Reviewer #1 (Public review):

Summary:

Using single-cell RNA sequencing and bioinformatics approaches, the authors aimed to discover if and how cells carrying mutations common to clonal haematopoiesis were more adherent to endothelial cells.

Strengths:

(1) The authors used matched blood and adipose tissue samples from the same patients (with the exception of the control people) to conduct their analysis.

(2) The use of bioinformatics and in-silico approaches helped to fast-track their aims to test specific inhibitors in their model cell adhesion system.

Weaknesses:

(1) The analysis was done on pooled cells; it would have been interesting to know if the same adhesion gene signatures were observed across the donors.

(2) The adhesion assays were conducted under static conditions; shear flow adhesion experiments would have been better. Mixed cultures using cell trackers would have been even better.

(3) In the intervention studies, the authors should have directly targeted the monocytes (not the endothelial cells) and should have also included DNMT3A mutant/KO cells to show specificity to TET2 CHIP.

Reviewer #2 (Public review):

Summary:

The authors describe potential mechanisms underlying the changes in endothelial-monocyte interactions in patients with clonal hematopoiesis of indeterminate potential (CHIP), including reduced velocity and increased ligand interactions of CHIP-mutated monocytes. They use a combination of transcriptomics (some for the first time in these tissues in patients with CHIP), in silico analyses, and ex vivo approaches to outline the changes that occur in blood monocytes derived from patients with CHIP. These findings advance the current field, which has previously mostly used mice and/or has been focused on cancer outcomes. The authors identify distinct alterations in signaling downstream of DNTM3A or TET2 mutations, which further distinguish two major mutations that contribute to CHIP.

Strengths:

(1) Combinatorial transcriptomics was used to identify potential therapeutic targets, which is an important proof-of-concept for multiple fields.

(2) The authors identify distinct ligand interactions downstream of TET2 and DNMT3A mutations.

Weaknesses:

(1) The authors extrapolate findings in adipose tissue in diabetic patients to vascular disease (ostensibly in the carotid or cardiac arteries), citing the difficulty of using tissue-matched samples. Broad-reaching conclusions need to be backed up in the relevant systems, considering how different endothelial cells in various vascular beds react. Considering these data were obtained with n=3 patients being sufficient to identify these changes, it seems that this can be performed (perhaps in silico) in the correct tissue.

(2) The selection/exclusion criteria for the diabetes samples are not noted, and therefore, the relevant conclusions cannot be fully evaluated, nor is the source of adipose tissue stated.

Appraisal:

While authors describe how to as well as the technical feasibility of integrating a number of transcriptomic techniques, they do not seem to do so to produce highly compelling data or targets within this manuscript. The potential is there to drill down to mechanisms; however, the data gathered herein do not highlight novel targets. For example, CXCL2 and 3 are already shown to be differentially expressed in TET2 loss combined with LDL treatment in the macrophages of mice. Furthermore, these authors then show that in humans, the prototypical CXC chemokine, IL8 (which mice lack), is significantly higher in TET2-mutated patients (DOI: 10.1056/NEJMoa1701719). The authors should demonstrate the utility of their transcriptomics by identifying and testing novel targets and focusing on the proper disease states. This could easily be a deep dive into CHIP in adipose tissue in diabetic patients.

Reviewer #1 (Public review):

Summary:

The study by Castro et al. presents an interesting blueprint for designing influenza immunogens that can induce cross-group influenza-specific antibodies. The authors used a structure-based design to transplant receptor binding site (RBS) residues from H5 and H3 into an H1 scaffold. In addition, they assembled the transplanted structures as heterotrimers. They characterized the constructs structurally and used them to immunize mice to define ELISA binding and neutralizing antibodies (Abs) to different influenza strains.

Strengths and Weaknesses:

The authors succeeded in generating the different, correctly folded immunogens. The heterotrimers would benefit from more characterization: it remains unclear whether they are even formed or whether the sample is a mix of homotrimers and whether some combinations are more likely than others. While some of these questions are complex to answer, authors should at least confirm the presence of heterotrimers.

While all constructs were able to elicit H1-specific Abs, different immunogens displayed differential ability to induce a response to the transplanted epitope. While H3-transplant resulted in H3-specific Abs, this was not the case for H5 or the heterotrimers. The importance of the finding is that authors are able to elicit polyclonal Abs neutralizing group 1 and group 2 influenza viruses with a single immunogen. A more in-depth discussion on why the H3-transplant but not the H5-transplant resulted in those specific Abs could be beneficial.

Overall, the work is a proof of concept that H1-H3 chimeric proteins can be produced and an important first step towards computational vaccines, inducing Abs to multiple groups.

Reviewer #2 (Public review):

Summary:

The manuscript from Castro et al describes the engineering of influenza hemagglutinin H1-based head domains that display receptor-binding-site residues from H5 and H3 HAs. The initial head-only chimeras were able to bind to FluA20, which recognizes the trimer interface, but did not bind well to H5 or H3-specific antibodies. Furthermore, these constructs were not particularly stable in solution as assessed by low melting temperatures. Crystal structures of each chimeric head in complex with FluA20 were obtained, demonstrating that the constructs could adopt the intended conformation upon stabilization with FluA20. The authors next placed the chimeric heads onto an H1 stalk to create homotrimeric HA ectodomains, as well as a heterotrimeric HA ectodomain. The homotrimeric chimeric HAs were better behaved in solution, and H3- and H5-specific antibodies bound to these trimers with affinities that were only about 10-fold weaker compared to their respective wildtype HAs. The heterotrimeric chimeric HA showed transient stability in solution and could bind more weakly to the H3- and H5-specific antibodies. Mice immunized with these trimers elicited cross-reactive binding antibodies, although the cross-neutralizing titers were less robust. The most positive result was that the H1H3 trimer was able to elicit sera that neutralized both H1 and H3 viruses.

Strengths:

The manuscript is very well-written with clear figures. The biophysical and structural characterizations of the antigen were performed to a high standard. The engineering approach is novel, and the results should provide a basis for further iteration and improvement of RBS transplantation.

Weaknesses:

The main limitation of the study is that there are no statistical tests performed for the immunogenicity results shown in Figures 4 and 5. It is therefore unknown whether the differences observed are statistically significant. Additionally, fits of the BLI data in Figure 3 to the binding model used to determine the binding constants should be shown.

Reviewer #1 (Public review):

Summary:

This is a careful and comprehensive study demonstrating that effector-dependent conformational switching of the MT lattice from compacted to expanded deploys the alpha tubulin C-terminal tails so as to enhance their ability to bind interactors.

Strengths:

The authors use 3 different sensors for the exposure of the alpha CTTs. They show that all 3 sensors report exposure of the alpha CTTs when the lattice is expanded by GMPCPP, or KIF1C, or a hydrolysis-deficient tubulin. They demonstrate that expansion-dependent exposure of the alpha CTTs works in tissue culture cells as well as in vitro.

Weaknesses:

There is no information on the status of the beta tubulin CTTs. The study is done with mixed isotype microtubules, both in cells and in vitro. It remains unclear whether all the alpha tubulins in a mixed isotype microtubule lattice behave equivalently, or whether the effect is tubulin isotype-dependent. It remains unclear whether local binding of effectors can locally expand the lattice and locally expose the alpha CTTs.

Appraisal:

The authors have gone to considerable lengths to test their hypothesis that microtubule expansion favours deployment of the alpha tubulin C-terminal tail, allowing its interactors, including detyrosinase enzymes, to bind. There is a real prospect that this will change thinking in the field. One very interesting possibility, touched on by the authors, is that the requirement for MAP7 to engage kinesin with the MT might include a direct effect of MAP7 on lattice expansion.

Impact:

The possibility that the interactions of MAPS and motors with a particular MT or region feed forward to determine its future interaction patterns is made much more real. Genuinely exciting.

Reviewer #2 (Public review):

The unstructured α- and β-tubulin C-terminal tails (CTTs), which differ between tubulin isoforms, extend from the surface of the microtubule, are post-translationally modified, and help regulate the function of MAPs and motors. Their dynamics and extent of interactions with the microtubule lattice are not well understood. Hotta et al. explore this using a set of three distinct probes that bind to the CTTs of tyrosinated (native) α-tubulin. Under normal cellular conditions, these probes associate with microtubules only to a limited extent, but this binding can be enhanced by various manipulations thought to alter the tubulin lattice conformation (expanded or compact). These include small-molecule treatment (Taxol), changes in nucleotide state, and the binding of microtubule-associated proteins and motors. Overall, the authors conclude that microtubule lattice "expanders" promote probe binding, suggesting that the CTT is generally more accessible under these conditions. Consistent with this, detyrosination is enhanced. Mechanistically, molecular dynamics simulations indicate that the CTT may interact with the microtubule lattice at several sites, and that these interactions are affected by the tubulin nucleotide state.

Strengths:

Key strengths of the work include the use of three distinct probes that yield broadly consistent findings, and a wide variety of experimental manipulations (drugs, motors, MAPs) that collectively support the authors' conclusions, alongside a careful quantitative approach.

Weaknesses:

The challenges of studying the dynamics of a short, intrinsically disordered protein region within the complex environment of the cellular microtubule lattice, amid numerous other binders and regulators, should not be understated. While it is very plausible that the probes report on CTT accessibility as proposed, the possibility of confounding factors (e.g., effects on MAP or motor binding) cannot be ruled out. Sensitivity to the expression level clearly introduces additional complications. Likewise, for each individual "expander" or "compactor" manipulation, one must consider indirect consequences (e.g., masking of binding sites) in addition to direct effects on the lattice; however, this risk is mitigated by the collective observations all pointing in the same direction.

The discussion does a good job of placing the findings in context and acknowledging relevant caveats and limitations. Overall, this study introduces an interesting and provocative concept, well supported by experimental data, and provides a strong foundation for future work. This will be a valuable contribution to the field.

Reviewer #3 (Public review):

Summary:

In this study, the authors investigate how the structural state of the microtubule lattice influences the accessibility of the α-tubulin C-terminal tail (CTT). By developing and applying new biosensors, they reveal that the tyrosinated CTT is largely inaccessible under normal conditions but becomes more accessible upon changes to the tubulin conformational state induced by taxol treatment, MAP expression, or GTP-hydrolysis-deficient tubulin. The combination of live imaging, biochemical assays, and simulations suggests that the lattice conformation regulates the exposure of the CTT, providing a potential mechanism for modulating interactions with microtubule-associated proteins. The work addresses a highly topical question in the microtubule field and proposes a new conceptual link between lattice spacing and tail accessibility for tubulin post-translational modification.

Strengths:

(1) The study targets a highly relevant and emerging topic-the structural plasticity of the microtubule lattice and its regulatory implications.

(2) The biosensor design represents a methodological advance, enabling direct visualization of CTT accessibility in living cells.

(3) Integration of imaging, biochemical assays, and simulations provides a multi-scale perspective on lattice regulation.

(4) The conceptual framework proposed lattice conformation as a determinant of post-translational modification accessibility is novel and potentially impactful for understanding microtubule regulation.

Weaknesses:

There are a number of weaknesses in the paper, many of which can be addressed textually. Some of the supporting evidence is preliminary and would benefit from additional experimental validation and clearer presentation before the conclusions can be considered fully supported.

In particular, the authors should directly test in vitro whether Taxol addition can induce lattice exchange (see comments below).

Reviewer #1 (Public review):

The remodeling of macromolecular substrates by AAA+ proteins is an essential aspect of life at the molecular scale, and understanding conserved and divergent features of substrate recognition across the AAA+ protein family remains an ongoing area of research. AAA+ proteins are highly modular and typically combine N-terminal recognition domain(s) with ATPase domain(s) to recognize and unfold some macromolecular target, such as dsDNA or protein substrates. This can be coupled to activity by additional C-terminal domains that further modify the substrate, such as a protease domain that hydrolyzes the extended, unstructured protein chain that emerges from the ATPase domain during substrate processing.

This work focuses on one such AAA+ protease, LONP1. LONP1 is an essential AAA+ protein involved in mitochondrial proteostasis, and disruption of its function in vivo has serious developmental consequences. This work explores the processing of two new mitochondrial protein substrates (StAR, TFAM) by LONP1 and presents new conformational states of LONP1 with closed configurations and no substrate threaded through the ATPase pores. The quality of the reconstructions and models is very good. Critically, one of these states (LONP1C3) has a completely occluded ATPase pore from the N-terminal side of the ATPase ring, where three of the six NTDs/CCDs interact tightly to form a C3-symmetric substructure preventing substrate ingress. The authors note several key interactions between amino acids forming these substructures, and perform ATPase assays on mutant LONP1 proteins to determine hydrolysis rates in the absence or presence of substrate. These patterns are recapitulated in casein disassembly assays as well. Based on these results, the authors note that the mutants have differential effects depending on the "foldedness" of the substrate, and surmise that disruption of the C3-symmetric substructure from the EM experiments is responsible for these effects - an intriguing idea. In addition to the C3 state, the authors observe additional intermediates which they place on the same conformational coordinate. One such structure is the LONP1C2 state with two splits, hinting at a conformational transition from LONP1C3 to the closed/active state.

Taken together, these results form the basis of an interesting story. However, I feel that more experimentation and analysis are needed to address several key points, or that the conclusions should be toned down. First and foremost, I note that while the hypothesis that the LONP1C3 state is a critical step in recognizing substrate "foldedness" is an interesting one, the claim is made solely on the basis of biochemical experiments with mutant LONP1, and that there is no substrate density associated with LONP1C3. In the absence of substrate density and/or structural data for the mutants, this seems like a very strong claim. More generally, the manuscript invokes the conformational landscape of LONP1C3 in multiple instances, but no such landscape is presented to show how LONP1C3 and the other states are quantitatively linked. Finally, I note the prevalence of ADP-only active sites in these intermediates, and am concerned that this might be related to the depletion of ATP under the on-grid reaction conditions. The inclusion of an ATP regeneration system may be a useful way to ensure that ATP/ADP concentrations are more physiological and that excessive ADP will not bias the conformations of the ring systems.

In summary, I believe this manuscript is exciting but would benefit from a paring back of claims, or the inclusion of some additional data to fill in some of the conceptual gaps outlined above.

Reviewer #2 (Public review):

This paper by Mindrebo et al. reveals multiple novel conformations of the human LONP1 protease. AAA+ proteases, like LONP1, are needed for maintaining proteostasis in cells and organelles. While structures of fully active (closed) and fully inactive (open) conformations of LONP1 are now established, the dynamics between these states and how changes in conformations may contribute to or be triggered by substrates and nucleotides are unclear. In this work, the authors characterize a novel C3-symmetric state of LONP1 bound to TFAM (a native substrate), suggesting that this C3-state is an intermediate in the open to closed cycle, and make mutations to test this model biochemically. Deeper inspection of their TFAM-bound LONP1 dataset reveals additional conformations, including a C2-symmetric and two asymmetric intermediates. All these conformations are synthesized by the authors to propose a model for how LONP1 transitions from an inactive OFF state to an active ENZ state. There are clear, interesting structural aspects to this work, revealing alternate conformations to shed light on the dynamics of LONP1. However, some of the conclusions interpret well beyond the scope of the experiments shown, and this is discussed below.

Overall, there are two major comments with the work as written that, if addressed, would make the results more compelling. First, the order of events and existence of intermediate states is primarily from static structural snapshots and fitting these structures to a possible mechanism. It would be ideal to have some biochemical or kinetic data supporting these steps and the existence of these intermediates. For example, the model is that the C3-state is an ADP-bound intermediate that blocks access and acts as a checkpoint for progression to the ENZ state of LONP1. The major evidence for this comes from a mutation (D449A) that fails to degrade TFAM as well as StAR or casein, which is taken as evidence that failure to form the C3 state reduces the ability to degrade more 'folded' substrates. A prediction of this model would be that destabilizing TFAM through mutation should improve D449A degradation. Ideally, other measures of conformational changes, such as FRET or HDX-MS, could be used to visualize this C3-state in unmutated LONP1 during the process of substrate engagement and degradation. At a minimum, using ATP hydrolysis as a proxy for forming the ENZ state and the assumption that different substrates will differentially promote formation of the C3-state means that measuring ATP hydrolysis of wt LONP1 with different substrates will be informative.

The second major comment is that the primary evidence for the importance of the C3 state is a mutation (D449A) that, based on the cryoEM structure, is incompatible with this conformation but should not affect any other state. A concern that arises is whether this mutation is doing more than simply destabilizing the C3 state and affecting substrate recognition/enzymatic activity in some other manner. To address this point, the authors could perform cryoEM characterization of the D449A mutant, which should show reduced or no presence of the C3-state, but still an intact ability to form the closed ENZ state.

Reviewer #3 (Public review):

Summary:

The AAA+ protease LON1P is a central component of mitochondrial protein quality control and has crucial functions in diverse processes. Cryo-EM structures of LON1P defined inactive and substrate-processing active states. Here, the authors determined multiple new LON1P structural states by cryo-EM in the presence of diverse substrates. The structures are defined as on-pathway intermediates to LON1P activation. A C3-symmetry state is suggested to function as a checkpoint to scan for LON1P substrates and link correct substrate selection to LON1P activation.

Strengths:

The determination of multiple structures provides relevant information on substrate-triggered activation of LON1P. The authors support structural data by biochemical analysis of structure-based mutants.

Weaknesses:

How substrate selection is achieved remains elusive, also because substrates are not detectable in the diverse structures. It also remains in parts unclear whether mutant phenotypes can be specifically linked to a single structural state (C3). Some mutant phenotypes appear complex and do not seem to be in line with the model proposed.

Reviewer #1 (Public review):

Summary:

This work provides evidence that slender T. brucei can initiate and complete cyclical development in Glossina morsitans without GlcNAc supplementation, in both sexes, and importantly in non-teneral flies, including salivary-gland infections.

Comparative transcriptomics show early divergence between slender- and stumpy-initiated differentiation (distinct GO enrichments), with convergence by ~72 h, supporting an alternative pathway into the procyclic differentiation program.

The work addresses key methodological criticisms of earlier studies and supports the hypothesis that slender forms may contribute to transmission at low parasitaemia.

Strengths:

(1) Directly tackles prior concerns (no GlcNAc, both sexes, non-teneral flies) with positive infections through to the salivary glands.

(2) Transcriptomic time course adds some mechanistic depth.

(3) Clear relevance to the "transmission paradox"; advances an important debate in the field.

Weaknesses:

(1) Discrepancy with Ngoune et al. (2025) remains unresolved; no head-to-head control for colony/blood source or microbiome differences that could influence vector competence.

(2) Lacks in vivo feeding validation (e.g., infecting flies directly on parasitaemic mice) to strengthen ecological relevance.

(3) Mechanistic inferences are largely correlative (although not requested, there is no functional validation of genes or pathways emerging from the transcriptomics).

(4) Reliance on a single parasite clone (AnTat 1.1) and one vector species limits external validity.

