of the mouse auditory cortex. Using sequences with different inter-stimulus intervals, they further demonstrate that these sequence violation responses cannot be explained by stimulus-specific adaption.

Strengths:

The work is based on a very clever use of a sequence violation paradigm (local-global paradigm) and provides convincing evidence for the interpretation that there are at least two types of sequence violation responses and that these cannot be explained by stimulus-specific adaption. Most of the conclusions are based on a large dataset, and are compelling.

Weaknesses:

The final part of the paper focuses on the responses of VIP and PV-positive interneurons. The responses of VIP interneurons appear somewhat variable and difficult to interpret (e.g. VIP neurons exhibit omission responses in the A block, but not the B block). The conclusions based on these data appear less solid.

Reviewer #3 (Public review):

Summary:

In their manuscript entitled "Parallel mechanisms signal a hierarchy of sequence structure violations in the auditory cortex", Jamali et al. provide evidence for cellular-level mechanisms in the auditory cortex of mice for the encoding of predictive information on different temporal and contextual scales. The study design separates more clearly than previous studies between the effects of local and global deviants and separates their respective effects on the neuronal responses clearly through the use of various contextual conditions and short and long time scales. Further, it identifies a contribution from a small set of VIP interneurons to the detection of omitted sounds, and shows the influence of isofluorane anesthesia on the neural responses.

Strengths:

(1) The study provides a rather encompassing set of experimental techniques to study the cellular level responses, using two complementary recording techniques in the same animal and similar cortical location.

(2) Comparison between awake and anesthetized states are conducted in the same animals, which allows for rather a direct comparison of populations under different conditions, thus reducing sampling variability.

(3) The set of paradigms is well developed and specifically chosen to provide appropriate and meaningful controls/comparisons, which were missing from previous studies.

(4) The addition of cell-type specific recordings is valuable and in particular in combination with the contrast of awake and anesthetized animals provides novel insights into the cellular level representation of deviant signals, such as surprise, prediction error, and general adaptation.

(5) The analysis and presentation of the data are clear and quite complete, yet remain succinct and perform insightful contrasts.

(6) The study will have an impact on multiple levels, as it introduces important variations in the paradigm and analytical contrasts that both human and animal researchers can pick up and improve their studies. The cell-type-specific results are particularly intriguing, although these would likely require replication before being completely reliable. Further, the study provides a substantial and diverse dataset that others can explore.

Weaknesses:

(1) The responses from cells recorded via Neuropixel and 2p differ qualitatively, as noted by the authors, with NP-recorded cells showing much more inhibited/reduced responses between acoustic stimulations. The authors briefly qualify these differences as potentially indicating a sampling issue, however, this matter deserves more detailed consideration in my opinion. Specifically, the authors could try to compare the different depths at which these neurons were sampled or relate the locations in the cortex to each other (as the Neuropixel recordings were collected in the same animals, a subset of the 2p recordings could be compared to the Neuropixel recordings.).

(2) The current study did not monitor the attentional state of the mouse in relation to the stimulus by either including a behavioral component or pupil monitoring, which could influence the neural responses to deviant stimuli and omissions. .

(3) Given the complexity and variety of the paradigms, conditions, and analyzed cell-types, the manuscript could profit from a more visual summary figure that provides an easy-to-access overview of what was found.

Reviewer #1 (Public review):

Summary:

This manuscript combined rat fMRI, optogenetics, and electrophysiology to examine the large-scale functional network of the olfactory system as well as its alteration in an aged rat model.

Strengths:

Overall methodology is very solid and the results provided an interesting perspective on large-scale functional network perturbation of the olfactory system.

Weaknesses:

The biological relevance and validation of the current results can be improved.

(1) Figure 1.1, on the top of the figure, CHR2 may be replaced by CHR2-mCherry, as only mCherry is fluorescent. And also, it's somewhat surprising that in AON and Pir regions (where only axon fibers should be labelled as red), most fluorescence appeared dot-like and looked more similar to cell body instead of typical fiber. The authors may want to double-check this.

(2) The authors primarily presented 1Hz stimulation results. What is the most biologically relevant frequency (e.g., perhaps firing frequency under natural odor stimulation) among all frequencies that were used?

(3) In Figure 2, the statistical thresholding is confusing: in the figure legend, it was stated that "t > 3.1 corresponding to P < 0.001" but later "further corrected for multiple comparisons with threshold-free cluster enhancement with family-wise error rate (TFCE-FWE) at P < 0.05"? Regardless of the statistical thresholding, such BOLD activation seemed to be widespread (almost whole-brain activation). Does such activation remain specific to the optogenetic stimulation, or something more general (e.g., arousal level change)? Furthermore, how those results (I assume they are group-level results) were obtained was not described very clearly. Is it just a simple average of individual-level results, or (more conventionally) second-level analysis?

(4) In Figure 2, why use AUC to quantify the activation, not the more conventional beta value in the GLM analysis?

(5) For Figure 2D, the way that it was quantified can be better described as "relative" activation within one condition, and I don't how to interpret the comparison among the relative fraction of activated regions. Perhaps comparison using percentage change (i.e., beta values) is more straightforward.

(6) For Figure 3, it may be more convenient for readers to include the results of 1st activation for direct comparison. The current layout makes it difficult to make direct, visual comparisons among all 3 activations. Again I think using beta values (instead of AUC) may be more conventional.

(7) Can the DCM results (at least part of it) be verified using the current electrophysiological data? For example, the long-range inhibitory effective connectivity of AON is rather intriguing. If that can be verified using ephys. data, it would be really great. In the current form, the DCM and ephys. results seem to be totally unrelated.

(8) In Figure 6, it would be great if the adaptation of BOLD and ephys. signals can be correlated at the brain region level. The current figure only demonstrated there is adaptation in ephys. signal, but did not show if such adaptation is related to the BOLD adaptation.

Reviewer #2 (Public review):

Summary:

Ma and colleagues presented a study on the characterization of brain-wide spatio-temporal impact of olfactory cortical outputs. They take advantage of multi-modal techniques on rats: fMRI, optogenetics, and electrophysiology. In addition, they used cutting-edge analytical techniques and modeling to support and interpret their data. The main findings of the study are:

(1) The neurons in the Olfactory Bulb (OB) predominantly activate primary olfactory network regions, while stimulation of OB afferents in Anterior Olfactory Nucleus (AON) and Piriform Cortex (Pir) primarily orthodromically activates hippocampal/striatal and limbic networks, respectively.

(2) Non-specified adaptation or habituation mechanisms may play a significant role in modulating olfactory outputs over subsequent fMRI sessions.

(3) Artificially induced aging in rats induces profound modification in the functional interaction between olfactory cortices and multiple brain regions.

The results on AON are of particular interest because of the lack of functional information on this region, despite its recognized importance in shaping OB output and behavior (odor localization tasks).

Strengths:

The manuscript is very accurate. The figures are well-crafted, and clear and provide much information with the most appropriate plots and graphics. The study's amount and data quality are remarkable, and the experimental size adequately addresses the scientific questions. I particularly appreciated the details in the description of the methods regarding the missing data and the size of the different animal groups. The supplementary data complete the leading figures and provide information at a single animal level.

Weaknesses:

(1) One of the main reasons the Piriform Cx is understudied in rodents is because of the proximity to air, which creates artifacts in fMRI images. This issue becomes more critical at ultra-high magnetic fields, but I would expect it also at 7T. One main achievement of this study is, indeed, the acquisition of fMRI data from Piriform, and this point should be highlighted by showing raw functional data from a rat. The best would be if an fMRI data sample for a rat, no matter which stimulation, is shared on a public repository, like Zenodo or similar. I am curious to check the quality of the BOLD data from such an 'enormous' field of view, particularly in the OB, with a single-shot sequence. Also, the visual inspection of raw data is essential to appreciate how many 0.5 x 0.5 x 1 mm voxels fit into AON, and others analyzed small brain structures, like the amygdala, etc. Was the amygdala entirely visible in BOLD, or did the air in the ear channel make an artifact partially shadowing it?

(2) Surprisingly, the only information missing in the methods is the post-surgery period and the time between two consecutive fMRI sessions. How much time was accorded to rats to recover from the surgeries, and what time interval between two scans? This information is crucial for interpreting the decrease in most BOLD responses in subsequent recordings. The supposed adaptation should fit into the known time frames for odor adaptation. Usually, fast adaptation does not last for days (and it should be measured within a single experiment: is it the case?), while for long-lasting adaptation the stimulus (odor or opto) should be maintained constantly ON. This does not seem to be the case in this study. The hypothesis, alternative to adaptation, of a less efficient light activation, for example, due to gliosis around the fiber tips, should be discarded with more evidence than the preservation of OB > Pir responses or acknowledged in the manuscript.

(3) The D-galactose experiments were conducted only after administering the aging molecule, with no baseline/reference data on the same animals. Then, comparisons were made with healthy rats, but the two groups not only can be discriminated with respect to D-galactose administration but also with age (10 VS 18 weeks). A control group for 18-weeks-old rats with no D-galactose treatment would better compare the D-galactose effect and avoid any potential bias from group comparisons of rats at different ages. Do you confirm that D-galactose was injected into each rat 56 times/day in a row, or am I mistaken?

Overall, if my concerns are addressed, this is outstanding work, and I congratulate the authors.

Reviewer #1 (Public Review):

Summary:

The emergence of Drosophila EM connectomes has revealed numerous neurons within the associative learning circuit. However, these neurons are inaccessible for functional assessment or genetic manipulation in the absence of cell-type-specific drivers. Addressing this knowledge gap, Shuai et al. have screened over 4000 split-GAL4 drivers and correlated them with identified neuron types from the "Hemibrain" EM connectome by matching light microscopy images to neuronal shapes defined by EM. They successfully generated over 800 split-GAL4 drivers and 22 split-LexA drivers covering a substantial number of neuron types across layers of the mushroom body associative learning circuit. They provide new labeling tools for olfactory and non-olfactory sensory inputs to the mushroom body; interneurons connected with dopaminergic neurons and/or mushroom body output neurons; potential reinforcement sensory neurons; and expanded coverage of intrinsic mushroom body neurons. Furthermore, the authors have optimized the GR64f-GAL4 driver into a sugar sensory neuron-specific split-GAL4 driver and functionally validated it as providing a robust optogenetic substitute for sugar reward. Additionally, a driver for putative nociceptive ascending neurons, potentially serving as optogenetic negative reinforcement, is characterized by optogenetic avoidance behavior. The authors also use their very large dataset of neuronal anatomies, covering many example neurons from many brains, to identify neuron instances with atypical morphology. They find many examples of mushroom body neurons with altered neuronal numbers or mistargeting of dendrites or axons and estimate that 1-3% of neurons in each brain may have anatomic peculiarities or malformations. Significantly, the study systematically assesses the individualized existence of MBON08 for the first time. This neuron is a variant shape that sometimes occurs instead of one of two copies of MBON09, and this variation is more common than that in other neuronal classes: 75% of hemispheres have two MBON09's, and 25% have one MBON09 and one MBON08. These newly developed drivers not only expand the repertoire for genetic manipulation of mushroom body-related neurons but also empower researchers to investigate the functions of circuit motifs identified from the connectomes. The authors generously make these flies available to the public. In the foreseeable future, the tools generated in this study will allow important advances in the understanding of learning and memory in Drosophila.

Strengths:

(1) After decades of dedicated research on the mushroom body, a consensus has been established that the release of dopamine from DANs modulates the weights of connections between KCs and MBONs. This process updates the association between sensory information and behavioral responses. However, understanding how the unconditioned stimulus is conveyed from sensory neurons to DANs, and the interactions of MBON outputs with innate responses to sensory context remains less clear due to the developmental and anatomic diversity of MBONs and DANs. Additionally, the recurrent connections between MBONs and DANs are reported to be critical for learning. The characterization of split-GAL4 drivers for 30 major interneurons connected with DANs and/or MBONs in this study will significantly contribute to our understanding of recurrent connections in mushroom body function.

(2) Optogenetic substitutes for real unconditioned stimuli (such as sugar taste or electric shock) are sometimes easier to implement in behavioral assays due to the spatial and temporal specificity with which optogenetic activation can be induced. GR64f-GAL4 has been widely used in the field to activate sugar sensory neurons and mimic sugar reward. However, the authors demonstrate that GR64f-GAL4 drives expression in other neurons not necessary for sugar reward, and the potential activation of these neurons could introduce confounds into training, impairing training efficiency. To address this issue, the authors have elaborated on a series of intersectional drivers with GR64f-GAL4 to dissect subsets of labeled neurons. This approach successfully identified a more specific sugar sensory neuron driver, SS87269, which consistently exhibited optimal training performance and triggered ethologically relevant local searching behaviors. This newly characterized line could serve as an optimized optogenetic tool for sugar reward in future studies.

(3) MBON08 was first reported by Aso et al. 2014, exhibiting dendritic arborization into both ipsilateral and contralateral γ3 compartments. However, this neuron could not be identified in the previously published Drosophila brain connectomes. In the present study, the existence of MBON08 is confirmed, occurring in one hemisphere of 35% of imaged flies. In brains where MBON08 is present, its dendrite arborization disjointly shares contralateral γ3 compartments with MBON09. This remarkable phenotype potentially serves as a valuable resource for understanding the stochasticity of neurodevelopment and the molecular mechanisms underlying mushroom body lobe compartment formation.

Comments on revised version:

I only suggested minor changes, and these have been resolved.

Reviewer #2 (Public Review):

Summary:

The article by Shuai et al. describes a comprehensive collection of over 800 split-GAL4 and split-LexA drivers, covering approximately 300 cell types in Drosophila, aimed at advancing the understanding of associative learning. The mushroom body (MB) in the insect brain is central to associative learning, with Kenyon cells (KCs) as primary intrinsic neurons and dopaminergic neurons (DANs) and MB output neurons (MBONs) forming compartmental zones for memory storage and behavior modulation. This study focuses on characterizing sensory input as well as direct upstream connections to the MB both anatomically and, to some extent, behaviorally. Genetic access to specific, sparsely expressed cell types is crucial for investigating the impact of single cells on computational and functional aspects within the circuitry. As such, this new and extensive collection significantly extends the range of targeted cell types related to the MB and will be an outstanding resource to elucidate MB-related processes in the future.

Strengths:

The work by Shuai et al. provides novel and essential resources to study MB-related processes and beyond. The resulting tools are publicly available and, together with the linked information, will be foundational for many future studies. The importance and impact of this tool development approach, along with previous ones, for the field cannot be overstated. One of many interesting aspects arises from the anatomical analysis of cell types that are less stereotypical across flies. These discoveries might open new avenues for future investigations into how such asymmetry and individuality arise from development and other factors, and how it impacts the computations performed by the circuitry that contains these elements.

Comments on revised version:

From my side they have addressed the few issues I had sufficiently.

Reviewer #3 (Public Review):

Summary:

Previous research on the Drosophila mushroom body (MB) has made this structure the best-understood example of an associative memory center in the animal kingdom. This is in no small part due to the generation of cell-type specific driver lines that have allowed consistent and reproducible genetic access to many of the MB's component neurons. The manuscript by Shuai et al. now vastly extends the number of driver lines available to researchers interested in studying learning and memory circuits in the fly. It is an 800-plus collection of new cell-type specific drivers target neurons that either provide input (direct or indirect) to MB neurons or that receive output from them. Many of the new drivers target neurons in sensory pathways that convey conditioned and unconditioned stimuli to the MB. Most drivers are exquisitely selective, and researchers will benefit from the fact that whenever possible, the authors have identified the targeted cell types within the Drosophila connectome. Driver expression patterns are beautifully documented and are publicly available through the Janelia Research Campus's Flylight database where full imaging results can be accessed. Overall, the manuscript significantly augments the number of cell type-specific driver lines available to the Drosophila research community for investigating the cellular mechanisms underlying learning and memory in the fly. Many of the lines will also be useful in dissecting the function of the neural circuits that mediate sensorimotor circuits.

Strengths:

The manuscript represents a huge amount of careful work and leverages numerous important developments from the last several years. These include the thousands of recently generated split-Gal4 lines at Janelia and the computational tools for pairing them to make exquisitely specific targeting reagents. In addition, the manuscript takes full advantage of the recently released Drosophila connectomes. Driver expression patterns are beautifully illustrated side-by-side with corresponding skeletonized neurons reconstructed by EM. A comprehensive table of the new lines, their split-Gal4 components, their neuronal targets, and other valuable information will make this collection eminently useful to end-users. In addition to the anatomical characterization, the manuscript also illustrates the functional utility of the new lines in optogenetic experiments. In one example, the authors identify a specific subset of sugar reward neurons that robustly promotes associative learning.

Comments on revised version:

Overall, I thought the authors addressed my comments well with the possible exception of what is actually new here. This was the most important thing that I thought should be included in the revision. Although the authors rewrote the paragraph describing the lines presented in the paper, I still can't tell exactly which ones haven't been previously published. Their revised paragraph says that 40 lines have been "previously used," but Supplemental Table 1 shows references for over 200 of the lines, which sounds more reasonable based on papers that have come out.

Also, in the revised paragraph they state that "All transgenic lines newly generated in this study are listed in Supplementary File 2" but that table lists only the 36 LexA hemidriver lines! Confusingly, this comment cites the same 8 references as are cited for the 40 line that they say were previously published. I am thus only more confused about how many previously uncharacterized lines are presented in this paper.

Further clarification would be helpful. On the one hand, I think this paper is a very nice summary of a ton of work and brings it all under one umbrella in a way that will be useful for many in the field. In that sense, the manuscript is worth publishing simply as a useful resource even if all the lines were previously published. On the other hand, it would be useful for readers to know which lines were previously characterized in other publications and which ones were not. This information may or may not be in Supplementary Tables 1 and 2 (but I can't tell).

Reviewer #1 (Public review):

Summary:

The paper by Shelton et al investigates some of the anatomical and physiological properties of the mouse claustrum. First, they characterize the intrinsic properties of claustrum excitatory and inhibitory neurons and determine how these different claustrum neurons receive input from different cortical regions. Next, they perform in vitro patch clamp recordings to determine the extent of intraclaustrum connectivity between excitatory neurons. Following these experiments, in vivo axon imaging was performed to determine how claustrum-retrosplenial cortex neurons are modulated by different combinations of auditory, visual, and somatosensory input. Finally, the authors perform claustrum lesions to determine if claustrum neurons are required for performance on a multisensory discrimination task

Strengths:

An important potential contribution the authors provide is the demonstration of intra-claustrum excitation. In addition, this paper does provide the first experimental data where two cortical inputs are independently stimulated in the same experiment (using 2 different opsins). Overall, the in vitro patch clamp experiments and anatomical data provide confirmation that claustrum neurons receive convergent inputs from areas of frontal cortex. These experiments were conducted with rigor and are of high quality.

Weaknesses:

The title of the paper states that claustrum neurons integrate information from different cortical sources. However, the authors did not actually test or measure integration in the manuscript. They do show physiological convergence of inputs on claustrum neurons in the slice work. Testing integration through simultaneous activation of inputs was not performed. The convergence of cortical input has been recently shown by several other papers (Chia et al), and the current paper largely supports these previous conclusions. The in vivo work did test for integration, because simultaneous sensory stimulations were performed. However, integration was not measured at the single cell (axon) level because it was unclear how activity in a single claustrum ROI changes in response to (for example) visual, tactile, and visual-tactile stimulations. Reading the discussion, I also see the authors speculate that the sensory responses in the claustrum could arise from attentional or salience related inputs from an upstream source such as the PFC. In this case, claustrum cells would not integrate anything (but instead respond to PFC inputs).

The different experiments in different figures often do not inform each other. For example, the authors show in Figure 3 that claustrum-RSP cells (CTB cells) do not receive input from the auditory cortex. But then, in Figure 6 auditory stimuli are used. Not surprisingly, claustrum ROIs respond very little to auditory stimuli (the weakest of all sensory modalities). Then, in Figure 7 the authors use auditory stimuli in the multisensory task. It seems that these experiments were done independently and were not used to inform each other.

One novel aspect of the manuscript is the focus on intraclaustrum connectivity between excitatory cells (Figure 2). The authors used wide-field optogenetics to investigate connectivity. However, the use paired patch clamp recordings remains the ground truth technique for determining the rate of connectivity between cell types, and paired recordings were not performed here. It is difficult to understand and gain appreciation for intraclaustrum connectivity when only wide-field optogenetics is used.

In Figure 2, CLA-rsp cells express Chrimson, and the authors removed cells from the analysis with short latency responses (which reflect opsin expression). But wouldn't this also remove cells that express opsin and receive monosynaptic inputs from other opsin expressing cells, therefore underestimating the connectivity between these CLA-rsp neurons? I think this needs to be addressed.

In Figure 5J the lack of difference in the EPSC-IPSC timing in the RSP is likely due to 1 outlier EPSC at 30ms which is most likely reflecting polysynaptic communication. Therefore, I do not feel the argument being made here with differences in physiology is particularly striking.

In the text describing Figure 5, the authors state "These experiments point to a complex interaction ....likely influenced by cell type of CLA projection and intraclaustral modules in which they participate". How does this slice experiment stimulating axons from one input relate to different CLA cell types or intra-claustrum circuits? I don't follow this argument.

In Figure 6G and H the blank condition yields a result similar to many of the sensory stimulus conditions. This blank condition (when no stimulus was presented) serves as a nice reference to compare the rest of the conditions. However, the remainder of the stimulation conditions were not adjusted relative to what would be expected by chance. For example, the response of each cell could be compared to a distribution of shuffled data, where time-series data are shuffled in time by randomly assigned intervals and a surrogate distribution of responses generated. This procedure is repeated 200-1000x to generate a distribution of shuffled responses. Then the original stimulus triggered response (1s post) could be compared to shuffled data. Currently, the authors just compare pre/post mean data using a Mann Whitney test from the mean overall response, which could be biased by a small number of trials. Therefore, I think a more conservative and statistically rigorous approach is warranted here, before making the claim of a 20% response probability or 50% overall response rate.

Regarding Figure 6, a more conventional way to show sensory responses is to display a heatmap of the z-scored responses across all ROIs, sorted by their post-stimulus response. This enables the reader to better visualize and understand the claims being made here, rather than relying on the overall mean which could be influenced by a few highly responsive ROIs.

For Figure 6 it would also help to display some raw data showing responses at the single ROI level and the population level. If these sensory stimulations are modulating claustrum neurons, then this will be observable on the mean population vector (averaged df/f across all ROIs as a function of time) within a given experiment and would add support to the conclusions being made.

As noted by the authors, there is substantial evidence in the literature showing that motor activity arises in mice during these types of sensory stimulation experiments. It is foreseeable that at least some of the responses measured here arise from motor activity. It would be important to identify to what extent this is the case.

All claims in the results for Figure 6 such as "the proportion of responsive axons tended to be highest when stimuli were combined" should be supported by statistics.

For Figure 7, the authors state that mice learned the structure of the task. How is this the case, when the number of misses are 5-6x greater than the number of hits on audiovisual trials (S Fig 19). I don't get the impression that mice perform this task correctly. As shown in Figure 7I, the hit rate is exceptionally low on the audiovisual port in controls. I just can't see how control and lesion mice can have the same hit rate and false alarm rate yet have different d'. Indeed, I might be missing something in the analysis. However, given that both groups of mice are not performing the task as designed, I fail to see how the authors claim regarding multisensory integration by the claustrum is supported. Even if there is some difference in the d' measure, what does that matter when the hits are the least likely trial outcome here for both groups.

In the discussion, it is stated that "While axons responded inconsistently to individual stimulus presentations, their responsivity remained consistent between stimuli and through time on average...". I do not understand this part of the sentence. Does this mean axons are consistently inconsistent?

In the discussion the authors state their axon imaging results contrast with recent studies in mice. Why not actually do the same analysis that Ollerenshaw did, so this statement is supported by fact? As pointed out above, the criteria used to classify an axon as responsive to stimuli was very liberal in this current manuscript.

I find the discussion wildly speculative and broad. For example, "the integrative properties of the CLA could act as a substrate for transforming the information content of its inputs (e.g. reducing trial to trial variability of responses to conjunctive stimuli...)". How would a claustrum neuron responding with a 10% reliability to a stimuli (or set of stimuli) provide any role in reducing trial to trial variability of sensory activity in the cortex?

Comments on the latest version: The authors have revised the manuscript, by adding 1 new supplementary figure, and some minor changes to the text. Overall, my comments regarding the manuscript were not sufficiently addressed. Here is one example:

The authors don't seem to be taking the comments regarding the statistical significance of the sensory responses seriously. If there is a response in 10% of the axons in the blank condition, and a 11 % response in the auditory stimulation, then that means that it is more accurate to say that 1% of axons actually respond to auditory stimulation. "leaving to reader to make their own decisions" as the authors suggest, but then having authors read text such as "All modalities could evoke responses in at least some claustrum neurons", is misleading because no attempt was made to correct for a chance level of detection that is clearly observed in the blank condition. Another interpretation of the authors data would be that in the case of the auditory/visual/somatosensory combined stimuli resulted in 21%(observed) - 10% (blank) = 11% of axons. Therefore, a conclusion that more accurately reflects the data would be that 89% of claustrum axons do not respond, even when the mouse received multisensory stimuli. I tried to get the authors to run some basic stats to more accurately test the true degree of responsiveness, but these changes did not appear in the manuscript.

Reviewer #2 (Public review):

Summary:

In this manuscript, Shelton et al. explore the organization of the Claustrum. To do so, they focus on a specific claustrum population, the one projecting to the retrosplenial cortex (CLA-RSP neurons). Using elegant technical approach, they first described electrophysiological properties of claustrum neurons, including the CLA-RSP ones. Further, they showed that CLA-RSP neurons 1) directly excite other CLA neurons, in a 'projection-specific' pattern, i.e. CLA-RSP neurons mainly excite claustrum neurons not projecting to the RSP and 2) received excitatory inputs from multiple cortical territories (mainly frontal ones). In an effort to confirm the 'integrative' property of claustrum networks, they then imaged claustrum axons in the cortex during single- or multi-sensory stimulations. Finally, they investigated the effect of CLA-RSP lesion on performance in a sensory detection task.

Strengths:

Overall, this is a really good study, using state of the art technical approaches to probe the local/global organization of the Claustrum. The in-vitro part is impressive, and the results are compelling.

Weaknesses:

One noteworthy concern arises from the terminology used throughout the study. The authors claimed that the claustrum is an integrative structure. Yet, integration has a specific meaning, i.e. the production of a specific response by a single neuron (or network) in response to a specific combination of several input signals. In this study, the authors showed compelling results in favor of convergence rather than integration. On a lighter note, the in-vivo data are less convincing, and do not entirely support the claim of "integration" made by the authors.

Reviewer #3 (Public review):

Public review:

The claustrum is one of the most enigmatic regions of the cerebral cortex, with a potential role in consciousness and integrating multisensory information. Despite extensive connections with almost all cortical areas, its functions and mechanisms are not well understood. In an attempt to unravel these complexities, Shelton et al. employed advanced circuit mapping technologies to examine specific neurons within the claustrum. They focused on how these neurons integrate incoming information and manage the output. Their findings suggest that claustrum neurons selectively communicate based on cortical projection targets and that their responsiveness to cortical inputs varies by cell type.

Imaging studies demonstrated that claustrum axons respond to both single and multiple sensory stimuli. Extended inhibition of the claustrum significantly reduced animals' responsiveness to multisensory stimuli, highlighting its critical role as an integrative hub in the cortex.

However, the study's conclusions at times rely on assumptions that may undermine their validity. For instance, the comparison between RSC projecting and non-RSC projecting neurons is problematic due to potential false negatives in the cell labeling process, which might not capture the entire neuron population projecting to a brain area. This issue casts doubt on the findings related to neuron interconnectivity and projections, suggesting that the results should be interpreted with caution. The study's approach to defining neuron types based on projection could benefit from a more critical evaluation or a broader methodological perspective.

Nevertheless, the study sets the stage for many promising future research directions. Future work could particularly focus on exploring the functional and molecular differences between E1 and E2 neurons and further assess the implications of the distinct responses of excitatory and inhibitory claustrum neurons for internal computations. Additionally, adopting a different behavioral paradigm that more directly tests the integration of sensory information for purposeful behavior could also prove valuable.

Reviewer #1 (Public review):

Summary:

This is an interesting and valuable study that uses multiple approaches to understand the role of bursting involving voltage-gated calcium channels within the mediodorsal thalamus in the sedative-hypnotic effects of alcohol. Given its unique functional roles and connectivity pattern, the finding that the mediodorsal thalamus has a fundamental role in regulating alcohol-induced transitions in consciousness state is both important for researchers investigating thalamocortical dynamics and more broadly interesting for understanding brain function. In addition, the author's examination of the role of the voltage-gated calcium channel Cav3.1 provides considerable evidence that burst-firing mediated by this channel in the thalamus is functionally important for behavioral-state transitions. While many previous studies have suggested an analogous role for these channels in sleep-state regulation, the evidence for a role of this type of bursting in sedative-induced transitions is more limited so the evidence presented is of considerable value to the field. By performing comparative experiments across multiple thalamic nuclei which have been implicated in controlling state-transitions, the authors also validate their claim and establish the unique role of the mediodorsal thalamus. Overall, this study provides substantial mechanistic insight into how the thalamus influences drug induced transitions between different states of consciousness and opens avenues for future research into how thalamocortical interactions enable brain function.

Strengths:

This study employes multiple, complementary research approaches including behavioral assays, sh-RNA based localized knockdown, single-unit recordings, and patterned optogenetic interventions to examine the role of activity in the mediodorsal thalamus in the sedative-hypnotic effects of alcohol. Experiments and analysis included in the manuscript generally appear well conceived and generally well executed. Sample sizes are sufficiently large and statistical analysis appears generally appropriate. The findings presented are novel and provide interesting insight into the role of the thalamus as well as voltage gated calcium channels within this region in controlling behavioral state-transitions induced by alcohol. In particular, the observed effects of selective knockout along with recordings in total knockout oof the voltage gated calcium channel, Cav3.1, which has previously been implicated in bursting dynamics as well as state transitions, particularly in sleep, together suggest that the transition of thalamic neurons to a bursting pattern of firing from a more constant firing is important for transition to the sedated state produced by ethanol intoxication. While previous studies have similarly implicated Cav3.1 bursting in behavioral state-transitions, the direct optogenetic interventions and single-unit recordings provide valuable new insight. These findings may also have valuable implications for the relationship between sleep process disruption associated with ethanol dependence.

Weaknesses:

While the authors have made substantial improvements to the analysis and presented important additional results, some of the methods given in the supplemental are still somewhat minimal in their description of the methods employed. In addition, the text of the manuscript still has multiple problematic issues with writing and editing that should be addressed. Such writing issues appear throughout the manuscript including in the abstract as well as in all other sections. While they do not reduce the value of the findings presented, they do make them more difficult to understand and so should be corrected.

Reviewer #2 (Public review):

This study explores the role of the mediodorsal thalamus (MD) and the T-type calcium channel Cav3.1 in ethanol-induced behavioral changes, focusing on transitions between sedation and shifts in brain-states. The authors utilize genetic knockdown, optogenetic manipulation, and electrophysiological recording techniques in mice to assess the contribution of MD Cav3.1 channels to ethanol's sedative effects. The central hypothesis is that Cav3.1-mediated burst firing in the MD is essential for regulating ethanol-induced sedation and arousal transitions.