Reviewer #2 (Public review):

Summary:

This paper is an exciting follow-up to two recent publications in eLife: one from the same lab, reporting that slender forms can successfully infect tsetse flies (Schuster, S et al., 2021), and another independent study claiming the opposite (Ngoune, TMJ et al., 2025). Here, the authors address four criticisms raised against their original work: the influence of N-acetyl-glucosamine (NAG), the use of teneral and male flies, and whether slender forms bypass the stumpy stage before becoming procyclic forms.

Strengths:

We applaud the authors' efforts in undertaking these experiments and contributing to a better understanding of the T. brucei life cycle. The paper is well-written and the figures are clear.

Weaknesses:

We identified several major points that deserve attention.

(1) What is a slender form? Slender-to-stumpy differentiation is a multi-step process, and most of these steps unfortunately lack molecular markers (Larcombe et al, 2023). In this paper, it is essential that the authors explicitly define slender forms. Which parameters were used? It is implicit that slender forms are replicative and GFP::PAD1-negative. Isn't it possible that some GFP::PAD1-negative cells were already transitioning toward stumpy forms, but not yet expressing the reporter? Transcriptomically, these would be early transitional cells that, upon exposure to "tsetse conditions" (in vitro or in vivo), could differentiate into PCF through an alternative pathway, potentially bypassing the stumpy stage (as suggested in Figure 4). Given the limited knowledge of early molecular signatures of differentiation, we cannot exclude the possibility that the slender forms used here included early differentiating cells. We suggest:

1.1 Testing the commitment of slender forms (e.g., using the plating assay in Larcombe et al., 2023), assessing cell-cycle profile, and other parameters that define slender forms.

1.2 In the Discussion, acknowledging the uncertainty of "what is a slender?" and being explicit about the parameters and assumptions.

1.3 Clarifying in the Materials and Methods how cultures were maintained in the 3-4 days prior to tsetse infections, including daily cell densities. Ideally, provide information on GFP expression, cell cycle, and morphology. While this will not fully resolve the concern, it will allow future reinterpretation of the data when early molecular events are better understood.

(2) Figure 1: This analysis lacks a positive control to confirm that NAG is working as expected. It would strengthen the paper if the authors showed that NAG improves stumpy infection. Once confirmed, the authors could discuss possible differences in the tsetse immune response to slender vs. stumpy forms to explain the absence of an effect on slender infections.

(3) Figure 2. To conclude that teneral flies are less infected than non-teneral flies, data from Figures 1 and 2 must be directly comparable. Were these experiments performed simultaneously? Please clarify in the figure legends. Moreover, the non-teneral flies here are still relatively young (6-7 days old), limiting comparisons with Ngoune, TMJ et al. 2025, where flies were 2-3 weeks old.

(4) Figure 3. The PCA plot (A) appears to suggest the opposite of the authors' interpretation: slender differentiation seems to proceed through a transcriptome closer to stumpy profiles. Plotting DEG numbers (panel C) is informative, but how were paired conditions selected? Besides, plotting of the number of DEGs between consecutive time points within and between parasite types is also necessary. There may also be better computational tools to assess temporal relationships. Finally, how does PAD1 transcript abundance change over time in both populations? It would also be important to depict the upregulation of procyclic-specific genes.

(5) Could methylcellulose in the medium sensitize parasites to QS-signal, leading to more frequent and/or earlier differentiation, despite low densities? If so, cultures with vs. without methylcellulose might yield different proportions of early-differentiating (yet GFP-negative) parasites. This could explain discrepancies between the Engstler and Rotureau labs despite using the same strain. The field would benefit from reciprocal testing of culture conditions. Alternatively, the authors could compare infectivity and transcriptomes of their slender forms under three conditions: (i) in vitro with methylcellulose, (ii) in vitro without methylcellulose, and (iii) directly from mouse blood.

Reviewer #1 (Public review):

This is a re-review following an author revision. I will go point-by-point in response to my original critiques and the authors' responses. I appreciate the authors taking the time to thoughtfully respond to the reviewer critiques.

Query 1. Based on the authors' description of their contribution to the algorithm design, it sounds like a hyperparameter search wrapped around existing software tools. I think that the use of their own language to describe these modules is confusing to potential users as well as unintentionally hides the contributions of the original LigBuilder developers. The authors should just explain the protocol plainly using language that refers specifically to the established software tools. Whether they use LigBuilder or something else, at the end of the day the description is a protocol for a specific use of an existing software rather than the creation of a new toolkit.

Query 2. I see. Correct me if I am mistaken, but it seems as though the authors are proposing using the Authenticator to identify the best distributions of compounds based on an in silico oracle (in this case, Vina score), and train to discriminate them. This is similar to training QSAR models to predict docking scores, such as in the manuscript I shared during the first round of review. In principle, one could perform this in successive rounds to create molecules that are increasingly composed of features that yield higher docking scores. This is an established idea that the authors demonstrate in a narrow context, but it also raises concern that one is just enriching for compounds with e.g., an abundance of hydrogen bond donors and acceptors. Regarding points (4) and (5), it is unclear to me how the authors perform train/test splits on unlabeled data with supervised machine learning approaches in this setting. This seems akin to a Y-scramble sanity check. Finally, regarding the discussion on the use of experimental data or FEP calculations for the determination of HABs and LABs, I appreciate the authors' point; however, the concern here is that in the absence of any true oracle the models will just learn to identify and/or generate compounds that exploit limitations of docking scores. Again, please correct me if I am mistaken. It is unclear to me how this advances previous literature in CADD outside of the specific context of incorporating some ideas into a GPCR-Gprotein framework.

Query 3. The authors mention that the hyperparameters for the ML models are just the package defaults in the absence of specification by the user. I would be helpful to know specifically what the the hyperparameters were for the benchmarks in this study; however, I think a deeper concern is still that these models are almost certainly far overparameterized given the limited training data used for the models. It is unclear why the authors did not just build a random forest classifier to discriminate their HABs and LABs using ligand- or protein-ligand interaction fingerprints or related ideas.

Query 4. It is good, and expected, that increasing the fraction of the training set size in a random split validation all the way to 100% would allow the model to perfectly discriminate HABs and LABs. This does not demonstrate that the model has significant enrichment in prospective screening, particularly compared to simpler methods. The concern remains that these models are overparameterized and insufficiently validated. The authors did not perform any scaffold splits or other out-of-distribution analysis.

Query 5. The authors contend that Gcoupler uniquely enables training models when data is scarce and ultra-large screening libraries are unavailable. Today, it is rather straightforward to dock a minimum of thousands of compounds. Using tools such as QuickVina2-GPU (https://pubs.acs.org/doi/10.1021/acs.jcim.2c01504), it is possible to quite readily dock millions in a day with a single GPU and obtain the AutoDock Vina score. GPU-acclerated Vina has been combined with cavity detection tools likely multiple times, including here (https://arxiv.org/abs/2506.20043). There are multiple cavity detection tools, including the ones the authors use in their protocol.

Query 6. The authors contend that the simulations are converged, but they elected not to demonstrate stability in the predicting MM/GBSA binding energies with block averaging across the trajectory. This could have been done through the existing trajectories without additional simulation.

Reviewer #1 (Public review):

This is a re-review following an author revision. I will go point-by-point in response to my original critiques and the authors' responses. I appreciate the authors taking the time to thoughtfully respond to the reviewer critiques.

Query 1. Based on the authors' description of their contribution to the algorithm design, it sounds like a hyperparameter search wrapped around existing software tools. I think that the use of their own language to describe these modules is confusing to potential users as well as unintentionally hides the contributions of the original LigBuilder developers. The authors should just explain the protocol plainly using language that refers specifically to the established software tools. Whether they use LigBuilder or something else, at the end of the day the description is a protocol for a specific use of an existing software rather than the creation of a new toolkit.

Query 2. I see. Correct me if I am mistaken, but it seems as though the authors are proposing using the Authenticator to identify the best distributions of compounds based on an in silico oracle (in this case, Vina score), and train to discriminate them. This is similar to training QSAR models to predict docking scores, such as in the manuscript I shared during the first round of review. In principle, one could perform this in successive rounds to create molecules that are increasingly composed of features that yield higher docking scores. This is an established idea that the authors demonstrate in a narrow context, but it also raises concern that one is just enriching for compounds with e.g., an abundance of hydrogen bond donors and acceptors. Regarding points (4) and (5), it is unclear to me how the authors perform train/test splits on unlabeled data with supervised machine learning approaches in this setting. This seems akin to a Y-scramble sanity check. Finally, regarding the discussion on the use of experimental data or FEP calculations for the determination of HABs and LABs, I appreciate the authors' point; however, the concern here is that in the absence of any true oracle the models will just learn to identify and/or generate compounds that exploit limitations of docking scores. Again, please correct me if I am mistaken. It is unclear to me how this advances previous literature in CADD outside of the specific context of incorporating some ideas into a GPCR-Gprotein framework.

Query 3. The authors mention that the hyperparameters for the ML models are just the package defaults in the absence of specification by the user. I would be helpful to know specifically what the the hyperparameters were for the benchmarks in this study; however, I think a deeper concern is still that these models are almost certainly far overparameterized given the limited training data used for the models. It is unclear why the authors did not just build a random forest classifier to discriminate their HABs and LABs using ligand- or protein-ligand interaction fingerprints or related ideas.

Query 4. It is good, and expected, that increasing the fraction of the training set size in a random split validation all the way to 100% would allow the model to perfectly discriminate HABs and LABs. This does not demonstrate that the model has significant enrichment in prospective screening, particularly compared to simpler methods. The concern remains that these models are overparameterized and insufficiently validated. The authors did not perform any scaffold splits or other out-of-distribution analysis.

Query 5. The authors contend that Gcoupler uniquely enables training models when data is scarce and ultra-large screening libraries are unavailable. Today, it is rather straightforward to dock a minimum of thousands of compounds. Using tools such as QuickVina2-GPU (https://pubs.acs.org/doi/10.1021/acs.jcim.2c01504), it is possible to quite readily dock millions in a day with a single GPU and obtain the AutoDock Vina score. GPU-acclerated Vina has been combined with cavity detection tools likely multiple times, including here (https://arxiv.org/abs/2506.20043). There are multiple cavity detection tools, including the ones the authors use in their protocol.

Query 6. The authors contend that the simulations are converged, but they elected not to demonstrate stability in the predicting MM/GBSA binding energies with block averaging across the trajectory. This could have been done through the existing trajectories without additional simulation.

Reviewer #1 (Public review):

The authors have implemented several clarifications in the text and improved the connection between their findings and previous work. As stated in my initial review, I had no major criticisms of the previous version of the manuscript, and I continue to consider this a solid and well-written study. However, the revised manuscript still largely reiterates existing findings and does not offer novel conceptual or experimental advances. It supports previous conclusions suggesting a likely conserved sex determination locus in aculeate hymenopterans, but does so without functional validation (i.e., via experimental manipulation) of the candidate locus in O. biroi. I also wish to clarify that I did not intend to imply that functional assessments in the Pan et al. study were conducted in more than one focal species; my previous review explicitly states that the locus's functional role was validated in the Argentine ant.

Reviewer #3 (Public review):

The authors have made considerable efforts to conduct functional analyses to the fullest extent possible in this study; however, it is understandable that meaningful results have not yet been obtained. In the revised version, they have appropriately framed their claims within the limits of the current data and have adjusted their statements as needed in response to the reviewers' comments.

Reviewer #1 (Public review):

Summary:

In this manuscript, Subhramanian et al. carefully examined how microglia adapt their surveillance strategies during chronic neurodegeneration, specifically in prion-infected mice. The authors used ex vivo time-lapse imaging and in vitro strategies, finding that reactive microglia exhibit a highly mobile, "kiss-and-ride" behavior, which contrasts with the more static surveillance typically observed in homeostatic microglia. The manuscript provides fundamental mechanistic insights into the dynamics of microglia-neuron interactions, implicates P2Y6 signaling in regulating mobility, and suggests that intrinsic reprogramming of microglia might underlie this behavior. The conclusions are therefore compelling.

Strengths:

(1) The novelty of the study is high, in particular, the demonstration that microglia lose territorial confinement and dynamically migrate from neuron to neuron under chronic neurodegeneration.

(2) The possible implications of a stimulus-independent high mobility in reactive microglia are particularly striking. Although this is not fully explored (see comments below).

(3) The use of time-lapse imaging in organotypic slices rather than overexpression models provided a more physiological approach.

(4) Microglia-neuron interactions in neurodegeneration have broad implications for understanding the progression of other diseases that are associated with chronic inflammation, such as Alzheimer's and Parkinson's.

Weaknesses:

(1) The Cx3cr1/EGFP line labels all myeloid cells, which makes it difficult to conclude that all observed behaviors are attributable to microglia rather than infiltrating macrophages. The authors refer to this and include it as a limitation. Nonetheless, complementary confirmation by additional microglia markers would strengthen their claims.

(2) Although the authors elegantly describe dynamic surveillance and envelopment hypothesis, it is unclear what the role of this phenotype is for disease progression, i.e., functional consequences. For example, are the neurons that undergo sustained envelopment more likely to degenerate?

(3) Moreover, although the increase in mobility is a relevant finding, it would be interesting for the authors to further comment on what the molecular trigger(s) is/are that might promote this increase. These adaptations, which are at least long-lasting, confer apparent mobility in the absence of external stimuli.

(4) The authors performed, as far as I could understand, the experiments in cortical brain regions. There is no clear rationale for this in the manuscript, nor is it clear whether the mobility is specific to a particular brain region. This is particularly important, as microglia reactivity varies greatly depending on the brain region.

(5) It would be relevant information to have an analysis of the percentage of cells in normal, sub-clinical, early clinical, and advanced stages that became mobile. Without this information, the speed/distance alone can have different interpretations.

Reviewer #2 (Public review):

This is a nice paper focused on the response of microglia to different clinical stages of prion infection in acute brain slices. The key here is the use of time-lapse imaging, which captures the dynamics of microglial surveillance, including morphology, migration, and intracellular neuron-microglial contacts. The authors use a myeloid GFP-labeled transgenic mouse to track microglia in SSLOW-infected brain slices, quantifying differences in motility and microglial-neuron interactions via live fluorescence imaging. Interesting findings include the elaborate patterns of motility among microglia, the distinct types and duration of intracellular contacts, the potential role of calcium signaling in facilitating hypermobility, and the fact that this motion-promoting status is intrinsic to microglia, persisting even after the cells have been isolated from infected brains. Although largely a descriptive paper, there are mechanistic insights, including the role of calcium in supporting movement of microglia, where bursts of signaling are identified even within the time-lapse format, and inhibition studies that implicate the purinergic receptor and calcium transient regulator P2Y6 in migratory capacity.

Strengths:

(1) The focus on microglia activation and activity in the context of prion disease is interesting.

(2) Two different prions produce largely the same response.

(3) Use of time-lapse provides insight into the dynamics of microglia, distinguishing between types of contact - mobility vs motility - and providing insight into the duration/transience and reversibility of extensive somatic contacts that include brief and focused connections in addition to soma envelopment.

(4) Imaging window selection (3 hours) guided by prior publications documenting preserved morphology, activity, and gene expression regulation up to 4 hours.

(5) The distinction between high mobility and low mobility microglia is interesting, especially given that hyper mobility seems to be an innate property of the cells.

(6) The live-imaging approach is validated by fixed tissue confocal imaging.

(7) The variance in duration of neuron/microglia contacts is interesting, although there is no insight into what might dictate which status of interaction predominates.

(8) The reversibility of the enveloping action, that is not apparently a commitment to engulfment, is interesting, as is the fact that only neurons are selected for this activity.

(9) The calcium studies use the fluorescent dye calbryte-590 to pick up neuronal and microglial bursts - prolonged bursts are detected in enveloped neurons and in the hyper-mobile microglia - the microglial lead is followed up using MRS-2578 P2Y6 inhibitor that blunts the mobility of the microglia.

Weaknesses:

(1) The number of individual cells tracked has been provided, but not the number of individual mice. The sex of the mice is not provided.

(2) The statistical approach is not clear; was each cell treated as a single observation?

(3) The potential for heterogeneity among animals has not been addressed.

(4) Validation of prion accumulation at each clinical stage of the disease is not provided.

(5) How were the numerous captures of cells handled to derive morphological quantitative values? Based on the videos, there is a lot of movement and shape-shifting.

(6) While it is recognized that there are limits to what can be measured simultaneously with live imaging, the authors appear to have fixed tissues from each time point too - it would be very interesting to know if the extent or prion accumulation influences the microglial surveillance - i.e., do the enveloped ones have greater pathology>

Reviewer #3 (Public review):

Summary:

The authors developed a new phenological lag metric and applied this analytical framework to a global dataset to synthesize shifts in spring phenology and assess how abiotic constraints influence spring phenology.

Strengths:

The dataset developed in this study is extensive, and the phenological lag metric is valuable.

Weaknesses:

The stability of the method used to calculate forcing requirements needs improvement, for example by including different base temperature thresholds. In addition, the visualization of the results should be improved.

Reviewer #1 (Public review):

In the current article, Octavia Soegyono and colleagues study "The influence of nucleus accumbens shell D1 and D2 neurons on outcome-specific Pavlovian instrumental transfer", building on extensive findings from the same lab. While there is a consensus about the specific involvement of the Shell part of the Nucleus Accumbens (NAc) in specific stimulus-based actions in choice settings (and not in General Pavlovian instrumental transfer - gPIT, as opposed to the Core part of the NAc), mechanisms at the cellular and circuitry levels remain to be explored. In the present work, using sophisticated methods (rat Cre-transgenic lines from both sexes, optogenetics and the well-established behavioral paradigm outcome-specific PIT - sPIT), Octavia Soegyono and colleagues decipher the differential contribution of dopamine receptors D1 and D2 expressing-spiny projection neurons (SPNs).

After validating the viral strategy and the specificity of the targeting (immunochemistry and electrophysiology), the authors demonstrate that while both NAc Shell D1- and D2-SPNs participate in mediating sPIT, NAc Shell D1-SPNs projections to the Ventral Pallidum (VP, previously demonstrated as crucial for sPIT), but not D2-SPNs, mediates sPIT. They also show that these effects were specific to stimulus-based actions, as value-based choices were left intact in all manipulations.

This is a well-designed study and the results are well supported by the experimental evidence. The paper is extremely pleasant to read and add to the current literature.

Reviewer #2 (Public review):

Summary:

This manuscript by Soegyono et a. describes a series of experiments designed to probe the involvement of dopamine D1 and D2 neurons within the nucleus accumbens shell in outcome-specific Pavlovian-instrumental transfer (osPIT), a well-controlled assay of cue-guided action selection based on congruent outcome associations. They used an optogenetic approach to phasically silence NAc shell D1 (D1-Cre mice) or D2 (A2a-Cre mice) neurons during a subset of osPIT trials. Both manipulations disrupted cue-guided action selection but had no effects on negative control measures/tasks (concomitant approach behavior, separate valued guided choice task), nor were any osPIT impairments found in reporter only control groups. Separate experiments revealed that selective inhibition of NAc shell D1 but not D2 inputs to ventral pallidum were required for osPIT expression, thereby advancing understanding of the basal ganglia circuitry underpinning this important aspect of decision making.

Strengths:

The combinatorial viral and optogenetic approaches used here were convincingly validated through anatomical tract-tracing and ex vivo electrophysiology. The behavioral assays are sophisticated and well-controlled to parse cue and value guided action selection. The inclusion of reporter only control groups is rigorous and rules out nonspecific effects of the light manipulation. The findings are novel and address a critical question in the literature. Prior work using less decisive methods had implicated NAc shell D1 neurons in osPIT but suggested that D2 neurons may not be involved. The optogenetic manipulations used in the current study provides a more direct test of their involvement and convincingly demonstrate that both populations play an important role. Prior work had also implicated NAc shell connections to ventral pallidum in osPIT, but the current study reveals the selective involvement of D1 but not D2 neurons in this circuit. The authors do a good job of discussing their findings, including their nuanced interpretation that NAc shell D2 neurons may contribute to osPIT through their local regulation of NAc shell microcircuitry.

Weaknesses:

The current study exclusively used an optogenetic approach to probe the function of D1 and D2 NAc shell neurons. Providing a complementary assessment with chemogenetics or other appropriate methods would strengthen conclusions, particularly the novel demonstration for D2 NAc shell involvement. Likewise, the null result of optically inhibiting D2 inputs to ventral pallidum leaves open the possibility that a more complete or sustained disruption of this pathway may have impaired osPIT.

Conclusions:

The research described here was successful in providing critical new insights into the contributions of NAc D1 and D2 neurons in cue-guided action selection. The authors' data interpretation and conclusions are well reasoned and appropriate. They also provide a thoughtful discussion of study limitations and implications for future research. This research is therefore likely to have a significant impact on the field.

Comments on the previous version:

I have reviewed the rebuttal and revised manuscript and have no remaining concerns.

Reviewer #1 (Public review):

The paper reports some interesting patterns in epistasis in a recently published large fitness landscape dataset. The results may have implications for our understanding of fitness landscapes and protein evolution. However, this version of the paper remains fairly descriptive and has significant deficiencies in clarity and rigor.

The authors have addressed some of my criticisms (e.g., I appreciate the additional analysis of synonymous mutations, and a more rigorous approach to calling fitness peaks), but many of the issues raised in my first round of review remain in the current version. Frankly, I am quite disappointed that the authors did not address my comments point by point, which is the norm. The remaining (and some new) issues are below.

(1a) (Modified from first round) I previously suggested to dissect what appears to be three different patterns of epistasis: "strong" and "weak" global epistasis and what one can could "purely idiosyncratic", i.e., not dependent on background fitness. The authors attempted to address this, but I don't think what they have done is sufficient. They make a statement "The lethal mutations have a slope smaller than -0.7 and average slope of -0.98. The remaining mutations all have a slope greater than -0.56" (LL 274-276)", but there is no evidence provided to support this claim. This is a strong and I think interesting statement (btw, how is "lethal" defined?) and warrants a dedicated figure. This statement suggests that the mixed patterns shown in Figure 5 can actually be meaningfully separated. Why don't the authors show this? Instead, they still claim "overall, global epistasis is not very strong on the folA landscape" (LL. 273-274). I maintain that this claim does not quite capture the observations.

Later in the text there is a whole section called "Only a small fraction of mutations exhibit strong global epistasis", which also seems related to this issue. First, I don't follow the logic here. Why is this section separate from this initial discussion? Second, here the authors claim "only a small subset of mutations exhibits strong global epistasis (R^2 > 0.5)" and then "This sharp contrast suggests a binary behavior of mutations: they either exhibit strong global epistasis (R2 > 0.5), or not (R2 < 0.5)." But this R^2 threshold seems arbitrary, and I don't see any statistical support for this binary nature.

(1b) (Verbatim from first round) Another rather remarkable feature of this plot is that the slopes of the strong global epistasis patterns sem to be very similar across mutations. Is this the case? Is there anything special about this slope? For example, does this slope simply reflect the fact that a given mutation becomes essentially lethal (i.e., produces the same minimal fitness) in a certain set of background genotypes?

(1c) (Verbatim from first round) Finally, how consistent are these patterns with some null expectations? Specifically, would one expect the same distribution of global epistasis slopes on an uncorrelated landscape? Are the pivot points unusually clustered relative to an expectation on an uncorrelated landscape?

(1d) (Verbatim from first round) The shapes of the DFE shown in Figure 7 are also quite interesting, particularly the bimodal nature of the DFE in high-fitness (HF) backgrounds. I think this bimodalilty must be a reflection of clustering of mutation-background combinations mentioned above. I think the authors ought to draw this connection explicitly. Do all HF backgrounds have a bimodal DFE? What mutations occupy the "moving" peak?

(1e) (Modified from first round). I still don't understand why there are qualitative differences in the shape of the DFE between functional and non-functional backgrounds (Figure 8B,C). Why is the transition between bimodal DFE in Figure 8B and unimodal DFE in Figure 8C is so abrupt? Perhaps the authors can plot the DFEs for all backgrounds on the same plot and just draw a line that separates functional and non-functional backgrounds so that the reader can better see whether DFE shape changes gradually or abruptly.

(1f) (Modified from first round) I am now more convinced that synonymous mutations alter epistasis and behave differently than non-synonymous mutations, but I still have some questions. (i) I would have liked a side-by-side comparison of synonymous and non-synonymous mutations, both in terms of their effects on fitness and on epistasis.<br /> (ii) The authors claim (LL 278-286) that "synonymous substitutions tend to follow two recurring behaviors" but this is not shown. To demonstrate this, the authors ought to plot (for example) the distribution of slopes of regression lines. Is this distribution actually bimodal? (iii) Later in the same paragraph the authors say "synonymous changes do not exhibit very strong background fitness-dependence". I don't see how this follows from the previous discussion.

(2) The authors claim to have improved statistical rigor of their analysis, but the Methods section is really thin and inadequate for understanding how the statistical analyses were done.

(3) In general, I notice a regrettable lack of attention to detail in the text, which makes me worried about a similar problem in the actual data analysis. Here are a few examples. (i) Throughout the text, the authors now refer to functional and non-functional genotypes, but several figures and captions retained the old HF and LF designations. (ii) Figure 7 is called Figure 8. (iii) Figure 3B is not discussed, though it logically precedes Figure 3A and 3C. (iv) Many of my comments, especially minor, were not addressed at all.

Reviewer #3 (Public review):

Summary:

The authors have studied a previously published large dataset on the fitness landscape of a 9 base-pair region of the folA gene. The objective of the paper is to understand various aspects of epistasis in this system, which the authors have achieved through detailed and computationally expensive exploration of the landscape. The authors describe epistasis in this system as "fluid", meaning that it depends sensitively on the genetic background, thereby reducing the predictability of evolution at the genetic level. However, the study also finds some robust patterns. The first is the existence of a "pivot point" for a majority of mutations, which is a fixed growth rate at which the effect of mutations switches from beneficial to deleterious (consistent with a previous study on the topic). The second is the observation that the distribution of fitness effects (DFE) of mutations is predicted quite well by the fitness of the genotype, especially for high-fitness genotypes. While the work does not offer a synthesis of the multitude of reported results, the information provided here raises interesting questions for future studies in this field.

Strengths:

A major strength of the study is its multifaceted approach, which has helped the authors tease out a number of interesting epistatic properties. The study makes a timely contribution by focusing on topical issues like global epistasis, the existence of pivot points, and the dependence of DFE on the background genotype and its fitness.

The authors have classified pairwise epistasis into six types, and found that the type of epistasis changes depending on background mutations. Switches happen more frequently for mutations at functionally important sites. Interestingly, the authors find that even synonymous mutations can alter the epistatic interaction between mutations in other codons, and this effect is uncorrelated with the direct fitness effects of the synonymous mutations. Alongside the observations of "fluidity", the study reports limited instances of global epistasis (which predicts a simple linear relationship between the size of a mutational effect and the fitness of the genetic background in which it occurs). Overall, the work presents strong evidence for the genetic context-dependent nature of epistasis in this system.

Weaknesses:

Despite the wealth of information provided by the study, there are a few points of concern.

The authors find that in non-functional genotypic backgrounds, most pairs of mutations display no epistasis. However, we do not know if this simply because a significant epistatic signal is hard to detect since all the fitness values involved in calculating epistasis are small (and therefore noise-prone). A control can be done by determining whether statistically significant differences exist among the fitness values themselves. In the absence of such information, it is hard to understand whether the classification of epistasis for non-functional backgrounds into discrete categories, such as in Fig 1C, is meaningful.

The authors have looked for global epistasis (i.e. a negative dependence of mutational fitness effect on background fitness) in all 108 (9x12) mutations in the landscape. They report that the majority of the mutations (77/108 or about 71 per cent) display weak correlation between fitness effect and background fitness (R^2<0.2), and a relatively small proportion show particularly strong correlation (R^2>0.5). They therefore conclude that global epistasis in this system is 'binary'-meaning that strong global epistasis is restricted to a few sites, whereas weak global epistasis occurs in the rest (Figure 5). Precise definitions of 'strong' and 'weak' are not given in the text, but the authors do mention that they are interested here primarily in detecting whether a correlation with background fitness exists or not. This again raises the question of the extent to which the low (and possibly noisy) fitness values of non-functional backgrounds can confound the results. For example, would the results be much the same if the analysis was repeated with only high-fitness backgrounds or only those sets of genotypes where the fitness differences between backgrounds and mutants were significant?<br /> Apart from this, I am also a bit conceptually perplexed by the term 'binary behavior', which suggests that the R^2 values should belong to two distinct classes; but, even assuming that the reported results are robust, Figure S12 shows that most values are 0.2 or less whereas higher values are more or less evenly distributed in the range 0.2-1.0, rather than showing an overall bimodal pattern. An especially confusing remark by the authors in this regard is the following; "This sharp contrast suggests a binary behavior of mutations: they either exhibit strong global epistasis (R^2 > 0.5), or not (R^2 < 0.5)'.

Conclusions: As large datasets on empirical fitness landscapes become increasingly available, more computational studies are needed to extract as much information from them as possible. The authors have made a timely effort in this direction. It is particularly instructive to learn from the work that higher-order epistasis is pervasive in the studied intragenic landscape, at least in functional genotypic backgrounds. Some of the analysis and interpretations in the paper require careful scrutiny, and the lack of a synthesis of the multitude of reported results leaves something to be desired. But the paper contains intriguing observations that can fuel further research into the factors shaping the topography of complex landscapes.

Reviewer #1 (Public review):

Summary:

Dendrotweaks provides to its users a solid tool to implement, visualize, tune, validate, understand, and reduce single-neuron models that incorporate complex dendritic arbors with differential distribution of biophysical mechanisms. The visualization of dendritic segments and biophysical mechanisms therein provide users an intuitive way to understand and appreciate dendritic physiology.

Reviewer #2 (Public review):

The paper by Makarov et al. describes the software tool called DendroTweaks, intended for examination of multi-compartmental biophysically detailed neuron models. It offers extensive capabilities for working with very complex distributed biophysical neuronal models and should be a useful addition to the growing ecosystem of tools for neuronal modeling.

Strengths

• This Python-based tool allows for visualization of a neuronal model's compartments.

• The tool works with morphology reconstructions in the widely used .swc and .asc formats.

• It can support many neuronal models using the NMODL language, which is widely used for neuronal modeling.

• It permits one to plot the properties of linear and non-linear conductances in every compartment of a neuronal model, facilitating examination of model's details.

• DendroTweaks supports manipulation of the model parameters and morphological details, which is important for exploration of the relations of the model composition and parameters with its electrophysiological activity.

• The paper is very well written - everything is clear, and the capabilities of the tool are described and illustrated with great attention to details.

Weaknesses

• Not a really big weakness, but it would be really helpful if the authors showed how the performance of their tool scales. This can be done for an increasing number of compartments - how long does it take to carry out typical procedures in DendroTweaks, on a given hardware, for a cell model with 100 compartments, 200, 300, and so on? This information will be quite useful to understand the applicability of the software.

Let me also add here a few suggestions (not weaknesses, but something that can be useful, and if the authors can easily add some of these for publication, that would strongly increase the value of the paper).

• It would be very helpful to add functionality to read major formats in the field, such as NeuroML and SONATA.

• Visualization is available as a static 2D projection of the cell's morphology. It would be nice to implement 3D interactive visualization.

• It is nice that DendroTweaks can modify the models, such as revising the radii of the morphological segments or ionic conductances. It would be really useful then to have the functionality for writing the resulting models into files for subsequent reuse.

• If I didn't miss something, it seems that DendroTweaks supports allocation of groups of synapses, where all synapses in a group receive the same type of Poisson spike train. It would be very useful to provide more flexibility. One option is to leverage the SONATA format, which has ample functionality for specifying such diverse inputs.

• "Each session can be saved as a .json file and reuploaded when needed" - do these files contain the whole history of the session or the exact snapshot of what is visualized when the file is saved? If the latter, which variables are saved, and which are not? Please clarify.

Comments on revisions:

In this revised version of the paper, the authors addressed all my comments. While many of the suggestions were addressed by textual changes in the manuscript or an explanation in the response to the reviewers (rather than adding substantial new functionality to the tool), DendroTweaks in its current updated state does represent an advanced and useful tool. Further extensions can be added as the development of the tool continues, in interaction with the community.

Reviewer #1 (Public review):

Summary:

The authors conducted a human neuroimaging study investigating the role of context in the representation of fear associations when the contingencies between a conditioned stimulus and shock unconditioned stimulus switches between contexts. The novelty of the analysis centered on neural pattern similarity to derive a measure of context and cue stability and generalization across different regions of the brain. Given the complexity and nuance of the results, it is kind of difficult to provide a concise summary. But during fear and reversal, there was cue generalization (between current CS+ cues) in the canonical fear network, and "item stability" for cues that changed their association with the shock in the IFG and precuneus. Reinstatement was quantified as pattern similarity for items or sets of cues from the earlier phases to the test phases, and they found different patterns in the IFG and dmPFC. A similar analytical strategy was applied to contexts.

Strengths:

Overall, I found this to be a novel use of MVPA to study the role of context in reversal/extinction of human fear conditioning that yielded interesting results. The paper was overall well-written, with a strong introduction and fairly detailed methods and results. The lack of any univariate contrast results from the test phases was used as motivation for the neural pattern similarity approach, which I appreciated as a reader.

I have no additional or new comments. The authors adequately addressed my major comments and concerns.

Reviewer #2 (Public review):

Summary:

This is a timely and original study on the geometry of macroscopic (2.5 mm) brain representations of multiple cues and contexts in Pavlovian fear conditioning. The authors report that these representations differ between initial learning, and reversal learning, and remain stable during extinction.

Strengths:

The authors address an important question and use a rigorous experimental methodology.

Weaknesses:

The findings are limited by the chosen spatial resolution (2.5 mm) which is far away from what modern fMRI can achieve. Also, region-of-interesting findings should be considered exploratory due to the chosen FDR method for correction for multiple comparison (which is transparently reported).

Reviewer #1 (Public review):

Summary:

In this manuscript, Henning et al. examine the impact of GABAergic feedback inhibition on the motion-sensitive pathway of flies. Based on a previous behavioral screen, the authors determined that C2 and C3, two GABAergic inhibitory feedback neurons in the optic lobes of the fly, are required for the optomotor response. Through a series of calcium imaging and disruption experiments, connectomics analysis, and follow-up behavioral assays, the authors concluded that C2 and C3 play a role in temporally sharpening visual motion responses. While this study employs a comprehensive array of experimental approaches, I have some reservations about the interpretation of the results in their current form. I strongly encourage the authors to provide additional data to solidify their conclusions. This is particularly relevant in determining whether this is a general phenomenon affecting vision or a specific effect on motion vision. Knowing this is also important for any speculation on the mechanisms of the observed temporal deficiencies.

Strengths:

This study uses a variety of experiments to provide a functional, anatomical, and behavioral description of the role of GABAergic inhibition in the visual system. This comprehensive data is relevant for anyone interested in understanding the intricacies of visual processing in the fly.

Weaknesses:

(1) The most fundamental criticism of this study is that the authors present a skewed view of the motion vision pathway in their results. While this issue is discussed, it is important to demonstrate that there are no temporal deficiencies in the lamina, which could be the case since C2 and C3, as noted in the connectomics analysis, project strongly to laminar interneurons. If the input dynamics are indeed disrupted, then the disruption seen in the motion vision pathway would reflect disruptions in temporal processing in general and suggest that these deficiencies are inherited downstream. A simple experiment could test this. Block C2, C3, and both together using Kir2.1 and Shibire independently, then record the ERG. Alternatively, one could image any other downstream neuron from the lamina that does not receive C2 or C3 input.

(2) Figure 6c. More analysis is required here, since the authors claim to have found a loss in inhibition (ND). However, the difference in excitation appears similar, at least in absolute magnitude (see panel 6c), for PD direction for the T4 C2 and C3 blocks. Also, I predict that C2 & C3 block statistically different from C3 only, why? In any case, it would be good to discuss the clear trend in the PD direction by showing the distribution of responses as violin plots to better understand the data. It would also be good to have some raw traces to be able to see the differences more clearly, not only polar plots and averages.

(3) The behavioral experiments are done with a different disruptor than the physiological ones. One blocks chemical synapses, the other shunts the cells. While one would expect similar results in both, this is not a given. It would be great if the authors could test the behavioral experiments with Kir2.1, too.

Reviewer #2 (Public review):

Summary:

The work by Henning et al. explores the role of feedback inhibition in motion vision circuits, providing the first identification of inhibitory inheritance in motion-selective T4 and T5 cells of Drosophila. This work advances our current knowledge in Drosophila motion vision and sets the way for further exploring the intricate details of direction-selective computations.

Strengths:

Among the strengths of this work is the verification of the GABAergic nature of C2 and C3 with genetic and immunohistochemical approaches. In addition, double-silencing C2&C3 experiments help to establish a functional role for these cells. The authors holistically use the Drosophila toolbox to identify neural morphologies, synaptic locations, network connectivity, neuronal functions, and the behavioral output.

Weaknesses:

The authors claim that C2 and C3 neurons are required for direction selectivity, as per the publication's title; however, even with their double silencing, the directional T4 & T5 responses are not completely abolished. Therefore, the contribution of this inherited feedback in direction-selective computations is not a prerequisite for its emergence, and the title could be re-adjusted.

Connectivity is assessed in one out of the two available connectome datasets; therefore, it would make the study stronger if the same connectivity patterns were identified in both datasets.

The mediating neural correlates from C2 & C3 to T4 & T5 are not clarified; rather, Mi1 is found to be one of them. The study could be improved if the same set of silencing experiments performed for C2-Mi1 were extended to C2 &C3-Tm1 or Tm4 to find the T5 neural mediators of this feedback inhibition loop. Stating more clearly from the connectomic analysis, the potential T5 mediators would be equally beneficial. Future experiments might also disentangle the parallel or separate functions of C2 and C3 neurons.