The authors' detailed responses to reviewers' comments significantly improved the manuscript, particularly regarding experimental specificity and methodological transparency. They addressed concerns about the specificity of MD knockdowns versus neighboring thalamic nuclei by adding quantifications, enhancing figure clarity, and providing lesion localization data. The revised figures, with added quantification panels, strengthened the claim that the manipulations specifically targeted the MD. Improvements in lesion validation figures and electrode placement explanations further clarified the accuracy of their methods.

One major limitation, as highlighted by Reviewer 1, is the lack of direct evidence from inhibitory optogenetic studies to validate the role of Cav3.1 channels in modulating ethanol-induced transitions in the MD. While the authors acknowledged the challenges of such experiments, citing technical issues like the inability of Cav3.1 knockout to allow rebound burst firing, the absence of these controls limits definitive causal conclusions about the MD's role. Alternative experiments with varying ethanol doses and data on tonic versus burst firing were presented, but these do not fully compensate for the missing inhibitory optogenetics, leaving some uncertainty regarding the attribution of observed behavioral effects solely to Cav3.1-mediated burst activity in the MD.<br /> Another challenge is the complexity of distinguishing the specific contribution of the MD from that of other thalamic nuclei involved in regulating arousal and brain-states. Although additional quantification was provided to demonstrate MD specificity, control experiments targeting adjacent regions like the central lateral nucleus (CL) would have strengthened the manuscript. While the practical constraints are understandable, this limitation slightly weakens the argument regarding the MD's unique role in state transitions. The provided explanations about spatial targeting and electrophysiological methods were reasonable, but a broader set of thalamic controls would have offered a more comprehensive understanding.

Overall, the authors successfully achieved their aims, providing strong evidence that Cav3.1-mediated burst firing in the MD is crucial for ethanol-induced sedation. The knockdown experiments showed a clear reduction in ethanol sensitivity, and the behavioral assays supported the conclusion that MD Cav3.1 activity plays a key role in regulating arousal states. The combined use of Cav3.1 knockdown and optogenetic stimulation effectively linked MD activity to ethanol-induced behavioral changes. The evidence presented establishes a clear mechanistic connection between neuronal activity and behavioral responses.

The expanded discussion and clarifications in response to reviewer feedback enhanced the manuscript's coherence, and the revisions to the figures improved the transparency of the findings. Despite not implementing all the additional experiments suggested by Reviewer 1, the authors provided sufficient alternative evidence and a clear explanation of practical limitations, making their conclusions credible given the available data.

This study significantly advances our understanding of thalamic involvement in behavioral state transitions, particularly ethanol-induced sedation. By clarifying the role of Cav3.1-mediated burst firing in the MD, the research provides new insights into how specific neuronal activity patterns influence global brain states and behavioral arousal, which has implications for understanding mechanisms underlying anesthesia, sedation, and sleep regulation. Moreover, the transparency in data sharing and detailed methodological revisions make this work a valuable resource for replication or adaptation in similar studies.

Reviewer #1 (Public review):

Summary:

This is an interesting study on AD(H)D. The authors combine a variety of neural and physiological metrics to study attention in a VR classroom setting. The manuscript is well written and the results are interesting, ranging from an effect of group (AD(H)D vs. control) on metrics such as envelope tracking, to multivariate regression analyses considering alpha-power, gaze, TRF, ERPs, and behaviour simultaneously. I find the first part of the results clear and strong. The multivariate analyses in Tables 1 and 2 are good ideas, but I think they would benefit from additional clarification. Overall, I think that the methodological approach is useful in itself. The rest is interesting in that it informs us on which metrics are sensitive to group effects and correlated with each other. I think this might be one interesting way forward. Indeed, much more work is needed to clarify how these results change with different stimuli and tasks. So, I see this as an interesting first step into a more naturalistic measurement of speech attention.

Strengths:

I praise the authors for this interesting attempt to tackle a challenging topic with naturalistic experiments and metrics. I think the results broadly make sense and they contribute to a complex literature that is far from being linear and cohesive.

Weaknesses:

Nonetheless, I have a few comments that I hope will help the authors improve the manuscript. Some aspects should be clearer, some methodological steps were unclear (missing details on filters), and others were carried out in a way that doesn't convince me and might be problematic (e.g., re-filtering). I also suggested areas where the authors might find some improvements, such as deriving distinct markers for the overall envelope reconstruction and its change over time, which could solve some of the issues reported in the discussion (e.g., the lack of correlation with TRF metrics).

I also have some concerns regarding reproducibility. Many details are imprecise or missing. And I did not find any comments on data and code sharing. A clarification would be appreciated on that point for sure.

There are some minor issues, typically caused by some imprecisions in the write-up. There are a few issues that could change things though (e.g., re-filtering; the worrying regularisation optimisation choices), and there I'll have to see the authors' reply to determine whether those are major issues or not. Figures should also be improved (e.g., Figure 4B is missing the ticks).

Reviewer #2 (Public review):

Summary:

While selective attention is a crucial ability of human beings, previous studies on selective attention are primarily conducted in a strictly controlled context, leaving a notable gap in underlying the complexity and dynamic nature of selective attention in a naturalistic context. This issue is particularly important for classroom learning in individuals with ADHD, as selecting the target and ignoring the distractions are pretty difficult for them but are the prerequisites of effective learning. The authors of this study have addressed this challenge using a well-motivated study. I believe the findings of this study will be a nice addition to the fields of both cognitive neuroscience and educational neuroscience.

Strengths:

To achieve the purpose of setting up a naturalistic context, the authors have based their study on a novel Virtual Reality platform. This is clever as it is usually difficult to perform such a study in a real classroom. Moreover, various techniques such as brain imaging, eye-tracking, and physiological measurement are combined to collect multi-level data. They found that, different from the controls, individuals with ADHD had higher neural responses to the irrelevant rather than the target sounds, and reduced speech tracking of the teacher. Additionally, the power of alpha-oscillations and frequency of gaze shifts away from the teacher are found to be associated with ADHD symptoms. These results provide new insights into the mechanism of selective attention among ADHD populations.

Weaknesses:

It is worth noting that nowadays there have been some studies trying to do so in the real classroom, and thus the authors should acknowledge the difference between the virtual and real classroom context and foresee the potential future changes.

The approach of combining multi-level data has the advantage of obtaining reliable results, but also raises significant difficulty for the readers to understand the main results.

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions.

As expected, individuals with ADHD showed anomalous patterns of neural responses, and eye-tracking patterns, compared to the controls. But there are also some similarities between groups such as the amount of time paying attention to teachers, etc. In general, their conclusions are supported.

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community, would highlight the contributions of the work.

The findings are an extension of previous efforts in understanding selective attention in the naturalistic context. The findings of this study are particularly helpful in inspiring teacher's practice and advancing the research of educational neuroscience. This study demonstrates, again, that it is important to understand the complexity of cognitive processes in the naturalistic context.

Reviewer #3 (Public review):

Summary:

The authors conducted a well-designed experiment, incorporating VR classroom scenes and background sound events, with both control and ADHD participants. They employed multiple neurophysiological measures, such as EEG, eye movements, and skin conductance, to investigate the mechanistic underpinnings of paying attention in class and the disruptive effects of background noise.

The results revealed that individuals with ADHD exhibited heightened sensory responses to irrelevant sounds and reduced tracking of the teacher's speech. Overall, this manuscript presented an ecologically valid paradigm for assessing neurophysiological responses in both control and ADHD groups. The analyses were comprehensive and clear, making the study potentially valuable for the application of detecting attentional deficits.

Strengths:

• The VR learning paradigm is well-designed and ecologically valid.

• The neurophysiological metrics and analyses are comprehensive, and two physiological markers are identified capable of diagnosing ADHD.

• This research provides a valuable dataset that could serve as a benchmark for future studies on attention deficits.

Weaknesses:

• Several results are null results, i.e., no significant differences were found between ADHD and control populations.

• Although the paradigm is well-designed and ecologically valid, the specific contributions or insights from the results remain unclear.

• Lack of information regarding code and data availability.

Reviewer #1 (Public review):

Summary:

In this manuscript, Jin et. al., describe SMARTR, an image analysis strategy optimized for analysis of dual-activity ensemble tagging mouse reporter lines. The pipeline performs cell segmentation, then registers the location of these cells into an anatomical atlas, and finally, calculates the degree of co-expression of the reporters in cells across brain regions. They demonstrate the utility of the method by labeling two ensemble populations during two related experiences: inescapable shock and subsequent escapable shock as part of learned helplessness.

Strengths:

(1) We appreciated that the authors provided all documentation necessary to use their method and that the scripts in their publicly available repository are well commented.

(2) The manuscript was well-written and very clear, and the methods were generally highly detailed.

Weaknesses:

(1) The heatmaps (for example, Figure 3A, B) are challenging to read and interpret due to their size. Is there a way to alter the visualization to improve interpretability? Perhaps coloring the heatmap by general anatomical region could help? We feel that these heatmaps are critical to the utility of the registration strategy, and hence, clear visualization is necessary.

(2) Additional context in the Introduction on the use of immediate early genes to label ensembles of neurons that are specifically activated during the various behavioral manipulations would enable the manuscript and methodology to be better appreciated by a broad audience.

(3) The authors mention that their segmentation strategies are optimized for the particular staining pattern exhibited by each reporter and demonstrate that the manually annotated cell counts match the automated analysis. They mention that alternative strategies are compatible, but don't show this data.

(4) The authors provided highly detailed information for their segmentation strategy, but the same level of detail was not provided for the registration algorithms. Additional details would help users achieve optimal alignment.

Reviewer #2 (Public review):

Summary:

This manuscript describes a workflow and software package, SMARTR, for mapping and analyzing neuronal ensembles tagged using activity-dependent methods. They showcase this pipeline by analyzing ensembles tagged during the learned helplessness paradigm. This is an impressive effort, and I commend the authors for developing open-source software to make whole-brain analyses more feasible for the community. Software development is essential for modern neuroscience and I hope more groups make the effort to develop open-source, easily usable packages. However, I do have concerns over the usability and maintainability of the SMARTR package. I hope that the authors will continue to develop this package, and encourage them to make the effort to publish it within either the Bioconductor or CRAN framework.

Strengths:

This is a novel software package aiming to make the analysis of brain-wide engrams more feasible, which is much needed. The documentation for the package and workflow is solid.

Weaknesses:

While I was able to install the SMARTR package, after trying for the better part of one hour, I could not install the "mjin1812/wholebrain" R package as instructed in OSF. I also could not find a function to load an example dataset to easily test SMARTR. So, unfortunately, I was unable to test out any of the packages for myself. Along with the currently broken "tractatus/wholebrain" package, this is a good example of why I would strongly encourage the authors to publish SMARTR on either Bioconductor or CRAN in the future. The high standards set by Bioc/CRAN will ensure that SMARTR is able to be easily installed and used across major operating systems for the long term.

The package is quite large (several thousand lines include comments and space). While impressive, this does inherently make the package more difficult to maintain - and the authors currently have not included any unit tests. The authors should add unit tests to cover a large percentage of the package to ensure code stability.

Why do the authors choose to perform image segmentation outside of the SMARTR package using ImageJ macros? Leading segmentation algorithms such as CellPose and StarMap have well-documented APIs that would be easy to wrap in R. They would likely be faster as well. As noted in the discussion, making SMARTR a one-stop shop for multi-ensemble analyses would be more appealing to a user.

Given the small number of observations for correlation analyses (n=6 per group), Pearson correlations would be highly susceptible to outliers. The authors chose to deal with potential outliers by dropping any subject per region that was> 2 SDs from the group mean. Another way to get at this would be using Spearman correlation. How do these analyses change if you use Spearman correlation instead of Pearson? It would be a valuable addition for the author to include Spearman correlations as an option in SMARTR.

I see the authors have incorporated the ability to adjust p-values in many of the analysis functions (and recommend the BH procedure) but did not use adjusted p-values for any of the analyses in the manuscript. Why is this? This is particularly relevant for the differential correlation analyses between groups (Figures 3P and 4P). Based on the un-adjusted p-values, I assume few if any data points will still be significant after adjusting. While it's logical to highlight the regional correlations that strongly change between groups, the authors should caution ¬ which correlations are "significant" without adjusting for multiple comparisons. As this package now makes this analysis easily usable for all researchers, the authors should also provide better explanations for when and why to use adjusted p-values in the online documentation for new users.

The package was developed in R3.6.3. This is several years and one major version behind the current R version (4.4.3). Have the authors tested if this package runs on modern R versions? If not, this could be a significant hurdle for potential users.

Joint Public Review:

Summary:

The authors present a new application of the high-content image-based morphological profiling Cell Painting (CP) to single cell type classification in mixed heterogeneous induced pluripotent stem cell-derived mixed neural cultures. Machine learning models were trained to classify single cell types according to either "engineered" features derived from the image or from the raw CP multiplexed image. The authors systematically evaluated experimental (e.g., cell density, cell types, fluorescent channels) and computational (e.g., different models, different cell regions) parameters and convincingly demonstrated that focusing on the nucleus and its surroundings contain sufficient information for robust and accurate cell type classification. Models that were trained on mono-cultures (i.e., containing a single cell type) could generalize for cell type prediction in mixed co-cultures, and to describe intermediate states of the maturation process of iPSC-derived neural progenitors to differentiation neurons.

Strengths:

Automatically identifying single cell types in heterogeneous mixed cell populations hold great promise to characterize mixed cell populations and to discover new rules of spatial organization and cell-cell communication. Although the current manuscript focuses on the application of quality control of iPSC cultures, the same approach can be extended to a wealth of other applications including in depth study of the spatial context. The simple and high-content assay democratizes use and enables adoption by other labs.

The manuscript is supported by comprehensive experimental and computational validations that raises the bar beyond the current state of the art in the field of high-content phenotyping and makes this manuscript especially compelling. These include (i) Explicitly assessing replication biases (batch effects); (ii) Direct comparison of feature-based (a la cell profiling) versus deep-learning-based classification (which is not trivial/obvious for the application of cell profiling); (iii) Systematic assessment of the contribution of each fluorescent channel; (iv) Evaluation of cell-density dependency; (v) explicit examination of mistakes in classification; (vi) Evaluating the performance of different spatial contexts around the cell/nucleus; (vii) generalization of models trained on cultures containing a single cell type (mono-cultures) to mixed co-cultures; (viii) application to multiple classification tasks.

Comments on latest version:

I have consulted with Reviewer #3 and both of us were impressed by revised manuscript, especially by the clear and convincing evidence regarding the nucleocentric model use of the nuclear periphery and its benefit for the case of dense cultures. However, there are two issues that are incompletely addressed (see below). Until these are resolved, the "strength of evidence" was elevated to "compelling".

First, the analysis of the patch size is not clearly indicating that the 12-18um range is a critical factor (Fig. 4E). On the contrary, the performance seems to be not very sensitive to the patch size, which is actually a desired property for a method. Still, Fig. 4B convincingly shows that the nucleocentric model is not sensitive to the culture density, while the other models are. Thus, the authors can adjust their text saying that the nucleocentric approach is not sensitive to the patch size and that the patch size is selected to capture the nucleus and some margins around it, making it less prone to segmentation errors in dense cultures.

Second, the GitHub does not contain sufficient information to reproduce the analysis. Its current state is sparse with documentation that would make reproducing the work difficult. What versions of the software were used? Where should data be downloaded? The README contains references to many different argparse CLI arguments, but sparse details on what these arguments actually are, and which parameters the authors used to perform their analyses. Links to images are broken. Ideally, all of these details would be present, and the authors would include a step-by-step tutorial on how to reproduce their work. Fixing this will lead to an "exceptional" strength of evidence.

Reviewer #1 (Public review):

To understand spinal locomotor circuits, we need to reveal how various types of spinal interneurons work in them. So far, the general roles of the cardinal groups of spinal interneurons (dI6, V0, V1, V2a, V2b, and V3) in locomotion have been studied but not fully understood. Each group is believed to contain some subgroups with more detailed functional differences. However, each character and function of these subgroups has yet to be elucidated.

In this study, Worthy et al. investigated V1 neurons, one of the main groups of inhibitory neurons in the spinal cord. Previous reports proposed four major clades in V1 neurons defined by the expression of transcription factors (MafA/MafB, Foxp2, sp8, and pou6f2). The authors investigated the birth time for V1 neurons in each of the four clades and showed the postnatal location in the spinal cord with different birthdates. Next, the authors investigated the Foxp2-V1 population in detail using genetically labeled Foxp2-V1 mice. They found some FoxP2-V1 located near LMC motor neurons that innervate limbs. They showed that most of the synapses of V1 neurons on the cell bodies of LMC motor neurons were from Foxp2-V1 and Renshaw cells, and the proportion of Foxp2-V1 synapses in V1 synapses on motor neurons was relatively high in LMC compared to other motor columns. They also proposed that Foxp2-V1 can be further classified according to the expression of transcription factors Otp and Foxp4. The results of this paper are well supported by the data obtained using widely used methods.

This study will be helpful for future analyses of the development and function of V1 neurons. In particular, the discovery of strong synaptic connections between Foxp2-V1 and LMC motor neurons will be beneficial in analyzing the role of V1 neurons in motor circuits that generate movement of the limbs.

Reviewer #2 (Public review):

Summary:

This work brings important information regarding the composition of interneurons in the mammalian spinal cord, with a developmental perspective. Indeed, for the past decades, tools inspired from developmental biology have opened up promising avenues for challenging the functional heterogeneity in the spinal cord. They rely on the fact that neurons sharing similar mature properties also share a largely similar history of expression of specific transcription factor (TF) genes during embryogenic and postnatal development. For instance, neurons originating from p1 progenitors and expressing the TF Engrailed-1, form the V1 neuronal class. While such "cardinal" neuronal classes defined by one single RF indeed share numerous features - e.g., for the case of V1 neurons, a ventral positioning, an inhibitory nature and ipsilatetal projections - there is accumulating evidence for a finer-grained diversity and specialization in each class which is still largely obscure. The present work studies the heterogeneity of V1 interneurons and describes multiple classes based on their birthdate, final positioning, and expression of additional TF. It brings in particular a solid characterization of the Foxp2-expressing V1 interneurons for which authors also delve into the connectivity, and hence, possible functional implication. The work will be of interest to developmental biologists and those interested in the organization of the locomotor spinal network.

Strengths:

This study has deeply analyzed the diversity of V1 neurons by intersecting multiple criteria: TF expression, birthdate, location in the spinal cord, diversity along the rostro-caudal axis, and for some subsets, connectivity. This illustrates and exemplifies the absolute need to not consider cardinal classes, defined by one single TF, as homogeneous. Rather, it highlights the limits of single-TF classification and exemplifies the existence of further diversity within the cardinal class.

Experiments are generally well performed with a satisfactory number of animals and adequate statistical tests.

Authors have also paid strong attention to potential differences in cell-type classification when considering neurons currently expressing of a given TF (e.g., using antibodies), from those defined as having once expressed that TF (e.g., defined by a lineage-tracing strategy). This ambiguity is a frequent source of discrepancy of findings across studies.

Furthermore, there is a risk in developmental studies to overlook the fact that the spinal cord is functionally specialized rostro-caudally, and to generalize features that may only be applicable to a specific segment and hence to a specific motor pool. While motoneurons share the same dorso-ventral origin and appear homogenous on a ChAT staining, specific clusters are dedicated to specific muscle groups, e.g., axial, hypaxial or limb muscles. Here, the authors make the important distinction between different lumbar levels and detail the location and connectivity of their neurons of interest with respect to specific clusters of MN.

Finally, the authors are fully transparent on inter-animal variability in their representation and quantification. This is crucial to avoid the overgeneralization of findings but to rather provide a nuanced understanding of the complexities of spinal circuits.

Weaknesses:

The different V1 populations have been investigated in detail regarding their development and positioning, but their functional ambition is not directly investigated through gain or loss of function experiments in the present study. While the putative inputs onto motoneurons are interesting and suggestive of differences between V1 pools, they are only a little predictive of function.

Reviewer #1 (Public review):

Summary:

In this manuscript by Napoli et al, the authors study the intracellular function of Cytosolic S100A8/A9 a myeloid cell soluble protein that operates extracellularly as an alarmin, whose intracellular function is not well characterized. Here, the authors utilize state-of-the-art intravital microscopy to demonstrate that adhesion defects observed in cells lacking S100A8/A9 (Mrp14-/-) are not rescued by exogenous S100A8/A9, thus highlighting an intrinsic defect. Based on this result subsequent efforts were employed to characterize the nature of those adhesion defects.

Strengths:

The authors convincingly show that Mrp14-/- neutrophils have normal rolling but defective adhesion caused by impaired CD11b activation (deficient ICAM1 binding). Analysis of cellular spreading (defective in Mrp14-/- cells) are also sound. The manuscript then focuses on selective signaling pathways and calcium measurements. Overall, this is a straightforward study of biologically important proteins and mechanisms.

Weaknesses:

Some suggestions are included below to improve this manuscript.

Reviewer #2 (Public review):

Summary:

Napoli et al. provide a compelling study showing the importance of cytosolic S100A8/9 in maintaining calcium levels at LFA-1 nano clusters at the cell membrane, thus allowing the successful crawling and adherence of neutrophils under shear stress. The authors show that cytosolic S100A8/9 is responsible for retaining stable and high concentrations of calcium specifically at LFA-1 nanoclusters upon binding to ICAM-1, and imply that this process aids in facilitating actin polymerisation involved in cell shape and adherence. The authors show early on that S100A8/9 deficient neutrophils fail to extravasate successfully into the tissue, thus suggesting that targeting cytosolic S100A8/9 could be useful in settings of autoimmunity/acute inflammation where neutrophil-induced collateral damage is unwanted.

Strengths:

Using multiple complementary methods from imaging to western blotting and flow cytometry, including extracellular supplementation of S100A8/9 in vivo, the authors conclusively prove a defect in intracellular S100A8/9, rather than extracellular S100A8/9 was responsible for the loss in neutrophil adherence, and pinpointed that S100A8/9 aided in calcium stabilisation and retention at the plasma membrane.

Weaknesses:

(1) Extravasation is shown to be a major defect of Mrp14-/- neutrophils, but the Giemsa staining in Figure 1H seems to be quite unspecific to me, as neutrophils were determined by nuclear shape and granularity, which could be affected by the angle at which the nucleus is viewed. It would have perhaps been cleaner/clearer to use immunofluorescence staining for neutrophils instead as seen in Supplementary Figure 1A (staining for Ly6G or other markers instead of S100A9).

Addressed issues:

(1) The representative image for Mrp14-/- neutrophils used in Figure 4K to demonstrate the Ripley's K function seems to be very different from that shown above in Figure 4C and 4F. In their response to reviewers, the authors reassure that all data has been included in the analysis.

(2) In the initial submission the authors needed to provide a more direct linkage between cytosolic S100A8/9 and actin polymerisation, which subsequently results in the arrest and adherence of neutrophils. The authors did an additional experiment indicating the co-localization of S100A8/9 with LFA-1, indicating that the spatial localisation of S100A8/9 does shift towards the membrane with activation. Further, the authors confirm that the defect is only apparent only in conditions of shear stress, as transwell migration of Mrp14-/- neutrophils is not affected.

Reviewer #1 (Public review):

Summary:

The study characterized the cellular and molecular mechanisms of spike timing-dependent long-term depression (t-LTD) at the synapses between excitatory afferents from lateral (LPP) and medial (MPP) perforant pathways to granule cells (GC) of the dentate gyrus (DG) in mice.

Strengths:

The electrophysiological experiments are thorough. The experiments are systematically reported and support the conclusions drawn.<br /> This study extends current knowledge by elucidating additional plasticity mechanisms at PP-GC synapses, complementing existing literature.

Comments on the revised version:

The revised study introduces two additional approaches to confirm astrocyte involvement in t-LTD: loading astrocytes with tetanus toxin light chain to inhibit exocytosis, and using Evans blue to block vesicular glutamate uptake. These new findings further reinforce the conclusion that t-LTD relies on Ca2+-dependent glutamate exocytosis from astrocytes.

Reviewer #2 (Public review):

Summary:

This work reports the existence of spike timing-dependent long-term depression (t-LTD) of excitatory synaptic strength at two synapses of the dentate gyrus granule cell, which are differently connected to the entorhinal cortex via either the lateral or medial perforant pathways (LPP or MPP, respectively). Using patch-clamp electrophysiological recording of tLTD in combination with either pharmacology or a genetically modified mouse model, they provide information on the differences in the molecular mechanism underlying this t-LTD at the two synapses.

Strengths:

The two synapses analyzed in this study have been understudied. This new data thus provides interesting new information on a plasticity process at these synapses, and the authors demonstrate subtle differences in the underlying molecular mechanisms at play. Experiments are in general well controlled and provide robust data that are properly interpreted.<br /> The data provided to demonstrate that glutamate release from astrocytes is necessary for these plasticity mechanisms are strong. This is particularly interesting as another example of how astrocytes regulate synapse plasticity.

Weaknesses:

This work was performed at young synapses and the highlighted mechanisms are therefore pertinent to this age, as acknowledged by the authors. We currently don't know if these mechanisms are still at play at the adult synapse.

Significance:

While this is the first report of t-LTD at these synapses, this plasticity process has been mechanistically well investigated at other synapses in the hippocampus and in the cortex. Nevertheless, this new data suggests that mechanistic differences in the induction of t-LTD at these two DG synapses could contribute to the differences in the physiological influence of the LPP and MPP pathways.

Reviewer #3 (Public review):

Coatl et al. investigated the mechanisms of synaptic plasticity of two important hippocampal synapses, the excitatory afferents from lateral and medial perforant pathways (LPP and MPP, respectively) of the entorhinal cortex (EC) connecting to granule cells of the hippocampal dentate gyrus (DG). They find that these two different EC-DG synaptic connections in mice show a presynaptically expressed form of long-term depression (LTD) requiring postsynaptic calcium, eCB synthesis, CB1R activation, astrocyte activity, and metabotropic glutamate receptor activation. Interestingly, LTD at MPP-GC synapses requires ionotropic NMDAR activation whereas LTD at LPP-GC synapse is NMDAR independent. Thus, they discovered two novel forms of t-LTD that require astrocytes at EC-GC synapses. Although plasticity of EC-DG granule cell (GC) synapses has been studied using classical protocols, These are the first analyses of the synaptic plasticity induced by spike timing dependent protocols at these synapses. Interestingly, the data also indicate that t-LTD at each type of synapse require different group I mGluRs, with LPP-GC synapses dependent on mGluR5 and MPP-GC t-LTD requiring mGluR1.

The authors performed a detailed analysis of the coefficient of variation of the EPSP slopes, miniature responses and different approaches (failure rate, PPRs, CV, and mEPSP frequency and amplitude analysis) they demonstrate a decrease in the probability of neurotransmitter release and a presynaptic locus for these two forms of LTD at both types of synapses. By using elegant electrophysiological experiments and taking the advantage of the conditional dominant-negative (dn) SNARE mice in which doxycycline administration blocks exocytosis and impairs vesicle release by astrocytes, they demonstrate that both LTD forms require the release of gliotransmitters from astrocytes. These data add in an interesting way to the ongoing discussion on whether LTD induced by STDP participates in refining synapses potentially weakening excitatory synapses under the control of different astrocytic networks. The conclusions of this paper are well supported by data.

Reviewer #1 (Public review):

Summary

The main goal of the study was to tease apart the associative and non-associative elements of cued fear conditioning that could influence which defensive behaviors are expressed. To do this, the authors compared groups conditioned with paired, unpaired, or shock only procedures followed by extinction of the cue. The cue used in the study was not typical; serial presentation of a tone followed by a white noise (or reversed) was used in order to assess switches in behavior across the transition from tone to white noise. Many defensive behaviors beyond the typical freezing assessments were measured, and both male and female mice were included throughout. The authors found changes in behavioral transitions from freezing to flight during conditioning as the tone transitioned into white noise, and a switch in freezing during extinction such that it became high during the white noise as flight behavior decreased. Overall, this was an interesting analysis of transitions in defensive behaviors to a serially presented cue consisting of two auditory stimuli during conditioning and then extinction.

Strengths

The highlights in this study were the significant switches in freezing and escape-like behaviors as the cue transitioned between the two auditory stimuli during fear conditioning, and then adjustment of those behaviors across extinction.

These main findings were a result of thorough behavioral analyses with key control groups (reversed stimulus order, unpaired conditioning, and shock only groups), assessing freezing, jumping, darting and tail rattling to try to parse out associative versus non-associative features of the behavioral profiles.

Weaknesses

While the detailed analyses of defensive behaviors in mice in a situation of signaled imminent threat adds valuable knowledge to those studying fear conditioning, the caveat is that it is unclear how broadly applicable these findings truly will be. It makes sense that similar transitions in defensive behaviors will occur across organisms, but each organism and each psychiatric disorder will have unique profiles.

Reviewer #2 (Public review):

Summary:

The authors examined several defensive responses elicited during Pavlovian conditioning using a serial compound stimulus (SCS) as the conditioned stimulus (CS) and a shock unconditioned stimulus (US) in male and female mice. The SCS consisted of a tone pips followed by white noise. Their design included conditions in which mice were exposed to the CS and US in a paired fashion, in an unpaired fashion, or only exposed to the shock US, as well as paired and unpaired conditions that reversed the order of the SCS. They compared freezing, jumping, darting, and tail rattling across all groups during conditioning and extinction. During conditioning, strong freezing responses to the tone pips followed by strong jumping and darting responses to the white noise were present in the paired group but less robust or not present in the unpaired or shock only groups. During extinction, tone-induced freezing diminished while the jumping was replaced by freezing and darting in the paired group. Together, these findings support the idea that associative pairings are necessary for conditioned defensive responses.

Strengths:

The study has strong control groups including a group that receives the same stimuli in an unpaired fashion and another control group that only receives the shock US and no CS to test the associative value of the SCS to the US. The authors examine a wide variety of defensive behaviors that emerge during conditioning and shift throughout extinction: in addition to the standard freezing response, jumping, darting, and tail rattling were also measured.

The revised version has greatly strengthened this study by including additional control groups (e.g., reversing the order of the compound stimuli in both paired and unpaired conditions).

Reviewer #1 (Public review):

Plasticity in the basolateral amygdala (BLA) is thought to underlie the formation of associative memories between neutral and aversive stimuli, i.e. fear memory. Concomitantly, fear learning modifies the expression of BLA theta rhythms, which may be supported by local interneurons. Several of these interneuron subtypes, PV+, SOM+, and VIP+, have been implicated in the acquisition of fear memory. However, it was unclear how they might act synergistically to produce BLA rhythms that structure the spiking of principal neurons so as to promote plasticity. Cattani et al. explored this question using small network models of biophysically detailed interneurons and principal neurons.

Using this approach, the authors had four principal findings:

(1) Intrinsic conductances in VIP+ interneurons generate a slow theta rhythm that periodically inhibits PV+ and SOM+ interneurons, while disinhibiting principal neurons.<br /> (2) A gamma rhythm arising from the interaction between PV+ and principal neurons establishes the precise timing needed for spike-timing-dependent plasticity.<br /> (3) Removal of any of the interneuron subtypes abolishes conditioning-related plasticity.<br /> (4) Learning-related changes in principal cell connectivity enhance expression of slow theta in the local field potential.

The strength of this work is that it explores the role of multiple interneuron subtypes in the formation of associative plasticity in the basolateral amygdala. The authors use biophysically detailed cell models that capture many of their core electrophysiological features, which helps translate their results into concrete hypotheses that can be tested in vivo. Moreover, they try to align the connectivity and afferent drive of their model with those found experimentally.

A drawback to this study is the construction of the afferent drive to the network, which does not elicit activities that are consistent with the majority of those observed to similar stimuli. The authors discuss this issue in depth, and provide potential mechanisms that may overcome it.

Setting aside the issues with the conditioning protocol, the study offers a model for the generation of multiple rhythms in the BLA that is ripe for experimental testing. The most promising avenue would be in vivo experiments testing the role of local VIP+ neurons in the generation of slow theta. That would go a long way to resolving whether BLA theta is locally generated or inherited from medial prefrontal cortex or ventral hippocampus afferents.

The broader importance of this work is that it illustrates that we must examine the function of neurons not just in terms of their behavioral correlates, but by their effects on the microcircuit they are embedded within. No one cell type is instrumental in producing fear learning in the BLA. Each contributes to the orchestration of network activity to produce plasticity. Moreover, this study reinforces a growing literature highlighting the crucial role of theta and gamma rhythms in BLA function.

Reviewer #1 (Public Review):

Summary:

In this study the authors demonstrated that ablation of astrocytes in lumbar spinal cord not only reduced neuropathic pain but also caused microglia activation. Furthermore, RNA sequencing and bioinformatics revealed an activation of STING/type I IFNs signal pathway in spinal cord microglia after astrocyte ablation.

Strengths:

The findings are novel and interesting and provide new insights into astrocyte-microglia interaction in neuropathic pain. This study may also offer a new therapeutic strategy for the treatment of debilitating neuropathic pain in patients with SCI.

Weaknesses:

The authors have provided a satisfactory explanation of the comments on sample size, statistics, and the sex of the animals. The statistic was reworked.

Reviewer #2 (Public Review):

Summary:

In the manuscript, Zhao et al. have carried out a thorough examination of the effects of targeted ablation of resident astrocytes on behavior, cellular responses, and gene expression after spinal cord injury. Employing transgenic mice models alongside pharmacogenetic techniques, the authors have successfully achieved the selective removal of these resident astrocytes. This intervention led to a notable reduction in neuropathic pain and induced a shift in microglial cell reactivation states within the spinal cord, significantly altering transcriptome profiles predominantly associated with interferon (IFN) signaling pathways.

Strengths:

The findings presented add considerable value to the current understanding of the role of astrocyte elimination in neuropathic pain, offering convincing evidence that supports existing hypotheses and valuable insights into the interactions between astrocytes and microglial cells, likely through IFN-mediated mechanisms. This contribution is highly relevant and suggests that further exploration in this direction could yield meaningful results.

Weaknesses:

The authors have satisfactorily addressed the comments regarding further clarifications and statistical methods.

Reviewer #1 (Public review):

The manuscript under review investigates the role of periosteal stem cells (P-SSC) in bone marrow regeneration using a whole-bone subcutaneous transplantation model. While the model is somewhat artificial, the findings were interesting, suggesting the migration of periosteal stem cells into the bone marrow and their potential to become bone marrow stromal cells. This indicates a significant plasticity of P-SSC consistent with previous reports using fracture models (Cell Stem Cell 29:1547, Dev Cell 59:1192).

Major Concerns

(1) The authors assert that the periosteal layer was completely removed in their model, which is crucial for their conclusions. To substantiate this claim, it is recommended that the authors provide evidence of the successful removal of the entire periosteal stem cell (P-SSC) population. A colony-forming assay, with and without periosteal removal, could serve as a suitable method to demonstrate this.

(2) The observation that P-SSCs do not express Kitl or Cxcl12, while their bone marrow stromal cell (BM-MSC) derivatives do, is a key finding. To strengthen this conclusion, the authors are encouraged to repeat the experiment using Cxcl12 or Scf reporter alleles. Immunofluorescence staining that confirms the migration of periosteal cells and their transformation into Cxcl12- or Scf-reporter-positive cells would significantly enhance the paper's key conclusion.

(3) On page 8, line 20, the authors' statement regarding the detection of Periostin+ cells outside the periosteum layer could be misinterpreted due to the use of the periostin antibody. Given that periostin is an extracellular matrix protein, the staining may not accurately represent Periostin-expressing cells but rather the presence of periostin in the extracellular matrix. The authors should revise this section for greater precision.

Reviewer #2 (Public review):

Summary:

The authors have established a femur graft model that allows the study of hematopoietic regeneration following transplantation. They have extensively characterized this model, demonstrating the loss of hematopoietic cells from the donor femur following transplantation, with recovery of hematopoiesis from recipient cells. They also show evidence that BM MSCs present in the graft following transplantation are graft-derived. They have utilized this model to show that following transplantation, periosteal cells respond by first expanding, then giving rise to more periosteal SSCs, and then migrating into the marrow to give rise to BM MSCs.

Strengths:

These studies are notable in several ways:

(1) Establishment of a novel femur graft model for the study of hematopoiesis;

(2) Use of lineage tracing and surgery models to demonstrate that periosteal cells can give rise to BM MSCs.

Weaknesses:

There are a few weaknesses. First, the authors do not definitively demonstrate the requirement of periosteal SSC movement into the BM cavity for hematopoietic recovery. Hematopoiesis recovers significantly before 5 months, even before significant P-SSC movement has been shown, and hematopoiesis recovers significantly even when periosteum has been stripped. Second, it is not clear how the periosteum is changing in the grafts. Which cells are expanding is unclear, and it is not clear if these cells have already adopted a more MSC-like phenotype prior to entering the marrow space. Indeed, given the presence of host-derived endothelial cells in the BM, these studies are reminiscent of prior studies from this group and others that re-endothelialization of the marrow may be much more important for determining hematopoietic regeneration, rather than the P-SSC migration. Third, the studies exploring the preferential depletion of BM MSCs vs P-SSCs are difficult to interpret. The single metabolic stress condition chosen was not well-justified, and the use of purified cell populations to study response to stress ex vivo may have introduced artifacts into the system.

Reviewer #3 (Public review):

Summary:

Marchand, Akinnola, et al. describe the use of the novel model to study BM regeneration. Here, they harvest intact femurs and subcutaneously graft them into recipient mice. Similar to standard BM regeneration models, there is a rapid decrease in cellularity followed by a gradual recovery over 5 months within the grafts. At 5 months, these grafts have robust HSC activity, similar to HSCs isolated from the host femur. They find that periosteum skeletal stem cells (p-SSCs) are the primary source of BM-MSCs within the grafted femur and that these cells are more resistant to the acute stress of grafting the femur.

Strengths:

This is an interesting manuscript that describes a novel model to study BM regeneration. The model has tremendous promise.

Weaknesses:

The authors claim that grafting intact femurs subcutaneously is a model of BM regeneration and can be used as a replacement for gold standard BM regeneration assays such as sublethal chemo/irradiation. However, there isn't enough explanation as to how this model is equivalent or superior to the traditional models. For instance, the authors claim that this model allows for the study of "BM regeneration in vivo in response to acute injury using genetic tools." This can and has been done numerous times with established, physiologically relevant BM regeneration models. The onus is on the authors to discuss or perform the necessary experiments to justify the use of this model. For example, standard BM regeneration models involve systemic damage that is akin to therapies that require BM regeneration. How is studying the current model that provides only an acute injury more relevant and useful than other models? As it stands, it seems as if the authors could have done all the experiments demonstrating the importance of these p-SSCs in the traditional myelosuppressive BM regeneration models to be more physiologically relevant. Along these lines, the use of a standard BM regeneration model (e.g., sublethal chemo/irradiation) as a critical control is missing and should be included. Even if the control doesn't demonstrate that p-SSCs can contribute to the BM-MSC during regeneration, it will still be important because it could be the justification for using the described model to specifically study p-SSCs' regulation of BM regeneration.

The authors perform some analysis that suggests that grafting a whole femur mimics BM regeneration, but there are many experiments missing from the manuscript that will be necessary to support the use of this model. To demonstrate that this new model mimics current BM regeneration models, the authors need to perform a careful examination of the early kinetics of hematopoietic recovery post-transplant. Complete blood counts should be performed on the grafts, focusing on white blood cells (particularly neutrophils), red blood cells, platelets, all critical indicators of BM regeneration. This analysis should be done at early time points that include weekly analysis for a minimum of 28 days following the graft. Additionally, understanding how and when the vasculature recovers is critical. This is particularly important because it is well-established that if there is a delay in vascular recovery, there is a delay in hematopoietic recovery. As mentioned above, a standard BM regeneration model should be used as a control.

The contribution of donor and host cells to the BM regeneration of the graft is interesting. Particularly, the chimerism of the vasculature. One can assume that for the graft to undergo BM regeneration, there needs to be the delivery of nutrients into the graft via the vasculature. The chimerism of the vascular network suggests that host endothelial cells anastomose with the graft. Host mice should have their vascular system labeled with a dye such as dextran to determine if anastomosis has occurred. If not, the authors need to explain how this graft survives up to 5 months. If anastomosis does occur, then it is very surprising that the hematopoietic system of the graft is not a chimera because this would essentially be a parabiosis model. This needs to be explained.

Most of the data presented for the resistance of p-SSCs to stress suggests DNA damage response. Do p-SSCs demonstrate a higher ability to resolve DNA damage? Do they accumulate less DNA damage? Staining for DNA damage foci or performing comet assays could be done to further define the mechanism of stress resistance properties of p-SSCs.

Given the importance of BM-MSCs in hematopoiesis and that the majority of the emerging BM-MSCs appear to be derived from p-SSCs, the authors should perform experiments to determine if p-SSC-derived BM-MSCs are critical regulators of BM regeneration. For example, the authors could test this by crossing the Postn-creER mice with iDTR mice to ablate these cells and see if recovery is inhibited or delayed. This should be done with the described periosteum-wrapped femur graft model as well as a control BM regeneration model. Demonstrating that the deletion of these cells affects BM regeneration in both models would further justify the physiological relevance and utility of the femur graft model.

Reviewer #1 (Public review):

Summary:

The manuscript by Cao et al. examines an important but understudied question of how chronic exposure to heat drives changes in affective and social behaviors. It has long been known that temperature can be a potent driver of behaviors and can lead to anxiety and aggression. However, the neural circuitry that mediates these changes is not known. Cao et al. take on this question by integrating optical tools of systems neuroscience to record and manipulate bulk activity in neural circuits, in combination with a creative battery of behavior assays. They demonstrate that chronic daily exposure to heat leads to changes in anxiety, locomotion, social approach, and aggression. They identify a circuit from the preoptic area (POA) to the posterior paraventricular thalamus (pPVT) in mediating these behavior changes. The POA-PVT circuit increases activity during heat exposure. Further, manipulation of this circuit can drive affective and social behavioral phenotypes even in the absence of heat exposure. Moreover, silencing this circuit during heat exposure prevents the development of negative phenotypes. Overall the manuscript makes an important contribution to the understudied area of how ambient temperature shapes motivated behaviors.

Strengths

The use of state-of-the-art systems neuroscience tools (in vivo optogenetics and fiber photometry, slice electrophysiology), chronic temperature-controlled experiments, and a rigorous battery of behavioral assays to determine affective phenotypes. The optogenetic gain of function of affective phenotypes in the absence of heat, and loss of function in the presence of heat are very convincing manipulation data. Overall a significant contribution to the circuit-level instantiation of temperature-induced changes in motivated behavior, and creative experiments.

Weaknesses

(1) There is no quantification of cFos/rabies overlap shown in Figure 2, and no report of whether the POA-PVT circuit has a higher percentage of Fos+ cells than the general POA population. Similarly, there is no quantification of cFos in POA recipient PVT cells for Figure 2 Supplement 2.

(2) The authors do not address whether stimulation of POA-PVT also increases core body temperature in Figure 3 or its relevant supplements. This seems like an important phenotype to make note of and could be addressed with a thermal camera or telemetry.

(3) In Figure 3G: is Day 1 vs Day 22 "pre-heat" significant? The statistics are not shown, but this would be the most conclusive comparison to show that POA-PVT cells develop persistent activity after chronic heat exposure, which is one of the main claims the authors make in the text. This analysis is necessary in order to make the claim of persistent circuit activity after chronic heat exposure.

(4) In Figure 4, the control virus (AAV1-EYFP) is a different serotype and reporter than the ChR2 virus (AAV9-ChR2-mCherry). This discrepancy could lead to somewhat different baseline behaviors.

(5) In Figure 5G, N for the photometry data: the authors assess the maximum z-score as a measure of the strength of calcium response, however the area under the curve (AUC) is a more robust and useful readout than the maximum z score for this. Maximum z-score can simply identify brief peaks in amplitude, but the overall area under the curve seems quite similar, especially for Figure 5N.

(6) For Fig 5V: the authors run the statistics on behavior bouts pooled from many animals, but it is better to do this analysis as an animal average, not by compiling bouts. Compiling bouts over-inflates the power and can yield significant p values that would not exist if the analysis were carried out with each animal as an n of 1.

(7) In general this is an excellent analysis of circuit function but leaves out the question of whether there may be other inputs to pPVT that also mediate the same behavioral effect. Future experiments that use activity-dependent Fos-TRAP labeling in combination with rabies can identify other inputs to heat-sensitive pPVT cells, which may have convergent or divergent functions compared to the POA inputs.

Reviewer #2 (Public review):

Summary

The study by Cao et al. highlights an interesting and important aspect of heat- and thermal biology: the effect of repetitive, long-term heat exposure and its impact on brain function.<br /> Even though peripheral, sensory temperature sensors and afferent neuronal pathways conveying acute temperature information to the CNS have been well established, it is largely unknown how persistent, long-term temperature stimuli interact with and shape CNS function, and how these thermally-induced CNS alterations modulate efferent pathways to change physiology and behavior. This study is therefore not only novel but, given global warming, also timely.

The authors provide compelling evidence that neurons of the paraventricular thalamus change plastically over three weeks of episodic heat stimulation and they convincingly show that these changes affect behavioral outputs such as social interactions, and anxiety-related behaviors.

Strengths

(1) It is impressive that the assessed behaviors can be (i) recruited by optogenetic fiber activation and (ii) inhibited by optogenetic fiber inhibition when mice are exposed to heat. Technically, when/how long is the fiber inhibition performed? It says in the text "3 min on and 3 min off". Is this only during the 20-minute heat stimulation or also at other times?

(2) It is interesting that the frequency of activity in pPVT neurons, as assessed by fiber photometry, stays increased after long-term heat exposure (day 22) when mice are back at normal room temperature. This appears similar to a previous study that found long-term heat exposure to transform POA neurons plastically to become tonically active (https://www.biorxiv.org/content/10.1101/2024.08.06.606929v1 ). Interestingly, the POA neurons that become tonically active by persistent heat exposure described in the above study are largely excitatory, and thus these could drive the activity of the pPVT neurons analyzed in this study.

(3) How can it be reconciled that the majority of the inputs from the POA are found to be largely inhibitory (Fig. 2H)? Is it possible that this result stems from the fact that non-selective POA-to-pPVT projections are labelled by the approach used in this study and not only those pathways activated by heat? These points would be nice to discuss.

(4) It is very interesting that no LTP can be induced after chronic heat exposure (Figures K-M); the authors suggest that "the pathway in these mice were already saturated" (line 375). Could this hypothesis be tested in slices by employing a protocol to extinguish pre-existing (chronic heat exposure-induced) LTP? This would provide further strength to the findings/suggestion that an important synaptic plasticity mechanism is at play that conveys behavioral changes upon chronic heat stimulation.

(5) It is interesting that long-term heat does not increase parameters associated with depression (Figure 1N-Q), how is it with acute heat stress, are those depression parameters increased acutely? It would be interesting to learn if "depression indicators" increase acutely but then adapt (as a consequence of heat acclimation) or if they are not changed at all and are also low during acute heat exposure.

Weaknesses/suggestions for improvements

(1) The introduction and general tenet of the study is, to us, a bit too one-sided/biased: generally, repetitive heat exposure --heat acclimation-- paradigms are known to not only be detrimental to animals and humans but also convey beneficial effects in allowing the animals and humans to gain heat tolerance (by strengthening the cardiovascular system, reducing energy metabolism and weight, etc.).

(2) The point is well taken that these authors here want to correlate their model (90 minutes of heat exposure per day) to heat waves. Nevertheless, and to more fully appreciate the entire biology of repetitive/chronic/persistent heat exposure (heat acclimation), it would be helpful to the general readership if the authors would also include these other aspects in their introduction (and/or discussion) and compare their 90-minute heat exposure paradigm to other heat acclimation paradigms. For example, many past studies (using mice or rats) have used more subtle temperatures but permanently (and not only for 90 minutes) stimulated them over several days and weeks (for example see PMID: 35413138). This can have several beneficial effects related to cardiovascular fitness, energy metabolism, and other aspects. In this regard: 38{degree sign}C used in this study is a very high temperature for mice, in particular when they are placed there without acclimating slowly to this temperature but are directly placed there from normal ambient temperatures (22{degree sign}C-24{degree sign}C) which is cold/coolish for mice. Since the accuracy of temperature measurement is given as +/- 2{degree sign}C, it could also be 40{degree sign}C -- this temperature, 40{degree sign}C, non-heat acclimated C57bl/6 mice will not survive for long.

The authors could consider discussing that this very strong, short episodic heat-stress model used here in this study may emphasize detrimental effects of heat, while more subtle long-term persistent exposure may be able to make animals adapt to heat, become more tolerant, and perhaps even prevent the detrimental cognitive effects observed in this study (which would be interesting to assess in a follow-up study).

(3) Line 140: It would help to be clear in the text that the behaviors are measured 1 day after the acute heat exposure - this is mentioned in the legend to the figure, but we believe it is important to stress this point also in the text. Similarly, this is also relevant for chronic heat stimulation: it needs to be made very clear that the behavior is measured 1 day after the last heat stimulus. If the behaviors had been measured during the heat stimulus, the results would likely be very different.

(4) Figure 2 D and Figure 2- Figure Supplement 1: since there is quite some baseline cFos activity in the pPVT region we believe it is important to include some control (room temperature) mice with anterograde labelling; in our view, it is difficult/not possible to conclude, based on Fig 2 supplement 2C, that nearly 100% of the cfos positive cells are contacted by POA fibre terminals (line 168). By eye there are several green cells that don't have any red label on (or next to) them; additionally, even if there is a little bit of red signal next to a green cell: this is not definitive proof that this is a synaptic contact. It is therefore advisable to revisit the quantification and also revisit the interpretation/wording about synaptic contacts.

In relation to the above: Figure 2h suggests that all neurons are connected (the majority receiving inhibitory inputs), is this really the case, is there not a single neuron out of the 63 recorded pPVT neurons that does not receive direct synaptic input from the POA?

(5) It would be nice to characterize the POA population that connects to the pPVT, it is possible/likely that not only warm-responsive POA neurons connect to that region but also others. The current POA-to-pPVT optogenetic fibre stimulations (Figure 4) are not selective for preoptic warm responsive neurons; since the POA subserves many different functions, this optogenetic strategy will likely activate other pathways. The referees acknowledge that molecular analysis of the POA population would be a major undertaking. Instead, this could be acknowledged in the discussion, for example in a section like "limitation of this study".

(6) Figure 3a the strategy to express Gcamp in a Cre-dependent manner: it seems that the Gcamp8f signal would be polluted by EGFP (coming from the Cre virus injected into the POA): The excitation peak for both is close to 490nm and emission spectra/peaks of GCaMP8f (510-520 nm) and EGFP (507-510 nm) are also highly overlapping. We presume that the high background (EGFP) fluorescence signal would preclude sensitive calcium detection via Gcamp8f, how did the authors tackle this problem?

(7) How did the authors perform the social interaction test (Figures 1F, G)? Was the intruder mouse male or female? If it was a male mouse would the interaction with the female mouse be a form of mating behavior? If so, the interpretation of the results (Figures 1F, G) could be "episodic heat exposure over the course of 3 weeks reduces mating behavior".

Reviewer #3 (Public review):

In this study, Cao et al. explore the neural mechanisms by which chronic heat exposure induces negative valence and hyperarousal in mice, focusing on the role of the posterior paraventricular nucleus (pPVT) neurons that receive projections from the preoptic area (POA). The authors show that chronic heat exposure leads to heightened activity of the POA projection-receiving pPVT neurons, potentially contributing to behavioral changes such as increased anxiety level and reduced sociability, along with heightened startle responses. In addition, using electrophysiological methods, the authors suggest that increased membrane excitability of pPVT neurons may underlie these behavioral changes. The use of a variety of behavioral assays enhances the robustness of their claim. Moreover, while previous research on thermoregulation has predominantly focused on physiological responses to thermal stress, this study adds a unique and valuable perspective by exploring how thermal stress impacts affective states and behaviors, thereby broadening the field of thermoregulation. However, a few points warrant further consideration to enhance the clarity and impact of the findings.

(1) The authors claim that behavior changes induced by chronic heat exposure are mediated by the POA-pPVT circuit. However, it remains unclear whether these changes are unique to heat exposure or if this circuit represents a more general response to chronic stress. It would be valuable to include control experiments with other forms of chronic stress, such as chronic pain, social defeat, or restraint stress, to determine if the observed changes in the POA-pPVT circuit are indeed specific to thermal stress or indicative of a more universal stress response mechanism.

(2) The authors use the term "negative emotion and hyperarousal" to interpret behavioral changes induced by chronic heat (consistently throughout the manuscript, including the title and lines 33-34). However, the term "emotion" is broad and inherently difficult to quantify, as it encompasses various factors, including both valence and arousal (Tye, 2018; Barrett, L. F. 1999; Schachter, S. 1962). Therefore, the reviewer suggests the authors use a more precise term to describe these behaviors, such as valence. Additionally, in lines 117 and 137-139, replacing "emotion" with "stress responses," a term that aligns more closely with the physiological observations, would provide greater specificity and clarity in interpreting the findings.

(3) Related to the role of POA input to pPVT,<br /> a) The authors showed increased activity in pPVT neurons that receive projections from the POA (Figure 3), and these neurons are necessary for heat-induced behavioral changes (Figures 4N-W). However, is the POA input to the pPVT circuit truly critical? Since recipient pPVT neurons can receive inputs from various brain regions, the reviewer suggests that experiments directly inhibiting the POA-to-pPVT projection itself are needed to confirm the role of POA input. Alternatively, the authors could show that the increased activity of pPVT neurons due to chronic heat exposure is not observed when the POA is blocked. If these experiments are not feasible, the reviewer suggests that the authors consider toning down the emphasis on the role of the POA throughout the manuscript and discuss this as a limitation.<br /> b) In the electrophysiology experiments shown in Figures 6A-I, the authors conducted in vitro slice recordings on pPVT neurons. However, the interpretation of these results (e.g., "The increase in presynaptic excitability of the POA to pPVT excitatory pathway suggested plastic changes induced by the chronic heat treatment.", lines 349-350) appears to be an overclaim. It is difficult to conclude that the increased excitability of pPVT neurons due to heat exposure is specifically caused by inputs from the POA. To clarify this, the reviewer suggests the authors conduct experiments targeting recipient neurons in the pPVT, with anterograde labeling from the POA to validate the source of excitatory inputs.

(4) The authors focus on the excitatory connection between the POA and pPVT (e.g., "Together, our results indicate that most of the pPVT-projecting POA neurons responded to heat treatment, which would then recruit their downstream neurons in the pPVT by exerting a net excitatory influence.", lines 169-171). However, are the POA neurons projecting to the pPVT indeed excitatory? This is surprising, considering i) the electrophysiological data shown in Figures 2E-K that inhibitory current was recorded in 52.4% of pPVT neurons by stimulation of POA terminal, and ii) POA projection neurons involved in modulating thermoregulatory responses to other brain regions are primarily GABAergic (Tan et al., 2016; Morrison and Nakamura, 2019). The reviewer suggests showing whether the heat-responsive POA neurons projecting to the pPVT are indeed excitatory (This could be achieved by retrogradely labeling POA neurons that project to the pPVT and conducting fluorescence in situ hybridization (FISH) assays against Slc32a1, Slc17a6, and Fos to label neurons activated by warmth). Alternatively, demonstrate, at least, that pPVT-projecting POA neurons are a distinct population from the GABAergic POA neurons that project to thermoregulatory regions such as DMH or rRPa. This would clarify how the POA-pPVT circuit integrates with the previously established thermoregulatory pathways.

Reviewer #1 (Public review):

This paper presents a model of the whole somatosensory non-barrel cortex of the rat, with 4.2 million morphologically and electrically detailed neurons, with many aspects of the model constrained by a variety of data. The paper focuses on simulation experiments, testing a range of observations. These experiments are aimed at understanding how the multiscale organization of the cortical network shapes neural activity.

Strengths:

(1) The model is very large and detailed. With 4.2 million neurons and 13.2 billion synapses, as well as the level of biophysical realism employed, it is a highly comprehensive computational representation of the cortical network.

(2) Large scope of work - the authors cover a variety of properties of the network structure and activity in this paper, from dendritic and synaptic physiology to multi-area neural activity.

(3) Direct comparisons with experiments, shown throughout the paper, are laudable.

(4) The authors make a number of observations, like describing how high-dimensional connectivity motifs shape patterns of neural activity, which can be useful for thinking about the relations between the structure and the function of the cortical network.

(5) Sharing the simulation tools and a "large subvolume of the model" is appreciated.

Weaknesses:

(1) A substantial part of this paper - the first few figures - focuses on single-cell and single-synapse properties, with high similarity to what was shown in Markram et al., 2015. Details may differ, but overall it is quite similar.

(2) Although the paper is about the model of the whole non-barrel somatosensory cortex, out of all figures, only one deals with simulations of the whole non-barrel somatosensory cortex. Most figures focus on simulations that involve one or a few "microcolumns". Again, it is rather similar to what was done by Markram et al., 2015 and constitutes relatively incremental progress.

(3) With a model like this, one has an opportunity to investigate computations and interactions across an extensive cortical network in an in vivo-like context. However, the simulations presented are not addressing realistic specific situations corresponding to animals performing a task or perceiving a relevant somatosensory stimulus. This makes the insights into the roles of cell types or connectivity architecture less interesting, as they are presented for relatively abstract situations. It is hard to see their relationship to important questions that the community would be excited about - theoretical concepts like predictive coding, biophysical mechanisms like dendritic nonlinearities, or circuit properties like feedforward, lateral, and feedback processing across interacting cortical areas. In other words, what do we learn from this work conceptually, especially, about the whole non-barrel somatosensory cortex?

(4) Most comparisons with in vivo-like activity are done using experimental data for whisker deflection (plus some from the visual stimulation in V1). But this model is for the non-barrel somatosensory cortex, so exactly the part of the cortex that has less to do with whiskers (or vision). Is it not possible to find any in vivo neural activity data from the non-barrel cortex?

(5) The authors almost do not show raw spike rasters or firing rates. I am sure most readers would want to decide for themselves whether the model makes sense, and for that, the first thing to do is to look at raster plots and distributions of firing rates. Instead, the authors show comparisons with in vivo data using highly processed, normalized metrics.

(6) While the authors claim that their model with one set of parameters reproduces many experimentally established metrics, that is not entirely what one finds. Instead, they provide different levels of overall stimulation to their model (adjusting the target "P_FR" parameter, with values from 0 to 1, and other parameters), and that influences results. If I get this right (the figures could really be improved with better organization and labeling), simulations with P_FR closer to 1 provide more realistic firing rate levels for a few different cases, however, P_FR of 0.3 and possibly above tends to cause highly synchronized activity - what the authors call bursting, but which also could be called epileptic-like activity in the network.

(7) The authors mention that the model is available online, but the "Resource availability" section does not describe that in substantial detail. As they mention in the Abstract, it is only a subvolume that is available. That might be fine, but more detail in appropriate parts of the paper would be useful.

Reviewer #2 (Public review):

Summary:

This paper is a companion to Reminann et al. (2022), presenting a large-scale, data-driven, biophysically detailed model of the non-barrel primary somatosensory cortex (nbS1). To achieve this unprecedented scale of a bottom-up model, approximately 140 times larger than the previous model (Markram et al., 2015), they developed new methods to account for inputs from missing brain areas, among other improvements. Isbister et al. focus on detailing these methodological advancements and describing the model's ability to reproduce in vivo-like spontaneous, stimulus-evoked, and optogenetically modified activity.

Strengths:

The model generated a series of predictions that are currently impossible in vivo, as summarized in Table S1. Additionally, the tools used in this study are made available online, fostering community-based exploration. Together with the companion paper, this study makes significant contributions by detailing the model's constraints, validations, and potential caveats, which are likely to serve as a basis for advancing further research in this area.

Weaknesses:

That said, I have several suggestions to improve clarity and strengthen the validation of the model's in vivo relevance.

Major:

(1) For the stimulus-response simulations, the authors should also reference, analyze, and compare data from O'Connor et al. (2010; https://pubmed.ncbi.nlm.nih.gov/20869600/) and Yu et al .(2016; https://pubmed.ncbi.nlm.nih.gov/27749825/) in addition to Yu et al. 2019, which is the only data source the authors consider for an awake response. The authors mentioned bias in spike rate measurements, but O'Connor et al. used cell-attached recordings, which do not suffer from activity-based selection bias (in addition, they also performed Ca2+ imaging of L2/3). This was done in the exact same task as Yu et al., 2019, and they recorded from over 100 neurons across layers. Combining this data with Yu et al., 2019 would provide a comprehensive view of activity across layers and inhibitory cell types. Additionally, Yu et al. (2016) recorded VPM neurons in the same task, alongside whole-cell recordings in L4, showing that L4 PV neurons filter movement-related signals encoded in thalamocortical inputs during active touch. This dataset is more suitable for extracting VPM activity, as it was collected under the same behavior and from the same species (Unlike Diamond et al., 1992, which used anesthetized rats). Furthermore, this filtering is an interesting computation performed by the network the authors modeled. The validation would be significantly strengthened and more biologically interesting if the authors could also reproduce the filtering properties, membrane potential dynamics, and variability in the encoding of touch across neurons, not just the latency (which is likely largely determined by the distance and number of synapses).

(2) The authors mention that in the model, the response of the main activated downstream area was confined to L6. Is this consistent with in vivo observations? Additionally, is there any in vivo characterization of the distance dependence of spiking correlation to validate Figure 8I?

(3) Across the figures, activity is averaged across neurons within layers and E or I cell types, with a limited description of single-cell type and single-cell responses. Were there any predictions regarding the responses of particular cell types that significantly differ from others in the same layer? Such predictions could be valuable for future investigations and could showcase the advantages of a data-driven, biophysically detailed model.

(4) 2.4: Are there caveats to assuming the OU process as a model for missing inputs? Inputs to the cortex are usually correlated and low-dimensional (i.e., communication subspace between cortical regions), but the OU process assumes independent conductance injection. Can (weakly) correlated inputs give rise to different activity regimes in the model? Can you add a discussion on this?

(5) 2.6: The network structure is well characterized in the companion paper, where the authors report that correlations in higher dimensions were driven by a small number of neurons with high participation ratios. It would be interesting to identify which cell types exhibit high node participation in high-dimensional simplices and examine the spiking activity of cells within these motifs. This could generate testable predictions and inform theoretical cell-type-specific point neuron models for excitatory/inhibitory balanced networks and cortical processing.