Finally, the authors' conclusions derive from the set of experiments they performed in a logical manner. Nonetheless, the Discussion could benefited from a more extensive explanation on the following matters: why do the ON-selective C2 and C3 neurons control OFF-generated behaviors, why the T4&T5 responses after C2&C3 silencing differ between stationary and moving stimuli and finally why C2 and not C3 had an effect in T5 DS responses, as the connectivity suggests C3 outputting to two out of the four major T5 cholinergic inputs.

Reviewer #3 (Public review):

Summary:

This article is about the neural circuitry underlying motion vision in the fruit fly. Specifically, it regards the roles of two identified neurons, called C2 and C3, that form columnar connections between neurons in the lamina and medulla, including neurons that are presynaptic to the elementary motion detectors T4 and T5. The approach takes advantage of specific fly lines in which one can disable the synaptic outputs of either or both of the C2/3 cell types. This is combined with optical recording from various neurons in the circuit, and with behavioral measurements of the turning reaction to moving stimuli.

The experiments are planned logically. The effects of silencing the C2/C3 neurons are substantial in size. The dominant effect is to make the responses of downstream neurons more sustained, consistent with a circuit role in feedback or feedforward inhibition. Silencing C2/C3 also makes the motion-sensitive neurons T4/T5 less direction-selective. However, the turning response of the fly is affected only in subtle ways. Detection of motion appears unaffected. But the response fails to discriminate between two motion pulses that happen in close succession. One can conclude that C2/C3 are involved in the motion vision circuit, by sharpening responses in time, though they are not essential for its basic function of motion detection.

Strengths:

The combination of cutting-edge methods available in fruit fly neuroscience. Well-planned experiments carried out to a high standard. Convincing effects documenting the role of these neurons in neural processing and behavior.

Weaknesses:

The report could benefit from a mechanistic argument linking the effects at the level of single neurons, the resulting neural computations in elementary motion detectors, and the altered behavioral response to visual motion.

Reviewer #1 (Public review):

This work by Reitz, Z. L. et al. developed an automated tool for high-throughput identification of microbial metallophore biosynthetic gene clusters (BGCs) by integrating knowledge of chelating moiety diversity and transporter gene families. The study aimed to create a comprehensive detection system combining chelator-based and transporter-based identification strategies, validate the tool through large-scale genomic mining, and investigate the evolutionary history of metallophore biosynthesis across bacteria.

Major strengths include providing the first automated, high-throughput tool for metallophore BGC identification, representing a significant advancement over manual curation approaches. The ensemble strategy effectively combines complementary detection methods, and experimental validation using HPLC-HRMS strengthens confidence in computational predictions. The work pioneers a global analysis of metallophore diversity across the bacterial kingdom and provides a valuable dataset for future computational modeling.

Some limitations merit consideration. First, ground truth datasets derived from manual curation may introduce selection bias toward well-characterized systems, potentially affecting performance assessment accuracy. Second, the model's dependence on known chelating moieties and transporter families constrains its ability to detect novel metallophore architectures, limiting discovery potential in metagenomic datasets. Third, while the proposed evolutionary hypothesis is internally consistent, it lacks direct validation and remains speculative without additional phylogenetic studies.

The authors successfully achieved their stated objectives. The tool demonstrates robust performance metrics and practical utility through large-scale application to representative genomes. Results strongly support their conclusions through rigorous validation, including experimental confirmation of predicted metallophores via HPLC-HRMS analysis.

The work provides a significant and immediate impact by enabling the transition from labor-intensive manual approaches to automated screening. The comprehensive phylogenetic framework advances understanding of bacterial metal acquisition evolution, informing future studies on microbial metal homeostasis. Community utility is substantial, since the tool and accompanying dataset create essential resources for comparative genomics, algorithm development, and targeted experimental validation of novel metallophores.

Reviewer #2 (Public review):

Summary:

This study presents a systematic and well-executed effort to identify and classify bacterial NRP metallophores. The authors curate key chelator biosynthetic genes from previously characterized NRP-metallophore biosynthetic gene clusters (BGCs) and translate these features into an HMM-based detection module integrated within the antiSMASH platform.

The new algorithm is compared with a transporter-based siderophore prediction approach, demonstrating improved precision and recall. The authors further apply the algorithm to large-scale bacterial genome mining and, through reconciliation of chelator biosynthetic gene trees with the GTDB species tree using eMPRess, infer that several chelating groups may have originated prior to the Great Oxidation Event.

Overall, this work provides a valuable computational framework that will greatly assist future in silico screening and preliminary identification of metallophore-related BGCs across bacterial taxa.

Strengths:

(1) The study provides a comprehensive curation of chelator biosynthetic genes involved in NRP-metallophore biosynthesis and translates this knowledge into an HMM-based detection algorithm, which will be highly useful for the initial screening and annotation of metallophore-related BGCs within antiSMASH.

(2) The genome-wide survey across a large bacterial dataset offers an informative and quantitative overview of the taxonomic distribution of NRP-metallophore biosynthetic chelator groups, thereby expanding our understanding of their phylogenetic prevalence.

(3) The comparative evolutionary analysis, linking chelator biosynthetic genes to bacterial phylogeny, provides an interesting and valuable perspective on the potential origin and diversification of NRP-metallophore chelating groups.

Weaknesses:

(1) Although the rule-based HMM detection performs well in identifying major categories of NRP-metallophore biosynthetic modules, it currently lacks the resolution to discriminate between fine-scale structural or biochemical variations among different metallophore types.

(2) While the comparison with the transporter-based siderophore prediction approach is convincing overall, more information about the dataset balance and composition would be appreciated. In particular, specifying the BGC identities, source organisms, and Gram-positive versus Gram-negative classification would improve transparency. In the supplementary tables, the "Just TonB" section seems to include only BGCs from Gram-negative bacteria - if so, this should be clearly stated, as Gram type strongly influences siderophore transport systems.

Reviewer #1 (Public review):

Summary:

The study explores the use of Transport-based morphometry (TBM) to predict hematoma expansion and growth 24 hours post-event, leveraging Non-Contrast Computed Tomography (NCCT) scans combined with clinical and location-based information. The research holds significant clinical potential, as it could enable early intervention for patients at high risk of hematoma expansion, thereby improving outcomes. The study is well-structured, with detailed methodological descriptions and a clear presentation of results. However, the practical utility of the predictive tool requires further validation, as the current findings are based on retrospective data. Additionally, the impact of this tool on clinical decision-making and patient outcomes needs to be further investigated.

Strengths

(1) Clinical Relevance: The study addresses a critical need in clinical practice by providing a tool that could enhance diagnostic accuracy and guide early interventions, potentially improving patient outcomes.

(2) Feature Visualization: The visualization and interpretation of features associated with hematoma expansion risk are highly valuable for clinicians, aiding in the understanding of model-derived insights and facilitating clinical application.

(3) Methodological Rigor: The study provides a thorough description of methods, results, and discussions, ensuring transparency and reproducibility.

Comments on revisions:

The authors have addressed my concerns.

Reviewer #1 (Public review):

Summary:

In this study, Donofrio et al. investigated cerebellar Purkinje cell (PC) degeneration during normal aging using both mouse and human samples. They found that PC loss followed a stripe pattern rather than occurring randomly. Although this pattern resembled the pattern of zebrin II expression in the anterior cerebellum, the overall pattern was different from zebrin II expression. Surviving PCs exhibited severe degeneration, including thickened axons, axonal torpedoes and shrunken dendrites. These structural changes were accompanied by functional deficits in motor coordination and tremor. Understanding why certain PC subpopulations are more vulnerable than others may provide insight into regional susceptibility (or resilience) to aging and inform potential therapeutic strategies for age-related neurological disorders. Overall, the findings are novel and significant, supported by compelling evidence from structural and functional analyses. The authors have fully addressed my previous concerns and improved the clarity of their presentation. I believe this work will have a significant impact in the field.

Reviewer #2 (Public review):

Summary:

The cerebellum is known to be vulnerable to aging, yet specific cell type vulnerability remains understudied. This important study convincingly demonstrate that the normal aged mouse cerebellum exhibits Purkinje cell loss, and that the vulnerable PCs to age are arranged on the basis of known Zebrin stripe pattern that represents a particular subtype of the PCs. As the authors wrote, future studies should investigate why this PC loss phenotype occurs stochastically across the population, and whether these findings parallel human cerebellar aging.

Strength:

• Banding pattern of PC loss is very clearly demonstrated by combining immunostaining for Zebrin.

• A critical methodological concern that a standard PC marker, Calbindin, could be compromised in aging has been addressed by performing control experiments with appropriate counterstaining and a transgenic mouse.

• Parallels with neurodegenerative phenotype would be helpful to understand the mechanisms of age-related PC loss in future.

Weakness:

• Limited strain diversity: The study exclusively uses C57BL/6J mice despite known genetic and motor differences among even closely related strains like C57BL/6N, weakening the generalizability of the findings. However, on the other hand, the presence of age-related PC loss makes C57BL/6J an interesting mouse model for studying aging of the cerebellum.

• Linkages with normal human aging and cerebellar function is not supported well. It remains unclear whether this PC loss phenomenon is universal or specific to a particular individual, and whether specific to human PC subtype.

Reviewer #3 (Public review):

Donofrio et al. report a new observation that in normal aging mice, anti-calbindin whole-mount staining and coronal immunohistochemistry in the cerebellum often show a sagittally patterned loss of Purkinje cells with age. The authors address a central concern that calbindin antibody staining alone is not sufficient to definitively assess Purkinje cell loss, and corroborate their antibody staining data with transgenic Pcp2-CRE x flox-GFP reporter mice and Neutral Red staining. The authors then investigate whether this patterned Purkinje loss correlates with the known parasagittal expression of zebrin-II, finding a strong but imperfect correlation with zebrin-II antibody staining. They next draw a connection between this age-related Purkinje loss to the age-related decline in motor function in mice, with trending but non-significant statistical association between the severity/patterning of Purkinje loss and motor phenotypes within cohorts of aged mice. Finally, the authors look at post-mortem human cerebellar tissues from deceased healthy donors between 21 and 74 years of age, finding a positive correlation between Purkinje degeneration and age, but with unknown spatial patterning.

The conclusions drawn from this study are well supported by the data provided, with image quantification corroborating visual observations. The authors highlight several examples of parasagittal patterning of Purkinje cell degeneration in disease, and they show that proper methodologies must be used to account for these patterns to avoid highly variable data in the sagittal plane. The authors aptly point out that additional work is needed to investigate the spatial patterns of Purkinje cell loss in the human cerebellum.

Reviewer #1 (Public review):

In their paper entitled "Combined transcriptomic, connectivity, and activity profiling of the medial amygdala using highly amplified multiplexed in situ hybridization (hamFISH)" Edwards et al. present a new method designated as hamFISH (highly amplified multiplexed in situ hybridization) that enables sequential detection of {less than or equal to}32 genes using multiplexed branched DNA amplification. As proof-of-principle, the authors apply the new technique - in conjunction with connectivity, and activity profiling - to the medial amygdala (MeA) of the mouse, which is a critical nucleus for innate social and defensive behaviors.

As mentioned by Edwards et al., hamFISH could prove beneficial as an affordable alternative to other in situ transcriptomic methods, including commercial platforms, that are resource-intensive and require complex analysis pipelines. Thus, the authors envision that the method they present could democratize in situ cell-type identification in individual laboratories.

The data presented by Edwards et al. is convincing. The authors use the appropriate and validated methodology in line with the current state-of-the-art. The paper makes a strong case for the benefits of hamFISH when combining transcriptomics studies with connectivity tracing and immediate early gene-based activity profiling. Notably, the authors also discuss the caveats and limitations of their study/approach in an open and transparent manner.

Comments on revisions:

In their revised paper, Edwards et al. have made an effort to improve manuscript clarity. Revisions made address the non-public "recommendations for the authors." The main criticism that prevents a more enthusiastic overall assessment, i.e., absence of some more in-depth hypothesis-based analysis (though, as originally mentioned, maybe beyond the study's scope), is still valid.

Reviewer #2 (Public review):

The authors describe the development and implementation of hamFISH, a sensitive multiplexed ISH method. They leverage a pre-existing scRNA-seq dataset for the MeA to design 32 probes that combinatorically represent MeA neuronal populations - ~80% of MeA neurons express at least three of these 32 markers. Using these markers to assess the spatial organization of the MeA, the authors identify a novel population of Ndnf+ projection neurons and characterize their connectivity with anterograde and retrograde labeling. They additionally combine hamFISH with CTB labeling of three principal MeA projections sites to show that 75% of MeA neurons have only a single projection target. Finally, they engage adult male mice in encounters with other adult males (aggression), females (mating), and pups (infanticide), followed with hamFISH and c-fos labeling to relate cell identity to behavior. Their overall conclusion is that hamFISH-defined cell types are broadly active to multiple sensory stimuli. However, the data presented are not sufficient to conclude that no selectivity exists.

A strength of the manuscript is the novel hamFISH approach, which is technically innovative and could potentially be adopted by many labs. However, a weakness is that the 32 selected hamFISH marker genes employed here are predominantly neuropeptides. These genes, such as Tac1, Cartpt, Adcyap1, Calb1, and Gal, are expressed throughout the MeA, and many other brain regions and are not selective for transcriptomic cell types or developmental lineages. The use of hamFISH probes that provide a more stringent classification of cell type or cell identity could potentially provide a different picture of sensory response selectivity within the MeA. Thus, although the data in the manuscript are exemplary, the biological insight into MeA function is more limited.

Reviewer #3 (Public review):

Summary:

In this manuscript, Edwards et al. describe hamFISH, a customizable and cost-efficient method for performing targeted spatial transcriptomics. hamFISH utilizes highly amplified multiplexed branched DNA amplification, and the authors extensively describe hamFISH development and its advantages over prior variants of this approach.

The authors then used hamFISH to investigate an important circuit in the mouse brain for social behavior, the medial amygdala (MeA). To develop a hamFISH probe set capable of distinguishing MeA neurons, the authors mined published single cell RNA-sequencing datasets of the MeA, ultimately creating a panel of 32 hamFISH probes that mostly cover the identified MeA cell types. They evaluated over 600,000 MeA cells and classified neurons into 16 inhibitory and 10 excitatory types, many of which are spatially clustered.

The authors combined hamFISH with viral and other circuit tracer injections to determine whether the identified MeA cell populations sent and/or received unique inputs from connected brain regions, finding evidence that several cell types had unique patterns of input and output. Finally, the authors performed hamFISH on the brains of male mice that were placed in behavioral conditions that elicit aggressive, infanticidal, or mating behaviors, finding that some cell populations are selectively activated (as assessed by c-fos mRNA expression) in specific social contexts.

Strengths:

(1) The authors developed an optimized tissue preparation protocol for hamFISH and implemented oligopools instead of individually synthesized oligonucleotides to reduce costs. The branched DNA amplification scheme improved smFISH signal compared to previous methods, and multiple variants provide additional improvements in signal intensity and specificity. Compared to other spatial transcriptomics methods, the pipeline for imaging and analysis is streamlined, and is compatible with other techniques like fluorescence-based circuit tracing. This approach is cost-effective and has several advantages that make it a valuable addition to the list of spatial transcriptomics toolkits.

(2) Using 31 probes, hamFISH was able to detect 16 inhibitory and 10 excitatory neuron types in the MeA subregions, including the vast majority of cell types identified by other transcriptomics approaches. The authors quantified the distributions of these cell types along the anterior-posterior, dorsal-ventral, and medial-lateral axes, finding spatial segregation among some, but not all, MeA excitatory and inhibitory cell types. The authors additionally identified a class of inhibitory neurons expressing Ndnf (and a subset of these that express Chrna7) that project to multiple social chemosensory circuits.

(3) The authors combined hamFISH with MeA input and output mapping, finding cell-type biases in the projections to the MPOA, BNST, and VMHvl, and inputs from multiple regions.

(4) The authors identified excitatory and inhibitory cell types, and patterns of activity across cell types, that were selectively activated during various social behaviors, including aggression, mating, and infanticide, providing new insights and avenues for future research into MeA circuit function.

Weaknesses:

(1) Gene selection for hamFISH is likely to still be a limiting factor, even with the expanded (32-probe) capacity. This may have contributed to the lack of ability to identify sexually dimorphic cell types (Fig. S2B). This is an expected tradeoff for a method that has major advantages in terms of cost and adaptability.

(2) Adaptation of hamFISH, for example, to adapt it to other brain regions or tissues, may require extensive optimization. This does not preclude it from being highly useful for other brain regions with extra effort.

(3) Pairing this method with behavioral experiments is likely to require further optimization, as c-fos mRNA expression is an indirect and incomplete survey of neuronal activity (e.g. not all cell types upregulate c-fos when electrically active). As such, there is a risk of false negative results that limit its utility for understanding circuit function.

(4) The incompatibility of hamFISH with thicker tissue samples and minimal optical sectioning introduce additional technical limitations. For example, it would be difficult to densely sample larger neural circuits using serial 20 micron sections.

Reviewer #1 (Public review):

In the current article, Octavia Soegyono and colleagues study "The influence of nucleus accumbens shell D1 and D2 neurons on outcome-specific Pavlovian instrumental transfer", building on extensive findings from the same lab. While there is a consensus about the specific involvement of the Shell part of the Nucleus Accumbens (NAc) in specific stimulus-based actions in choice settings (and not in General Pavlovian instrumental transfer - gPIT, as opposed to the Core part of the NAc), mechanisms at the cellular and circuitry levels remain to be explored. In the present work, using sophisticated methods (rat Cre-transgenic lines from both sexes, optogenetics and the well-established behavioral paradigm outcome-specific PIT - sPIT), Octavia Soegyono and colleagues decipher the differential contribution of dopamine receptors D1 and D2 expressing-spiny projection neurons (SPNs).

After validating the viral strategy and the specificity of the targeting (immunochemistry and electrophysiology), the authors demonstrate that while both NAc Shell D1- and D2-SPNs participate in mediating sPIT, NAc Shell D1-SPNs projections to the Ventral Pallidum (VP, previously demonstrated as crucial for sPIT), but not D2-SPNs, mediates sPIT. They also show that these effects were specific to stimulus-based actions, as value-based choices were left intact in all manipulations.

This is a well-designed study and the results are well supported by the experimental evidence. The paper is extremely pleasant to read and add to the current literature.

Comments on revisions:

We thank the authors for their detailed responses and for addressing our comments and concerns.

To further improve consistency and transparency, we kindly request that the authors provide, for Supplemental Figures S1-S4, panels E (raw data for lever presses during the PIT test), the individual data points together with the corresponding statistical analyses in the figure legends.

In addition, regarding Supplemental Figure S3, panel E, we note the absence of a PIT effect in the eYFP group under the ON condition, which appears to differ from the net response reported in the main Figure 5, panel B. Could the authors clarify this apparent discrepancy?