Minor:

(1) Since the previous model was published in 2015, the neuroscience field has seen significant advancements in single-cell and single-nucleus sequencing, leading to the clustering of transcriptomic cell types in the entire mouse brain. For instance, the Allen Institute has identified ~10 distinct glutamatergic cell types in layer 5, which exceeds the number incorporated into the current model. Could you discuss 1) the relationship between the modeled me-types and these transcriptomic cell types, and 2) how future models will evolve to integrate this new information? If there are gaps in knowledge in order to incorporate some transcriptome cell types into your model, it would be helpful to highlight them so that efforts can be directed toward addressing these areas.

(2) For the optogenetic manipulation, it would be interesting if the model could reproduce the paradoxical effects (for example, Mahrach et al. reported paradoxical effects caused by PV manipulation in S1; https://pubmed.ncbi.nlm.nih.gov/31951197/). This seems a more relevant and non-trivial network phenomenon than the V1 manipulation the authors attempted to replicate.

Reviewer #1 (Public review):

Summary:

The study by Deng et al reports single-cell expression analysis of developing mouse hearts and examines the requirements for cardiac fibroblasts in heart maturation. Much of this work is overlapping with previous studies, but the single-cell gene expression data may be useful to investigators in the field. The significance and scope of new findings are limited and major conclusions are largely based on correlative data.

Strengths:

The strengths of the manuscript are the new single-cell datasets and comprehensive approach to ablating cardiac fibroblasts in pre and postnatal development in mice.

Weaknesses:

There are several major weaknesses in the analysis and interpretation of the results.

(1) The major conclusions regarding collagen signaling and heart maturation are based on gene expression patterns and are not functionally validated. The potential downstream signaling pathways were not examined and known structural contributions of fibrillar collagen to heart maturation are not discussed.

(2) The heterogeneity of fibroblast populations and contributions to multiple structures in the developing heart are not well-considered in the analysis. The developmental targeting of fibroblasts will likely affect multiple structures in the embryonic heart and other organs. Lethality is described in some of these studies, but additional analysis is needed to determine the effects on heart morphogenesis or other organs beyond the focus on cardiomyocyte maturation being reported. In particular, the endocardial cushions and developing valves are likely to be affected in the prenatal ablations, but these structures are not included in the analyses.

(3) ECM complexity and extensive previous work on specific ECM proteins in heart development and maturation are not incorporated into the current study. Different types of collagen (basement membrane Col4, filamentous Col6, and fibrillar Col1) are known to be expressed in fibroblast populations in the developing heart and have been studied extensively. Much also has been reported for other ECM components mentioned in the current work.

Reviewer #2 (Public review):

This study aims to elucidate the role of fibroblasts in regulating myocardium and vascular development through signaling to cardiomyocytes and endothelial cells. This focus is significant, given that fibroblasts, cardiomyocytes, and vascular endothelial cells are the three primary cell types in the heart. The authors employed a Pdgfra-CreER-controlled diphtheria toxin A (DTA) system to ablate fibroblasts at various embryonic and postnatal stages, characterizing the resulting cardiac defects, particularly in myocardium and vasculature development. scRNA-seq analysis of the ablated hearts identified collagen as a crucial signaling molecule from fibroblasts that influences the development of cardiomyocytes and vascular endothelial cells.

This is an interesting manuscript; however, there are several major issues, including an over-reliance on the scRNA-seq data, which shows inconsistencies between replicates.<br /> Some of the major issues are described below.

(1) The CD31 immunostaining data (Figures 3B-G) indicate a reduction in endothelial cell numbers following fibroblast deletion using PdgfraCreER+/-; RosaDTA+/- mice. However, the scRNA-seq data show no percentage change in the endothelial cell population (Figure 4D). Furthermore, while the percentage of Vas_ECs decreased in ablated samples at E16.5, the results at E18.5 were inconsistent, showing an increase in one replicate and a decrease in another, raising concerns about the reliability of the RNA-seq findings.

(2) Similarly, while the percentage of Ven_CMs increased at E18.5, it exhibited differing trends at E16.5 (Figure 4E), further highlighting the inconsistency of the scRNA-seq analysis with the other data.

(3) Furthermore, the authors noted that the ablated samples had slightly higher percentages of cardiomyocytes in the G1 phase compared to controls (Figures 4H, S11D), which aligns with the enrichment of pathways related to heart development, sarcomere organization, heart tube morphogenesis, and cell proliferation. However, it is unclear how this correlates with heart development, given that the hearts of ablated mice are significantly smaller than those of controls (Figure 3E). Additionally, the heart sections from ablated samples used for CD31/DAPI staining in Figure 3F appear much larger than those of the controls, raising further inconsistencies in the manuscript.

(4) The manuscript relies heavily on the scRNA-seq dataset, which shows inconsistencies between the two replicates. Furthermore, the morphological and histological analyses do not align with the scRNA-seq findings.

(5) There is a lack of mechanistic insight into how collagen, as a key signaling molecule from fibroblasts, affects the development of cardiomyocytes and vascular endothelial cells.

(6) In Figure 1B, Col1a1 expression is observed in the epicardial cells (Figure 1A, E11.5), but this is not represented in the accompanying cartoon.

(7) What is the genotype of the control animals used in the study?

(8) Do the PdgfraCreER+/-; RosaDTA+/- mice survive after birth when induced at E15.5, and do they exhibit any cardiac defects?

Reviewer #3 (Public review):

The authors investigated fibroblasts' communication with key cell types in developing and neonatal hearts, with a focus on the critical roles of fibroblast-cardiomyocyte and fibroblast-endothelial cell networks in cardiac morphogenesis. They tried to map the spatial distribution of these cell types and reported the major pathways and signaling molecules driving the communication. They also used Cre-DTA system to ablate Pdgfra labeled cells and observed myocardial and endothelial cell defects at development. They screened the pathways and genes using sequencing data of ablated hearts. Lastly, they reported compensatory collagen expression in long-term ablated neonate hearts. Overall, this study provides us with important insight into fibroblasts' roles in cardiac development and will be a powerful resource for collagens and ECM-focused research.

Strengths:

The authors utilized good analyzing tools to investigate multiple databases of single-cell sequencing and Multi-seq. They identified significant pathways and cellular and molecular interactions of fibroblasts. Additionally, they compared some of their analytic findings with a human database, and identified several groups of ECM genes with varying roles in mice.

Weaknesses:

This study is majorly based on sequencing data analysis. At the bench, they used a very strident technique to study fibroblast functions by ablating one of the major cell populations of the heart. Considering the importance of the fibroblast population, intriguing in vivo findings were expected. Also, they analyzed the downstream genes in ablated hearts, but did not execute any experimental validation for any of the targets.

Reviewer #1 (Public review):

(1) Significance of findings and strength of evidence.<br /> (a) The work presented in this manuscript is intended to support the authors' novel idea that HIV DNA integration strongly favors "triple-stranded" R-loops in DNA formed either during transcription of many, but not all, genes or by strand invasion of silent DNA by transcripts made elsewhere, and that HIV infection promotes R-loop formation mediated by incoming virions in the absence of reverse transcription. The authors were able to demonstrate a reverse transcription-independent increase in R-loop formation early during HIV infection, while also demonstrating increased integration into sequences that contain R-loop structures. Furthermore, this manuscript also identifies that R-loops are present in both transcriptional active and silent regions of the genome and that HIV integrase interacts with R-loops. Although the work presented supports a correlation between R-loop formation and HIV DNA integration, it does not prove the authors' hypothesis that R-loops are directly targeted for integration. Direct experimentation, such as in vitro integration into defined DNA targets, will be required. Further, the authors provide no explanation as to how current sophisticated structural models of concerted retroviral DNA integration into both strands of double-stranded DNA targets can accommodate triple-stranded structures. Finally, there are serious technical concerns with interpretation of the integration site analyses.<br /> This resubmitted manuscript has corrected some of the issues raised by the previous reviews - particularly the quality of the English - but otherwise the text and figures remain very much the same and concerns regarding the conclusions drawn regarding integration site specificity remain. The manuscript also still suffers from a lack of description of experimental detail necessary to understand the results as presented. In many cases, explanations given privately in the rebuttal o the earlier reviews need to be made available to all readers, not just the reviewers.

(2) Public review with guidance for readers around how to interpret the work, highlighting important findings but also mentioning caveats.<br /> (a) Introduction: The authors provide an excellent introduction to R-loops but they base the rationale for this study on mis-citation of earlier studies regarding integration in transcriptionally silent regions of the genome. The "most favored locus" cited in the very old reference 6 comprises only 5 events and has not been reproduced in more recent, much larger datasets For example, see the study of over 300.000 sites in ref 14. The laundry list of IN interactors in lines 43-44 is based on old experiments. It is now quite clear that the only direct interaction of importance is with LEDGF and that should be discussed here. Also discussed should be the role of the capsid in the nuclear entry and targeting. For example, one of the references cited, as well as a mention in the discussion (Line 326) concerns CPSF-6, which is now known to modulate nuclear entry and specificity by interacting with capsid, not integrase. The statement on lines 46-47 regarding that some highly expressed genes are, nonetheless, poor targets for integration is correct, but the experiment cited was done in PBMC with wild-type HIV-1and it is possible that those genes were expressed in non-target cells like B-cells or monocytes.

(b) Figure 1: Demonstrates models for HIV infections in both cell lines and primary human CD4+ T cells. R-loop formation was determined through a method called DRIPc-seq which utilizes an anti-body specific for DNA-RNA hybrid structures and sequences these regions of the genome using RNaseH treatment to show that when RNA-DNA hybrids are absent then no R-loops are detected. In these models of in vitro and ex vivo infection, the authors show that R-Loop formation increases following HIV infection between 6 hr. post-infection and 12 hrs. post-infection, depending on the cell model. However, these figures lack a mock infected control for each cell model to assess R-loop formation at the same time points. They would also benefit from a control showing that virus entry is necessary, such as omitting the VSV G protein donor.

(c) Figure 2: This figure shows that cells infected with HIV show more R-loops as well as longer sequences containing R-loop structures. Panel B shows that these R-loops were distributed throughout different genomic features, such as both genic and intergenic regions of the genome. However, the data are presented in such a way that it is impossible to determine the proportion of R-loops in each type of genomic feature. The reader has no way to tell, for example, the proportion of R-loops in genic vs intergenic DNA and how this value changes with time. Furthermore, increased R-loop formation due to HIV infection showed poor correlation with gene expression, suggesting that R-loops were not forming due to transcriptional activation, although the difference between 0 and the remaining timepoints is not apparent, nor is the meaning of the absurd p values.

The experiments presented in Figures 1 and 2 show that treatment of cells with VSV G-pseudotyped HIV-1 leads to a significant increase in R loops in all parts of the genome. Accumulation of R-loops at so soon after infection, as well as its resistance to RT and Integration inhibitors, rules out the involvement of newly synthesized viral DNA or any newly made viral protein (Figure S3). Rather, some component(s) of the virion, possibly protease, or an accessory gene product such as Vpr or Vif, must be directly responsible e (although the authors neglect to draw this conclusion in the description of these experiments, lines 125-135, leaving it hanging until the Discussion).

On the whole, and as a non-expert in this area, I find the overall conclusions of this part of the study convincing, but, as pointed out in one of the earlier reviews, the virologic significance of early effects seen at high multiplicity of infection (likely hundreds of particles per cell) needs to be taken with a grain of salt. At a minimum, this point should be discussed. Also, the study would have been greatly strengthened by a simple experiment to identify the virion protein responsible for the effect.<br /> Based on the results in the first two figures, the authors hypothesize that R-Loop induction early in infection plays an important role in HIV replication, specifically by interacting with the intasome and thus directing integration to regions of the host genome favorable for expression of the provirus. Experiments to test this idea and probe the mechanism are described in the remaining 3 figures, which, despite comments in the previous reviews, are unchanged from the previous version and still suffer from serious defects in experimental design and interpretation.

(d) Figure 3: This figure shows the use of cell lines carrying R-loop inducible (mAIRN) or non-inducible (ECFP) genes to model association of HIV integration with R-loop structures. The authors demonstrate the functional validation of R-loop induction in the cell line model. Additionally, when R-loops are induced there is a significant increase in HIV integration in the R-loop forming vector sequence when R-loops are induced with doxycycline. This result shows a correlation between expression and integration that is much stronger in the R-loop forming gene than in the unreferenced ECFP gene but does not prove that integration directly targets R-loops. It is possible, for example, that some feature of the DNA sequence, such as base composition affects both integration and R-loop formation independently. As described more fully below, there is also a serious concern regarding the method used to quantitate the integration frequencies. As before, There are a number of problems here.<br /> (1) The authors use a classic, but suboptimal integration site assay comprising restriction enzyme digestion followed by PCR to assess integration site distribution, and (despite statements to the contrary in the rebuttal) read counts to quantitate relative frequencies of target site use. See the legend and axis labels in Fig 3E, F, and G. This approach leads to serious bias in the ability to detect and count the use of integration sites that are either too close or too far from the sites of cleavage and can lead to artefactual misrepresentation of their chromosomal distribution.<br /> (2) The result shown in Figure 3D is uninterpretable. It is simply not possible that the 3-fold increase in luciferase activity is due addition of 25 10-kb sequences leading to A 3-fold increase in integration frequency into the target sequence, particularly when panel E shows that the measured frequency is on the order of 20 reads per million. Something else must be going on here.<br /> (3) Panels 3F and G show the read count distribution in the introduced target sequences plotted in a completely nonstandard way and is explained so poorly that I could not be sure what the authors were trying to show. The numbers on the bottom of the 2 plots appear to represent the only sites of integration seen in the 10-kb region studied. If so, this is not the expected result for the authors claim of greatly increasing regional integration. As can easily be seen in the figures of ref 14, high frequency gene targets are characterized by large numbers of sites, not by more frequent targeting of small numbers of sites as implied by the figures.

(e) Figure 4: This figure shows evidence of increased HIV integration within regions of the genome containing R-loops with additional preference with integration within the R-loop and decrease in frequency of integration further from the R-loop. Identifying a preference for R-loops is very intriguing but the authors do also demonstrate that integration does occur when R-loops are not present. Also Panel A, which shows that regions of cell DNA that form R-loops have a higher frequency of Integration sites than those that do not, should also be controlled for the level of gene expression of the two types of region. the result shown cannot be interpreted to mean that R-loops have anything to do with integration targeting. It is already well-established that about 80% of HIV integration sites are in expressed genes, which comprise about 20% of the genome. Since a gene must be expressed to contain an R-loop, the non-R-loop fraction will contain the 80% of the genome that is a 20-fold poorer target, giving the result shown, whether R-loops are involved or not. The rather weak correlation between R-Loop locations and integration site distribution in Fig 4C and D hardly seems consistent with the curves seen in 4B. Can the authors refute the hypothesis that the apparent correlation is simply because both integration and R-Loop formation frequency must correlate with level of gene expression and therefore their correlation with one another cannot be used to infer causality/ As pointed out in prior reviews, R-loops themselves cannot be targets for integration. In their rebuttal, the authors agree and have made slight modifications to their conclusion in the text, now concluding that Integration favors the vicinity of an R-loop. Why then do the peaks in correlation curves in Fig 4B center exactly on the center of the R-loops? It seems that this result would be more consistent with integration and R-loop formation favoring the same sites, but for different reasons (base composition for example).

(f) Figure 5: In this figure the authors demonstrate that HIV integrase binds to R-loops through a number of protein assays, but does not show that this binding is associated with enzymatic activity. EMSA of integrase identified increased binding to DNA-RNA over dsDNA. Additionally, precipitation of RNA-DNA hybrids pulled down HIV integrase. A proximity ligation assay detecting R-loops and HIV-integrase showed co-localization within the nucleus of HeLa cells. HeLa cells were probably used due to their efficiency of transduction but are not physiologically relevant cell types. Figure 5 suffers greatly in interpretability from the failure of the authors to use assembled intasomes, since the DNA binding properties are likely to be quite different. The authors excuse that they were unable to prepare intasomes (which needs to be included in the text, not just in the rebuttal) explains but does not justify the use of monomeric IN protein. Figure 5A shows that the IN binding is NOT specific to R-loops, since any single-stranded DNA binds equally. The authors should make this point in the text.<br /> The experiment using integrase overexpression in cells brings up some déjà vu to a retrovirologist. There is some history in retrovirology of experiments like this having been used to draw conclusions (like the role of integrase in nuclear import) that have since proven to be wrong. Also, Fig 5G is not interpretable quantitively, since the distribution of neither IN nor R-loops is probed, and we have no idea what proportion of each is in the PLA spots. Overall, this section would be much more convincing if it also included some direct experimentation, such as in vitro integration using intasomes, or infection of cells with viral mutants (or in the presence of inhibitors) affecting the function of whatever virion protein found to be important for R-loop formation.

(g) Discussion: In the discussion, the authors address how their work relates to previous evidence of HIV integration by association of LEDGF/p75 and CPSF6. They also cite that LEDGF/p75 has possible R-loop binding capabilities. They also discuss what possible mechanisms are driving increases in R-loop formation during HIV infection, pointing to possible HIV accessory proteins. They also state that how HIV integrates in transcriptionally silent regions is still unknown but do point out that they were able to show R-loops appear in many different regions of the genome but did not show that R-loops in transcriptional inactive regions are integration targets. More seriously, they failed to make a connection between their work and current understanding of the biochemical and structural mechanism of the integration reaction.

Reviewer #3 (Public review):

In this manuscript, Park and colleagues describe a series of experiments that investigate the role of R-loops in HIV-1 genome integration. The authors show that during HIV-1 infection, R-loops levels on the host genome accumulate. Using a synthetic R-loop prone gene construct, they show that HIV-1 integration sites target sites with high R-loop levels. They further show that integration sites on the endogenous host genome are correlated with sites prone to R-loops. Using biochemical approaches, as well as in vivo co-IP and proximity ligation experiments, the authors show that HIV-1 integrase physically interacts with R-loop structures.

The major strengths of this work is that the investigators use multiple independent experimental systems and multiple cell types to support their conclusions, including in vivo and biochemical experiments. Furthermore, their use of genome-wide analyses help to support their conclusion that HIV targets genomic regions enriched with R-loops versus those lacking such enrichment.

This work may have a significant impact on the field of HIV genomic integration by elucidating why transcription levels are not the sole determinant of HIV integration sites.

Reviewer #1 (Public review):

This paper investigates the dynamics of excitatory synaptic weights under a calcium-based plasticity rule, in long (up to 10 minutes) simulations of a 211,000-neuron biophysically detailed model of a rat cortical network.

Strengths

(1) A very detailed network model, with a large number of neurons, connections, synapses, etc., and with a huge number of biological considerations implemented in the model.

(2) A carefully developed calcium-based plasticity rule, which operates with biologically relevant variables like calcium concentration and NMDA conductances.

(3) The study itself is detailed and thorough, covering many aspects of the cellular and network anatomy and properties and investigating their relationships to plasticity.

(4) The model remains stable over long periods of simulations, with the plasticity rule maintaining reasonable synaptic weights and not pushing the network to extremes.

(5) The variety of insights the authors derive in terms of relationships between the cellular and network properties and dynamics of the synaptic weights are potentially interesting for the field.

(6) Sharing the model and the associated methods and tools is a big plus.

Weaknesses

(1) Conceptually, there seems to be a missed opportunity here in that it is not clear what the network learns to do. The authors present 10 different input patterns, the network does some plasticity, which is then analyzed, but we do not know whether the learning resulted in anything functionally significant. Did the network learn to discriminate the patterns much better than at the beginning, to capture or anticipate the timing of pattern presentation, detect similarities between patterns, etc.? This is important to understand if one wants to assess the significance of synaptic changes due to plasticity. For example, if the network did not learn much new functionally, relative to its initial state, then the observed plasticity could be considered minor and possibly insufficient. In that case, were the network to learn something substantial, one would potentially observe much more extensive plasticity, and the results of the whole study could change, possibly including the stability of the network. While this could be a whole separate study, this issue is of central importance, and it is hard to judge the value of the results when we do not know what the network learned to do, if anything.

(2) In this study, plasticity occurs only at E-to-E connections but not at others. However, it is well known that inhibitory connections in the cortex exhibit at the very least a substantial short-term plasticity. One would expect that not including these phenomena would have substantial consequences on the results.

(3) Lines 134-135: "We calibrated layer-wise spontaneous firing rates and evoked activity to brief VPM inputs matching in vivo data from Reyes-Puerta et al. (2015)."

(4) Can the authors show these results? It is an important comparison, and so it would be great to see firing rates (ideally, their distributions) for all the cell types and layers vs. experimental data, for the evoked and spontaneous conditions.

(5) That being said, the Reyes-Puerta et al. paper reports firing rates for the barrel cortex, doesn't it? Whereas here, the authors are simulating a non-barrel cortex. Is such a comparison appropriate?

(6) Comparison with STDP on pages 5-7 and Figure 2: if I got this right, the authors applied STDP to already generated spikes, that is, did not run a simulation with STDP. That seems strange. The spikes they use here were generated by the system utilizing their calcium-based plasticity rule. Obviously, the spikes would be different if STDP was utilized instead. The traces of synaptic weights would then also be different. The comparison therefore is not quite appropriate, is it?

(7) Section 2.3 and Figure 5: I am not sure this analysis adds much. The main finding is that plasticity occurs more among cells in assemblies than among all cells. But isn't that expected given what was shown in the previous figures? Specifically, the authors showed that for cells that fire more, plasticity is more prominent. Obviously, cells that fire little or not at all won't belong to any assemblies. Therefore, we expect more plasticity in assemblies.

(8) Section 2.4 and Figure 6: It is not clear that the results truly support the formulation of the section's title ("Synapse clustering contributes to the emergence of cell assemblies, and facilitates plasticity across them") and some of the text in the section. What I can see is that the effect on rho is strong for non-clustered synapses (Figure 6C and Figure S8A). In some cases, it is substantially higher than what is seen for clustered synapses. Furthermore, the wording "synapse clustering contributes to the emergence of cell assemblies" suggests some kind of causal role of clustered synapses in determining which neurons form specific cell assemblies. I do not see how the data presented supports that. Overall, it appears that the story about clustered synapses is quite complicated, with both clustered and non-clustered synapses driving changes in rho across the board.

(9) Section 2.5 and Figure 7: Can we be certain that it is the edge participation that is a particularly good predictor of synaptic changes and/or strength, as opposed to something simpler? For example, could it be the overall number of synapses, excitatory synapses, or something along these lines, that the source and/or target neurons receive, that determine the rho dynamics? And then, I do not understand the claim that edge participation allows one to "delineate potentiation from depression". The only related data I can find is in Figure 7A3, about which the authors write "this effect was stronger for potentiation than depression". But I don't see what they mean. For both depression and facilitation, the changes observed are in the range of ~12% of probability values. And even if the effect is stronger, does it mean one can "delineate" potentiation from depression better? What does it mean, to "delineate"? If it is some kind of decoding based on the edge participation, then the authors did not show that.

(10) "test novel predictions in the MICrONS (2021) dataset, which while pushing the boundaries of big data neuroscience, was so far only analyzed with single cells in focus instead of the network as a whole (Ding et al., 2023; Wang et al., 2023)." That is incorrect. For example, the whole work of Ding et al. analyzes connectivity and its relation to the neuron's functional properties at the network level.

Reviewer #2 (Public review):

Summary:

This paper aims to understand the effects of plasticity in shaping the dynamics and structure of cortical circuits, as well as how that depends on aspects such as network structure and dendritic processing.

Strengths:

The level of biological detail included is impressive, and the numerical simulations appear to be well executed. Additionally, they have done a commendable job in open-sourcing the model.

Weaknesses:

The main result of this work is that activity in their network model remains stable without the need for a homeostatic mechanism. However, as the authors acknowledge, this has been demonstrated in previous studies (e.g., Higgins et al. 2014). In those studies, stability was attributed to calcium-based rules combined with calcium concentrations at in vivo levels and background neuronal activity. Since the authors use the same calcium-based rule, it is unclear what new result, if any, is being presented. If the authors are suggesting that the mechanism in their simulations differs, that should be stated clearly, and evidence supporting that claim should be provided.

The other findings discussed in the paper are related to a characterization of the dependency of plastic changes on network structure. While this analysis is potentially interesting, it has the following limitations.

First, I believe the authors should include an analysis of the generality and specificity of their results. All the findings seem to be derived from a single run of the simulation. How do the results vary with different network initializations, simulation times, or parameter choices?

Second, the presentation of the results is difficult to follow. The characterization comes across as a long list of experiments, making it hard to identify a central message or distinguish key findings from minor details. The authors provide little intuition about why certain outcomes arise, and the complexity of the simulation makes it challenging - if not impossible - to determine which model elements are essential for specific results and which mechanisms drive emergent properties. Additionally, the text often lacks crucial details. For instance, the description of k-edge participation should be expanded, and an explanation of what this method quantifies should be included. Overall, I believe the authors should focus on a smaller set of significant results and provide a more in-depth discussion.

The comparison of the model with the MICrONS dataset could be improved. In Figure 7B, the authors should show how the same quantification looks in a network model without plasticity. In Figure 8B, the data aligns with the model before plasticity, so it's unclear how this serves as a verification of the theoretical predictions.

Reviewer #3 (Public review):

Summary:

Ecker et al. utilized a biologically realistic, large-scale cortical model of the rat's non-barrel somatosensory cortex, incorporating a calcium-dependent plasticity rule to examine how various factors influence synaptic plasticity under in vivo-like conditions. Their analysis characterized the resulting plastic changes and revealed that key factors, including the co-firing of stimulus-evoked neuronal ensembles, the spatial organization of synaptic clusters, and the overall network topology, play an important role in affecting the extent of synaptic plasticity.

Strengths:

The detailed, large-scale model employed in this study enables the evaluation of diverse factors across various levels that influence the extent of plastic changes. Specifically, it facilitates the assessment of synaptic organization at the subcellular level, network topology at the macroscopic level, and the co-activation of neuronal ensembles at the activity level. Moreover, modeling plasticity under in vivo-like conditions enhances the model's relevance to experiments.

Weaknesses:

(1) The authors claimed that, under in vivo-like conditions and in the presence of plasticity, firing rates and weight distributions remain stable without additional homeostatic mechanisms during a 10-minute stimulation period. However, the weights do not reach the steady state immediately after the 10-minute stimulation. Therefore, extended simulations are necessary to substantiate the claim.

(2) Another major limitation of the paper lies in its lack of mechanistic insights into the observed phenomena (particularly on aspects that are typically impossible to assess in traditional simplified models, like layer-specific and layer-to-layer pathways-specific plasticity changes), as well as the absence of discussions on the potential computational implications of the corresponding observed plastic changes.

Reviewer #1 (Public review):

Summary:

The study by Jena et al. addresses important questions on the fundamental mechanisms of genetic adaptation, specifically, does adaptation proceed via changes of copy number (gene duplication and amplification "GDA") or by point mutation. While this question has been worked on (for example by Tomanek and Guet) the authors add several important aspects relating to resistance against antibiotics and they clarify the ability of Lon protease to reduce duplication formation (previous work was more indirect).

A key finding Jena et al. present is that point mutations after significant competition displace GDA. A second one is that alternative GDA constantly arise and displace each other (see work on GDA-2 in Figure 3). Finally, the authors found epistasis between resistance alleles that was contingent on lon. Together this shows an intricate interplay of lon proteolysis for the evolution and maintenance of antibiotic resistance by gene duplication.

Strengths:

The study has several important strengths: (i) the work on GDA stability and competition of GDA with point mutations is a very promising area of research and the authors contribute new aspects to it, (ii) rigorous experimentation, (iii) very clearly written introduction and discussion sections. To me, the best part of the data is that deletion of lon stimulates GDA, which has not been shown with such clarity until now.

Weaknesses:

The minor weaknesses of the manuscript are a lack of clarity in parts of the results section (Point 1) and the methods (Point 2).

Reviewer #2 (Public review):

Summary:

In this strong study, the authors provide robust evidence for the role of proteostasis genes in the evolution of antimicrobial resistance, and moreover, for stabilizing the proteome in light of gene duplication events.

Strengths:

This strong study offers an important interaction between findings involving GDA, proteostasis, experimental evolution, protein evolution, and antimicrobial resistance. Overall, I found the study to be relatively well-grounded in each of these literatures, with experiments that spoke to potential concerns from each arena. For example, the literature on proteostasis and evolution is a growing one that includes organisms (even micro-organisms) of various sorts. One of my initial concerns involved whether the authors properly tested the mechanistic bases for the rule of Lon in promoting duplication events. The authors assuaged my concern with a set of assays (Figure 8).

More broadly, the study does a nice job of demonstrating the agility of molecular evolution, with responsible explanations for the findings: gene duplications are a quick-fix, but can be out-competed relative to their mutational counterparts. Without Lon protease to keep the proteome stable, the cell allows for less stable solutions to the problem of antibiotic resistance.

The study does what any bold and ambitious study should: it contains large claims and uses multiple sorts of evidence to test those claims.

Weaknesses:

While the general argument and conclusion are clear, this paper is written for a bacterial genetics audience that is familiar with the manner of bacterial experimental evolution. From the language to the visuals, the paper is written in a boutique fashion. The figures are even difficult for me - someone very familiar with proteostasis - to understand. I don't know if this is the fault of the authors or the modern culture of publishing (where figures are increasingly packed with information and hard to decipher), but I found the figures hard to follow with the captions. But let me also consider that the problem might be mine, and so I do not want to unfairly criticize the authors.

For a generalist journal, more could be done to make this study clear, and in particular, to connect to the greater community of proteostasis researchers. I think this study needs a schematic diagram that outlines exactly what was accomplished here, at the beginning. Diagrams like this are especially important for studies like this one that offer a clear and direct set of findings, but conduct many different sorts of tests to get there. I recommend developing a visual abstract that would orient the readers to the work that has been done.

Next, I will make some more specific suggestions. In general, this study is well done and rigorous, but doesn't adequately address a growing literature that examines how proteostasis machinery influences molecular evolution in bacteria.

While this paper might properly test the authors' claims about protein quality control and evolution, the paper does not engage a growing literature in this arena and is generally not very strong on the use of evolutionary theory. I recognize that this is not the aim of the paper, however, and I do not question the authors' authority on the topic. My thoughts here are less about the invocation of theory in evolution (which can be verbose and not relevant), and more about engagement with a growing literature in this very area.

The authors mention Rodrigues 2016, but there are many other studies that should be engaged when discussing the interaction between protein quality control and evolution.

A 2015 study demonstrated how proteostasis machinery can act as a barrier to the usage of novel genes: Bershtein, S., Serohijos, A. W., Bhattacharyya, S., Manhart, M., Choi, J. M., Mu, W., ... & Shakhnovich, E. I. (2015). Protein homeostasis imposes a barrier to functional integration of horizontally transferred genes in bacteria. PLoS genetics, 11(10), e1005612

A 2019 study examined how Lon deletion influenced resistance mutations in DHFR specifically: Guerrero RF, Scarpino SV, Rodrigues JV, Hartl DL, Ogbunugafor CB. The proteostasis environment shapes higher-order epistasis operating on antibiotic resistance. Genetics. 2019 Jun 1;212(2):565-75.

A 2020 study did something similar: Thompson, Samuel, et al. "Altered expression of a quality control protease in E. coli reshapes the in vivo mutational landscape of a model enzyme." Elife 9 (2020): e53476.

And there's a new review (preprint) on this very topic that speaks directly to the various ways proteostasis shapes molecular evolution:<br /> Arenas, Carolina Diaz, Maristella Alvarez, Robert H. Wilson, Eugene I. Shakhnovich, C. Brandon Ogbunugafor, and C. Brandon Ogbunugafor. "Proteostasis is a master modulator of molecular evolution in bacteria."

I am not simply attempting to list studies that should be cited, but rather, this study needs to be better situated in the contemporary discussion on how protein quality control is shaping evolution. This study adds to this list and is a unique and important contribution. However, the findings can be better summarized within the context of the current state of the field. This should be relatively easy to implement.

Reviewer #3 (Public review):

Summary:

This paper investigates the relationship between the proteolytic stability of an antibiotic target enzyme and the evolution of antibiotic resistance via increased gene copy number. The target of the antibiotic trimethoprim is dihydrofolate reductase (DHFR). In Escherichia coli, DHFR is encoded by folA and the major proteolysis housekeeping protease is Lon (lon). In this manuscript, the authors report the results of the experimental evolution of a lon mutant strain of E. coli in response to sub-inhibitory concentrations of the antibiotic trimethoprim and then investigate the relationship between proteolytic stability of DHFR mutants and the evolution of folA gene duplication. After 25 generations of serial passaging in a fixed concentration of trimethoprim, the authors found that folA duplication events were more common during the evolution of the lon strain, than the wt strain. However, with continued passaging, some folA duplications were replaced by a single copy of folA containing a trimethoprim resistance-conferring point mutation. Interestingly, the evolution of the lon strain in the setting of increasing concentrations of trimethoprim resulted in evolved strains with different levels of DHFR expression. In particular, some strains maintained two copies of a mutant folA that encoded an unstable DHFR. In a lon+ background, this mutant folA did not express well and did not confer trimethoprim resistance. However, in the lon- background, it displayed higher expression and conferred high-level trimethoprim resistance. The authors concluded that maintenance of the gene duplication event (and the absence of Lon) compensated for the proteolytic instability of this mutant DHFR. In summary, they provide evidence that the proteolytic stability of an antibiotic target protein is an important determinant of the evolution of target gene copy number in the setting of antibiotic selection.

Strengths:

The major strength of this paper is identifying an example of antibiotic resistance evolution that illustrates the interplay between the proteolytic stability and copy number of an antibiotic target in the setting of antibiotic selection. If the weaknesses are addressed, then this paper will be of interest to microbiologists who study the evolution of antibiotic resistance.

Weaknesses:

Although the proposed mechanism is highly plausible and consistent with the data presented, the analysis of the experiments supporting the claim is incomplete and requires more rigor and reproducibility. The impact of this finding is somewhat limited given that it is a single example that occurred in a lon strain and compensatory mutations for evolved antibiotic resistance mechanisms are described. In this case, it is not clear that there is a functional difference between the evolution of copy number versus any other mechanism that meets a requirement for increased "expression demand" (e.g. promoter mutations that increase expression and protein stabilizing mutations).

Reviewer #1 (Public review):

Summary:

Frelih et al. investigated both periodic and aperiodic activity in EEG during working memory tasks. In terms of periodic activity, they found post-stimulus decreases in alpha and beta activity, while in terms of aperiodic activity, they found a bi-phasic post-stimulus steepening of the power spectrum, which was weakly predictive of performance. They conclude that it is crucial to properly distinguish between aperiodic and periodic activity in event-related designs as the former could confound the latter. They also add to the growing body of research highlighting the functional relevance of aperiodic activity in the brain.

Strengths:

This is a well-written, timely paper that could be of interest to the field of cognitive neuroscience, especially to researchers investigating the functional role of aperiodic activity. The authors describe a well-designed study that looked at both the oscillatory and non-oscillatory aspects of brain activity during a working memory task. The analytic approach is appropriate, as a state-of-the-art toolbox is used to separate these two types of activity. The results support the basic claim of the paper that it is crucial to properly distinguish between aperiodic and periodic activity in event-related designs as the former could confound the latter. They also add to the growing body of research highlighting the functional relevance of aperiodic activity in the brain. Commendably, the authors include replications of their key findings on multiple independent data sets.

Weaknesses:

The authors also claim that their results speak to the interplay between oscillatory and non-oscillatory activity, and crucially, that task-related changes in the theta frequency band - often attributed to neural oscillations in the field - are in fact only a by-product of non-oscillatory changes. I believe these claims are too bold and are not supported by compelling evidence in the paper. Some control analyses - e.g., contrasting the scalp topographies of purported theta and non-oscillatory effects - could help strengthen the latter argument, but it may be safest to simply soften these two claims.

In terms of the methodology used, I suggest the authors make it clearer to readers that the primary results were obtained on a sample of middle-aged-to-older-adults, some with subjective cognitive complaints, and note that while stimulus-locked event-related potentials (ERPs) were removed from the data prior to analyses, response-locked ERPs were not. This could potentially confound aperiodic findings. Contrasting the scalp topographies of response-related ERPs and the identified aperiodic components, especially the latter one, could bring some clarity here too.

I also found certain parts of the introduction to be somewhat confusing.

Reviewer #2 (Public review):

Summary:

In this manuscript, Frelih et al investigate the relationship between aperiodic neural activity, as measured by EEG, and working memory performance, and compare this to the more commonly analyzed periodic, and in particular theta, measures that are often associated with such tasks. To do so, they analyze a primary dataset of 57 participants engaging in an n-back task, as well as a replication dataset, and use spectral parameterization to measure periodic and aperiodic features of the data, across time. In doing so, they find both periodic and aperiodic features that relate to the task dynamics, but importantly the aperiodic component appears to explain away what otherwise looks like theta activity in a more traditional analysis. This study, therefore, helps to establish that aperiodic activity is a task-relevant dynamic feature in working memory tasks, and may be the underlying change in many other studies that reported 'theta' changes but did not use methods that could differentiate periodic and aperiodic features.

Strengths:

Key strengths of this paper include that it addresses an important question - that of properly adjudicating which features of EEG recordings relate to working memory tasks - and in doing so provides a compelling answer, with important implications for considering prior work and contributing to understanding the neural underpinnings of working memory. I do not find any significant faults or errors with the design, analysis, and main interpretations as presented by this paper, and as such, find the approach taken to be valid and well-enacted. The use of multiple variants of the working memory task, as well as a replication dataset significantly strengthens this manuscript, by demonstrating a degree of replicability and generalizability. This manuscript is also an important contribution to motivating best practices for analyzing neuro-electrophysiological data, including in relation to using baselining procedures.

Weaknesses:

Overall, I do not find any obvious weaknesses in this manuscript and its analyses that challenge the key results and conclusions. There are some minor reporting notes, on the methods and conclusions that I believe could be improved (details in the suggestions for authors). One aspect that could be improved is that while the figures demonstrate the main findings convincingly, the results as written could have more detailed quantifications of the analyzed effects (including, for example, more on the model results, effect sizes, and quantifications of the different features), in order to more fully report the dynamics of the analyzed features and to provide the reader with more information on the findings.

Reviewer #3 (Public review):

Summary:

Using a specparam (1/f) analysis of task-evoked activity, the authors propose that "substantial changes traditionally attributed to theta oscillations in working memory tasks are, in fact, due to shifts in the spectral slope of aperiodic activity." This is a very bold and ambitious statement, and the field of event-related EEG would benefit from more critical assessments of the role of aperiodic changes during task events. Unfortunately, the data shown here does not support the main conclusion advanced by the authors.

Strengths:

The field of event-related EEG would benefit from more critical assessments of the role of aperiodic changes during task events. The authors perform a number of additional control analyses, including different types of baseline correction, ERP subtraction, as well as replication of the experiment with two additional datasets.

Weaknesses:

The authors did not first show that their first task successfully evoked theta power, nor that specparam is capable of quantifying the background around a short theta burst, nor that theta effects are different between baseline corrected vs. spectral parameterized quantifications.

Reviewer #2 (Public review):

Summary:

The authors conduct a causal analysis of years of secondary education on brain structure in late life. They use a regression discontinuity analysis to measure the impact of a UK law change in 1972 that increased the years of mandatory education by 1 year. Using brain imaging data from the UK Biobank, they find essentially no evidence for 1 additional year of education altering brain structure in adulthood.

Strengths:

The authors pre-registered the study and the regression discontinuity was very carefully described and conducted. They completed a large number of diagnostic and alternate analyses to allow for different possible features in the data. (Unlike a positive finding, a negative finding is only bolstered by additional alternative analyses).

Weaknesses:

While the work is of high quality for the precise question asked, ultimately the exposure (1 additional year of education) is a very modest manipulation and the outcome is measured long after the intervention. Thus a null finding here is completely consistent educational attainment (EA) in fact having an impact on brain structure, where EA may reflect elements of training after a second education (e.g. university, post-graduate qualifications, etc) and not just stopping education at 16 yrs yes/no.

The work also does not address the impact of the UK Biobank's well-known healthy volunteer bias (Fry et al., 2017) which is yet further magnified in the imaging extension study (Littlejohns et al., 2020). Under-representation of people with low EA will dilute the effects of EA and impact the interpretation of these results.

References:

Fry, A., Littlejohns, T. J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T., Collins, R., & Allen, N. E. (2017). Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. American Journal of Epidemiology, 186(9), 1026-1034. https://doi.org/10.1093/aje/kwx246

Littlejohns, T. J., Holliday, J., Gibson, L. M., Garratt, S., Oesingmann, N., Alfaro-Almagro, F., Bell, J. D., Boultwood, C., Collins, R., Conroy, M. C., Crabtree, N., Doherty, N., Frangi, A. F., Harvey, N. C., Leeson, P., Miller, K. L., Neubauer, S., Petersen, S. E., Sellors, J., ... Allen, N. E. (2020). The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nature Communications, 11(1), 2624. https://doi.org/10.1038/s41467-020-15948-9

Reviewer #1 (Public review):

Summary:

This fascinating manuscript studies the effect of education on brain structure through a natural experiment. Leveraging the UK BioBank, these authors study the causal effect of education using causal inference methodology that focuses on legislation for an additional mandatory year of education in a regression discontinuity design.

Strengths:

The methodological novelty and study design were viewed as strong, as was the import of the question under study. The evidence presented is solid. The work will be of broad interest to neuroscientists

Weaknesses:

There were several areas which might be strengthed from additional consideration from a methodological perspective.

Reviewer #3 (Public review):

Summary:

This study investigates evidence for a hypothesised, causal relationship between education, specifically the number of years spent in school, and brain structure as measured by common brain phenotypes such as surface area, cortical thickness, total volume, and diffusivity.

To test their hypothesis, the authors rely on a "natural" intervention, that is, the 1972 ROSLA act that mandated an extra year of education for all 15-year-olds. The study's aim is to determine potential discontinuities in the outcomes of interest at the time of the policy change, which would indicate a causal dependence. Naturalistic experiments of this kind are akin to randomised controlled trials, the gold standard for answering questions of causality.

Using two complementary, regression-based approaches, the authors find no discernible effect of spending an extra year in primary education on brain structure. The authors further demonstrate that observational studies showing an effect between education and brain structure may be confounded and thus unreliable when assessing causal relationships.

Strengths:

(1) A clear strength of this study is the large sample size totalling up to 30k participants from the UK Biobank. Although sample sizes for individual analyses are an order of magnitude smaller, most neuroimaging studies usually have to rely on much smaller samples.

(2) This study has been preregistered in advance, detailing the authors' scientific question, planned method of inquiry, and intended analyses, with only minor, justifiable changes in the final analysis.

(3) The analyses look at both global and local brain measures used as outcomes, thereby assessing a diverse range of brain phenotypes that could be implicated in a causal relationship with a person's level of education.

(4) The authors use multiple methodological approaches, including validation and sensitivity analyses, to investigate the robustness of their findings and, in the case of correlational analysis, highlight differences with related work by others.

(5) The extensive discussion of findings and how they relate to the existing, somewhat contradictory literature gives a comprehensive overview of the current state of research in this area.

Weaknesses:

(1) This study investigates a well-posed but necessarily narrow question in a specific setting: 15-year-old British students born around 1957 who also participated in the UKB imaging study roughly 60 years later. Thus conclusions about the existence or absence of any general effect of the number of years of education on the brain's structure are limited to this specific scenario.

(2) The authors address potential concerns about the validity of modelling assumptions and the sensitivity of the regression discontinuity design approach. However, the possibility of selection and cohort bias remains and is not discussed clearly in the paper. Other studies (e.g. Davies et al 2018, https://www.nature.com/articles/s41562-017-0279-y) have used the same policy intervention to study other health-related outcomes and have established ROSLA as a valid naturalistic experiment. Still, quoting Davies et al. (2018), "This assumes that the participants who reported leaving school at 15 years of age are a representative sample of the sub-population who left at 15 years of age. If this assumption does not hold, for example, if the sampled participants who left school at 15 years of age were healthier than those in the population, then the estimates could underestimate the differences between the groups.". Recent studies (Tyrrell 2021, Pirastu 2021) have shown that UK Biobank participants are on average healthier than the general population. Moreover, the imaging sub-group has an even stronger "healthy" bias (Lyall 2022).

(3) The modelling approach used in this study requires that all covariates of no interest are equal before and after the cut-off, something that is impossible to test. Mentioned only briefly, the inclusion and exclusion of covariates in the model are not discussed in detail. Standard imaging confounds such as head motion and scanning site have been included but other factors (e.g. physical exercise, smoking, socioeconomic status, genetics, alcohol consumption, etc.) may also play a role.

Reviewer #1 (Public review):

Summary:

This manuscript by Vogt et al examines how the synaptic composition of AMPA and NMDA receptors changes over sleep and wake states. The authors perform whole-cell patch clamp recordings to quantify changes in silent synapse number across conditions of spontaneous sleep, sleep deprivation, and recovery sleep after deprivation. They also perform single nucleus RNAseq to identify transcriptional changes related to AMPA/NMDA receptor composition following spontaneous sleep and sleep deprivation. The findings of this study are consistent with a decrease in silent synapse number during wakefulness and an increase during sleep. However, these changes cannot be conclusively linked to sleep/wake states. Measurements were performed in motor cortex, and sleep deprivation was achieved by forced locomotion, raising the possibility that recent patterns of neuronal activity, rather than sleep/wake states, are responsible for the observed results.

Strengths:

This study examines an important question. Glutamatergic synaptic transmission has been a focus of studies in the sleep field, but AMPA receptor function has been the primary target of these studies. Silent synapses, which contain NMDA receptors but lack AMPA receptors, have important functional consequences for the brain. Exploring the role of sleep in regulating silent synapse number is important to understanding the role of sleep in brain function. The electrophysiological approach of measuring the failure rate ratio, supported by AMPA/NMDA ratio measurements, is a rigorous tool to evaluate silent synapse number.

The authors also perform snRNAseq to identify genes differentially expressed in the spontaneous sleep and sleep deprivation groups. This analysis reveals an intriguing pattern of upregulated genes controlled by HDAC4 and Mef2c, along with synaptic shaping component genes and genes associated with autism spectrum disorder, across cell types in the sleep deprivation group. This unbiased approach identifies candidate genes for follow-up studies. The finding that ASD-risk genes are differentially expressed during SD also raises the intriguing possibility that normal sleep function is disrupted in ASD.

Weaknesses:

A major consideration to the interpretation of this study is the use of forced locomotion for sleep deprivation. Measurements are made from motor cortex, and therefore the effects observed could be due to differences in motor activity patterns across groups, rather than lack of sleep per se. Considering that other groups have failed to find a difference in AMPA/NMDA ratio in mice with different spontaneous sleep/wake histories (Bridi et al., Neuron 2020), confirmation of these findings in a different brain region would greatly strengthen the study.

The electrophysiological measurements and statistical analyses raise several questions. Input resistance (cutoffs and actual values) are not provided, making it difficult to assess recording quality. Parametric one-way ANOVAs were used, although the data do not appear to be normally distributed. In addition, for the AMPA/NMDA and FRR measurements (Figures 1E, F), the SD group (rather than the control sleep group) was used as the control group for post-hoc comparisons, but it is unclear why. While the data appear in line with the authors' conclusions, the number of mice (3/group) and cells recorded is low, and adding more would better account for inter-animal variability and increase the robustness of the findings.

The snRNAseq data are intriguing. However, several genes relevant to the AMPA/NMDA ratio are mentioned, but the encoded proteins would be expected to have variable effects on AMPA/NMDA receptor trafficking and function, making the model presented in Figure 4C oversimplified. A more thorough discussion of the candidate genes and pathways that are upregulated during sleep deprivation, the spatiotemporal/posttranslational control of protein expression, and their effects on AMPA/NMDA trafficking vs function is warranted.

Reviewer #2 (Public review):

Summary:

Here Vogt et al., provide new insights into the need for sleep and the molecular and physiological response to sleep loss. The authors expand on their previously published work (Bjorness et al., 2020) and draw from recent advances in the field to propose a neuron-centric molecular model for the accumulation and resolution of sleep need and basis of restorative sleep function. While speculative, the proposed model successfully links important observations in the field and provides a framework to stimulate further research and advances on the molecular basis of sleep function. In my review, I highlight the important advances of this current work, the clear merits of the proposed model, and indicate areas of the model that can serve to stimulate further investigation.

Strengths:

Reviewer comment on new data in Vogt et al., 2024<br /> Using classic slice electrophysiology, the authors conclude that wakefulness (sleep deprivation (SD)) drives a potentiation of excitatory glutamate synapses, mediated in large part by "un-silencing" of NMDAR-active synapses to AMPAR-active synapses. Using a modern single nuclear RNAseq approach the authors conclude that SD drives changes in gene expression primarily occurring in glutamatergic neurons. The two experiments combined highlight the accumulation and resolution of sleep need centered on the strength of excitatory synapses onto excitatory neurons. This view is entirely consistent with a large body of extant and emerging literature and provides important direction for future research.

Consistent with prior work, wakefulness/SD drives an LTP-type potentiation of excitatory synaptic strength on principle cortical neurons. It has been proposed that LTP associated with wake, leads to the accumulation of sleep need by increasing neuronal excitability, and by the "saturation" of LTP capacity. This saturation subsequently impairs the capacity for further ongoing learning. This new data provides a satisfying mechanism of this saturation phenomenon by introducing the concept of silent synapses. The new data show that in mice well rested, a substantial number of synapses are "silent", containing an NMDAR component but not AMPARs. Silent synapses provide a type of reservoir for learning in that activity can drive the un-silencing, increasing the number of functional synapses. SD depletes this reservoir of silent synapses to essentially zero, explaining how SD can exhaust learning capacity. Recovery sleep led to restoration of silent synapses, explaining how recovery sleep can renew learning capacity. In their prior work (Bjorness et al., 2020) this group showed that SD drives an increase in mEPSC frequency onto these same cortical neurons, but without a clear change in pre-synaptic release probability, implying a change in the number of functional synapses. This prediction is now born out in this new dataset.

The new snRNAseq dataset indicates the sleep need is primarily seen (at the transcriptional level) in excitatory neurons, consistent with a number of other studies. First, this conclusion is corroborated by an independent, contemporary snRNAseq analysis recently available as a pre-print (Ford et al., 2023 BioRxiv https://doi.org/10.1101/2023.11.28.569011). A recently published analysis on the effects of SD in drosophila imaged synapses in every brain region in a cell-type dependent manner (Weiss et al., PNAS 2024), concluding that SD drives brain wide increases in synaptic strength almost exclusively in excitatory neurons. Further, Kim et al., Nature 2022, heavily cited in this work, show that the newly described SIK3-HDAC4/5 pathway promotes sleep depth via excitatory neurons and not inhibitory neurons.

The new experiments provided in Fig1-3 are expertly conducted and presented. This reviewer has no comments of concern regarding the execution and conclusions of these experiments.

Reviewer comment on model in Vogt et al., 2024<br /> To the view of this reviewer the new model proposed by Vogt et al., is an important contribution. The model is not definitively supported by new data, and in this regard should be viewed as a perspective, providing mechanistic links between recent molecular advances, while still leaving areas that need to be addressed in future work. New snRNAseq analysis indicates SD drives expression of synaptic shaping components (SSCs) consistent with the excitatory synapse as a major target for the restorative basis of sleep function. SD induced gene expression is also enriched for autism spectrum disorder (ASD) risk genes. As pointed out by the authors, sleep problems are commonly reported in ASD, but the emphasis has been on sleep amount. This new analysis highlights the need to understand the impact on sleep's functional output (synapses) to fully understand the role of sleep problems in ASD.

Importantly, SD induced gene expression in excitatory neurons overlap with genes regulated by the transcription factor MEF2C and HDAC4/5 (Fig. 4). In their prior work, the authors show loss of MEF2C in excitatory neurons abolished the SD transcriptional response and the functional recovery of synapses from SD by recovery sleep. Recent advances identified HDAC4/5 as major regulators of sleep depth and duration (in excitatory neurons) downstream of the recently identified sleep promoting kinase SIK3. In Zhou et al., and Kim et al., Nature 2022, both groups propose a model whereby "sleep-need" signals from the synapse activate SIK3, which phosphorylates HDAC4/5, driving cytoplasmic targeting, allowing for the de-repression and transcriptional activation of "sleep genes". Prior work shows that HDAC4/5 are repressors of MEF2C. Therefore, the "sleep genes" derepressed by HDAC4/5 may be the same genes activated in response to SD by MEF2C. The new model thereby extends the signaling of sleep need at synapses (through SIK3-HDAC4/5) to the functional output of synaptic recovery by expression of synaptic/sleep genes by MEF2C. The model thereby links aspects of expression of sleep need with the resolution of sleep need by mediating sleep function: synapse renormalization.

Weaknesses:

Areas for further investigation.<br /> In the discussion section Vogt et al., explore the links between excitatory synapse strength, arguably the major target of "sleep function", and NREM slow-wave activity (SWA), the most established marker of sleep need. SIK3-HDAC4/5 have major effects on the "depth" of sleep by regulating NREM-SWA. The effects of MEF2C loss of function on NREM SWA activity are less obvious, but clearly impact the recovery of glutamatergic synapses from SD. The authors point out how adenosine signaling is well established as a mediator of SWA, but the links with adenosine and glutamatergic strength are far from clear. The mechanistic links between SIK3/HDAC4/5, adenosine signaling, and MEF2C, are far from understood. Therefore, the molecular/mechanistic links between a synaptic basis of sleep need and resolution with NREM-SWA activity require further investigation.

Additional work is also needed to understand the mechanistic links between SIK3-HDAC4/5 signaling and MEF2C activity. The authors point out that constitutively nuclear (cn) HDAC4/5 (acting as a repressor) will mimic MEF2C loss of function. This is reasonable, however, there are notable differences in the reported phenotypes of each. Notably, cnHDAC4/5 suppresses NREM amount and NREM SWA but had no effect on the NREM-SWA increase following SD (Zhou et al., Nature 2022). Loss of MEF2C in CaMKII neurons had no effect on NREM amount and suppressed the increase in NREM-SWA following SD (Bjorness et al., 2020). These instances indicate that cnHDAC4/5 and loss of MEF2C do not exactly match suggesting additional factors are relevant in these phenotypes. Likely HDAC4/5 have functionally important interactions with other transcription factors, and likewise for MEF2C, suggesting areas for future analysis.

One emerging theme may be that the SIK3-HDAC4/5 axis are major regulators of the sleep state, perhaps stabilizing the NREM state once the transition from wakefulness occurs. MEF2C is less involved in regulating sleep per se, and more involved in executing sleep function, by promoting restorative synaptic modifications to resolve sleep need.

Finally, advances in the roles of the respective SIK3-HDAC4/5 and MEF2C pathways point towards transcription of "sleep genes", as clearly indicated in the model of Fig.4. Clearly more work is needed to understand how the expression of such genes ultimately lead to resolution of sleep need by functional changes at synapses. What are these sleep genes and how do they mechanistically resolve sleep need? Thus, the current work provides a mechanistic framework to stimulate further advances in understanding the molecular basis for sleep need and the restorative basis of sleep function.

Reviewer #1 (Public review):

Summary:

This very interesting manuscript first shows that human, murine, and feline sperm penetrate the zona pellucida (ZP) of bovine oocytes recovered directly from the ovary, although first cleavage rates are reduced (Figure 1A). Similarly, bovine sperm can penetrate superovulated murine oocytes recovered directly from the ovary (Figure 1B). However, bovine oocytes incubated with oviduct fluid (30 min) are generally impenetrable by human sperm (Figure 1C).

Thereafter, the cytoplasm was aspirated from murine oocytes - obtained from the ovary (Figure 1D) or oviduct (Figure 1D). Binding and penetration by bovine and human sperm were reduced in both groups relative to homologous (murine) sperm. However, heterologous (bovine and human) sperm penetration was further reduced in oviduct vs. ovary derived empty ZP. These compelling data show that outer (ZP) not inner (cytoplasmic) oocyte alterations reduce heterologous sperm penetration as well as homologous sperm binding.

This was repeated using empty bovine ZP incubated (Figure 2B), or not (Figure 2A) with bovine oviduct fluid. Prior oviduct fluid exposure reduced non-homologous (human and murine) empty ZP penetration, polyspermy, and sperm binding. This demonstrates that species-specific oviduct fluid factors regulate ZP penetrability.

To test the hypothesis that OVGP1 is responsible, the authors obtained his-tagged bovine and murine OVGP1 and DDK-tagged human OVGP1 proteins. Tagging was to enable purification following overexpression in BHK-21 or HEK293T cells. The authors confirm these recombinant OVGP1 proteins bound to both murine (Figure 3C) and bovine (Figure 3D) oocytes. Moreover, previous data using oviduct fluid (Figure 1D-E and 2A-B) was mirrored using bovine oocytes supplemented with homologous (bovine) recombinant OVGP1 (Figure 4B) or not (Figure 4A). This confirms the hypothesis, at least in cattle.

Next, the authors exposed bovine (Figure 6A) and murine (Figure 6B) empty ZP to bovine, murine, and human recombinant OVGP1, in addition to bovine, murine, or human sperm. Interestingly, both species-specific ZP and OVGP1 seem to be required for optimal sperm binding and penetration.

Lastly, empty bovine (Figures 7A-B) and murine (Figures 7C-D) ZP were treated with neuraminidase, or not, with or without pre-treatment with homologous OVGP1. In each case, neuraminidase reduced sperm binding and penetration. This further demonstrates that both ZP and OVGP1 are required for optimal sperm binding and penetration.

Strengths:

The authors convincingly demonstrate that two mechanisms underpin mammalian sperm recognition and penetration, the first being specific (ZP-mediated) and the second non-specific (OVGP1-mediated). This may prove useful for improving porcine in vitro fertilization (IVF), which is notoriously prone to polyspermy, in addition to human IVF, for better intrinsic individual sperm selection.

Weaknesses:

In my estimation, the following would improve this manuscript:

(1) The physiological relevance of these data could be better highlighted. For instance, future work could revolve around incubating oocytes with oviduct fluid (or OVGP1) to reduce polyspermy in porcine IVF, and naturally improve sperm selection in human IVF.

(2) Biological and technical replicate values for each experiment are unclear - for semen, oocytes, and oviduct fluid pools. I suggest providing in the Materials and Methods and/or Figure legends.

(3) Although differences presented in the bar charts seem obvious, providing statistical analyses would strengthen the manuscript.

(4) Results are presented as {plus minus} SEM (line 677); however, I believe standard deviation is more appropriate.

(5) Given the many independent experimental variables and combinations, a schematic depiction of the experimental design may benefit readers.

(6) Attention to detail can be improved in parts, as delineated in the "author recommendation" review section.

Reviewer #2 (Public review):

In the manuscript entitled "Oviductin sets the species-specificity of the mammalian zona pellucida." The study analyzes the species specificity of sperm-egg recognition by looking at sperm binding and penetration of zonae pellucidae from different mammalian species and find a role for the oviductal protein OVGP1 in determining species specificity.

Strengths:

By combining sperm, oocytes, zona pellucida (ZP), and oviductal fluid from different mammalian species, they elucidate the essential role of OVGP1 in conferring species-specific fertilization.

Weaknesses:

The authors postulate a role for oviductal fluid in species-specific fertilization, but in my opinion, they cannot rule out hormonal effects or differences in the method of oocyte maturation employed.

They also cannot unequivocally prove that OVGP1 is the oviductal protein involved in the effect. Additional experiments are necessary to rule out these alternative explanations.

When performing the EZPT assay on mouse oocytes obtained either from the ovary or from the oviduct, the oocytes obtained from the ovary came from mice primed with eCG, whereas the ones collected from the oviduct were obtained from superovulated mice (eCG plus hCG). This difference in the hormonal environment may make a difference in the properties of the ZP. Additionally, the ones obtained from the ovary were in vitro matured, which is also different from the freshly ovulated eggs and, again, may change the properties of the ZP. I suggest doing this experiment superovulating both groups of mice but collecting the fully matured MII eggs from the ovary before they get ovulated. In that way the hormonal environment will be the same in both groups and in both groups, oocytes will be matured in vivo. Hence, the only difference will be the exposure to oviductal fluids.

Mice with OVGP1 deletion are viable and fertile. It would be quite interesting to investigate the species-specificity of sperm-ZP binding in this model. That would indicate whether OVGP1 is the only glycoprotein involved in determining species-specificity. Alternatively, the authors could immunodeplete OVGP1 from oviductal fluid and then ascertain whether this depleted fluid retains the ability to impede cross-species fertilization.

What is the concentration of OVGP1 in the oviduct? How did the authors decide what concentration of protein to use in the experiments where they exposed ZPs to purified OVGP1? Why did they use this experimental design to check the structure of the ZP by SEM? Why not do it on oocytes exposed to oviductal fluid, which would be more physiological?

None of the figures show any statistical analysis. Please perform analysis for all the data presented, include p values, and indicate which statistical tests were performed. The Statistical analysis section in the Methods indicating that repeated measures ANOVA was used must refer to the tables. Was normality tested? I doubt all the data are normally distributed, in which case using ANOVA is not appropriate.

Why was OVGP1 selected as the probable culprit of the species specificity? In the Results section entitled "Homology of bovine, human and murine OVGP1 proteins..." the authors delve into the possible role of this protein without any rationale for investigating it. What about other oviductal proteins?

Reviewer #3 (Public review):

Summary:

The present study reports findings from a series of experiments suggesting that bovine oviductal fluid and species-specific oviductal glycoprotein (OVGP1 or oviductin) from bovine, murine, or human sources modulate the species specificity of bovine and murine oocytes.

Strengths:

The study reported in the manuscript deals with an important topic of interest in reproductive biology.

Weaknesses:

The manuscript began with a well-written introduction, but problems started to surface in the Results section, in the Discussion, as well as in the Materials and Methods. Major concerns include inconsistencies, misinterpretation of results, lacking up-to-date literature search, numerous errors found in the figure legends, misleading and incorrect information given in the Materials and Methods, missing information regarding statistical analysis, and inadequate discussion. These concerns raise questions regarding the authenticity of the study, reliability of the findings, and interpretation of the results. The manuscript does not provide solid and convincing findings to support the conclusion.

Reviewer #1 (Public Review):

Summary:

The paper begins with phenotyping the DGRP for post-diapause fecundity, which is used to map genes and variants associated with fecundity. There are overlaps with genes mapped in other studies and also functional enrichment of pathways including most surprisingly neuronal pathways. This somewhat explains the strong overlap with traits such as olfactory behaviors and circadian rhythm. The authors then go on to test genes by knocking them down effectively at 10 degrees. Two genes, Dip-gamma and sbb are identified as significantly associated with post-diapause fecundity, which they also find the effects to be specific to neurons. They further show that the neurons in the antenna but not arista are required for the effects of Dip-gamma and sbb. They show that removing antenna has a diapause specific lifespan extending effect, which is quite interesting. Finally, ionotropic receptor neurons are shown to be required for the diapause associated effects.