We also note a discrepancy between the authors' statement in their response ("40 rats excluded based on post-mortem analyses") and the number of excluded animals reported in the Materials and Methods section, which adds up to 47. We kindly ask the authors to clarify this point for consistency.

Finally, as a minor point, we suggest indicating the total number of animals used in the study in the Materials and Methods section.

Reviewer #2 (Public review):

Summary:

This manuscript by Soegyono et a. describes a series of experiments designed to probe the involvement of dopamine D1 and D2 neurons within the nucleus accumbens shell in outcome-specific Pavlovian-instrumental transfer (osPIT), a well-controlled assay of cue-guided action selection based on congruent outcome associations. They used an optogenetic approach to phasically silence NAc shell D1 (D1-Cre mice) or D2 (A2a-Cre mice) neurons during a subset of osPIT trials. Both manipulations disrupted cue-guided action selection but had no effects on negative control measures/tasks (concomitant approach behavior, separate valued guided choice task), nor were any osPIT impairments found in reporter only control groups. Separate experiments revealed that selective inhibition of NAc shell D1 but not D2 inputs to ventral pallidum were required for osPIT expression, thereby advancing understanding of the basal ganglia circuitry underpinning this important aspect of decision making.

Strengths:

The combinatorial viral and optogenetic approaches used here were convincingly validated through anatomical tract-tracing and ex vivo electrophysiology. The behavioral assays are sophisticated and well-controlled to parse cue and value guided action selection. The inclusion of reporter only control groups is rigorous and rules out nonspecific effects of the light manipulation. The findings are novel and address a critical question in the literature. Prior work using less decisive methods had implicated NAc shell D1 neurons in osPIT but suggested that D2 neurons may not be involved. The optogenetic manipulations used in the current study provides a more direct test of their involvement and convincingly demonstrate that both populations play an important role. Prior work had also implicated NAc shell connections to ventral pallidum in osPIT, but the current study reveals the selective involvement of D1 but not D2 neurons in this circuit. The authors do a good job of discussing their findings, including their nuanced interpretation that NAc shell D2 neurons may contribute to osPIT through their local regulation of NAc shell microcircuitry.

Weaknesses:

The current study exclusively used an optogenetic approach to probe the function of D1 and D2 NAc shell neurons. Providing a complementary assessment with chemogenetics or other appropriate methods would strengthen conclusions, particularly the novel demonstration for D2 NAc shell involvement. Likewise, the null result of optically inhibiting D2 inputs to ventral pallidum leaves open the possibility that a more complete or sustained disruption of this pathway may have impaired osPIT.

Conclusions:

The research described here was successful in providing critical new insights into the contributions of NAc D1 and D2 neurons in cue-guided action selection. The authors' data interpretation and conclusions are well reasoned and appropriate. They also provide a thoughtful discussion of study limitations and implications for future research. This research is therefore likely to have a significant impact on the field.

Comments on revisions:

I have reviewed the rebuttal and revised manuscript and have no remaining concerns.

Reviewer #1 (Public review):

In the current article, Octavia Soegyono and colleagues study "The influence of nucleus accumbens shell D1 and D2 neurons on outcome-specific Pavlovian instrumental transfer", building on extensive findings from the same lab. While there is a consensus about the specific involvement of the Shell part of the Nucleus Accumbens (NAc) in specific stimulus-based actions in choice settings (and not in General Pavlovian instrumental transfer - gPIT, as opposed to the Core part of the NAc), mechanisms at the cellular and circuitry levels remain to be explored. In the present work, using sophisticated methods (rat Cre-transgenic lines from both sexes, optogenetics, and the well-established behavioral paradigm outcome-specific PIT-sPIT), Octavia Soegyono and colleagues decipher the differential contribution of dopamine receptors D1 and D2 expressing spiny projection neurons (SPNs).

After validating the viral strategy and the specificity of the targeting (immunochemistry and electrophysiology), the authors demonstrate that while both NAc Shell D1- and D2-SPNs participate in mediating sPIT, NAc Shell D1-SPNs projections to the Ventral Pallidum (VP, previously demonstrated as crucial for sPIT), but not D2-SPNs, mediates sPIT. They also show that these effects were specific to stimulus-based actions, as value-based choices were left intact in all manipulations.

This is a well-designed study, and the results are well supported by the experimental evidence. The paper is extremely pleasant to read and adds to the current literature.

Reviewer #2 (Public review):

Summary:

This manuscript by Soegyono et al. describes a series of experiments designed to probe the involvement of dopamine D1 and D2 neurons within the nucleus accumbens shell in outcome-specific Pavlovian-instrumental transfer (osPIT), a well-controlled assay of cue-guided action selection based on congruent outcome associations. They used an optogenetic approach to phasically silence NAc shell D1 (D1-Cre mice) or D2 (A2a-Cre mice) neurons during a subset of osPIT trials. Both manipulations disrupted cue-guided action selection but had no effects on negative control measures/tasks (concomitant approach behavior, separate valued guided choice task), nor were any osPIT impairments found in reporter-only control groups. Separate experiments revealed that selective inhibition of NAc shell D1 but not D2 inputs to ventral pallidum was required for osPIT expression, thereby advancing understanding of the basal ganglia circuitry underpinning this important aspect of decision making.

Strengths:

The combinatorial viral and optogenetic approaches used here were convincingly validated through anatomical tract-tracing and ex vivo electrophysiology. The behavioral assays are sophisticated and well-controlled to parse cue and value-guided action selection. The inclusion of reporter-only control groups is rigorous and rules out nonspecific effects of the light manipulation. The findings are novel and address a critical question in the literature. Prior work using less decisive methods had implicated NAc shell D1 neurons in osPIT but suggested that D2 neurons may not be involved. The optogenetic manipulations used in the current study provide a more direct test of their involvement and convincingly demonstrate that both populations play an important role. Prior work had also implicated NAc shell connections to ventral pallidum in osPIT, but the current study reveals the selective involvement of D1 but not D2 neurons in this circuit. The authors do a good job of discussing their findings, including their nuanced interpretation that NAc shell D2 neurons may contribute to osPIT through their local regulation of NAc shell microcircuitry.

Weaknesses:

The current study exclusively used an optogenetic approach to probe the function of D1 and D2 NAc shell neurons. Providing a complementary assessment with chemogenetics or other appropriate methods would strengthen conclusions, particularly the novel demonstration of D2 NAc shell involvement. Likewise, the null result of optically inhibiting D2 inputs to the ventral pallidum leaves open the possibility that a more complete or sustained disruption of this pathway may have impaired osPIT.

Reviewer #3 (Public review):

Summary:

The authors present data demonstrating that optogenetic inhibition of either D1- or D2-MSNs in the NAc Shell attenuates expression of sensory-specific PIT while largely sparing value-based decision on an instrumental task. They also provide evidence that SS-PIT depends on D1-MSN projections from the NAc-Shell to the VP, whereas projections from D2-MSNs to the VP do not contribute to SS-PIT.

Strengths:

This is clearly written. The evidence largely supports the authors' interpretations, and these effects are somewhat novel, so they help advance our understanding of PIT and NAc-Shell function.

Weaknesses:

I think the interpretation of some of the effects (specifically the claim that D1-MSNs do not contribute to value-based decision making) is not fully supported by the data presented.

Reviewer #1 (Public review):

Summary:

The authors used high-density probe recordings in the medial prefrontal cortex (PFC) and hippocampus during a rodent spatial memory task to examine functional sub-populations of PFC neurons that are modulated vs. unmodulated by hippocampal sharp-wave ripples (SWRs), an important physiological biomarker that is thought to have role in mediating information transfer across hippocampal-cortical networks for memory processes. SWRs are associated with reactivation of representations of previous experiences, and associated reactivation in hippocampal and cortical regions have been proposed to have a role in memory formation, retrieval, planning, and memory-guided behavior. This study focuses of awake SWRs that are prevalent during immobility periods during pauses in behavior. Previous studies have reported strong modulation of a subset of prefrontal neurons during hippocampal SWRs, with some studies reporting prefrontal reactivation during SWRs that have a role in spatial memory processes. The study seeks to extend these findings by examining activity of SWR-modulated vs. unmodulated neurons across PFC sub-regions, and whether there is a functional distinction between these two kinds of neuronal populations with respect to representing spatial information and supporting memory-guided decision making.

Strengths:

The major strength of the study is the use of Neuropixels 1.0 probes to monitor activity throughput the dorsal-ventral extent of the rodent medial prefrontal cortex, permitting an investigation of functional distinction in neuronal populations across PFC sub-regions. They are able to show that SWR-unmodulated neurons, in addition to having stronger spatial tuning than SWR-modulated neurons as previously reported, also show stronger directional selectivity, and theta-cycle skipping properties.

Weaknesses:

(1) The title and abstract have been updated to reflect the updated interpretation that prefrontal neurons are involved in spatial tuning and signaling upcoming choice independently from hippocampal SWRs, implying the negative that these functions do not happen during SWRs. The evidence presented, however, is lacking and the analyses has key limitations that preclude such a conclusion. First, the fact that prefrontal neurons decode past and future choices independently of the hippocampus, not just hippocampal SWRs, is well-established (for e.g., Baeg et al., 2003, 10.1016/s0896-6273(03)00597-x). Second, the statement that prefrontal neurons are involved in spatial tuning independently from SWRs is inconsistent, since spatial tuning is assessed during exploratory behaviors that are not associated with SWRs. Apart from showing that non-local decoding occurs in prefrontal cortex outside SWR time periods, which is already established, the conclusion needs evidence this does not occur during SWR time periods, which is not presented.

(2) The results show that SWR-modulated prefrontal neurons are more linked to hippocampal non-local representations, whereas SWR-unmodulated neurons encode upcoming choice independently of SWRs. This is logical, and implies that SWR-modulated prefrontal neurons are involved in non-local decoding during hippocampal non-local representations. This hints at potentially multiple mechanisms, one involving independent prefrontal non-local decoding, and another involving prefrontal and hippocampal non-local decoding.

(3) The analyses have key limitations. The Methods section notes that decoding was performed in 50ms bins, periods with running speed less than 15cm/s were excluded, then decoded probabilities summed for each maze segment, followed by grouping probabilities together for local and non-local decoding. This implies that decoding segments can span entire maze segments or long time periods - this needs to be clarified and quantified. When examining time-locking of decoding segments to hippocampal SWRs, only non-local segments that occurred within 2 secs of SWRs were used. This raises several concerns. First, prefrontal modulation by hippocampal SWRs lasts primarily <500ms, so a 2sec temporal proximity will lead to non-SWR modulation periods being included in the analyses. In addition, even for decoding segments that may be in close temporal proximity, these can be very long, based on the analyses description. This can lead to spurious results. Second, if only running speeds >15cm/s were included, immobility periods are necessarily being excluded, which is when SWRs occur. So, this analysis cannot be used to investigate decoding during SWRs; rather, a direct approach of extracting prefrontal activity during SWRs and then decoding this activity is required.

Reviewer #2 (Public review):

Summary:

This work by den Bakker and Kloosterman contributes to the vast body of research exploring the dynamics governing the communication between the hippocampus (HPC) and the medial prefrontal cortex (mPFC) during spatial learning and navigation. Previous research showed that population activity of mPFC neurons is replayed during HPC sharp-wave ripple events (SWRs), which may therefore correspond to privileged windows for the transfer of learned navigation information from the HPC, where initial learning occurs, to the mPFC, which is thought to store this information long term. Indeed, it was also previously shown that the activity of mPFC neurons contains task-related information that can inform about the location of an animal in a maze, which can predict the animals' navigational choices. Here, the authors aim to show that the mPFC neurons that are modulated by HPC activity (SWRs and theta rhythms) are distinct from those "encoding" spatial information. This result could suggest that the integration of spatial information originating from the HPC within the mPFC may require the cooperation of separate sets of neurons.

This observation may be useful to further extend our understanding of the dynamics regulating the exchange of information between the HPC and mPFC during learning. However, my understanding is that this finding is mainly based upon a negative result, which cannot be statistically proven by the failure to reject the null hypothesis. Moreover, in my reading, the rest of the paper mainly replicates phenomena that have already been described, with the original reports not correctly cited. My opinion is that the novel elements should be precisely identified and discussed, while the current phrasing in the manuscript, in most cases, leads readers to think that these results are new. Detailed comments are provided below.

Major concerns:

ORIGINAL COMMENT: (1) The main claim of the manuscript is that the neurons involved in predicting upcoming choices are not the neurons modulated by the HPC. This is based upon the evidence provided in Figure 5, which is a negative result that the authors employ to claim that predictive non-local representations in the mPFC are not linked to hippocampal SWRs and theta phase. However, it is important to remember that in a statistical test, the failure to reject the null hypothesis does not prove that the null hypothesis is true. Since this claim is so central in this work, the authors should use appropriate statistics to demonstrate that the null hypothesis is true. This can be accomplished by showing that there is no effect above some size that is so small that it would make the effect meaningless (see https://doi.org/10.1177/070674370304801108).

AUTHOR RESPONSE: We would like to highlight a few important points here. (1) We indeed do not intend to claim that the SWR-modulated neurons are not at all involved in predicting upcoming choice, just that the SWR-unmodulated neurons may play a larger role. We have rephrased the title and abstract to make this clearer.

REVIEWER COMMENT: The title has been rephrased but still conveys the same substantive claim. The abstract sentence also does not clearly state what was found. Using "independently" in the new title continues to imply that SWR modulation and prediction of upcoming choices are separate phenomena. By contrast, in your response here in the rebuttall you state only that "SWR-unmodulated neurons may play a larger role," which is a much more tempered claim than what the manuscript currently argues. Why is this clarification not adopted in the article? Moreover, the main text continues to use the same arguments as before; beyond the cosmetic changes of title and abstract, the claim itself has not materially changed.

AUTHOR RESPONSE: (2) The hypothesis that we put forward is based not only on a negative effect, but on the findings that: the SWR-unmodulated neurons show higher spatial tuning (Fig 3b), more directional selectivity (Fig 3d), more frequent encoding of the upcoming choice at the choice point (new analysis, added in Fig 4d), and higher spike rates during the representations of the upcoming choice (Fig 5b). This is further highlighted by the fact that the representations of upcoming choice in the PFC are not time locked to SWRs (whereas the hippocampal representations of upcoming choice are; see Fig 5a and Fig 6a), and not time-locked to hippocampal theta phase (whereas the hippocampal representations are; see Fig 5c and Fig 6c). Finally, the representations of upcoming and alternative choices in the PFC do not show a large overlap in time with the representations in the hippocampus (see updated Fig 4e were we added a statistical test to show the likelihood of the overlap of decoded timepoints). All these results together lead us to hypothesize that SWR-modulation is not the driving factor behind non-local decoding in the PFC.

REVIEWER COMMENT: I do not see how these precisions address my remark. The main claim in the title used to be "Neurons in the medial prefrontal cortex that are not modulated by hippocampal sharp-wave ripples are involved in spatial tuning and signaling upcoming choice." It is now "Neurons in the medial prefrontal cortex are involved in spatial tuning and signaling upcoming choice independently from hippocampal sharp-wave ripples." The substance has not changed. This specific claim is supported solely by Figure 5.

The other analyses cited describe functional characteristics of SWR-unmodulated neurons but, unless linked by explicit new analyses, do not substantiate independence/orthogonality between SWR modulation and non-local decoding in PFC. If there is an analysis that makes this link explicit, it should be clearly presented; as it stands, I cannot find an explanation in the manuscript for why "all these results together" justify the conclusion that "All these results together lead us to hypothesize that SWR-modulation is not the driving factor behind non-local decoding in the PFC". Also: is the main result of this work a "hypothesis"? If so, this should be clearly differentiated from a conclusion supported by results and analyses.

AUTHOR RESPONSE: (3) Based on the reviewers suggestion, we have added a statistical test to compare the phase-locking based of the non-local decoding to hippocampal SWRs and theta phase to shuffled posterior probabilities. Instead of looking at all SWRs in a -2 to 2 second window, we have now only selected the closest SWR in time within that window, and did the statistical comparison in the bin of 0-20 ms from SWR onset. With this new analysis we are looking more directly at the time-locking of the decoded segments to SWR onset (see updated Fig 5a and 6a).

REVIEWER COMMENT: I appreciate the added analysis focusing on the closest SWR and a 0-20 ms bin. My understanding is that you consider the revised analyses in Figures 5a and 6a sufficient to show that predictive non-local representations in mPFC are not linked to hippocampal SWRs and theta phase.

First, the manuscript should explicitly explain the rationale for this analysis and why it is sufficient to support the claim. From the main text it is not possible to understand what was done; the Methods are hard to follow, and the figure legends are not clearly described (e.g. the shuffle is not even defined there).

Specific points I could not reconcile:

i) The gray histograms in the revised Figures 5a and 6a now show a peak at zero lag, whereas in the previous version they were flat, although they are said to plot the same data. What changed?

ii) Why choose a 20 ms bin? A single narrow bin invites false negatives. Please justify this choice.

iii) Comparing to a shuffle is a useful control, but when the p-value is non-significant we only learn that no difference was detected under that shuffle-not that there is no difference or that the processes are independent.

ORIGINAL COMMENT: (2) The main claim of the work is also based on Figure 3, where the authors show that SWRs-unmodulated mPFC neurons have higher spatial tuning, and higher directional selectivity scores, and a higher percentage of these neurons show theta skipping. This is used to support the claim that SWRs-unmodulated cells encode spatial information. However, it must be noted that in this kind of task, it is not possible to disentangle space and specific task variables involving separate cognitive processes from processing spatial information such as decision-making, attention, motor control, etc., which always happen at specific locations of the maze. Therefore, the results shown in Figure 3 may relate to other specific processes rather than encoding of space and it cannot be unequivocally claimed that mPFC neurons "encode spatial information". This limitation is presented by Mashoori et al (2018), an article that appears to be a major inspiration for this work. Can the authors provide a control analysis/experiment that supports their claim? Otherwise, this claim should be tempered. Also, the authors say that Jadhav et al. (2016) showed that mPFC neurons unmodulated by SWRs are less tuned to space. How do they reconcile it with their results?

AUTHOR RESPONSE: The reviewer is right to assert caution when talking about claims such as spatial tuning where other factors may also be involved. Although we agree that there may be some other factors influencing what we are seeing as spatial tuning, it is very important to note that the behavioral task is executed on a symmetrical 4-armed maze, where two of the arms are always used for the start of the trajectory, and the other two arms (North and South) function as the goal (reward) arms. Therefore, if the PFC is encoding cognitive processes such as task phases related to decision-making and reward, we would not be able to differentiate between the two start arms and the two goal arms, as these represent the same task phases. Note also that the North and South arm are illuminated in a pseudo-random order between trials and during cue-based rule learning this is a direct indication of where the reward will be found. Even in this phase of the task, the PFC encodes where the animal will turn on a trial-to-trial basis (meaning the North and South arm are still differentiated correctly on each trial even though the illumination and associated reward are changing).