Strengths:

Overall I find the experiments rigorously done and interpretations sound. I have no further suggestions except an ANOVA to estimate heritability of the post-diapause fecundity trait, which is routinely done in the DGRP and offers a global parameter regarding how reliable phenotyping is. A minor point is I cannot find how many DGRP lines are used.

Weaknesses:

None noted.

Reviewer #2 (Public Review):

Summary

In this study, Easwaran and Montell investigated the molecular, cellular, and genetic basis of adult reproductive diapause in Drosophila using the Drosophila Genetic Reference Panel (DGRP). Their GWAS revealed genes associated with variation in post-diapause fecundity across the DGRP and performed RNAi screens on these candidate genes. They also analyzed the functional implications of these genes, highlighting the role of genes involved in neural and germline development. In addition, in conjunction with other GWAS results, they noted the importance of the olfactory system within the nervous system, which was supported by genetic experiments. Overall, their solid research uncovered new aspects of adult diapause regulation and provided a useful reference for future studies in this field.

Strengths:

The authors used whole-genome sequenced DGRP to identify genes and regulatory mechanisms involved in adult diapause. The first Drosophila GWAS of diapause successfully uncovered many QTL underlying post-diapause fecundity variations across DGRP lines. Gene network analysis and comparative GWAS led them to reveal a key role for the olfactory system in diapause lifespan extension and post-diapause fecundity.

Comments on revised version:

While the authors have addressed many of the minor concerns raised by the reviewers, they have not fully resolved some of the key criticisms. Notably, two reviewers highlighted significant concerns regarding the phenotype and assay of post-diapause fecundity, which are critical to the study. The authors acknowledged that this assay could be confounded by the 'cold temperature endurance phenotype,' potentially altering the interpretation of their results. However, they responded by stating that it is not obvious how to separate these effects experimentally. This leaves the analysis in this research ambiguous, as also noted by Reviewer #3.

Additionally, I raised concerns about the validity of prioritizing genes with multiple associated variants. Although the authors agreed with this point, they did not revise the manuscript accordingly. The statement that 'Genes with multiple SNPs are good candidates for influencing diapause traits' is not a valid argument within the context of population and quantitative genetics.

In summary, the authors have not fully utilized the peer-review process to address the critical weaknesses identified, which ultimately leaves the quality of their work in question.

Reviewer #2 (Public review):

Summary:

Weinberg et al. show that spike LCB minibinders can be used as the extracellular domain for SynNotch, SNIPR, and CAR. They evaluated their designs against cells expressing the target proteins and live virus.

Strengths:

This is a good fundamental demonstration of alternative use of the minibinder. The results are unsurprising but robust and solid in most cases.

Weaknesses:

The manuscript can benefit from better descriptions of the study's novelty. Given that LCB previously worked in SynNotch, what unexpected finding was uncovered by this study? It is well known that the extracellular domain of CAR is amendable to different types of binding domains (e.g., scFv, nanobody, DARPin, natural ligands). So, it is not surprising that a minibinder also works with CAR. We don't know if the minibinders are more or less likely to be compatible with CAR or SNIPR.

The demonstrations are all done using just 1 minibinder. It is hard to conclude that minibinders, as a unique class of protein binders, are generalizable in different contexts. All it can conclude is that this specific Spike minibinder can be used in synNotch, SNIPR, and CAR. The LCB3 minibinder seems to be much weaker.

The sensing of live viruses is interesting, but the output is very weak. It is difficult to imagine a utility for such a weak response.

Reviewer #1 (Public review):

In this manuscript, Ferhat and colleagues describe their study aimed at developing a blood brain barrier (BBB) penetrant agent that could induce hypothermia and provide neuroprotection from the sequelae of status epilepticus (SE) in mice. Hypothermia is used clinically in an attempt to reduce neurological sequelae of injury and disease. Hypothermia can be effective, but physical means used to reduce core body temperature is associated with untoward effects. Pharmacological means to induce hypothermia could be as effective with fewer untoward complications. Intracerebroventricularly applied neurotensin can cause hypothermia; however, neurotensin applied peripherally is degraded and does not cross the BBB. Here the authors develop and characterize a neurotensin conjugate that can reach the brain, induce hypothermia, and reduce seizures, cognitive changes, and inflammatory changes associated with status epilepticus.

Strengths:

(1) In general, the study is well reasoned, well designed, and seemingly well executed.<br /> (2) Strong dose-response assessment of multiple neurotensin conjugates in mice.<br /> (3) Solid assessment of binding affinity, in vitro stability ion blood, and brain uptake of the conjugate.<br /> (4) Appropriate inclusion of controls for SE and for drug injections.<br /> (5) Multifaceted assessment of neurodegeneration, inflammation, and mossy fiber sprouting in the different groups.<br /> (6) Inclusion of behavioral assessments.<br /> (7) Evaluate NSTR1 receptor distribution in multiple ways.<br /> (8) Demonstrate that this conjugate can induce hypothermia and have positive effects on the sequelae of SE. Could have great impact on the application of pharmacologically-induced hypothermia as a neuroprotective measure in patients.

Weaknesses:

(1) The data suggest that the neurotensin conjugate causes hypothermia AND has favorable effects on the sequelae of SE. There is a limitation that they do not definitely show that the hypothermia caused by the neurotensin conjugate is necessarily responsible for the effects they see. The authors recognize and discuss this limitation in the manuscript.

Reviewer #2 (Public review):

Summary:

The authors generated analogs consisting of modified neurotensin (NT) peptides capable of binding to low density lipoprotein (LDL) and NT receptors. Their lead analog was further evaluated for additional validation as a novel therapeutic. The putative mechanism of action for NT in its antiseizure activity is hypothermia, and as therapeutic hypothermia has been demonstrated in epilepsy, NT analogs may confer antiseizure activity and avoid the negative effects of induced hypothermia.

Strengths:

The authors demonstrate an innovative approach, i.e. using LDLR as a means of transport into the brain, that may extend to other compounds. They systematically validate their approach and its potential through binding, brain penetration, in vivo antiseizure efficacy, and neuroprotection studies.

Reviewer #1 (Public review):

Strengths:

This work adds another mouse model for LAMA2-MD that re-iterates the phenotype of previously published models. Such as dy3K/dy3K; dy/dy and dyW/dyW mice. The phenotype is fully consistent with the data from others.

One of the major weaknesses of the manuscript initially submitted was the overinterpretation and the overstatements. The revised version is clearly improved as the authors toned-down their interpretation and now also cite the relevant literature of previous work.

Weaknesses:

Unfortunately, the data on RNA-seq and scRNA-seq are still rather weak. scRNA-seq was conducted with only one mouse resulting in only 8000 nuclei. I am not convinced that the data allow us to interpret them to the extent of the authors. Similar to the first version, the authors infer function by examining expression. Although they are a bit more cautious, they still argue that the BBB is not functional in dyH/dyH mice without showing leakiness. Such experiments can be done using dyes, such as Evans-blue or Cadaverin. Hence, I would suggest that they formulate the text still more carefully.

A similar lack of evidence is true for the suggested cobblestone-like lissencephaly of the mice. There is no strong evidence that this is indeed occurring in the mice (might also be a problem because mice die early). Hence, the conclusions need to be formulated in such a way that readers understand that these are interpretations and not facts.

Finally, I am surprised that the only improvement in the main figures is the Western blot for laminin-alpha2. The histology of skeletal muscle still looks rather poor. I do not know what the problems are but suggest that the authors try to make sections from fresh-frozen tissue. I anticipate that the mice were eventually perfused with PFA before muscles were isolated. This often results in the big gaps in the sections.

Overall, the work is improved but still would need additional experiments to make it really an important addition to the literature in the LAMA-MD field.

Reviewer #2 (Public review):

Summary:

This revised manuscript describes the production of a mouse model for LAMA2-Related Muscular Dystrophy. The authors investigate changes in transcripts within the brain and blood barrier. The authors also investigate changes in the transcriptome associated with the muscle cytoskeleton.

Strengths:

(1) The authors produced a mouse model of LAMA2-CMD using CRISPR-Cas9

(2) The authors identify cellular changes that disrupted the blood-brain barrier.

Weaknesses:

(1) The authors throughout the manuscript overstate "discoveries" which have been previously described, published and not appropriately cited.

(2) Alternations in the blood brain barrier and in the muscle cell cytoskeleton in LAMA2-CMD have been extensively studied and published in the literature and are not cited appropriately.

(3) The authors have increased animal number to N=6, but this is still insufficient based on Power analysis results in statistical errors and conclusions that may be incorrect.

(4) The use of "novel mouse model" in the manuscript overstates the impact of the study.

(5) All studies presented are descriptive and do not more to the field except for producing yet another mouse model of LAMA2-CMD and is the same as all the others produced.

(6) Grip strength measurements are considered error prone and do not give an accurate measurement of muscle strength, which is better achieved using ex vivo or in vivo muscle contractility studies.

(7) A lack of blinded studies as pointed out of the authors is a concern for the scientific rigor of the study.

Reviewer #1 (Public review):

Suarez-Freire et al. analyzed here the function of the exocyst complex in the secretion of the glue proteins by the salivary glands of the Drosophila larva. This is a widely used, genetically accessible system in which the formation, maturation and precisely timed exocytosis of the glue secretory granules can be beautifully imaged. Using RNAi, the authors show that all units of the exocyst complex are required for exocytosis. They show that not just granule fusion with the plasma membrane is affected (canonical role), but also, with different penetrance, that glue protein is retained in the ER, secretory granules fail to fuse homotypically or fail to acquire maturation features. The authors document these phenotypes and postulate specific roles for the exocyst in these additional processes to explain them: exocyst as a Golgi-Golgi, Golgi-granule or granule-granule tether.

Compared to the initial submission, this revised version of the study presents strengthened evidence for these novel roles. In particular, authors show juxta-Golgi localization of exocyst components and disruption of the trans-Golgi compartment upon exocyst loss. Additionally, the revised study contains controls indicating that glue secretion defects prior to plasma membrane exocytosis are not due to polarity loss or unspecific poor health of cells.

Reviewer #2 (Public review):

The manuscript from Wappner and Melani labs claims a novel for the exocyst subunits in multiple aspects of secretory granule exocytosis. This an intriguing paper for it suggests multiple roles of the exocyst in granule maturation and fusion with roles at the ER/Golgi interface, TGN, granule homotypic fusion.

A key strength is the breadth of the assays and study of all 8 exocyst subunits in a powerful model system (fly larvae). But why do KD of different exocysts have different effects on presumed granule formation? Also it can be hard to disentangle direct vs. secondary effects, as much of the TGN seems to be altered in the KDs. The authors ascribe many of the results to the holocomplex, but there are major differences between the proteins -- this may be all related to the different levels of expression (as the authors propose), but only limited mRNA was examined.

Unresolved Comments:

(A) Explanation variability of exocyst KD on the appearance of MSG. What is remarkable is a highly variable effect of different subunit KD on the percentage of cells with MLS (Fig. 4C). Controls = 100 %, Exo70=~75% (at 19 deg), Sec3 = ~30%, Sec10 = 0%, Exo84 = 100% ... This is interesting for the functional exocyst is an octameric holocomples, thus why the huge subunit variability in the phenotypes? One explanation is that the levels of KD varied between the subunits. Another is that not all subunits have equivalent roles (as seen for instance in exocyst's roles in autophagy).

This should be addressed by quantification of the KD of the 8 different exocyst proteins (and or mRNA as only 2 subunits were studied). If their data holds up then the underlying mechanism here needs to be considered. (Note: there is some precedent from the autophagy field of differential exocyst effects).

(B) Golgi: It is unclear from their model (Fig. 5) why after exocyst KD of Sec15 the cis-Golgi is more preserved than the TGN, which appears as large vacuoles.

(C) Granule homotypic fusion. Over-expression of just one subunit, Sec15-GFP, made giant secretory granules (SG) that were over 8 microns big. Does it act like a seed to promote exocyst assembly as the authors propose? If so is there evidence that there is biochemically more holocomplex with expression of Sec15, but not other subunits?

(D) The authors should better frame their interpretations of other studies of the exocyst that includes role in autophagy, Palade body trafficking and differential roles of the subunits.

In summary, there clearly are striking new effects on secretory granule biogenesis by dysfunction of the exocyst which are important and should inspire other studies for new roles of the exocyst; e.g. in non cannonical roles. Secondly, the power of the system to partially deplete proteins (if further validated) suggests that one may need to consider protein expression as an important variable that can be used to unmask multiple phenotypes in granule maturation. Last this paper implies new roles of the exocyst in homotypic fusion, which could be investigated in future work.

Reviewer #3 (Public review):

Freire and co-authors examine the role of the exocyst complex during the formation and secretion of mucins from secretory granules in the larval salivary gland of Drosophila melanogaster. Using transgenic lines with a tagged Sgs3 mucin, the authors KD expression of exocyst subunit members and observe a defect in secretory granules with a heterogeneity of phenotypes. By carefully controlling RNAi expression using a Gal4-based system, the authors can KD exocyst subunit expression to varying degrees. The authors find that the stronger the inhibition of expression of the exocyst is, the earlier the defect is in the secretory pathway. The manuscript is well written, the model system is physiological, and the techniques are innovative.

In my initial review, my major concern was the pleiotropic effect of the loss of exocyst. The authors have responded to this point with clarity and have argued that the multiple localisations of exocyst during the Sgs3 synthesis programme indicate it is likely a direct phenotype. They also performed some analysis of PM lipids but did not detect a difference. I accept the arguments presented. However, I remain concerned that these are due to a pleiotropic effect. It is very hard to absolutely prove a direct effect, and due to the unusual claim and nature of the evidence (depletion levels), I think that there is still the possibility of this being an indirect effect. Perhaps it is just worth the authors writing a paragraph in the discussion, at least accepting the possibility that it is an indirect effect so future readers are aware of that.

Reviewer #3 (Public review):

Summary:

Juan Liu et al. investigated the interplay between habitat fragmentation and climate-driven thermophilization in birds in an island system in China. They used extensive bird monitoring data (9 surveys per year per island) across 36 islands of varying size and isolation from the mainland covering 10 years. The authors use extensive modeling frameworks to test a general increase of the occurrence and abundance of warm-dwelling species and vice versa for cold-dwelling species using the widely used Community Temperature Index (CTI), as well the relationship between island fragmentation in terms of island area and isolation from the mainland on extinction and colonization rates of cold- and warm-adapted species. They found that indeed there was thermophilization happening during the last 10 years, which was more pronounced for the CTI based on abundances and less clearly for the occurrence based metric. Generally, the authors show that this is driven by an increased colonization rate of warm-dwelling and an increased extinction rate of cold-dwelling species. Interestingly, they unravel some of the mechanisms behind this dynamic by showing that warm-adapted species increased while cold-dwelling decreased more strongly on smaller islands, which is - according to the authors - due to lowered thermal buffering on smaller islands (which was supported by air temperature monitoring done during the study period on small and large islands). They argue, that the increased extinction rate of cold-adapted species could also be due to lowered habitat heterogeneity on smaller islands. With regards to island isolation, they show that also both thermophilization processes (increase of warm and decrease of cold-adapted species) was stronger on islands closer to the mainland, due to closer sources to species populations of either group on the mainland as compared to limited dispersal (i.e. range shift potential) in more isolated islands.

The conclusions drawn in this study are sound, and mostly well supported by the results. Only few aspects leave open questions and could quite likely be further supported by the authors themselves thanks to their apparent extensive understanding of the study system.

Strengths:

The study questions and hypotheses are very well aligned with the methods used, ranging from field surveys to extensive modeling frameworks, as well as with the conclusions drawn from the results. The study addresses a complex question on the interplay between habitat fragmentation and climate-driven thermophilization which can naturally be affected by a multitude of additional factors than the ones included here. Nevertheless, the authors use a well balanced method of simplifying this to the most important factors in question (CTI change, extinction, colonization, together with habitat fragmentation metrics of isolation and island area). The interpretation of the results presents interesting mechanisms without being too bold on their findings and by providing important links to the existing literature as well as to additional data and analyses presented in the appendix.

Weaknesses:

The metric of island isolation based on distance to the mainland seems a bit too oversimplified as in real-life the study system rather represents an island network where the islands of different sizes are in varying distances to each other, such that smaller islands can potentially draw from the species pools from near-by larger islands too - rather than just from the mainland. Although the authors do explain the reason for this metric, backed up by earlier research, a network approach could be worthwhile exploring in future research done in this system. The fact, that the authors did find a signal of island isolation does support their method, but the variation in responses to this metric could hint on a more complex pattern going on in real-life than was assumed for this study.

Reviewer #1 (Public review):

Summary:

Fallah and colleagues characterize the connectivity between two basal ganglia output nuclei, the SNr and GPe, and the pedunculopontine nucleus, a brainstem nucleus that is part of the mesencephalic locomotor region. Through a series of systematic electrophysiological studies, they find that these regions target and inhibit different populations of neurons, with anatomical organization. Overall, SNr projects to PPN and inhibits all major cell types, while the GPe inhibits glutamatergic and GABAergic PPN neurons, and preferentially in the caudal part of the nucleus. Optogenetic manipulation of these inputs had opposing effects on behavior - SNr terminals in the PPN drove place aversion, while GPe terminals drove place preference.

Strengths:

This work is a thorough and systematic characterization of a set of relatively understudied circuits. They build on the classic notions of basal ganglia connectivity and suggest a number of interesting future directions to dissect motor control and valence processing in brainstem systems.

Weaknesses:

Characterization of the behavioral effects of manipulations of these PPN input circuits could be further parsed, for a better understanding of the functional consequences of the connections demonstrated in the ephys analyses.

All the cell type recording studies showing subtle differences in the degree of inhibition and anatomical organization of that inhibition suggest a complex effect of general optogenetic manipulation of SNr or GPe terminals in the PPN. It will be important to determine if SNr or GPe inputs onto a particular cell type in PPN are more or less critical for how the locomotion and valence effects are demonstrated here.

Reviewer #2 (Public review):

Summary:

Fallah et al carefully dissect projections from SNr and GPe - two key basal ganglia nuclei - to the PPN, an important brainstem nucleus for motor control. They consider inputs from these two areas onto 3 types of downstream PPN neurons: GABAergic, glutamatergic, and cholinergic neurons. They also carefully map connectivity along the rostrocaudal axis of the PPN.

Strengths:

The slice electrophysiology work is technically well done and provides useful information for further studies of PPN. The optogenetics and behavioral studies are thought-provoking, showing that SNr and GPe projections to PPN play distinct roles in behavior.

Weaknesses:

Although the optogenetics and behavioral studies are intriguing, they are somewhat difficult to fit together into a specific model of circuit function. Perhaps the authors can work to solidify the connection between these two arms of the work. Otherwise, there are a few questions whose answers could add context to the interpretation of these results:

(1) Male and female mice are used, but the authors do not discuss any analysis of sex differences. If there are no sex differences, it is still useful to report data disaggregated by sex in addition to pooled data.

(2) There is some lack of clarity in the current manuscript on the ages used - 2-5 months vs "at least 7 weeks." Is 7 weeks the time of virus injection surgery, then recordings 3 weeks later (at least 10 weeks)? Please clarify if these ages apply equally to electrophysiological and behavioral studies. If the age range used for the test is large, it may be useful to analyze and report if there are age-related effects.

(3) Were any exclusion criteria applied, e.g. to account for missed injections?

(4) 28-34degC is a fairly wide range of temperatures for electrophysiological recording, which could affect kinetics.

(5) It would be good to report the number of mice used for each condition in addition to n=cells. Statistically, it would be preferable not to assume that each cell from the same mouse is an independent measurement and to use a nested ANOVA.

Reviewer #3 (Public review):

Summary:

The study by Fallah et al provides a thorough characterization of the effects of two basal ganglia output pathways on cholinergic, glutamatergic, and GABAergic neurons of the PPN. The authors first found that SNr projections spread over the entire PPN, whereas GPe projections are mostly concentrated in the caudal portion of the nucleus. Then the authors characterized the postsynaptic effects of optogenetically activating these basal ganglia inputs and identified the PPN's cell subtypes using genetically encoded fluorescent reporters. Activation of inputs from the SNr inhibited virtually all PPN neurons. Activation of inputs from the GPe predominantly inhibited glutamatergic neurons in the caudal PPN, and to a lesser extent GABAergic neurons. Finally, the authors tested the effects of activating these inputs on locomotor activity and place preference. SNr activation was found to increase locomotor activity and elicit avoidance of the optogenetic stimulation zone in a real-time place preference task. In contrast, GPe activation reduced locomotion and increased the time in the RTPP stimulation zone.

Strengths:

The evidence of functional connectivity of SNr and GPe neurons with cholinergic, glutamatergic, and GABAergic PPN neurons is solid and reveals a prominent influence of the SNr over the entire PPN output. In addition, the evidence of a GPe projection that preferentially innervates the caudal glutamatergic PPN is unexpected and highly relevant for basal ganglia function.

Opposing effects of two basal ganglia outputs on locomotion and valence through their connectivity with the PPN.

Overall, these results provide an unprecedented cell-type-specific characterization of the effects of basal ganglia inputs in the PPN and support the well-established notion of a close relationship between the PPN and the basal ganglia.

Weaknesses:

The behavioral experiments require further analysis as some motor effects could have been averaged out by analyzing long segments. Additional controls are needed to rule out a motor effect in the real-time place preference task. Importantly, the location of the stimulation is not reported even though this is critical to interpret the behavioral effects.

There are some concerns about the possible recruitment of dopamine neurons in the SNr experiments.

Reviewer #2 (Public review):

Summary:

Non-canonical Wnt signaling plays an important role in morphogenesis, but how different components of the pathway are required to regulate different developmental events remains an open question. This paper focuses on elucidating the overlapping and distinct functions of dact1 and dact2, two Dishevelled-binding scaffold proteins, during zebrafish axis elongation and craniofacial development. By combining genetic studies, detailed phenotypic analysis, lineage tracing, and single cell RNA-sequencing, the authors aimed to understand (1) the relative function of dact1/2 in promoting axis elongation, (2) their ability to modulate phenotypes caused by mutations in other non-canonical wnt components, and (3) pathways downstream of dact1/2.

Corroborating previous findings, this paper showed that dact1/2 is required for convergent extension during gastrulation and body axis elongation. Qualitative evidence was also provided to support dact1/2's role in genetically modulating non-canonical wnt signaling to regulate body axis elongation and the morphology of the ethmoid plate (EP). However, the spatiotemporal function of dact1/2 remains unknown. The use of scRNA-seq identified novel pathways and targets downstream of dact1/2. Calpain 8 is one such example, and its overexpression in some of the dact1/2+/- embryos was able to phenocopy the dact1/2-/- mutant EP morphology, pointing to its sufficiency in driving the EP phenotype in a few embryos. However, the same effect was not observed in dact1-/-; dact2+/- embryos, leading to the question of how significant calpain 8 really is in this context. The requirement of calpain 8 in mediating the phenotype is unclear as well. This is the most novel aspect of the paper, but some weaknesses remain in convincingly demonstrating the importance of calpain 8.

Strengths:

(1) The generation of dact1/2 germline mutants and the use of genetic approaches to dissect their genetic interactions with wnt11f2 and gpc4 provide unambiguous and consistent results that inform the relative functions of dact1 and dact2, as well as their combined effects.<br /> (2) Because the ethmoid plate exhibits a spectrum of phenotypes in different wnt genetic mutants, it is a useful system for studying how tissue morphology can be modulated by different components of the wnt pathway.<br /> (3) The authors leveraged lineage tracing by photoconversion to dissect how dact1/2 differentially impacts the ability of different cranial neural crest populations to contribute to the ethmoid plate. This revealed that distinct mechanisms via dact1/2 and shh can lead to similar phenotypes.<br /> (4) The use of scRNA-seq was a powerful approach and identified potential novel pathways and targets downstream of dact1/2.

Weaknesses:

(1) Connecting the expression of dact1/2 and wnt11f2 to their mutant phenotypes: Given that dact1/2 and wnt11f2 expression are quite distinct, at least in the stages examined, the claim that dact1/2 function downstream of wnt11f2 is not well supported. That conclusion was based on shared craniofacial phenotypes between dact1/2-/-, wnt11f2-/-, and dact1/2-/-;wnt11f2-/- mutants. However, because the craniofacial phenotype is likely a secondary effect of dact1/2 deletion, using it to interpret the signaling axis between dact1/2 and wnt11f2 is not appropriate.<br /> (2) Spatiotemporal function of dact1/2: Germline mutations limit the authors' ability to study a gene's spatiotemporal functional requirement. They, therefore, cannot concretely attribute nor separate early-stage phenotypes (during gastrulation) to/from late stage phenotypes (EP morphological changes), which the authors postulated to result from secondary defects in floor plate and eye field morphometry. As a result, whether dact1/2 are directly involved in craniofacial development is not addressed, and the mechanisms resulting in the craniofacial phenotypes are also unclear.<br /> (3) The functional significance of calpain 8: Because calpain 8 was upregulated in many dact1/2-/- mutant cell populations (although not in the neural crest) during gastrulation, the authors tested its function by overexpressing capn8 mRNA in embryos. While only 1 out of 142 calpain 8-overexpressing wild type animals phenocopied dact1/2 mutants, 7.5% of dact1/2+/- embryos overexpressing capn8 exhibited dact1/2-like phenotypes. However, the same effect was not observed in dact1-/-; dact2+/- embryos. Given the expression pattern of calpain 8 and results from the overexpression study, the function of capn8 remains inconclusive. The requirement of calpain 8 in driving the phenotype remains unclear. The authors stated these limitations in their study.

Reviewer #3 (Public review):

Summary:

In this manuscript the authors explore the roles of dact1 and dact2 during zebrafish gastrulation and craniofacial development. Previous studies used morpholino (MO) knockdowns to show that these scaffolding proteins, which interact with dissheveled (Dsh), are expressed during zebrafish gastrulation and suggested that dact1 promotes canonical Wnt/B-catenin signaling, while dact2 promotes non-canonical Wnt/PCP-dependent convergent-extension (Waxman et al 2004). This study goes beyond this work by creating loss-of-function mutant alleles for each gene and unlike the MO studies finds little (dact2) to no (dact1) phenotypic defects in the homozygous mutants. Interestingly, dact1/2 double mutants have a more severe phenotype, which resembles those reported with MOs as well as homozygous wnt11/silberblick (wnt11/slb) mutants that disrupt non-canonical Wnt signaling (Heisenberg et al., 1997; 2000). Further analyses in this paper try to connect gastrulation and craniofacial defects in dact1/2 mutants with wnt11/slb and other wnt-pathway mutants. scRNAseq conducted in mutants identifies calpain 8 as a potential new target of dact1/2 and Wnt signaling.

Previous comments:<br /> Strengths:

When considered separately the new mutants are an improvement over the MOs and the paper contains a lot of new data.

Weaknesses:

However, the hypotheses are very poorly defined and misinterpret key previous findings surrounding the roles of wnt11 and gpc4, which results in a very confusing manuscript. Many of the results are not novel and focus on secondary defects. The most novel result overexpressing calpain8 in dact1/2 mutants is preliminary and not convincing.

The authors addressed some of our comments, but not our main criticisms, which we reiterate here:

(1) The authors argue that morpholino studies are unreliable and here they made new mutants to solve this uncertainty for dap 1/2. However, creating stable mutant lines to largely confirm previous results obtained by using morpholino knock-down phenotypes does not justify publication in eLife.

(2) The authors argue that since it has not been shown conclusively that craniofacial defects in wnt11 and dap1/2 mutants are secondary to gastrulation defects there is no solid evidence preventing them from investigating these craniofacial defects. However, since it is extremely likely that the rod-like ethmoid plates of wnt11f2- and dact1/2 mutants focused on here are secondary to gastrulation defects previously described by others (Heisenberg and NussleinVolhard 1997; Waxman et al., 2004), the burden of proof is on the authors to provide much stronger evidence against this interpretation.

(3) The data for calpain overexpression remains too preliminary.

Reviewer #1 (Public review):

Summary:

The manuscript gives a broad overview of how to write NeuroML, a brief description of how to use it with different simulators and for different purposes - cells to networks, simulation, optimization and analysis. From this perspective it can be an extremely useful document to introduce new users to NeuroML.

Strengths:

The modularity of NeuroML is indeed a great advantage. For example, the ability to specify the channel file allows different channels to be used with different morphologies without redundancy. The hierarchical nature of NeuroML also is commendable, and well illustrated.

The number of tools available to work with NeuroML is impressive.

Having a python API and providing examples using this API is fantastic. Exporting to NeuroML from python is also a great feature.

The tutorials should assist additional scientists in adopting NeuroML.

Weaknesses:

None noted.

Reviewer #2 (Public review):

Summary:

Developing neuronal models that are shareable, reproducible, and interoperable allows the neuroscience community to make better use of published models and to collaborate more effectively. In this manuscript, the authors present a consolidated overview of the NeuroML model description system along with its associated tools and workflows. They describe where different components of this ecosystem lay along the model development pathway and highlight resources, including documentation and tutorials, to help users employ this system.

Strengths:

The manuscript is well-organized and clearly written. It effectively uses the delineated model development life cycle steps, presented in Figure 1, to organize its descriptions of the different components and tools relating to NeuroML. It uses this framework to cover the breadth of the software ecosystem and categorize its various elements. The NeuroML format is clearly described, and the authors outline the different benefits to its particular construction. As primarily a means of describing models, NeuroML also depends on many other software components to be of high utility to computational neuroscientists; these include simulators (ones that both pre-date NeuroML and those developed afterwards), visualization tools, and model databases.

Overall, the rationale for the approach NeuroML has taken is convincing and well-described. The pointers to existing documentation, guides, and the example usages presented within the manuscript are useful starting points for potential new users. This manuscript can also serve to inform potential users of features or aspect of the ecosystem that they may have been unaware of, which could lower obstacles to adoption. While much of what is presented is not new to this manuscript, it still serves as a useful resource for the community looking for information about an established, but perhaps daunting, set of computational tools.

Weaknesses:

The manuscript in large part catalogs the different tools and functionalities that have been produced through the long development cycle of NeuroML. Overall, the interoperability of NeuroML is a benefit, but it does increase the complexity of choices facing users entering into the ecosystem.

In many respects this is an intractable fact of the current environment, but the authors do try to mitigate the issue with user guides (e.g., Table 1) and example code (e.g. Box 1) which address a range of target user audiences, from those learning about the ecosystem for the first time to those looking to implement specific model features. They also categorize different simulator options (Figure 5) and provide feature comparisons (Table 3), which could assist with the most daunting choice faced by new users.

Comments on revised version:

The authors have addressed my major concerns with the original manuscript. The discussion of simulators in particular is much clearer now, and the manuscript has been restructured so that specific details pertinent to a much more focused audience have been rewritten or shifted to more appropriate locations.

Reviewer #1 (Public review):

The conserved AAA-ATPase PCH-2 has been shown in several organisms including C. elegans to remodel classes of HORMAD proteins that act in meiotic pairing and recombination. In some organisms the impact of PCH-2 mutations is subtle but becomes more apparent when other aspects of recombination are perturbed. Patel et al. performed a set of elegant experiments in C. elegans aimed at identifying conserved functions of PCH-2. Their work provides such an opportunity because in C. elegans meiotically expressed HORMADs localize to meiotic chromosomes independently of PCH-2. Work in C. elegans also allows the authors to focus on nuclear PCH-2 functions as opposed to cytoplasmic functions also seen for PCH-2 in other organisms.

The authors performed the following experiments:

(1) They constructed C. elegans animals with SNPs that enabled them to measure crossing over in intervals that cover most of four of the six chromosomes. They then showed that double-crossovers, which were common on most of the four chromosomes in wild-type, were absent in pch-2. They also noted shifts in crossover distribution in the four chromosomes.

(2) Based on the crossover analysis and previous studies they hypothesized that PCH-2 plays a role at an early stage in meiotic prophase to regulate how SPO-11 induced double-strand breaks are utilized to form crossovers. They tested their hypothesis by performing ionizing irradiation and depleting SPO-11 at different stages in meiotic prophase in wild-type and pch-2 mutant animals. The authors observed that irradiation of meiotic nuclei in zygotene resulted in pch-2 nuclei having a larger number of nuclei with 6 or greater crossovers (as measured by COSA-1 foci) compared to wildtype. Consistent with this observation, SPO11 depletion, starting roughly in zygotene, also resulted in pch-2 nuclei having an increase in 6 or more COSA-1 foci compared to wild type. The increased number at this time point appeared beneficial because a significant decrease in univalents was observed.

(3) They then asked if the above phenotypes correlated with the localization of MSH-5, a factor that stabilizes crossover-specific DNA recombination intermediates. They observed that pch-2 mutants displayed an increase in MSH-5 foci at early times in meiotic prophase and an unexpectedly higher number at later times. They conclude based on the differences in early MSH-5 localization and the SPO-11 and irradiation studies that PCH-2 prevents early DSBs from becoming crossovers and early loading of MSH-5. By analyzing different HORMAD proteins that are defective in forming the closed conformation acted upon by PCH-2, they present evidence that MSH-5 loading was regulated by the HIM-3 HORMAD.

(4) They performed a crossover homeostasis experiment in which DSB levels were reduced. The goal of this experiment was to test if PCH-2 acts in crossover assurance. Interestingly, in this background PCH-2 negative nuclei displayed higher levels of COSA-1 foci compared to PCH-2 positive nuclei. This observation and a further test of the model suggested that "PCH-2's presence on the SC prevents crossover designation."

(5) Based on their observations indicating that early DSBS are prevented from becoming crossovers by PCH-2, the authors hypothesized that the DNA damage kinase CHK-2 and PCH-2 act to control how DSBs enter the crossover pathway. This hypothesis was developed based on their finding that PCH-2 prevents early DSBs from becoming crossovers and previous work showing that CHK-2 activity is modulated during meiotic recombination progression. They tested their hypothesis using a mutant synaptonemal complex component that maintains high CHK-2 activity that cannot be turned off to enable crossover designation. Their finding that the pch-2 mutation suppressed the crossover defect (as measured by COSA-1 foci) supports their hypothesis.

Based on these studies the authors provide convincing evidence that PCH-2 prevents early DSBs from becoming crossovers and controls the number and distribution of crossovers to promote a regulated mechanism that ensures the formation of obligate crossovers and crossover homeostasis. As the authors note, such a mechanism is consistent with earlier studies suggesting that early DSBs could serve as "scouts" to facilitate homolog pairing or to coordinate the DNA damage response with repair events that lead to crossing over. The detailed mechanistic insights provided in this work will certainly be used to better understand functions for PCH-2 in meiosis in other organisms. My comments below are aimed at improving the clarity of the manuscript.

Comments

(1) It appears from reading the Materials and Methods that the SNPs used to measure crossing over were obtained by mating Hawaiian and Bristol strains. It is not clear to this reviewer how the SNPs were introduced into the animals. Was crossing over measured in a single animal line? Were the wild-type and pch-2 mutations made in backgrounds that were isogenic with respect to each other? This is a concern because it is not clear, at least to this reviewer, how much of an impact crossing different ecotypes will have on the frequency and distribution of recombination events (and possibly the recombination intermediates that were studied).

(2) The authors state that in pch-2 mutants there was a striking shift of crossovers (line 135) to the PC end for all of the four chromosomes that were tested. I looked at Figure 1 for some time and felt that the results were more ambiguous. Map distances seemed similar at the PC end for wildtype and pch-2 on Chrom. I. While the decrease in crossing over in pch-2 appeared significant for Chrom. I and III, the results for Chrom. IV, and Chrom. X. seemed less clear. Were map distances compared statistically? At least for this reviewer the effects on specific intervals appear less clear and without a bit more detail on how the animals were constructed it's hard for me to follow these conclusions.

(3) Figure 2. I'm curious why non-irradiated controls were not tested side-by-side for COSA-1 staining. It just seems like a nice control that would strengthen the authors' arguments.

(4) Figure 3. It took me a while to follow the connection between the COSA-1 staining and DAPI staining panels (12 hrs later). Perhaps an arrow that connects each set of time points between the panels or just a single title on the X-axis that links the two would make things clearer.

Reviewer #2 (Public review):

Summary:

This paper has some intriguing data regarding the different potential roles of Pch-2 in ensuring crossing over. In particular, the alterations in crossover distribution and Msh-5 foci are compelling. My main issue is that some of the models are confusingly presented and would benefit from some reframing. The role of Pch-2 across organisms has been difficult to determine, the ability to separate pairing and synapsis roles in worms provides a great advantage for this paper.

Strengths:

Beautiful genetic data, clearly made figures. Great system for studying the role of Pch-2 in crossing over.

Weaknesses:

(1) For a general audience, definitions of crossover assurance, crossover eligible intermediates, and crossover designation would be helpful. This applies to both the proposed molecular model and the cytological manifestation that is being scored specifically in C. Elegans.

(2) Line 62: Is there evidence that DSBs are introduced gradually throughout the early prophase? Please provide references.

(3) Do double crossovers show strong interference in worms? Given that the PC is at the ends of chromosomes don't you expect double crossovers to be near the chromosome ends and thus the PC?

(4) Line 155 - if the previous data in Deshong et al is helpful it would be useful to briefly describe it and how the experimental caveats led to misinterpretation (or state that further investigation suggests a different model etc.). Many readers are unlikely to look up the paper to find out what this means.

(5) Line 248: I am confused by the meaning of crossover assurance here - you see no difference in the average number of COSA-1 foci in Pch-2 vs. wt at any time point. Is it the increase in cells with >6 COSA-1 foci that shows a loss of crossover assurance? That is the only thing that shows a significant difference (at the one time point) in COSA-1 foci. The number of dapi bodies shows the loss of Pch-2 increases crossover assurance (fewer cells with unattached homologs). So this part is confusing to me. How does reliably detecting foci vs. DAPI bodies explain this?

(6) Line 384: I am confused. I understand that in the dsb-2/pch2 mutant there are fewer COSA-1 foci. So fewer crossovers are designated when DSBs are reduced in the absence of PCH-2. How then does this suggest that PCH-2's presence on the SC prevents crossover designation? Its absence is preventing crossover designation at least in the dsb-2 mutant.

(7) Discussion Line 535: How do you know that the crossovers that form near the PCs are Class II and not the other way around? Perhaps early forming Class I crossovers give time for a second Class II crossover to form. In budding yeast, it is thought that synapsis initiation sites are likely sites of crossover designation and class I crossing over. Also, the precursors that form class I and II crossovers may be the same or highly similar to each other, such that Pch-2's actions could equally affect both pathways.

Reviewer #3 (Public review):

Summary:

This manuscript describes an in-depth analysis of the effect of the AAA+ ATPase PCH-2 on meiotic crossover formation in C. elegant. The authors reach several conclusions, and attempt to synthesize a 'universal' framework for the role of this factor in eukaryotic meiosis.

Strengths:

The manuscript makes use of the advantages of the 'conveyor' belt system within the c.elegans reproductive tract, to enable a series of elegant genetic experiments.

Weaknesses:

A weakness of this manuscript is that it heavily relies on certain genetic/cell biological assays that can report on distinct crossover outcomes, without clear and directed control over other aspects and variables that might also impact the final repair outcome. Such assays are currently out of reach in this model system.

In general, this manuscript could be more generally accessible to non-C.elegans readers. Currently, the manuscript is hard to digest for non-experts (even if meiosis researchers). In addition, the authors should be careful to consider alternative explanations for certain results. At several steps in the manuscript, results could ostensibly be caused by underlying defects that are currently unknown (for example, can we know for sure that pch-2 mutants do not suffer from altered DSB patterning, and how can we know what the exact functional and genetic interactions between pch-2 and HORMAD mutants tell us?). Alternative explanations are possible and it would serve the reader well to explicitly name and explain these options throughout the manuscript.

Reviewer #1 (Public review):

Summary:

Parsing speech into meaningful linguistic units is a fundamental yet challenging task that infants face while acquiring the native language. Computing transitional probabilities (TPs) between syllables is a segmentation cue well-attested since birth. In this research, the authors examine whether newborns compute TPs over any available speech feature (linguistic and non-linguistic), or whether by contrast newborns' favor the computation of TPs over linguistic content over non-linguistic speech features such as speaker's voice. Using EEG and the artificial language learning paradigm, they record the neural responses of two groups of newborns presented with speech streams in which either phonetic content or speaker's voice are structured to provide TPs informative of word boundaries, while the other dimension provides uninformative information. They compare newborns' neural responses to these structured streams to their processing of a stream in which both dimensions vary randomly. After the random and structured familiarization streams, the newborns are presented with (pseudo)words as defined by their informative TPs, as well as partwords (that is, sequences that straddle a word boundary), extracted from the same streams. Analysis of the neural responses shows that while newborns neural activity entrained to the syllabic rate (2 Hz) when listening to the random and structured streams, it additionally entrained at the word rate (4 Hz) only when listening to the structured streams, finding no differential response between the streams structured around voice or phonetic information. Newborns showed also different neural activity in response to the words and part words. In sum, the study reveals that newborns compute TPs over linguistic and non-linguistic features of speech, these are calculated independently, and linguistic features do not lead to a processing advantage.

Strengths:

This interesting research furthers our knowledge of the scope of the statistical learning mechanism, which is confirmed to be a general-purpose powerful tool that allows humans to extract patterns of co-occurring events while revealing no apparent preferential processing for linguistic features. To answer its question, the study combines a highly replicated and well-established paradigm, i.e. the use of an artificial language in which pseudowords are concatenated to yield informative TPs to word boundaries, with a state-of-the-art EEG analysis, i.e. neural entrainment. The sample size of the groups is sufficient to ensure power, and the design and analysis are solid and have been successfully employed before.

Weaknesses:

There are no significant weaknesses to signal in the manuscript. However, in order to fully conclude that there is no obvious advantage for the linguistic dimension in neonates, it would have been most useful to test a third condition in which the two dimensions were pitted against each other, that is, in which they provide conflicting information as to the boundaries of the words comprised in the artificial language. This last condition would have allowed us to determine whether statistical learning weighs linguistic and non-linguistic features equally, or whether phonetic content is preferentially processed.

To sum up, the authors achieved their central aim of determining whether TPs are computed over both linguistic and non-linguistic features, and their conclusions are supported by the results. This research is important for researchers working on language and cognitive development, and language processing, as well as for those working on cross-species comparative approaches.

Reviewer #2 (Public review):

Summary:

The manuscript investigates to what degree neonates show evidence for statistical learning from regularities in streams of syllables, either with respect to phonemes or with respect to speaker identity. Using EEG, the authors found evidence for both, stronger entrainment to regularities as well as ERP differences in response to violations of previously introduced regularities. In addition, violations of phoneme regularities elicited an ERP pattern which the authors argue might index a precursor of the N400 response in older children and adults.

Strengths:

All in all, this is a very convincing paper, which uses a clever manipulation of syllable streams to target the processing of different features. The combination of neural entrainment and ERP analysis allows for the assessment of different processing stages, and implementing this paradigm in a comparably large sample of neonates is impressive. I only have some smaller comments.

Weaknesses:

I am skeptical regarding the interpretation of the phoneme-specific ERP effect as a precursor of the N400 and would suggest toning it down. While the authors are correct in that infant ERP components are typically slower and more posterior compared to adult components, and the observed pattern is hence consistent with an adult N400, at the same time, it could also be a lot of other things. On a functional level, I can't follow the author's argument as to why a violation in phoneme regularity should elicit an N400, since there is no evidence for any semantic processing involved. In sum, I think there is just not enough evidence from the present paradigm to confidently call it an N400.

Why did the authors choose to include male and female voices? While using both female and male stimuli of course leads to a higher generalizability, it also introduces a second dimension for one feature that is not present for this other (i.e., phoneme for Experiment 1 and voice identity plus gender for Experiment 2). Hence, couldn't it also be that the infants extracted the regularity with which one gender voice followed the other? For instance, in List B, in the words, one gender is always followed by the other (M-F or F-M), while in 2/3 of the part-words, the gender is repeated (F-F and M-M). Wouldn't you expect the same pattern of results if infants learned regularities based on gender rather than identity?

Do you have any idea why the duplet entrainment effect occurs over the electrodes it does, in particular over the occipital electrodes (which seems a bit unintuitive given that this is a purely auditory experiment with sleeping neonates).

Reviewer #3 (Public review):

Summary:

This study is focused on testing whether statistical learning (a mechanism for parsing the speech signal into smaller chunks) preferentially operates over certain features of the speech at birth in humans. The features under investigation are phonetic content and speaker identity. Newborns are tested in an EEG paradigm in which they are exposed to a long stream of syllables. In Experiment 1, newborns are familiarized with a sound stream that comprises regularities (transitional probabilities) over syllables (e.g., "pe" followed by "tu" in "petu" with 1.0 probability) while the voices uttering the syllables remain random. In Experiment 2, newborns are familiarized with the same sound stream but, this time, the regularities are built over voices (e.g., "green voice" followed by "red voice" with 1.0 probability) while the concatenation of syllables stays random. At the test, all newborns listened to duplets (individual chunks) that either matched or violated the structure of the familiarization. In both experiments, newborns showed neural entrainment to the regularities implemented in the stream, but only the duplets defined by transitional probabilities over syllables (aka word forms) elicited a N400 ERP component. These results suggest that statistical learning operates in parallel and independently on different dimensions of the speech already at birth and that there seems to be an advantage for processing statistics defining word forms rather than voice patterns.

Strengths:

This paper presents an original experimental design that combines two types of statistical regularities in a speech input. The design is robust and appropriate for EEG with newborns. I appreciated the clarity of the Methods section. There is also a behavioral experiment with adults that acts like a control study for newborns. The research question is interesting, and the results add new information about how statistical learning works at the beginning of postnatal life, and on which features of the speech. The figures are clear and helpful in understanding the methods, especially the stimuli and how the regularities were implemented.

Weaknesses:

(1) I'm having a hard time understanding the link between the results of the study and the universality of statistical learning. The main goal of the study was testing whether statistical learning is a general mechanism for newborns that operates on any speech dimension, or whether it operates over linguistic features only. To test that, statistical regularities (TPs) were built over syllables (e.g., pe followed by tu in petu with 1.0 probability) or voices (e.g., green voice followed by red voice with 1.0 probability). Voices were considered as the non-linguistic dimension.

While it's true that voice is not essential for language (i.e., sign languages are implemented over gestures; the use of voices to produce non-linguistic sounds, like laughter), it is a feature of spoken languages. Thus I'm not sure if we can really consider this study as a comparison between linguistic and non-linguistic dimensions. In turn, I'm not sure that these results show that statistical learning at birth operates on non-linguistic features, being voices a linguistic dimension at least in spoken languages. I'd like to hear the authors' opinions on this.

Along the same line, in the Discussion section, the present results are interpreted within a theoretical framework showing statistical learning in auditory non-linguistic (string of tones, music) and visual domains as well as visual and other animal species. I'm not sure if that theoretical framework is the right fit for the present results.

(2) I'm not sure whether the fact that we see parallel and independent tracking of statistics in the two dimensions of speech at birth indicates that newborns would be able to do so in all the other dimensions of the speech. If so, what other dimensions are the authors referring to?

(3) Lines 341-345: Statistical learning is an evolutionary ancient learning mechanism but I do not think that the present results are showing it. This is a study on human neonates and adults, there are no other animal species involved therefore I do not see a connection with the evolutionary history of statistical learning. It would be much more interesting to make claims on the ontogeny (rather than philogeny) of statistical learning, and what regularities newborns are able to detect right after birth. I believe that this is one of the strengths of this work.

(4) The description of the stimuli in Lines 110-113 is a bit confusing. In Experiment 1, e.g., "pe" and "tu" are both uttered by the same voice, correct? ("random voice each time" is confusing). Whereas in Experiment 2, e.g., "pe" and "tu" are uttered by different voices, for example, "pe" by yellow voice and "tu" by red voice. If this is correct, then I recommend the authors to rephrase this section to make it more clear.

(5) Line 114: the sentence "they should compute a 36 x 36 TPs matrix relating each acoustic signal, with TPs alternating between 1/6 within words and 1/12 between words" is confusing as it seems like there are different acoustic signals. Can the authors clarify this point?

Reviewer #1 (Public review):

Summary:

The central question of this manuscript is the role of RNase III in supporting Salmonella infection. The authors begin with an RNAseq analysis of a collection of food or clinical Salmonella isolates from China, identifying RNase III (encoded by rnc) as an upregulated gene in clinical ("high virulence") isolates. Based on follow-up studies with knockout and complemented strains, the authors propose that RNase III has two roles - one in the upregulation of sodA expression to counter host-derived ROS, and the other in general degradation of dsRNA to dampen host immune responses. Overall, the manuscript is logical and the authors make largely reasonable interpretations of their data. However, the depth of supporting evidence limits the breadth of the authors' conclusions in their current form. Thus, this manuscript will be useful to researchers in directly related fields of study, but more work is required to understand how these proposed mechanisms function during infection.

Strengths:

(1) The use of comparative RNAseq between different isolates to identify potential virulence mechanisms is a powerful approach to understanding what makes certain strains more likely to cause infection over others.

(2) The experiments identifying dsRNA as the factor contributing to increased innate immune induction in the rnc knockout strain are particularly thorough.

(3) The authors observed an in vivo mammalian infection defect for RNase III-deficient Salmonella, a novel finding for the field and strong evidence that this protein is required to support pathogen fitness.

Weaknesses:

(1) The strengths of the manuscript are in places obscured by a lack of clarity and justification in the manuscript about strain selection and rationale for using some backgrounds over others. Moreover, several aspects of the organization and flow of the manuscript could be improved, as data is described out of order and the text description of results does not always align with the data presented.

(2) The specific claim that the relatively modest increase in expression of RNase III in some isolates (Figure 1A) accounts for their "virulence" is not well-supported, since the only comparisons in the study are between total knockouts or wild-type (and not overexpression) and the actual protein levels of RNase III are not quantified.

(3) Although the experiments on dsRNA are strong, they would have benefited from measurements of cytokine production/immune responses during infection with the actual knockout strains instead of transfected RNA along with quantification of Salmonella burdens.

(4) The contribution of RNase III catalytic activity (i.e., through the use of a catalytically dead mutant) was not assessed, which means that a role for general RNA binding or protein-protein interactions cannot be ruled out from this study.

(5) The in vivo work was limited to survival analysis, so whether the proposed mechanisms account for the defects observed could not be resolved.

(6) Statistical analysis throughout the manuscript is inconsistently applied, making it hard in places to determine whether the differences seen in phenotypes are biologically significant.

Reviewer #2 (Public review):

Summary:

This work attempted to investigate how the gene rnc, which showed higher expression in clinical strains of Salmonella Enteritidis compared to those isolated from food, affects the virulence of this bacteria through modulating dsRNA levels and the immune response of host cells.

Strengths:

The authors clearly demonstrated that the deletion of rnc Salmonella Enteritidis leads to an accumulation of dsRNA inside the cells, which further activates the immune response of host cells. It is also well demonstrated that the rnc gene deletion results in an increased ROS level through regulating the SodA protein.

Weaknesses:

(1) It is unclear whether the higher rnc expression in clinical strains of Salmonella Enteritidis is universal or just specific to several strains, because of the inadequate data provided and different strains used for different tests in this study.

(2) A lot of specific information is missing in the Figure legends and Method section, which makes it hard to understand some of the key results in the manuscript.

Reviewer #3 (Public review):

Summary:

Chan et al. evaluated the role of RNase III, encoded by the rnc gene, in Salmonella virulence. Chan et al. first identified rnc among the genes with upregulated mRNA levels in virulent Salmonella isolates. The authors further showed that deletion of rnc resulted in increased double-stranded RNA (dsRNA) and reduced invasion rate and replication rate in an in vitro macrophage model. The authors then showed that transfection of total RNA of rnc knock-out strains upregulates (with respect to a WT Salmonella strain) expression levels of immune-related genes (e.g., TNF-a, IL-1B, etc.) in a dsRNA-dependent manner. The authors reported reduced SodA protein accumulation in the rnc knock-out strains, despite higher levels of sodA mRNA, suggesting a role of SodA in the protection against reactive oxygen species. Finally, the authors showed, using a mice model, the partial contribution of sodA in the restoration of virulence levels in the rnc knock-out strains.

Strengths:

(1) The manuscript is well written.

(2) The authors evaluated the impact of rnc deletion in both in vitro and mice infection models. Both experiment setups supported the contribution of rnc to Salmonella virulence.

(3) The authors tested the effect of rnc deletion in different genetic backgrounds (i.e., different bacterial isolates) offering additional support to their claims.

(4) Measurement of SodA protein levels nicely complemented and informed initial findings at the mRNA level.

Weaknesses:

(1) The authors failed to discuss how their work differentiates from recent studies of rnc deletion strains in Salmonella (NIH PMID: 38182942) and Escherichia coli (NIH PMID: 35456749). Remarkably, the first publication performed genome-wide transcriptional profiling of a rnc deletion Salmonella strain. The second publication explored the link between rnc and sodA in E. coli.

(2) The authors should explain what the criteria for selecting food and clinical isolates for molecular characterization were. This information is valuable for the reader as they may wonder about the impact of isolate selection in the study's conclusions. Similarly, the authors need to explain how they selected their controls for baseline gene expression, virulence, etc.. Furthermore, I wondered if they could use an avirulent Salmonella strain as an additional control.

(3) The authors do not perform any analysis of the differentially expressed genes (DEGs) identified in their study. They should leverage DEGs to expand their mechanistic insights of other genes or functional processes putatively linked to rnc activity and virulence. Additionally, authors should make transcriptional data and the output of their differential expression analysis (and the list of differentially expressed genes-DEGs) available to the readers. In fact, it is not clear how the DEGS were defined.

Reviewer #1 (Public review):

Summary:

The manuscript by Wang et al. investigates the interesting relationship between Streptococcus suis (S. suis) growth phases and levels of virulence factor, specifically the capsular polysaccharide (CPS), in the bacterial cell wall. S. suis is a gram positive bacterial pathogen that causes important losses in the swine industry worldwide. Interestingly, S. suis is also a resident bacteria in the pig tonsils. Vaccination against bacterial infections such as S. suis can be difficult, and understanding how the serotype of a bacterial pathogen impacts what body sites are infected and the dynamics of pathogen dissemination is critical. In this case, this manuscript looks at neuroinvasion of S. suis following intranasal delivery because this pathogen causes meningitis in infected hosts. Further, understanding host - pathogen interactions at early time points in the upper respiratory tract may have broad implications for vaccine development.

The authors use an understudied mouse intranasal infection model of S. suis to connect growth phase related CPS abundance to the pathogenicity of the bacteria in the nose and blood.

Adoptive transfer of serum against either CPS or V5 (five other virulence factors) supports the idea that S. suis CPS levels are an important factor that shapes how this bacterium reaches different organs.

Some conclusions are not completely supported by the present data, and at times the manuscript is disjoint and hard to follow. While the work has some interesting observations, additional experiments and controls are warranted to support the claims of the manuscript .

Strengths:

The model of intranasal infection is compelling to expand upon work previously done in vitro and with systemic routes of infection. The histology and fluorescent imaging of the olfactory epithelium and olfactory bulb complement work in figure 2 about the attachment of S. Suis to epithelial cells and the bacterial burden over time in different organs of figure 3. Histology was performed at 1 hour and 9 days after intranasal infection with stationary phase S. Suis and drives home that this pathogen can invade the olfactory nerve and may potentially cause bacterial meningitis seen in some infected swine.

The adoptive transfer of either anti-CPS or anti-V5 to mice before infection at both longer (12 hr), and shorter (1 hr) time points is useful to demonstrate that the changes in cell wall composition between the NALT/CSF and blood compartments result in different efficacy in clearing bacteria from those locations. This is fundamental for the development of vaccines for the swine industry and begs those developing other bacterial vaccines to consider what virulence factors are the most useful as neutralizing antibody targets at the sight of bacterial invasion.

Demonstrating that the amount of CPS within the cell wall of S. suis is related to the growth phase of the bacteria is an important consideration for vaccine development. While others had previously shown that CPS levels were higher in the blood than in the CNS, and that CPS decreases the invasion of epithelial cells, the close look at the olfactory epithelium at an early time point of 1 hr ties together in vitro findings. The control of a CPS-negative strain was critical to understanding their findings. The location and the microbial community that bacterial pathogens live within may change the growth phase and therefore also the cell wall components.

Weaknesses:

While the authors present compelling data that is relevant to the development of anti-bacterial vaccinations, the data does not completely match their assertions and there are places where some further investigation would further the impact of their interesting study.

Major concerns for the manuscript:

-The intranasal infections were done with S. suis in the stationary phase which has been shown to have less CPS on the cell wall. While this mimics the literature that shows S. Suis to have less CPS in the CNS, the difference in the pathogenesis of a log phase vs. stationary phage intranasal infection would be interesting. Especially because the bacteria is a part of the natural microbial community of swine tonsils, it is curious if the change in growth phase and therefore CPS levels may be a causative reason for pathogenic invasion in some pigs.

-The authors should consider taking the bacteria from NALT/CSF and blood and compare the lag times bacteria from different organs take to enter a log growth phase to show whether the difference in CPS is because S. suis in each location is in a different growth phase. If log phase bacteria were intranasally delivered, would it adapt a stationary phase life strategy? How long would that take?

-Authors should be cautious about claims about S. suis downregulating CPS in the NALT for increased invasion and upregulating CPS to survive phagocytosis in blood. While it is true that the data shows that there are different levels of CPS in these locations, the regulation and mechanism of the recorded and observed cell wall difference are not investigated past the correlation to the growth phase.

- The mouse model used in this manuscript is useful but cannot reproduce the nasal environment of the natural pig host. It is not clear if the NALTs of pigs and mice have similar microbial communities and how this may affect the pathogenesis of S. Suis in the mouse. Because the authors show a higher infection rate in the mouse with acetic acid, they may want to consider investigating what the mouse NALT microenvironment is naturally doing to exclude more bacterial invasion. Is it simply a host mismatch or is there something about the microbiome or steady-state immune system in the nose of mice that is different from pigs?

-I have some concerns regarding the images shown for neuroinvasion because I think the authors mistake several compartments of the mouse nasal cavity as well as the olfactory bulb. These issues are critical because neuroinvasion is one of the major conclusions of this work.

Reviewer #2 (Public review):

In this manuscript from Wang et al., the authors seek to examine the role of capsular polysaccharides (CPS) in invasive S. suis pathogenesis. They show that CPS thickness variations associate with isolation from different compartments within the infected mouse and that CPS promotes resistance to blood borne immune mechanisms. The authors conclude that thick CPS inhibits colonization/invasion of the NALT and rather antisera against non-CPS. These results are interesting and thought provoking and provide the continued basis for future experiments that delve further into immune mechanisms. However, there are serious concerns about data collection and interpretation that require further data to provide an accurate conclusion. Some of these concerns are highlighted below:

In figure 2, the authors conclude that high levels of CPS confer resistance to phagocytic killing in blood exposed S. suis. However, it seems equally likely that this is resistance against complement mediated killing. It would be important to compare S. suis killing in animals depleted of complement components (C3 and C5-9).

Intranasal administration non-CPS antisera provides a nice contrast to intravenous administration, especially in light of the recently identified "blood-olfactory barrier". Can the authors provide any insight into how long and where this antibody would be located after intranasal administration? Would this be antibody mediated cellular resistance, or something akin to simple antibody "neutralization"

The micrographs in Figure 7 depict anatomy from the respiratory mucosa. While there is no histochemical identification of neurons, the tissues labeled OE are almost certainly not olfactory and in fact respiratory. However, more troubling is that in figures 7A,a,b,e, and f, the lateral nasal organ has been labeled as the olfactory bulb. This undermines the conclusion of CNS invasion, and also draws into question other experiments in which the brain and CSF are measured.

Micrographs of brain tissue in 7B are taken from distal parts of the brain, whereas if olfactory neuroinvasion were occurring, the bacteria would be expected to arrive in the olfactory bulb. It's also difficult to understand how an inflammatory process would be developed to this point in the brain -even if we were looking at the appropriate region of the brain -within an hour of inoculation (is there a control for acetic acid induced brain inflammation?). Some explanations about the speed of the immune responses recorded are warranted.

The detected presence of S. suis in the CSF 0.5hr following intranasal inoculation is difficult to understand from an anatomical perspective. This is especially true when the amount of S. suis is nearly the same as that found within the NALT. Even motile pathogens would need far longer than 0.5hr to get into the brain, so it's exceedingly difficult to understand how this could occur so extensively in under an hour. The authors are quantifying CSF as anything that comes out of the brain after mincing. Firstly, this should more accurately be referred to as "brain", not CSF. Secondly, is it possible that the lateral nasal organ -which is mistakenly identified as olfactory bulb in figure 7- is being included in the CNS processing? This would explain the equivalent amounts of S. suis in NALT and "CSF".

To support their conclusions about neuroinvasion along the olfactory route and /CSF titer the authors should provide more compelling images to support this conclusion: sections stained for neurons and S. suis, images of the actual olfactory bulb (neurons, glomerular structure etc).

Reviewer #1 (Public review):

Summary:

The authors propose a new technique which they name "Multi-gradient Permutation Survival Analysis (MEMORY)" that they use to identify "Genes Steadily Associated with Prognosis (GEARs)" using RNA-seq data from the TCGA database. The contribution of this method is one of the key stated aims of the paper. The vast majority of the paper focuses on various downstream analyses that make use of the specific GEARs identified by MEMORY to derive biological insights, with a particular focus on lung adenocarcinoma (LUAD) and breast invasive carcinoma (BRCA) which are stated to be representative of other cancers and are observed to have enriched mitosis and immune signatures, respectively. Through the lens of these cancers, these signatures are the focus of significant investigation in the paper.

Strengths:

The approach for MEMORY is well-defined and clearly presented, albeit briefly. This affords statisticians and bioinformaticians the ability to effectively scrutinize the proposed methodology and may lead to further advancements in this field.

The scientific aspects of the paper (e.g., the results based on the use of MEMORY and the downstream bioinformatics workflows) are conveyed effectively and in a way that is digestible to an individual who is not deeply steeped in the cancer biology field.