REVIEWER COMMENT: I appreciate that the departure location was pseudorandomized. However, this control does not rule out that PFC activity reflects motor preparation (left vs right turns) and associated perceptual decision-making/attentional processes that are inherently tied to a specific action. As such, it cannot by itself support the claim that PFC neurons "encode spatial information." Moreover, the authors acknowledge here that "other factors may also be involved," yet this caveat is not reflected in the manuscript. Why?

AUTHOR RESPONSE: Secondly, importantly, the reviewer mentions that we claimed that Jadhav et al. (2016) showed that mPFC neurons unmodulated by SWRs are less tuned to space, but this is incorrect. Jadhav et al. (2016) showed that SWR-unmodulated neurons had lower spatial coverage, meaning that they are more spatially selective (congruent with our results). We have rephrased this in the text to be clearer.

REVIEWER COMMENT: Thanks for clarifying this.

ORIGINAL COMMENT: (3) My reading is that the rest of the paper mainly consists of replications or incremental observations of already known phenomena with some not necessarily surprising new observations:<br /> a) Figure 2 shows that a subset of mPFC neurons is modulated by HPC SWRs and theta (already known), that vmPFC neurons are more strongly modulated by SWRs (not surprising given anatomy), and that theta phase preference is different between vmPFC and dmPFC (not surprising given the fact that theta is a travelling wave).

AUTHOR RESPONSE: The finding that vmPFC neurons are more strongly modulated by SWRs than dmPFC indeed matches what we know from anatomy, but that does not make it a trivial finding. A lot remains unknown about the mPFC subregions and their interactions with the hippocampus, and not every finding will be directly linked to the anatomy. Therefore, in our view this is a significant finding which has not been studied before due to the technical complexity of large-scale recordings along the dorsal-ventral axis of the mPFC.

REVIEWER COMMENT: This finding is indeed non-trivial; however, it seems completely irrelevant to the paper's main claim unless the Authors can argue otherwise.

AUTHOR RESPONSE: Similarly, theta being a traveling wave (which in itself is still under debate), does not mean we should assume that the dorsal and ventral mPFC should follow this signature and be modulated by different phases of the theta cycle. Again, in our view this is not at all trivial, but an important finding which brings us closer to understanding the intricate interactions between the hippocampus and PFC in spatial learning and decision-making.

REVIEWER COMMENT: Yes, but in what way does this support the manuscript's primary claim? This is unclear to me.

ORIGINAL COMMENT: b) Figure 4 shows that non-local representations in mPFC are predictive of the animal's choice. This is mostly an increment to the work of Mashoori et al (2018). My understanding is that in addition to what had already been shown by Mashoori et al here it is shown how the upcoming choice can be predicted. The author may want to emphasize this novel aspect.

AUTHOR RESPONSE: In our view our manuscript focuses on a completely different aspect of learning and memory than the paper the reviewer is referring to (Mashoori et al. 2018). Importantly, the Mashoori et al. paper looked at choice evaluation at reward sites and shows that disappointing reinforcements are associated with reactivations in the ACC of the unselected target. This points to the role of the ACC in error detection and evaluation. Although this is an interesting result, it is in essence unrelated to what we are focusing on here, which is decision making and prediction of upcoming choices. The fact that the turning direction of the animal can be predicted on a trial-to-trial basis, and even precedes the behavioral change over the course of learning, sheds light on the role of the PFC in these important predictive cognitive processes (as opposed to post-choice reflective processes).

REVIEWER COMMENT: Indeed, as I said, the new element here is that the upcoming choice can be predicted. This appears only incremental and could belong to another story; as the manuscript is currently written, it does not support the article's main claim. I would like to specify that, regarding this and the other points above, my inability to see how these minor results support the Authors' claim may reflect my misunderstanding; nevertheless, this suggests that the manuscript should be extensively rewritten and reorganized to make the Authors' meaning clear.

ORIGINAL COMMENT: c) Figure 6 shows that prospective activity in the HPC is linked to SWRs and theta oscillations. This has been described in various forms since at least the works of Johnson and Redish in 2007, Pastalkova et al 2008, and Dragoi and Tonegawa (2011 and 2013), as well as in earlier literature on splitter cells. These foundational papers on this topic are not even cited in the current manuscript.

AUTHOR RESPONSE: We have added these citations to the introduction (line 37).

REVIEWER COMMENT: This is an example of how the Authors fail to acknowledge the underlying problem with how the manuscript is written; the issue has not been addressed except with a cosmetic change like the one described above. The Results section contains a series of findings that are well-known phenomena described previously (see below). Prior results should be acknowledged at the beginning of each relevant paragraph, followed by an explicit statement of what is new, so that readers can distinguish replication from novelty. Here, I pointed specifically to the results of Figure 6, and the Authors deemed it sufficient simply to add the citations I indicated to an existing sentence in the Introduction, while keeping the Results description unchanged. As written, this reads as if these phenomena are being described for the first time. This is incorrect. It is hard to avoid the impression that the Authors did not take this concern seriously; the same issue appears elsewhere in the manuscript, and I fail to see how the Authors "have improved clarity of the text throughout to highlight the novelty of our results better."

Reviewer #2 (Public review):

This study presents a thorough investigation of remote memory deficits in the APP/PS1 mouse model of Alzheimer's disease, highlighting the progressive emergence of these deficits alongside selective hyperexcitability of PV interneurons in the mPFC. By combining viral-TRAP labeling and patch-clamp electrophysiology, the authors demonstrate increased inhibitory input onto engram cells in APP/PS1 mice, despite preserved engram size and reactivation. The revised manuscript successfully addresses earlier concerns by clarifying the relationship between amyloid pathology and circuit dysfunction, acknowledging the correlative nature of the findings, and integrating possible contributions of excitatory remodeling and broader network changes, including oscillatory disruptions. Although the precise mechanistic link between PV hyperexcitability, increased inhibition, and impaired remote memory remains to be empirically established, the study convincingly argues for inhibitory microcircuit alterations as an early contributor to cognitive decline in AD.

Reviewer #2 (Public review):

In this paper Chang et al follow up on their lab's previous findings about the secreted protein Shv and its role in activity-induced synaptic remodeling at the fly NMJ. Previously they reported that shv mutants have impaired synaptic plasticity. Normally a high stimulation paradigm should increase bouton size and GluR expression at synapses but this does not happen in shv mutants. The phenotypes relating to activity-dependent plasticity were completely recapitulated when Shv was knocked down only in neurons and could be completely rescued by incubation in exogenously applied Shv protein. The authors also showed that Shv activation of integrin signaling on both the pre- and post-synapse was the molecular mechanism underlying its function in plasticity. Here they extend their study to consider a role of Shv derived from glia in modulating synaptic features at baseline and remodeling conditions. The authors show evidence that Shv is expressed in both neurons and glia. Despite the fact that neuron-specific RNAi knockdown of Shv recapitulated the plasticity phenotypes seen in whole animal mutants, the authors asked whether glial-specific knockdown would have any effects. Surprisingly, knockdown of Shv only in glia also blocked plasticity, just like neuron-specific knockdown, and supporting an important role for glial-derived Shv in plasticity. Unlike neuronal knockdown, though, glial knockdown also caused abnormally high baseline GluR expression. Restoring Shv in ONLY glia in mutant animals is sufficient to completely rescue the plasticity phenotypes and baseline GluR expression, but glial-Shv does not appear to activate integrin signaling which was shown to be the mechanism for neuronally derived Shv to control plasticity. This suggests a different or indirect mechanism of action for glial-derived Shv. This led the authors to hypothesize that glial Shv might work via controlling the levels of neuronal Shv and/or extracellular glutamate. To test these hypotheses, they provide evidence that in the absence of glial Shv, synaptic levels of Shv go up overall, suggesting that glial Shv could somehow have a suppressive effect on release of neuronal Shv. This would indirectly modulate integrin signaling to control plasticity. Using an extracelluar glutamate sensor in presynaptic boutons, they also observe decreased signal (extracellular glutamate) from the sensor in glial Shv KD animals, and increased signal in glial Shv overexpression animals, supporting the hypothesis that glial Shv can regulate glutamate levels somehow. These results establish glia as an important source of Shv in these processes and identify some mechanisms for how this might be accomplished. Several outstanding questions remain-most importantly: how/why do glial-derived and neuronal-derived Shv have different effects when in the same space? No obvious isoform or size differences were found, and the same rescue construct expressed either in neurons or glia could have different effects on integrin activation or glutamate levels. Answering these questions using modified rescue constructs will be an important future direction to understand Shv function specifically and how neurons and glia work together in this context--and potentially many other contexts.

Comments on revisions:

The authors addressed my and the other reviewers' concerns from the original review adequately and this has strengthened the paper substantially.

One small omission to correct: In Figures 4 and 6, the graphs in the figures do not have a legend for the colored bars.

Reviewer #3 (Public review):

Summary:

The manuscript by Chang and colleagues provides compelling evidence that glia-derived Shriveled (Shv) modulates activity-dependent synaptic plasticity at the Drosophila neuromuscular junction (NMJ). This mechanism differs from the previously reported function of neuronally released Shv, which activates integrin signaling. They further show that this requirement of Shv is acute and that glial Shv supports synaptic plasticity by modulating neuronal Shv release and the ambient glutamate levels. However, there are a number of conceptual and technical issues that need to be addressed.

Major comments

(1) From the images provided for Fig 2B +RU486, the bouton size appears to be bigger in shv RNAi + stimulation, especially judging from the outline of GluR clusters.

(2) The shv result needs to be replicated with a separate RNAi.

(3) The phenotype of shv mutant resembles that of neuronal shv RNAi - no increased GluR baseline. Any insights why that is the case?

(4) In Fig 3B, SPG shv RNAi has elevated GluR baseline, while PG shv RNAi has a lower baseline. In both cases, there is no activity induced GluR increase. What could explain the different phenotypes?

(5) In Fig 4C, the rescue of PTP is only partial. Does that suggest neuronal shv is also needed to fully rescue the deficit of PTP in shv mutants?

(6) The observation in Fig 5D is interesting. While there is a reduction in Shv release from glia after stimulation, it is unclear what the mechanism could be. Is there a change in glial shv transcription, translation or the releasing machinery? It will be helpful to look at the full shv pool vs the released ones.

(7) In Fig 5E, what will happen after stimulation? Will the elevated glial Shv after neuronal shv RNAi be retained in the glia?

(8) It would be interesting to see if the localization of shv differs based on if it is released by neuron or glia, which might be able to explain the difference in GluR baseline. For example, by using glia-Gal4>UAS-shv-HA and neuronal-QF>QUAS-shv-FLAG. It seems important to determine if they mix together after release? It is unclear if the two shv pools are processed differently.

(9) Alternatively, do neurons and glia express and release different Shv isoforms, which would bind different receptors?

(10) It is claimed that Sup Fig 2 shows no observable change in gross glial morphology, further bolstering support that glial Shv does not activate integrin. This seems quite an overinterpretation. There is only one image for each condition without quantification. It is hard to judge if glia, which is labeled by GFP (presumably by UAS-eGFP?), is altered or not.

(11) The hypothesis that glutamate regulates GluR level as a homeostatic mechanism makes sense. What is the explanation of the increased bouton size in the control after glutamate application in Fig 6?

(12) What could be a mechanism that prevents elevated glial released Shv to activate integrin signaling after neuronal shv RNAi, as seen in Fig 5E?

(13) Any speculation on how the released Shv pool is sensed?

Comments on revisions:

The authors have addressed most of my previous comments and questions in their revision.

Reviewer #1 (Public review):

In this manuscript, Rishiq et al. investigate whether natural killer (NK) cells can interact with Fusobacterium nucleatum and identify the molecular mediators involved in this interaction. The authors propose that the bacterial adhesin RadD may bind to the activating NK cell receptor NKp46 (NCR1 in mice), leading to NK cell activation and tumor control. While the topic is of significant interest and the hypothesis intriguing, the manuscript lacks critical experimental evidence, contains several technical concerns, and requires substantial revisions.

Major Concerns:

(1) Lack of Direct Evidence for RadD-NKp46 Interaction

The central claim that RadD interacts with NKp46 is not formally demonstrated. A direct binding assay (e.g., Biacore, ELISA, or pull-down with purified proteins) is essential to support this assertion. The absence of this fundamental experiment weakens the mechanistic conclusions of the study.

(2) Figure 2: Binding Specificity and Bacterial Strains

A CEACAM1-Ig control should be included in all binding experiments to distinguish between specific and non-specific Ig interactions. There is differential Ig binding between strains ATCC 23726 and 10953. The authors should quantify RadD expression in each strain to determine if the difference in binding is due to variation in RadD levels.

(3) Figure 3: Flow Cytometry Inconsistencies and Missing Controls

What do the FITC-negative, Ig-negative events represent? The authors should clarify whether these are background signals, bacterial aggregates, or debris.

Panel B, CEACAM1-Ig binding appears markedly increased compared to WT bacteria. The reason for this enhancement should be discussed-does it reflect upregulation of the bacterial ligand or an artifact of overexpression? Fluorescence compensation should be carefully reviewed for the NKp46/NCR1-Ig binding assays to ensure that the signals are not due to spectral overlap or nonspecific binding. Importantly, binding experiments using the FadI/RadD double knockout strain are missing and should be included. This control is essential.

In Panel E, the basis for calculating fold-change in MFI is unclear. Please indicate the reference condition to which the change is normalized.

(4) Figure 4: Binding Inhibition and Receptor Sensitivity

Panel A lacks representative FACS plots and is currently difficult to interpret. Differences in the sensitivity of human vs. mouse NKp46 to arginine inhibition should be discussed, given species differences in receptor-ligand interactions. What are the inhibition results using F. nucleatum strains deficient in FadI?

In Panel B, CEACAM1-Ig and RadD-deficient bacteria must be included as negative controls for binding specificity upon anti-NKp46 blocking.

(5) Figure 5: Functional NK Activation and Tumor Killing

In Panels B and C, the key control condition (NK cells + anti-NKp46, without bacteria) is missing. This is needed to evaluate if NKp46 recognition is involved in tumor killing. The authors should explicitly test whether pre-incubation of NK cells with bacteria enhances their anti-tumor activity. Alternatively, could bacteria induce stress signals in tumor cells that sensitize them to NK killing? This distinction is critical.

(6) Figure 5D: Mechanism of Peripheral Activation

It is suggested that contact between bacteria and NK cells in the periphery leads to their activation. Can the authors confirm whether this pre-activation leads to enhanced killing of tumor targets, or if bacteria-tumor co-localization is required? The literature indicates that F. nucleatum localizes intracellularly within tumor cells. If so, how is RadD accessible to NKp46 on infiltrating NK cells?

(8) Figure 5E and In Vivo Relevance

Surprisingly, F. nucleatum infection is associated with increased tumor burden. Does this reflect an immunosuppressive effect? Are NK cells inhibited or exhausted in infected mice (TGIT, SIGLEC7...)? If NK cell activation leads to reduced tumor control in the infected context, the role of RadD-induced activation needs further explanation. RadD-deficient bacteria, which do not activate NK cells, result in even poorer tumor control. This paradox needs to be addressed: how can NK activation impair tumor control while its absence also reduces tumor control?

(9) NKp46-Deficient Mice: Inconsistencies

In Ncr1⁻/⁻ mice, infection with WT or RadD-deficient F. nucleatum has no impact on tumor burden. This suggests that NKp46 is dispensable in this context and casts doubt on the physiological relevance of the proposed mechanism. This contradiction should be discussed more thoroughly.

Reviewer #2 (Public review):

Summary:

In the present study, Rishiq et al. investigated whether the RadD protein expressed by Fusobacterium nucleatum subsp. Nucleatum serves as a natural ligand for the NK-activating receptor NKp46, and whether RadD-NKp46 interaction enhances NK cell cytotoxicity against tumor cells. To address this, the authors first performed an association analysis of F. nucleatum abundance and NKp46 expression in head and neck squamous cell carcinoma (HNSC) and colorectal cancer (CRC) using the TCMA and TCGA databases, respectively. While a positive association between NKp46⁺ and F. nucleatum⁺ status with improved overall survival was observed in HNSC patients, no such correlation was found in CRC.

Next, they examined the binding of NKp46-Ig to various F. nucleatum strains. To confirm that this interaction was mediated specifically by RadD, they employed a RadD-deficient mutant strain. Finally, to establish the functional relevance of the RadD-NKp46 interaction in promoting NK cell cytotoxicity and anti-tumor responses, they utilized a syngeneic mouse breast cancer model. In this setup, AT3 cells were orthotopically implanted into the mammary fat pad of C57BL/6 wild-type (WT) or Ncr1-deficient (NCR1⁻/⁻; murine orthologue of human NKp46) mice, followed by intravenous inoculation with either WT F. nucleatum or the ∆RadD mutant strain.

Strengths:

A notable strength of the work is that it identifies a previously unrecognized activating interaction between F. nucleatum RadD and the NK cell receptor NKp46, demonstrating that the same bacterial protein can engage distinct NK cell receptors (activating or inhibitory) to exert context-dependent effects on anti-tumor immunity. This dual-receptor insight adds depth to our understanding of F. nucleatum-immune interactions and highlights the complexity of microbial modulation of the tumor microenvironment.

Weaknesses:

(1) A previous study by this group (PMID: 38952680) demonstrated that RadD of F. nucleatum binds to NK cells via Siglec-7, thereby diminishing their cytotoxic potential. They further proposed that the RadD-Siglec-7 interaction could act as an immune evasion mechanism exploited by tumor cells. In contrast, the present study reports that RadD of F. nucleatum can also bind to the activating receptor NKp46 on NK cells, thereby enhancing their cytotoxic function.

While F. nucleatum-mediated tumor progression has been documented in breast and colon cancers, the current study proposes an NK-activating role for F. nucleatum in HNSC. However, it remains unclear whether tumor-infiltrating NK cells in HNSC exhibit differential expression of NKp46 compared to Siglec-7. Furthermore, heterogeneity within the NK cell compartment, particularly in the relative abundance of NKp46⁺ versus Siglec-7⁺ subsets, may differ substantially among breast, colon, and HNSC tumors. Such differences could have been readily investigated using publicly available single-cell datasets. A deeper understanding of this subset heterogeneity in NK cells would better explain why F. nucleatum is passively associated with a favorable prognosis in HNSC but correlates with poor outcomes in breast and colon cancers.

(2) The in vivo tumor data (Figure 5D-F) appear to contradict the authors' claims. Specifically, Figure 5E suggests that WT mice engrafted with AT3 breast tumors and inoculated with WT F. nucleatum exhibited an even greater tumor burden compared to mice not inoculated with F. nucleatum, indicating a tumor-promoting effect. This finding conflicts with the interpretation presented in both the results and discussion sections.

(3) Although the authors acknowledge that F. nucleatum may have tumor context-specific roles in regulating NK cell responses, it is unclear why they chose a breast cancer model in which F. nucleatum has been reported to promote tumor growth. A more appropriate choice would have been the well-established preclinical oral cancer model, such as the 4-nitroquinoline 1-oxide (4NQO)-induced oral cancer model in C57BL/6 mice, which would more directly relate to HNSC biology.

(4) Since RadD of F. nucleatum can bind to both Siglec-7 and NKp46 on NK cells, exerting opposing functional effects, the expression profiles of both receptors on intratumoral NK cells should be evaluated. This would clarify the balance between activating and inhibitory signals in the tumor microenvironment and provide a more mechanistic explanation for the observed tumor context-dependent outcomes.

Reviewer #1 (Public review):

Summary:

This is an interesting study on the role of FGF signaling in the induction of primitive streak like-cells (PS-LC) in human 2D-gastruloids. The authors use a previously characterized standard culture that generates a ring of PS-LCs (TBXT+) and correlate this with pERK staining. A requirement for FGF signaling in TBXT induction is demonstrated via pharmacological inhibition of MEK and FGFR activity. A second set of culture conditions (with no exogenous FGFs) suggests that endogenous FGFs are required for pERK and TBXT induction. The authors then characterize, via scRNA-seq, various components of the FGF pathway (genes for ligand, receptors, ERK regulators, HSPG regulation). They go on to characterize the pFGFR1, receptor isoforms and polarized localization of this receptor. Finally, they perform FGF4 inhibition and use a cell line with a limited FGF17 inactivation (heterozygous null) and show that loss of these FGFs reduce PS-LC and derivative cell types.

Strengths:

(1) As the authors point out, the role of FGF signaling in gastrulation is less well understood than other signaling pathways. Hence this is a valuable contribution to that field.

(2) The FGF4 and FGF17 loss-of-function experiments in Figure 5 are very intriguing. This is especially so given the intriguing observation that these FGFs appear to be dominating in this model of human gastrulation, in contrast to what FGFs dominate in mice, chick and frogs.

(3) In general this paper is valuable as a further development of the Human gastruloid system and the role of FGF signaling in the induction of PS-CLs. The wide net that the authors cast in characterizing FGF ligand gene, receptor isoforms, and downstream components provides a foundation for future work. As the authors write near the beginning of the Discussion "Many questions remain."

Weaknesses:

(1) FGFs are cell survival factors in various aspects of development. The authors fail to address cell death due to loss of FGF signaling in any of their experiments. For example, in Figure 1E (which requires statistical analysis) and 1G (the bottom FGFRi row), there appears to be a significant amount of cell loss. Is this due to cell death? The authors should address the question of whether the role of FGF/ERK signaling is to keep the cells alive.

(2) Regarding the sparse cells in 1G, is there a reduction in cell number only with FGFRi and not MEKi? Is this reproducible? Gattiglio et al (Development, 2023, PMID: 37530863) present data supporting a "community effect" in the FGF-induced mesoderm differentiation of mouse embryonic stem cells. Could a community effect be at play in this human system (especially given the images in the bottom row of 1G). If the authors don't address this experimentally they should at least address the ideas in Gattoglio et al.

(3) Do the FGF4 and FGF17 LOF experiments in Figure 5 affect cell number like FGFRi in Figure 1? Why examine PS-LC induction only in FGF17 heterozygous cells and not homozygous FGF17 nulls?

(4) The idea that FGF8 plays a dominant role during gastrulation of other species but not humans is so intriguing it warrants deeper testing. The authors dismiss FGF8 because its mRNA "...levels always remained low." (line 363) as well as the data published in Zhai et al (PMID: 36517595) and Tyser et al (PMID: 34789876). But there are cases in mouse development where a gene was expressed at levels so low, it might be dismissed, and yet LOF experiments revealed it played a role or even was required in a developmental process. The authors should consider FGF8 inhibition or inactivation to explore its potential role, despite its low levels of expression.

(5) Redundancy is a common feature in FGF genetics. What is the effect of inhibiting FGF4 in FGF17 LOF cells?

(6) I suggest stating that the authors take more caution describing FGF gradients. For example, in one Results heading they write "Endogenous FGF4 and FGF17 gradients underly the ERK activity pattern.", implying an FGF protein gradient. However, they only present data for FGF mRNA , not protein. This issue would be clarified if they used proper nomenclature for gene, mRNA (italics) and protein (no italics) throughout the paper.

Comments on revisions:

The authors have addressed my concerns.

Reviewer #2 (Public review):

Summary:

The role of FGFs in embryonic development and stem cell differentiation has remained unclear due to its complexity. In this study, the authors utilized a 2D human stem cell-based gastrulation model to investigate the functions of FGFs. They discovered that FGF-dependent ERK activity is closely linked to the emergence of primitive streak cells. Importantly, this 2D model effectively illustrates the spatial distribution of key signaling effectors and receptors by correlating these markers with cell fate markers, such as T and ISL1. Through inhibition and loss-of-function studies, they further corroborated the needs of FGF ligands. Their data shows that FGFR1 is the primary receptor, and FGF2/4/17 are the key ligands for primitive streak development, which aligns with observations in primate embryos. Additional experiments revealed that the reduction of FGF4 and FGF17 decreases ERK activity.

Strengths:

This study provides comprehensive data and improves our understanding of the role of FGF signaling in primate primitive streak formation. The authors provide new insights related to the spatial localization of the key components of FGF signaling and attempt to reveal the temporal dynamics of the signal propagation and cell fate decision, which has been challenging.

Reviewer #3 (Public review):

Jo and colleagues set out to investigate the origins and functions of localized FGF/ERK signaling for the differentiation and spatial patterning of primitive streak fates of human embryonic stem cells in a well-established micropattern system. They demonstrate that endogenous FGF signaling is required for ERK activation in a ring-domain in the micropatterns, and that this localized signaling is directly required for differentiation and spatial patterning of specific cell types. Through high-resolution microscopy and transwell assays, they show that cells receive FGF signals through basally localized receptors. Finally, the authors find that there is a requirement for exogenous FGF2 to initiate primitive streak-like differentiation, but endogenous FGFs, especially FGF4 and FGF17, fully take over at later stages.

Even though some of the authors' findings - such as the localized expression of FGF ligands during gastrulation and the importance of FGF/ERK signaling for cell differentiation in the primitive streak - have been reported in model organisms before, this is one of the first studies to investigate the role of FGF signaling during primitive streak-like differentiation of human cells. In doing so, the paper reports a number of interesting and valuable observations, namely the basal localization of FGF receptors which mirrors that of BMP and Nodal receptors, as well as the existence of a positive feedback loop centered on FGF signaling that drives primitive-streak differentiation. In the revised version of their work, the authors have furthermore dissected the role of different FGFs through knockdown approaches. These experiments reveal discrete functions for different FGF genes in their system, as well as interesting differences between the role of specific FGFs in human compared to model systems.

Comments on revisions:

The authors have appropriately addressed all comments and suggestions from the previous round of review. The only textual change that I would still like to suggest is to write explicitly in the main text corresponding to Fig. 1 that the mTESR1 medium used for these initial experiments already contains FGF. This is something that is probably known to experts in the field, but not necessarily to a broader readership.

Reviewer #1 (Public review):

This manuscript puts forward the concept that there is a specific time window during which YAP/TAZ driven transcription provides feedback for optimal endothelial cell adhesion, cytoskeletal organization and migration. The study follows up on previous elegant findings from this group and others which established the importance of YAP/TAZ-mediated transcription for persistent endothelial cell migration. The data presented here extends the concept at two levels: first, the data may explain why there are differences between experimental setups where YAP/TAZ activity are inhibited for prolonged times (e.g. cultures of YAP knockdown cells), versus experiments in which the transient inhibition of YAP/TAZ and (global) transcription affects endothelial cell dynamics prior to their equilibrium.

All experiments are convincing, clearly visualized and quantified.

The strength of the paper is that it clearly indicates that there are temporal controlled feedback systems, which is important knowledge for understanding the mechanisms that drive endothelial collective cell behavior.

A potential limitation of the in vivo experiments is that the inhibitors may include off-target effects as well. To solve this caveat in future research endeavours, which is beyond the scope of the current study, it would be interesting to study this process in knockout models, combined with optogenetics and transgenic zebrafish lines that visualize endothelial cell functional properties such as proliferation and migration.

Reviewer #2 (Public review):

Summary:

Here the effect of overall transcription blockade, and then specifically depletion of YAP/TAZ transcription factors was tested on cytoskeletal responses, starting from a previous paper showing YAP/TAZ-mediated effects on the cytoskeleton and cell behaviors. Here, primary endothelial cells were assessed on substrates of different stiffness and parameters such as migration, cell spreading, and focal adhesion number/length were tested upon transcriptional manipulation. Zebrafish subjected to similar manipulations were also assessed during the phase of intersegmental vessel elongation. The conclusion was that there is a feedback loop of 4 hours that is important for the effects of mechanical changes to be translated into transcriptional changes that then permanently affect the cytoskeleton.

The idea is intriguing and a previous paper contains data supporting the overall model. The fish washout data is quite interesting and supports the kinetics conclusions. New transcriptional profiling in this version supports that cytoskeletal genes are differentially regulated with YAP/TAZ manipulations.

Major strengths:

The combination of in vitro and in vivo assessment provides evidence for timing in physiologically relevant contexts, and rigorous quantification of outputs is provided. The idea of defining temporal aspects of the system is quite interesting. New RNA profiling supports the model.

Weaknesses:

Actinomycin D blocks most transcription so exposure for hours likely leads to secondary and tertiary effects and perhaps effects on viability.

Comments on latest version:

I read the author response to previous reviews, and it seems they agree with the weaknesses stated in the reviews but did not provide any text or data revisions.

Reviewer #4 (Public review):

Summary:

Mason DE et al. have extended their previous study on continuous migration of cells regulated by a feedback loop that controls gene expression by YAP and TAZ. Time scale of the negative feedback loop is derived from the authors' adhesion-spreading-polarization-migration (ASPM) assay. Involvement of transcription-translation in the negative feedback loop is evidenced by the experiments using Actinomycin D. The time scale of mechanotransduction-dependent feedback demonstrated by cytoskeletal alteration in the actinomycin D-treated endothelial colony forming cells (ECFCs) and that shown in the ECFCs depleted of YAP/TAZ by siRNA. The authors examine the time scale when ECFCs are attached to MeHA matrics (soft, moderate, and stiff substrate) and show the conserved time scale among the conditions they use, although instantaneous migration, cell area, and circularity vary. Finally, they tried to confirm that the time scale of the feedback loop-dependent endothelial migration by the effect of washout of Actinomycin D (inhibition of gene transcription), Puromycin (translational inhibition), and Verteporfin (YAP/TAZ inhibitor) on ISV extension during sprouting angiogenesis. They conclude that endothelial motility required for vascular morphogenesis is regulated by a mechanotransduction-mediated feedback loop that is dependent on YAP/TAZ-dependent transcriptional regulation.

Strengths:

The authors conduct ASPM assay to find the time scale of feedback when ECFCs attach to three different matrics. They observe the common time scale of feedback. Thus, under very specific conditions they use, the reproducibility is validated by their ASPM assay. The feedback loop mediated by inhibition of gene expression by Actinomycin D is similar to that obtained from YAP/TAZ-depleted cells, suggesting the mechanotranduction might be involved in the feedback loop. The time scale representing infection point might be interesting when considering the continuous motility in cultured endothelial cells, although it might not account for the migration of endothelial cells that is controlled by a wide variety of extracellular cues. In vivo, stiffness of extracellular matrix is merely one of the cues.

Weaknesses:

ASPM assay is based on attachment-dependent phenomenon. The time scale, including the inflection point determined by ASPM experiments using cultured cells and the mechanotransduction-based theory, do not seem to fit in vivo ISV elongation. Although it is challenging to find the conserved theory of continuous cell motility of endothelial cells, the data is preliminary and does not support the authors' claim. There is no evidence that mechanotransduction solely determines the feedback loop during elongation of ISVs.

Comments on revisions:

The authors' methods using ASPM assay might suggest the feedback loop by their in vitro culture assay. They still need to confirm the loop in vivo using zebrafish intersegmental vessels. The time course of the feedback loop is supported by the ASPM assay. However, the feedback loop is not confirmed in vivo, although it might be suggested by the phenotypes of the ISV treated with drugs. Thus, in the abstract and in the results section, they had better rewrite the interpretation. They have not yet confirmed the feedback loop in vivo.

Reviewer #3 (Public review):

Summary:

The manuscript explores behavioral responses of C. elegans to hydrogen sulfide, which is known to exert remarkable effects on animal physiology in a range of contexts. The possibility of genetic and precise neuronal dissection of responses to H2S motivates the study of responses in C. elegans.

The authors have followed up observations in the initial version of the manuscript, and their data do not support the direct sensing of H2S by the ASJ neurons or other sensory neurons. Genetic and parallel analysis of O2 and CO2 responsive pathways do not reveal further insights regarding potential mechanisms underlying H2S sensing. Gene expression analysis extends prior work. Finally, the authors have examined how H2S-evoked locomotory behavioral responses are affected in mutants with altered stress and detoxification response to H2S, most notably hif-1 and egl-9. These data, while examining locomotion, are more suggestive that observed effects on animal locomotion are secondary to altered organismal toxicity as opposed to specific behavioral responedse

Overall, the manuscript provides a wide range of preliminary observations of genetic interactions that may influence locomotory responses to H2S, but mechanistic insight or a synthesis of disparate data is lacking.

Reviewer #4 (Public review):

Summary:

The authors establish a behavioral paradigm for avoidance of H2S and conduct a large candidate screen to identify genetic requirements. They follow up by genetically dissecting a large number of implicated pathways - insulin, TGF-beta, oxygen/HIF-1, and mitochondrial ROS, which have varied effects on H2S avoidance. They additionally assay whole-animal gene expression changes induced by varying concentrations and durations of H2S exposure.

Strengths:

The implicated pathways are tested extensively through mutants of multiple pathway molecules. The authors address previous reviewer concerns by directly testing the ability of ASJ to respond to H2S via calcium imaging. This allows the authors to revise their previous conclusion and determine that ASJ does not directly respond to H2S and likely does not initiate the behavioral response. Extensive experiments manipulating the mitochondrial ETC and ROS support the authors' revised model that mitochondrial toxicity is the major driver of H2S avoidance.

It seems possible that HIF-1 and SKN-1 signaling directly modulate ROS toxicity while ASJ neurons and the oxygen sensing circuit could modulate the avoidance behavior. How this neuronal interaction happens remains unknown.

Reviewer #1 (Public review):

Summary:

Okazaki et al. showed flickering stimuli to patients with unilateral spatial neglect (USN) and measured EEG responses. They compared this with another patient group (post-stroke, but no USN) and healthy controls. The author's rationale was to entrain intrinsic brain rhythms using the flicker of different frequencies (3-30 Hz). Effects found unique to the 9-Hz stimulation condition differentiate USN patients from the other groups, leading them to conclude that USN can be characterized by increased hemispheric alpha asymmetry, driven by a relatively increased response in the intact hemisphere.

Strengths:

This study is principled empirical work that benefits from access to special patient groups of considerable size (about 60 stroke patients in total, and 20 USN). The authors use state-of-the-art established methods to (1) deliver and (2) quantify the responses to the flicker stimulation in the EEG recordings. In addition, they use phase-coupling measures to investigate cross-frequency coupling (here: alpha-gamma) and a measure of directed connectivity between brain areas, transfer entropy. The results are supported by means of simulations using a coupled-oscillators model.

Weaknesses:

In my eyes, the major conceptual weakness of the study is that the authors make the a priori assumption that the flicker stimulation entrains intrinsic brain rhythms, especially alpha (9 Hz). To date, there is no direct (and only equivocal indirect) evidence that alpha rhythms can be entrained with periodic visual stimulation. In the present study, the assumption of alpha entrainment permeates some analytical decisions - where it would be possible to separate stimulus-driven from intrinsic rhythms more strongly than is currently the case, potentially yielding deeper insights into the oscillopathy of USN - and, ultimately, the interpretation of the results. Another potential issue to consider here is the analysis of gamma rhythms in EEG data, absent a control of miniature eye movements, a known problem (Yuval-Greenberg et al., 2008, https://doi.org/10.1016/j.neuron.2008.03.027) that may be exacerbated here, given that USN patients could show different auxiliary gaze behaviour.

Reviewer #2 (Public review):

This study investigates how altered neural oscillations may contribute to unilateral spatial neglect (USN) following right-hemisphere stroke. By combining steady-state visual evoked potentials (SSVEPs), phase-amplitude coupling (PAC), transfer entropy (TE), and computational modeling, the authors aim to show that USN arises from disrupted hemispheric synchronization dynamics rather than simply from lesion extent. The integration of empirical EEG data with a mechanistic model is a major strength and offers a valuable new perspective on how frequency-specific neural dynamics relate to clinical symptoms.

The work has several notable strengths. The combination of experimental and modeling approaches is innovative and powerful, and the findings provide a coherent mechanistic framework linking abnormal neural entrainment to attentional deficits. The study also provides concrete evidence to support the potential for frequency-specific neuromodulatory interventions, which could have translational relevance.

At the same time, there are areas where the evidence could be clarified or contextualized further. The manuscript would benefit from more detailed characterization of lesions, since differences in lesion topography (white vs. gray matter, occipital vs. parietal areas) could greatly improve our understanding of the physiopathology causing unilateral spatial neglect and the altered neural oscillations reported. Methodological choices, such as focusing analyses on occipital electrodes rather than parietal sites, and the potential influence of volume conduction in transfer entropy analyses, also need clearer justification/elaboration. In addition, while the authors report several neural metrics, it is not always clear why SSVEP power was chosen as the primary correlate of clinical severity over other measures. More broadly, the manuscript would be strengthened by clearer definitions of dependent variables and reporting of software and toolboxes used.

Overall, the study makes a significant contribution by demonstrating that USN can be conceptualized as a disorder of disrupted oscillatory dynamics. With some clarifications and expansions, the paper will provide readers with a clearer understanding of both the strengths and the limitations of the evidence, and it will stand as a valuable reference for future work on oscillatory mechanisms in stroke and attention.

Reviewer #1 (Public review):

Summary:

This study presents a novel toolkit for visualizing and manipulating neurotransmitter-specific vesicles in C. elegans neurons, addressing the challenge of tracking neurotransmitter dynamics at the level of individual synapses. The authors engineered endogenously tagged vesicular transporters for glutamate, GABA, acetylcholine, and monoamines, enabling cell-specific labeling while maintaining physiological function. Additionally, they developed conditional knockout strains to disrupt neurotransmitter synthesis in single neurons. The study reveals that over 10% of neurons in C. elegans exhibit co-transmission, with a detailed case study on the ADF sensory neuron, where serotonin and acetylcholine are trafficked in distinct vesicle pools. The approach provides a powerful platform for studying neurotransmitter identity, synaptic architecture, and co-transmission.

Strengths:

(1) This toolkit offers a generalizable framework that can be applied to other model organisms, advancing the ability to investigate synaptic plasticity and neural circuit logic with molecular precision.

(2) Through the use of this toolkit, the authors uncover molecular heterogeneity at individual synapses, revealing co-transmission in over 10% of neurons, and offer new insights into neurotransmitter trafficking and synaptic plasticity, advancing our understanding of synaptic organization.