Weaknesses:

I was surprised that comparatively little of the paper is devoted to the justification of MEMORY (i.e., the authors' method) for the identification of genes that are important broadly for the understanding of cancer. The authors' approach is explained in the methods section of the paper, but no rationale is given for why certain aspects of the method are defined as they are. Moreover, no comparison or reference is made to any other methods that have been developed for similar purposes and no results are shown to illustrate the robustness of the proposed method (e.g., is it sensitive to subtle changes in how it is implemented).

For example, in the first part of the MEMORY algorithm, gene expression values are dichotomized at the sample median and a log-rank test is performed. This would seemingly result in an unnecessary loss of information for detecting an association between gene expression and survival. Moreover, while dichotomizing at the median is optimal from an information theory perspective (i.e., it creates equally sized groups), there is no reason to believe that median-dichotomization is correct vis-à-vis the relationship between gene expression and survival. If a gene really matters and expression only differentiates survival more towards the tail of the empirical gene expression distribution, median-dichotomization could dramatically lower the power to detect group-wise differences.

Specifically, the authors' rationale for translating the Significant Probability Matrix into a set of GEARs warrants some discussion in the paper. If I understand correctly, for each cancer the authors propose to search for the smallest sample size (i.e., the smallest value of k_{j}) were there is at least one gene with a survival analysis p-value <0.05 for each of the 1000 sampled datasets. I base my understanding on the statement "We defined the sampling size k_{j} reached saturation when the max value of column j was equal to 1 in a significant-probability matrix. The least value of k_{j} was selected". Then, any gene with a p-value <0.05 in 80% of the 1000 sampled datasets would be called a GEAR for that cancer. The 80% value here seems arbitrary but that is a minor point. I acknowledge that something must be chosen. More importantly, do the authors believe this logic will work effectively in general? Presumably, the gene with the largest effect for a cancer will define the value of K_{j}, and, if the effect is large, this may result in other genes with smaller effects not being selected for that cancer by virtue of the 80% threshold. One could imagine that a gene that has a small-to-moderate effect consistently across many cancers may not show up as a gear broadly if there are genes with more substantive effects for most of the cancers investigated. I am taking the term "Steadily Associated" very literally here as I've constructed a hypothetical where the association is consistent across cancers but not extremely strong. If by "Steadily Associated" the authors really mean "Relatively Large Association", my argument would fall apart but then the definition of a GEAR would perhaps be suboptimal. In this latter case, the proposed approach seems like an indirect way to ensure there is a reasonable effect size for a gene's expression on survival.

The paper contains numerous post-hoc hypothesis tests, statements regarding detected associations and correlations, and statements regarding statistically significant findings based on analyses that would naturally only be conducted in light of positive results from analyses upstream in the overall workflow. Due to the number of statistical tests performed and the fact that the tests are sometimes performed using data-driven subgroups (e.g., the mitosis subgroups), it is highly likely that some of the findings in the work will not be replicable. Of course, this is exploratory science, and is to be expected that some findings won't replicate (the authors even call for further research into key findings). Nonetheless, I would encourage the authors to focus on the quantification of evidence regarding associations or claims (i.e., presenting effect estimates and uncertainty intervals), but to avoid the use of the term statistical significance owing to there being no clear plan to control type I error rates in any systematic way across the diverse analyses there were performed.

A prespecified analysis plan with hypotheses to be tested (to the extent this was already produced) and a document that defines the complete scope of the scientific endeavor (beyond that which is included in the paper) would strengthen the contribution by providing further context on the totality of the substantial work that has been done. For example, the focus on LUAD and BRCA due to their representativeness could be supplemented by additional information on other cancers that may have been investigated similarly but where results were not presented due to lack of space.

Reviewer #2 (Public review):

Summary:

The authors are trying to come up with a list of genes (GEAR genes) that are consistently associated with cancer patient survival based on TCGA database. A method named "Multi-gradient Permutation Survival Analysis" was created based on bootstrapping and gradually increasing the sample size of the analysis. Only the genes with consistent performance in this analysis process are chosen as potential candidates for further analyses.

Strengths:

The authors describe in detail their proposed method and the list of the chosen genes from the analysis. The scientific meaning and potential values of their findings are discussed in the context of published results in this field.

Weaknesses:

Some steps of the proposed method (especially the definition of survival analysis similarity (SAS) need further clarification or details since it would be difficult if anyone tries to reproduce the results. In addition, the multiplicity (a large number of p-values are generated) needs to be discussed and/or the potential inflation of false findings needs to be part of the manuscript.

If the authors can improve the clarity of the proposed method and there is no major mistake there, the proposed approach can be applied to other diseases (assuming TCGA type of data is available for them) to identify potential gene lists, based on which drug screening can be performed to identify potential target for development.

Reviewer #3 (Public review):

Summary:

The authors describe a valuable method to find gene sets that may correlate with a patient's survival. This method employs iterative tests of significance across randomised samples with a range of proportions of the original dataset. Those genes that show significance across a range of samples are chosen. Based on these gene sets, hub genes are determined from similarity scores.

Strengths:

MEMORY allows them to assess the correlation between a gene and patient prognosis using any available transcriptomic dataset. They present several follow-on analyses and compare the gene sets found to previous studies.

Weaknesses:

Unfortunately, the authors have not included sufficient details for others to reproduce this work or use the MEMORY algorithm to find future gene sets, nor to take the gene findings presented forward to be validated or used for future hypotheses.

Reviewer #4 (Public review):

The authors apply what I gather is a novel methodology titled "Multi-gradient Permutation Survival Analysis" to identify genes that are robustly associated with prognosis ("GEARs") using tumour expression data from 15 cancer types available in the TCGA. The resulting lists of GEARs are then interrogated for biological insights using a range of techniques including connectivity and gene enrichment analysis.

I reviewed this paper primarily from a statistical perspective. Evidently, an impressive amount of work has been conducted, and concisely summarised, and great effort has been undertaken to add layers of insight to the findings. I am no stranger to what an undertaking this would have been. My primary concern, however, is that the novel statistical procedure proposed, and applied to identify the gene lists, as far as I can tell offers no statistical error control or quantification. Consequently, we have no sense of what proportion of the highlighted GEAR genes and networks are likely to just be noise.

Major comments:

(1) The main methodology used to identify the GEAR genes, "Multi-gradient Permutation Survival Analysis" does not formally account for multiple testing and offers no formal error control. Meaning we are left with no understanding of what the family-wise (aka type 1) error rate is among the GEAR lists, nor the false discovery rate. I would generally recommend against the use of any feature selection methodology that does not provide some form of error quantification and/or control because otherwise we do not know if we are encouraging our colleagues and/or readers to put resources into lists of genes that contain more noise than not. There are numerous statistical techniques available these days that offer error control, including for lists of p-values from arbitrary sets of tests (see expansion on this and some review references below).

(2) Similarly, no formal significance measure was used to determine which of the strongest "SAS" connections to include as edges in the "Core Survival Network".

(3) There is, as far as I could tell, no validation of any identified gene lists using an independent dataset external to the presently analysed TCGA data.

(4) There are quite a few places in the methods section where descriptions were not clear (e.g. elements of matrices referred to without defining what the columns and rows are), and I think it would be quite challenging to re-produce some aspects of the procedures as currently described (more detailed notes below).

(5) There is a general lack of statistical inference offered. For example, throughout the gene enrichment section of the results, I never saw it stated whether the pathways highlighted are enriched to a significant degree or not.

Reviewer #1 (Public review):

Summary:

This study provides an in-depth analysis of syncytiotrophoblast (STB) gene expression at the single-nucleus (SN) and single-cell (SC) levels, using both primary human placental tissues and two trophoblast organoid (TO) models. The authors compare the older TO model, where STB forms internally (STBin), with a newer model where STB forms externally (STBout). Through a series of comparative analyses, the study highlights the necessity of using both SN and SC techniques to fully understand placental biology. The findings demonstrate that the STBout model shows more differentiated STBs with higher expression of canonical markers and hormones compared to STBin. Additionally, the study identifies both conserved and distinct gene expression profiles between the TO models and human placenta, offering valuable insights for researchers using TOs to study STB and CTB differentiation.

Strengths:

The study offers a comprehensive SC- and SN-based characterization of trophoblast organoid models, providing a thorough validation of these models against human placental tissues. By comparing the older STBin and newer STBout models, the authors effectively demonstrate the improvements in the latter, particularly in the differentiation and gene expression profiles of STBs. This work serves as a critical resource for researchers, offering a clear delineation of the similarities and differences between TO-derived and primary STBs. The use of multiple advanced techniques, such as high-resolution sequencing and trajectory analysis, further enhances the study's contribution to the field.

Weaknesses:

While the study is robust, some areas could benefit from further clarification. The importance of the TO model's orientation and its impact on outcomes could be emphasized more in the introduction. The differences in cluster numbers/names between primary tissue and TO data need a clearer explanation, and consistent annotation could aid in comparison. The rationale for using SN sequencing over SC sequencing for TO evaluations should be clarified, especially regarding the potential underrepresentation of certain trophoblast subsets. Additionally, more evidence could be provided to support the claims about STB differentiation in the STBout model and to determine whether its differentiation trajectory is unique or simply more advanced than in STBin.

Reviewer #2 (Public review):

Summary:

In this study, the authors aimed to elucidate the formation and differentiation of syncytiotrophoblast (STB) cells by analyzing placental tissue and trophoblast organoids (TOs) using single-nucleus (SN) and single-cell (SC) RNA sequencing. They identified three distinct nuclear subtypes within the STB and explored the relationship between STB gene expression changes, developmental stages, and environmental contexts. The study emphasizes the utility of TOs as models for understanding STB differentiation and highlights novel gene markers, such as RYBP, involved in STB development.

Strengths:

(1) The use of SN and SC RNA sequencing provides a detailed analysis of STB formation and differentiation.

(2) The identification of distinct STB subtypes and novel gene markers such as RYBP offers new insights into STB development.

Weaknesses:

(1) Inconsistencies in data presentation.

(2) Questionable interpretation of lncRNA signals: The use of long non-coding RNA (lncRNA) signals as cell type-specific markers may represent sequencing noise rather than true markers.

To improve the study's validity and significance, it is crucial to address the inconsistencies and to provide additional evidence for the claims. Supplementing with immunofluorescence staining for validating the distribution of STB_in, STB_out, and EVT_enrich in the organoid models is recommended to strengthen the results and conclusions.

Reviewer #3 (Public review):

In this report, Keenen et al. present a thoroughly characterized platform for identifying potential molecular mechanisms regulating syncytiotrophoblast cell functions in placental biology. The application of single-cell assessments to identify developmental trajectories of this lineage has been challenging due to the complex, multinucleated structure of the syncytium. The authors provide a comprehensive comparative assessment of term placental tissue and three independent trophoblast organoid models. They use single-cell and single-nucleus RNA sequencing followed by differential gene expression and pseudotime analyses to identify subpopulations and differentiation trajectories. They further compare the datasets generated in this study to publicly available datasets from first-trimester placental tissue. The work is timely as optimization of trophoblast organoids is an evolving topic in placental research. Careful characterization of in vitro models has been noted as essential for model selection and result interpretation in the field.

The study elucidates syncytiotrophoblast nucleus subtypes and proportions in three different organoid models and compares subtypes and gene expression signatures to placental tissues. This work advances the field by demonstrating the utility of different trophoblast organoids to model syncytiotrophoblast differentiation. The in-depth characterization of cell types comprising the different organoid models and how they compare to placental tissue will help to inform model selection for future experimentation in the field. Defining cell composition and cell differentiation trajectories will also aid in data interpretation for data generated by these tissue and model sources. Overall, the conclusions presented in the manuscript are well supported by the data. The figures, as presented, are informative and striking.

The authors present outstanding progress toward their aim of identifying, "the underlying control of the syncytiotrophoblast". They identify the chromatin remodeler, RYBP, as well as other regulatory networks that they propose are critical to syncytiotrophoblast development. This study is limited in fully addressing the aim, however, as functional evidence for the contributions of the factors/pathways to syncytiotrophoblast cell development is needed. Future experimentation testing the hypotheses generated by this work will define the essentiality of the identified factors to syncytiotrophoblast development and function. Localization and validation of the identified factors within tissue and at the protein level will also provide further contextual evidence to address the hypotheses generated.

Reviewer #1 (Public review):

Summary:

Ghone et al show that HIV-1 Vif causes a pseudo-metaphase arrest rather than a G2 arrest. The metaphase arrest correlates with misregulation of the kinetochore which could be explained by the loss of phosphatase functions that determine chromosome-microtubule interactions.

Strengths:

The single-cell imaging using different reporters of cell cycle progression is very elegant and the quantitation is convincing. The authors clearly show that what others have characterized as a G2 arrest by flow cytometry is somewhat later in metaphase and correlates with kinetochore misregulation.

Weaknesses:

(1) The major problem with the paper is trying to connect what is observed in tumor cell lines with actual infections in primary T cells. While all of the descriptive work in cell lines is convincing, none of these cells are relevant targets and tumor cells have different cell death and cell cycle regulation than primary T cells. Thus, while Vif might well do all of the things described in the manuscript, it is a stretch to connect any of it to what happens in vivo.

(2) Line 109 and elsewhere. The ability of Vif to cause cell cycle arrest and bind PP2A subunits is not a completely conserved feature. Rather, it is quite variable in different HIV-1 strains. (e.g. https://doi.org/10.1016/j.bbrc.2020.04.123 and https://elifesciences.org/articles/53036). Therefore, it is necessary for the authors to quite clearly use strain designations in the manuscript rather than a generic "Vif", and to more clearly describe the viruses being used.

(3) Figure 5: This figure shows disruption of PP2A-B56 at the kinetochores. However, is this specific to the kinetochores? Since Vif has been described to more broadly degrade PP2A-B56, could this not be a result of a more general decrease in PP2A activity throughout the cell?

Reviewer #2 (Public review):

Summary

The authors characterize the cell-cycle arrest induced by HIV-1 Vif in infected cells. They show this arrest is not at G2/M as previously thought but during metaphase. They show that the metaphase plate forms normally but progression to anaphase is massively delayed, and chromosome segregation is dysregulated in a manner consistent with impaired assembly of microtubules at the kinetochore. This correlates with the lack of recruitment of B56-subunits of PP2 phosphatase which are known degradation targets of Vif, suggesting that this weakens and unbalances the microtubule-mediated forces on the separating chromosomes.

Strengths

The authors present a very well-performed set of quantitative live cell imaging experiments that convincingly show a difference between Vif and Vpr-mediated cell cycle arrests. Through an in-depth characterization of the Vif-mediated block in metaphase, they make a strong case for this phenotype being tied to the degradation of PP2-B56 by Vif. Furthermore, it is important that they have performed most of these experiments with virally infected cells, meaning that their observations are observable at relevant viral expression levels of Vif.

Weaknesses

Experimentally there is very little to criticize with respect to the cellular systems used. Data from 10.1016/j.bbrc.2020.04.123 has identified selective mutants that fail to degrade B56 while maintaining A3G degradation by Cul5, and it would be nice to confirm that such a mutant behaves like the delta-Vif virus when examining metaphase, but selective ablation of B56 during mitosis to mimic Vif is would expect to be very challenging and beyond the scope.

Where I would raise some criticism is in the relevance of these observations to the replication and pathogenesis of the virus itself, which the authors do not address or discuss. Firstly, despite clear data that both Vpr and Vif can lead to a cell cycle arrest in cycling cells, it has never been particularly clear why the virus does this. While I would agree with the authors that Vif results in the metaphase arrest through targeting B56-PP2A, this may not be the reason WHY the virus targets one of the cell's major phosphatases, but rather a knock-on effect of doing so. I appreciate that this is beyond the scope of the study, but it is something I feel should be discussed rather than the narrow mechanistic points made in the discussion. Secondly, the authors suggest that this activity of Vif is a major cause of apoptosis in infected cells and perhaps CD4+ T cell depletion in vivo. It would be good to quantify how much apoptosis is Vif-dependent in infected primary human CD4+ T cells rather than transformed tumor cells, and whether this correlates with the Vif-mediated induction of a pseudometaphase.

Joint Public Review:

Summary:

The behavioral switch between foraging and mating is important for resource allocation in insects. This study investigated the role of the neuropeptide, sulfakinin, and of its receptor, the sulfakinin receptor 1 (SkR1), in mediating this switch in the oriental fruit fly, Bactrocera dorsalis. The authors use genetic disruption of sulfakinin and of SkR1 to provide strong evidence that changes in sulfakinin signaling alter odorant receptor expression profiles and antennal responses and that these changes mediate the behavioral switch. The combination of molecular and physiological data is a strength of the study. Additional work would be needed to determine whether the physiological and molecular changes observed account for the behavioral changes observed.

Strengths:

(1) The authors show that sulfakinin signaling in the olfactory organ mediates the switch between foraging and mating, thereby providing evidence that peripheral sensory inputs contribute to this important change in behavior.

(2) The authors' development of an assay to investigate the behavioral switch and their use of different approaches to demonstrate the role of sulfakinin and SkR1 in this process provides strong support for their hypothesis.

(3) The manuscript is overall well-organized and documented.

Weaknesses:

(1) The authors claim that sulfakinin acts directly on SkR1-positive neurons to modulate the foraging and mating behaviors in B. dorsalis. The authors also indicated in the schematic that satiation suppresses SkR1 expression. Additional experiments and more a detailed discussion of the results would help support these claims.

(2) The findings reported could be strengthened with additional experimental details regarding time of day versus duration of starvation effects and additional genetic controls, amongst others.

Reviewer #1 (Public review):

Summary:

This work computationally characterized the threat-reward learning behavior of mice in a recent study (Akiti et al.), which had prominent individual differences. The authors constructed a Bayes-adaptive Markov decision process model and fitted the behavioral data by the model. The model assumed (i) hazard function starting from a prior (with free mean and SD parameters) and updated in a Bayesian manner through experience (actually no real threat or reward was given in the experiment), (ii) risk-sensitive evaluation of future outcomes (calculating lower 𝛼 quantile of outcomes with free 𝛼 parameter), and (iii) heuristic exploration bonus. The authors found that (i) brave animals had more widespread hazard priors than timid animals and thereby quickly learned that there was in fact little real threat, (ii) brave animals may also be less risk-aversive than timid animals in future outcome evaluation, and (iii) the exploration bonus could explain the observed behavioral features, including the transition of behavior from the peak to steady-state frequency of bout. Overall, this work is a novel interesting analysis of threat-reward learning, and provides useful insights for future experimental and theoretical work. However, there are several issues that I think need to be addressed.

Strengths:

(1) This work provides a normative Bayesian account for individual differences in braveness/timidity in reward-threat learning behavior, which complements the analysis by Akiti et al. based on model-free threat reinforcement learning.

(2) Specifically, the individual differences were characterized by (i) the difference in the variance of hazard prior and potentially also (ii) the difference in the risk-sensitivity in the evaluation of future returns.

Weakness:

(1) Theoretically the effect of prior is diluted over experience whereas the effect of biased (risk-aversive) evaluation persists, but these two effects could not be teased apart in the fitting analysis of the current data.

(2) It is currently unclear how (whether) the proposed model corresponds to neurobiological (rather than behavioral) findings, different from the analysis by Akiti et al.

Major points:

(1) Line 219<br /> It was assumed that the exploration bonus was replenished at a steady rate when the animal was at the nest. An alternative way would be assuming that the exploration bonus slowly degraded over time or experience, and if doing so, there appears to be a possibility that the transition of the bout rate from peak to steady-state could be at least partially explained by such a decrease in the exploration bonus.

(2) Line 237- (Section 2.2.6, 2.2.7, Figures 7, 9)<br /> I was confused by the descriptions about nCVaR. I looked at the cited original literature Gagne & Dayan 2022, and understood that nCVaR is a risk-sensitive version of expected future returns (equation 4) with parameter α (α-bar) (ranging from 0 to 1) representing risk preference. Line 269-271 and Section 4.2 of the present manuscript described (in my understanding) that α was a parameter of the model. Then, isn't it more natural to report estimated values of α, rather than nCVaR, for individual animals in Section 2.2.6, 2.2.7, Figures 7, 9 (even though nCVaR monotonically depends on α)? In Figures 7 and 9, nCVaR appears to be upper-bounded to 1. The upper limit of α is 1 by definition, but I have no idea why nCVaR was also bounded by 1. So I would like to ask the authors to add more detailed explanations on nCVaR. Currently, CVaR is explained in Lines 237-243, but actually, there is no explanation about nCVaR rather than its formal name 'nested conditional value at risk' in Line 237.

(3) Line 333 (and Abstract)<br /> Given that animals' behaviors could be equally well fitted by the model having both nCVaR (free α) and hazard prior and the alternative model having only hazard prior (with α = 1), may it be difficult to confidently claim that brave (/timid) animals had risk-neutral (/risk-aversive) preference in addition to widespread (/low-variance) hazard prior? Then, it might be good to somewhat weaken the corresponding expression in the Abstract (e.g., add 'potentially also' to the result for risk sensitivity) or mention the inseparability of risk sensitivity and prior belief pessimism (e.g., "... although risk sensitivity and prior belief pessimism could not be teased apart").

Reviewer #2 (Public review):

Shen and Dayan build a Bayes adaptive Markov decision process model with three key components: an adaptive hazard function capturing potential predation, an intrinsic reward function providing the urge to explore, and a conditional value at risk (CvaR, closely related to probability distortion explanations of risk traits). The model itself is very interesting and has many strengths including considering different sources of risk preference in generating behavior under uncertainty. I think this model will be useful to consider for those studying approach/avoid behaviors in dynamic contexts.

The authors argue that the model explains behavior in a very simple and unconstrained behavioral task in which animals are shown novel objects and retreat from them in various manners (different body postures and patterns of motor chunks/syllables). The model itself does capture lots of the key mouse behavioral variability (at least on average on a mouse-by-mouse basis) which is interesting and potentially useful. However, the variables in the model - and the internal states it implies the mice have during the behavior - are relatively unconstrained given the wide range of explanations one can offer for the mouse behavior in the original study (Akiti et al). This reviewer commends the authors on an original and innovative expansion of existing models of animal behaviour, but recommends that the authors revise their study to reflect the obvious challenges. I would also recommend a reduction in claiming that this exercise gives a normative-like or at least quantitative account of mental disorders.

My main comment is that this paper is a very nice model creation that can characterize the heterogeneity rodent behavior in a very simple approach/avoid context (Akiti et al; when a novel object is placed in an arena) that itself can be interpreted in a multitude of ways. The use of terms like "exploration", "brave", etc in this context is tricky because the task does not allow the original authors (Akiti et al) to quantify these "internal states" or "traits" with the appropriate level of quantitative detail to say whether this model is correct or not in capturing the internal states that result in the rodent behavior. That said, the original behavioral setup is so simple that one could imagine capturing the behavioral variability in multiple ways (potentially without evoking complex computations that the original authors never showed the mouse brain performs). I would recommend reframing the paper as a new model that proposes a set of internal states that could give rise to the behavioral heterogeneity observed in Akiti et al, but nonetheless is at this time only a hypothesis. Furthermore, an explanation of what would be really required to test this would be appreciated to make the point clearer.

Reviewer #3 (Public review):

Summary:

The manuscript presents computational modelling of the behaviour of mice during encounters with novel and familiar objects, originally reported by Akiti et al. (Neuron 110, 2022). Mice typically perform short bouts of approach followed by a retreat to a safe distance, presumably to balance exploration to discover possible rewards with the potential risk of predation. However, there is considerable heterogeneity in this exploratory behaviour, both across time as an individual subject becomes more confident in approaching the object, and across subjects; with some mice rapidly becoming confident to closely explore the object, while other timid mice never become fully confident that the object is safe. The current work aims to explain both the dynamics of adaptation of individual animals over time, and the quantitative and qualitative differences in behaviour between subjects, by modelling their behaviour as arising from model-based planning in a Bayes adaptive Markov Decision Process (BAMDP) framework, in which the subjects maintain and update probabilistic estimates of the uncertain hazard presented by the object, and rationally balance the potential reward from exploring the object with the potential risk of predation it presents.

In order to fit these complex models to the behaviour the authors necessarily make substantial simplifying assumptions, including coarse-graining the exploratory behaviour into phases quantified by a set of summary statistics related to the approach bouts of the animal. Inter-individual variation between subjects is modelled both by differences in their prior beliefs about the possible hazard presented by the object and by differences in their risk preference, modelled using a conditional value at risk (CVaR) objective, which focuses the subject's evaluation on different quantiles of the expected distribution of outcomes. Interestingly these two conceptually different possible sources of inter-subject variation in brave vs timid exploratory behaviour turn out not to be dissociable in the current dataset as they can largely compensate for each other in their effects on the measured behaviour. Nonetheless, the modelling captures a wide range of quantitative and qualitative differences between subjects in the dynamics of how they explore the object, essentially through differences in how subject's beliefs about the potential risk and reward presented by the object evolve over the course of exploration, and are combined to drive behaviour.

Exploration in the face of risk is a ubiquitous feature of the decision-making problem faced by organisms, with strong clinical relevance, yet remains poorly understood and under-studied, making this work a timely and welcome addition to the literature.

Strengths:

(1) Individual differences in exploratory behaviour are an interesting, important, and under-studied topic.

(2) Application of cutting-edge modelling methods to a rich behavioural dataset, successfully accounting for diverse qualitative and qualitative features of the data in a normative framework.

(3) Thoughtful discussion of the results in the context of prior literature.

Limitations:

(1) The model-fitting approach used of coarse-graining the behaviour into phases and fitting to their summary statistics may not be applicable to exploratory behaviours in more complex environments where coarse-graining is less straightforward.

(2) Some aspects of the work could be more usefully clarified within the manuscript.

Reviewer #1 (Public review):

As a starting point, the authors discuss the so-called "additive partitioning" (AP) method proposed by Loreau & Hector in 2001. The AP is the result of a mathematical rearrangement of the definition of overyielding, written in terms of relative yields (RY) of species in mixtures relative to monocultures. One term, the so-called complementarity effect (CE), is proportional to the average RY deviations from the null expectations that plants of both species "do the same" in monocultures and mixtures. The other term, the selection effect (SE), captures how these RY deviations are related to monoculture productivity. Overall, CE measures whether relative biomass gains differ from zero when averaged across all community members, and SE, whether the "relative advantage" species have in the mixture, is related to their productivity. In extreme cases, when all species benefit, CE becomes positive. When large species have large relative productivity increases, SE becomes positive. This is intuitively compatible with the idea that niche complementarity mitigates competition (CE>0), or that competitively superior species dominate mixtures and thereby driver overyielding (SE>0).

However, it is very important to understand that CE and SE capture the "statistical structure" of RY that underlies overyielding. Specifically, CE and SE are not the ultimate biological mechanisms that drive overyielding, and never were meant to be. CE also does not describe niche complementarity. Interpreting CE and SE as directly quantifying niche complementarity or resource competition, is simply wrong, although it sometimes is done. The criticism of the AP method thus in large part seems unwarranted. The alternative methods the authors discuss (lines 108-123) are based on very similar principles.

The authors now set out to develop a method that aims at linking response patterns to "more true" biological mechanisms.

Assuming that "competitive dominance" is key to understanding mixture productivity, because "competitive interactions are the predominant type of interspecific relationships in plants", the authors introduce "partial density" monocultures, i.e. monocultures that have the same planting density for a species as in a mixture. The idea is that using these partial density monocultures as a reference would allow for isolating the effect of competition by the surrounding "species matrix".

The authors argue that "To separate effects of competitive interactions from those of other species interactions, we would need the hypothesis that constituent species share an identical niche but differ in growth and competitive ability (i.e., absence of positive/negative interactions)." - I think the term interaction is not correctly used here, because clearly competition is an interaction, but the point made here is that this would be a zero-sum game.

The authors use the ratio of productivity of partial density and full-density monocultures, divided by planting density, as a measure of "competitive growth response" (abbreviated as MG). This is the extra growth a plant individual produces when intraspecific competition is reduced.

Here, I see two issues: first, this rests on the assumption that there is only "one mode" of competition if two species use the same resources, which may not be true, because intraspecific and interspecific competition may differ. Of course, one can argue that then somehow "niches" are different, but such a niche definition would be very broad and go beyond the "resource set" perspective the authors adopt. Second, this value will heavily depend on timing and the relationship between maximum initial growth rates and competitive abilities at high stand densities.

The authors then progress to define relative competitive ability (RC), and this time simply uses monoculture biomass as a measure of competitive ability. To express this biomass in a standardized way, they express it as different from the mean of the other species and then divide by the maximum monoculture biomass of all species.

I have two concerns here: first, if competitive ability is the capability of a species to preempt resources from a pool also accessed by another species, as the authors argued before, then this seems wrong because one would expect that a species can simply be more productive because it has a broader niche space that it exploits. This contradicts the very narrow perspective on competitive ability the authors have adopted. This also is difficult to reconcile with the idea that specialist species with a narrow niche would outcompete generalist species with a broad niche. Second, I am concerned by the mathematical form. Standardizing by the maximum makes the scaling dependent on a single value.

As a final step, the authors calculate a "competitive expectation" for a species' biomass in the mixture, by scaling deviations from the expected yield by the product MG ⨯ RC. This would mean a species does better in a mixture when (1) it benefits most from a conspecific density reduction, and (2) has a relatively high biomass.

Put simply, the assumption would be that if a species is productive in monoculture (high RC), it effectively does not "see" the competitors and then grows like it would be the sole species in the community, i.e. like in the partial density monoculture.

Overall, I am not very convinced by the proposed method.

Comments on revised version:

Only minimal changes were made to the manuscript, and they do not address the main points that were raised.

Reviewer #2 (Public review):

This manuscript by Tao et al. reports on an effort to better specify the underlying interactions driving the effects of biodiversity on productivity in biodiversity experiments. The authors are especially concerned with the potential for competitive interactions to drive positive biodiversity-ecosystem functioning relationships by driving down the biomass of subdominant species. The authors suggest a new partitioning schema that utilizes a suite of partial density treatments to capture so-called competitive ability. While I agree with the authors that understanding the underlying drivers of biodiversity-ecosystem functioning relationships is valuable - I am unsure of the added value of this specific approach for several reasons.

Comments on revised version:

The authors changed only one minor detail in response to the last round of reviews.

Reviewer #3 (Public review):

Summary:

This manuscript claims to provide a new null hypothesis for testing the effects of biodiversity on ecosystem functioning. It reports that the strength of biodiversity effects changes when this different null hypothesis is used. This main result is rather inevitable. That is, one expects a different answer when using a different approach. The question then becomes whether the manuscript's null hypothesis is both new and an improvement on the null hypothesis that has been in use in recent decades.

Strengths:

In general, I appreciate studies like this that question whether we have been doing it all wrong and I encourage consideration of new approaches.

Weaknesses:

Despite many sweeping critiques of previous studies and bold claims of novelty made throughout the manuscript, I was unable to find new insights. The manuscript fails to place the study in the context of the long history of literature on competition and biodiversity and ecosystem functioning. The Introduction claims the new approach will address deficiencies of previous approaches, but after reading further I see no evidence that it addresses the limitations of previous approaches noted in the Introduction. Furthermore, the manuscript does not reproducibly describe the methods used to produce the results (e.g., in Table 1) and relies on simulations, claiming experimental data are not available when many experiments have already tested these ideas and not found support for them. Finally, it is unclear to me whether rejecting the 'new' null hypothesis presented in the manuscript would be of interest to ecologists, agronomists, conservationists, or others.

Comments on revised version:

Please see review comments on the previous version of this manuscript. The authors have not revised their manuscript to address most of the issues previously raised by reviewers.