Weaknesses:

(1) While the article introduces valuable tools for visualizing neurotransmitter vesicles in vivo, the core techniques are based on previously established methods. The study does not present significant technological breakthroughs, limiting the novelty of the methodological advancements.

(2) The article does not fully explore the potential implications or the underlying mechanisms governing this process, while the discovery of co-transmission in over 10% of neurons is an intriguing finding. A deeper investigation into the functional uniqueness and interactions of neurotransmitters released from individual co-transmitting neurons - perhaps through case study examples - would strengthen the study's impact.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors developed fluorescent reporters to visualize the subcellular localization of vesicular transporters for glutamate, GABA, acetylcholine, and monoamines in vivo. They also developed cell-specific knockout methods for these vesicular transporters. To my knowledge, this is the first comprehensive toolkit to label and ablate vesicular transporters in C. elegans. They carefully and strategically designed the reporters and clearly explained the rationale behind their construct designs. Meanwhile, they used previously established functional assays to confirm that the reporters are functional. They also tested and confirmed the effect of cell-specific and pan-neuronal knockout of several of these transporters.

Strengths:

The tools developed are versatile: they generated both green and red fluorescent reporters for easy combination with other reporters; they established the method for cell-type-specific KO to analyze the function of the neurotransmitter in different cell types. The reagents allow visualization of specific synapses among other processes and cell bodies. In addition, they also developed a binary expression method to detect co-transmission "We reasoned that if two neurotransmitters were co-expressed in the same neuron, driving Flippase under the promoter of one transmitter would activate the conditional reporter - resulting in fluorescence - only in cells also expressing a second neurotransmitter identity". Overall, this is a versatile and valuable toolkit with well-designed and carefully validated reagents. This toolkit will likely be widely used by the C. elegans community.

Weaknesses:

The authors evaluated the positions of fluorescent puncta by visually comparing their positions with the positions of synapses indicated by EM reconstruction. It would provide stronger supportive evidence if the authors also examined co-localization of these reporters with well-established synaptic reporters previously published by their lab, such as reporters that label presynaptic sites of AIY interneurons.

This toolkit will likely be widely used by the C. elegans community. To facilitate the adoption of the approach and method by worm labs, the authors should include their plan for the dissemination of all of the reagents included in the kit, along with all of the associated information, including construct sequences and the protocols for their use.

Reviewer #3 (Public review):

Summary:

Cuentas-Condori et al. generate cell-specific tools for visualizing the endogenous expression of, as well as knocking out, four different classes of neurotransmitter vesicular transporters (glutamatergic, cholinergic, GABAergic, and monoaminergic) in C. elegans. They then use these tools in an intersectional strategy to provide evidence for the co-expression of these transporters in individual neurons, suggesting co-transmission of the associated neurotransmitters.

Strengths:

A major strength of the work is the generation of several endogenous tools that will be of use to the community. Additionally, this adds to accumulating evidence of co-transmission of different classes of neurotransmitters in the nervous system.

Weaknesses:

A weakness of the study is a lack of comparison to previously published single-cell sequencing data. These tools are alternatively described in the manuscript as superior to the sequencing data and as validation of the sequencing data, but neither claim can be assessed without knowing how they compare and contrast to that data. It is thus not clear to what extent the conclusions of this paper are an advance over what could be determined from the sequencing data on its own. Finally, some technical considerations should be discussed as potential caveats to the robustness of their intersectional strategy for concluding that certain genes are indeed co-expressed. Overall, claims about co-transmission should be tempered by the caveats presented in the discussion, suggesting that co-expression of these transporters is not in and of itself sufficient for neurotransmitter release.

Reviewer #1 (Public review):

Jouary et al. present Megabouts, a Transformer-based classifier and Python toolbox for automated categorization of zebrafish movement bouts into 13 bout types. This is potentially a very useful tool for the zebrafish community. It is broadly applicable to a wide variety of behavioral paradigms and could help to unify behavioral quantification across labs. The overall implementation is technically sound and thoughtfully engineered. The choice of standard Transformer architecture is well-justified (e.g., it can handle long-term tracking data and process missing data, integrates posture and trajectory information over time, and shows robustness to variable frame rates and partial occlusion). The data augmentation strategies (e.g., downsampling, tail masking, and temporal jitter) are well designed to enhance cross-condition generalization. Thus, I very much support this work.

For the benefit of the end users of this tool, several clarifications and additional analyses would be helpful:

(1) What is the source and nature of the classification errors? The reported accuracy is <80% with trajectory data and still <90% with trajectory + tail data.

(1a) Is this due to model failure (is overfitting a concern? How unbiased were the test sets?), imperfections of the preprocessing step (how sensitive is this to noise in the input data?), or underlying ambiguity in the biological data (e.g., do some "errors" reflect intermediate patterns that don't map neatly onto the 13 discrete classes)?

(1b) A systematic error analysis would be helpful. Which classes are most often confused? Are errors systematic (e.g., slow swims vs. routine turns) or random?

(1c) Can confidence of classification be provided for each bout in the data? How would the authors recommend that the end user deal with misclassifications (e.g., by manual correction)?<br /> Overall, the end user would benefit greatly from more information on potential failure modes and their root causes.

(2) How well does the trained network generalize across labs and setups? To what extent have the authors tested this on datasets from other labs to determine how well the pretrained model transfers across datasets? Having tested the code provided by the authors on a short stretch of x-y zebrafish trajectory data obtained independently, the pipeline generates phantom movement annotations. The underlying cause is unclear.

(2a) One possibility is that preprocessing steps may be highly sensitive to slight noise in the x-y positional data, which leads to noise in the speed data. The neural net, in turn, classifies noise into movement annotations. It would be helpful if the authors could add Gaussian noise to the x-y trajectory data and then determine the extent to which the computational pipeline is robust to noise.

(2b) When testing the pipeline, some stationary periods are classified as movements. Which step of the pipeline gave rise to the issue is unclear. Thus, explicit cross-lab validation and robustness tests (e.g., adding Gaussian noise to trajectories) would strengthen the claims of this paper.

(2c) Lastly, given the potential issue of generalization across labs, it would be helpful to provide/outline the steps for users in different labs to retrain and fine-tune the model.

Reviewer #2 (Public review):

Summary:

Overall, the manuscript is well organized and clearly written. However, in this reviewer's opinion, the manuscript suffers from multiple major weaknesses.

Strengths:

The strengths of the paper are unclear; they have not been articulated well by the authors.

Weaknesses:

The pipeline is designed to analyze larval zebrafish behaviors, which by definition is considered a highly specialized, if not niche, application. Hence, the scope of this manuscript is extremely narrow, and consequently, the overall significance and the broader impact on the field of behavioral neuroscience are rather low. Broadening the scope would significantly improve the manuscript's impact. Second, it was noted that the authors neglect to present an unbiased discussion of how their pipeline compares to well-established and time-proven pipelines used to track larval zebrafish behaviors. This reviewer also failed to detect any new biological insights presented or improvements compared to existing methods, further questioning the overall significance and impact of this manuscript. Finally, the core claim of the manuscript lacks meaningful experimental data that would allow an unbiased and more definitive evaluation of the claims made regarding the Megabouts pipeline. The critical experiment to achieve this would be to run an identical set of behavioral assays (e.g., PPI, social behaviors) on different platforms (e.g., a commercial and a non-commercial one) and then determine if Megabouts correctly analyzes and integrates the results. While this might sound to the authors like an 'outside the scope' experiment, this reviewer would argue that it is the only meaningful experiment to validate the central claim put forward in this manuscript.

Reviewer #3 (Public review):

In this manuscript, the authors introduce Megabouts, a software package designed to standardize the analysis of larval zebrafish locomotion, through clustering the 2D posture time series into canonical behavioral categories. Beyond a first, straightforward segmentation that separates glides from powered movements, Megabouts uses a Transformer neural network to classify the powered movements (bouts). This Transformer network is trained with supervised examples. The authors apply their approach to improve the quantification of sensorimotor transformations and enhance the sensitivity of drug-induced phenotype screening. Megabouts also includes a separate pipeline that employs convolutional sparse coding to analyze the less predictable tail movements in head-restrained fish.

I presume that the software works as the authors intend, and I appreciate the focus on quantitative behavior. My primary concerns reflect an implicit oversimplification of animal behavior. Megabouts is ultimately a clustering technique, categorizing powered locomotion into distinct, labelled states which, while effective for analysis, may confuse the continuous and fluid nature of animal behavior. Certainly, Megabouts could potentially miss or misclassify complex, non-stereotypical movements that do not fit the defined categories. In fact, it appears that exactly this situation led the authors to design a new clustering for head-restrained fish. Can we anticipate even more designs for other behavioral conditions?

Ultimately, I am not yet convinced that Megabouts provides a justifiable picture of behavioral control. And if there was a continuous "control knob", which seems very likely, wouldn't that confuse the clustering process, as many distinct clusters would correspond to, say, different amplitudes of the same control knob?

There has been tremendous recent progress in the measurement and analysis of animal behavior, including both continuous and discrete perspectives. However, the supervised clustering approach described here feels like a throwback to an earlier era. Yes, it's more automatic and quantifiable, and the amount of data is fantastic. But ultimately, the method is conceptually bound to the human eye in conditions where we are already familiar.

Reviewer #1 (Public review):

Summary:

This manuscript investigates the interplay between spontaneous attention and melody formation during polyphonic music listening. The authors use EEG recordings during uninstructed listening to examine how attention bias influences melody processing, employing both behavioural measures and computational modelling with music transformers. The study introduces a very clever pitch-inversion manipulation design to dissociate high-voice superiority from melodic salience, and proposes a "weighted integration" model where attention dynamically modulates how multiple voices are combined into perceived melody.

Strengths:

(1) The attention bias findings (Figure 2) are compelling and methodologically sound, with convergent evidence from both behavioral and neural measures.

(2) The pitch-inversion manipulation appears to super elegantly dissociate two competing factors (high-voice superiority vs melodic salience), moreover, the authors claim that the chosen music lends itself perfectly to his PolyInv condition. A claim I cannot really evaluate, but which would make it even more neat.

(3) Nice bridge between hypotheses and operationalisations.

Weaknesses:



The results in Figure 3 are very striking, but I have a number of questions before I can consider myself convinced. 


(1) Conceptual questions about surprisal analysis:


The pattern of results seems backwards to me. Since the music is inherently polyphonic in PolyOrig, I'd expect the polyphonic model to fit the brain data better - after all, that's what the music actually is. These voices were composed to interact harmonically, so modeling them as independent monophonic streams seems like a misspecification. Why would the brain match this misspecified model better?
<br /> Conversely, it would seem to me the pitch inversion in PolyInv disrupts (at least to some extent) the harmonic coherence, so if anywhere, I'd a priori expect that in this condition, listeners would rather be processing streams separately - making the monophonic model fit better there (or less bad), not in PolyOrig. The current pattern is exactly opposite to what seems logical to me.


(2) Missing computational analyses:


If the transformer is properly trained, it should "understand" (i.e., predict/compress) the polyphonic music better, right? Can the authors demonstrate this via perplexity scores, bits-per-byte, or other prediction metrics, comparing how well each model (polyphonic vs monophonic) handles the music in both conditions? Similarly, if PolyInv truly maintains musical integrity as claimed, the polyphonic model should handle it as well as PolyOrig. But if the inversion does disrupt the music, we should see this reflected in degraded prediction scores. These metrics would validate whether the experimental manipulation works as intended. Also, how strongly are the surprisal streams correlated? There are many non-trivial modelling steps that should be reported in more detail.


(3) Methodological inconsistencies:

Why are the two main questions (Figures 2 and 3) answered with completely different analytical approaches? The switch from TRF to CCA with match-vs-mismatch classification seems unmotivated. I think it's very important to provide a simpler model comparison - just TRF with acoustic features plus either polyphonic or monophonic surprisal - evaluated on relevant electrodes or the full scalp. This would make the results more comparable and interpretable.

(4) Presentation and methods:

a) Coming from outside music/music theory, I found the paper somewhat abstract and hard to parse initially. The experimental logic becomes clearer with reflection, but you're doing yourselves a disservice with the jargon-heavy presentation. It would be useful to include example stimuli.

b) The methods section is extremely brief - no details whatsoever are provided regarding the modelling: What specific music transformer architecture? Which implementation of this "anticipatory music transformer"? Pre-trained on what corpus - monophonic, polyphonic, Western classical only? What constituted "technical issues" for the 9 excluded participants? What were the channel rejection criteria?

Reviewer #2 (Public review):

Summary:

The authors sought to understand the drivers of spontaneous attentional bias and melodic expectation generation during listening to short two-part classical pieces. They measured scalp EEG data in a monophonic condition and trained a model to reconstruct the audio envelope from the EEG. They then used this model to probe which of the two voices was best reflected in the neural signal during two polyphonic conditions. In one condition, the original piece was presented, in the other, the voices were switched in an attempt to distinguish between effects of (a) the pitch range of one voice compared to the other and (b) intrinsic melodic features. They also collected a behavioural measure of attentional bias for a subset of the stimuli in a separate study. Further modelling assessed whether expectations of how the melody would unfold were formed based on an integrated percept of melody across the two voices, or based on a single voice. The authors sought to relate the findings to different theories of how musical/auditory scene analysis occurs, based on divided attention, figure-ground perception, and stream integration.

Strengths:

(1) A clever but simple manipulation - transposing the voices such that the higher one became the lower one - allowed an assessment of different factors that might affect the allocation of attention.

(2) State-of-the-art analytic techniques were applied to (a) build a music attention decoder (these are more commonly encountered for speech) and (b) relate the neural data to features of the stimulus at the level of acoustics and expectation.

(3) The effects appeared robust across the group, not driven by a handful of participants.

Weaknesses:

(1) A key goal of the work is to establish the relative importance for the listener's attention of a voice's (a) mean pitch in the context of the two voices (high-voice superiority) and (b) intrinsic melodic statistics/motif attractiveness. The rationale of the experimental manipulation is that switching the relative height of the lines allows these to be dissociated by imparting the same high-voice benefit to the new high-voice and the same preferred intrinsic melodic statistics to the new low voice. However, previous work suggests that the high-voice superiority effect is not all-or-nothing. Electrophysiology supported by auditory nerve modelling found it to depend on the degree of voice separation in a non-monotonic way (see https://doi.org/10.1016/j.heares.2013.07.014 at p. 68). Although the authors keep the overall pitch of the lower (and upper) line fixed across conditions, systematically different contour patterns across the voices could give rise to a sub-optimal distribution of separations in the PolyInv versus PolyOrig condition. This could weaken the high-voice superiority effect in PolyInv and explain the pattern of results. One could argue that such contour differences are examples of the "intrinsic melodic statistics" put forward as the effect working in opposition to high-voice superiority, but it is their interaction across voices that matters here.

(2) Although melody statistics are mentioned throughout, none have been calculated. It would be helpful to see the features that presumably lead to "motif attractiveness" quantified, as well as how they differ across lines. The work of David Huron, such as at https://dl.acm.org/doi/abs/10.1145/3469013.3469016, provides examples that could be calculated with ease and compared across the two lines: "the tendency for small over large pitch movements, for large leaps to ascend, for musical phrases to fall in pitch, and for phrases to begin with an initial pitch rise". The authors also mention differences in ornamentation. Such comparisons would make it more tangible for the reader as to what differs across the original "melody" and "support" line. In particular, as the authors themselves note, lines in double-counterpoint pieces can, to a degree, operate interchangeably. Bach's inventions in particular use a lot of direct repetition (up to octave invariance), which one would expect to minimise differences in the statistics mentioned. The references purporting to relate to melodic statistics (11-14 in original numbering) seem rather to relate to high-voice superiority.

(3) The exact nature of the transposition manipulation is obscured by a confusing Figure 1B, which shows an example in which the transposed line does not keep the same note-to-note interval structure as the original line.

(4) The transformer model is barely described in the main text. Even readers who are familiar with the Hidden Markov Models (e.g., in IDyOM) previously used by some of the authors to model melodic surprise and entropy would benefit from a brief description in the main text at least of how transformer models are different. The Methods section goes a little further but does not mention what the training set was, nor the relative weight given to long- and short-term memory models.

(5) The match-mismatch procedure should be explained in enough detail for readers to at least understand what value represents chance performance and why performance would be measured as an average over participants. Relatedly, there is no description at all of CCA or the match-mismatch procedure in the Methods.

(6) Details of how the integration model was implemented will be critical to interpreting the results relating to melodic expectations. It is not clear how "a single melody combining the two streams" was modelled, given that at least some notes presumably overlapped in time.

(7) The authors propose a weighted integration model, referring in the Discussion to dynamics and an integration rate. They do show that in the PolyOrig case, the top stream bias is highest and the monophonic model gives the best prediction, while in the PolyInv case, the top stream bias is weaker and the polyphonic model provides the best prediction. However, that doesn't seem to say anything about the temporal rate of integration, just the degree, which could be fixed over the whole stimulus. Relatedly, the terms "strong attention bias" and "weak attention bias" in Highlight 4 might give the impression of different attention modes for a given listener, or perhaps different types of listeners, but this seems to be shorthand for how attention is allocated for different types of stimuli (namely those that have or have not had their voices reversed).

(8) Another aspect of the presentation relating to temporal dynamics is that in places (e.g., Highlight 1), the authors suggest they are tracking attention dynamically. However, as acknowledged in the Discussion, neither the behavioural nor neural measure of attentional bias are temporally resolved. The measures indicate that on average participants attend more to the higher line (less so when it formed the lower line in the original composition).

(9) It is not clear whether the sung-back data were analysed (and if not why participants were asked to sing the melody back rather than just listen to the two components and report which they thought was the melody). It is also not stated whether the order in which the high and low voices were played back was randomised. If not, response biases or memory capacity might have affected the behavioural attention data.

Reviewer #3 (Public review):

Summary:

In this paper, Winchester and colleagues investigated melodic perception in natural music listening. They highlight the central role of attentional processes in identifying one particular stream in polyphonic material, and propose to compare several theoretical accounts, namely (1) divided attention, (2) figure-ground separation, and (3) stream integration. In parallel, the authors compare the relative strength of exogenous attentional effects (i.e., salience) produced by two common traits of melodies: high-pitch (compared to other voices), and attractive statistics. To ensure the generalisability of their results to real-life listening contexts, they developed a new uninstructed listening paradigm in which participants can freely attend to any part of a musical stimulus.

Major strengths and weaknesses of the methods and results:

(1) Winchester and colleagues capitalized on previous attention decoding techniques and proposed an uninstructed listening paradigm. This is an important innovation for the study of music perception in ecological settings, and it is used here to investigate the spontaneous attentional focus during listening. The EEG decoding results obtained are coherent with the behavioral data, suggesting that the paradigm is robust and relevant.

(2) The authors first evaluate the relative importance of high-pitch and statistics in producing an attentional bias (Figure 2). Behavioral results show a clear pattern, in which both effects are present, with a dominance of the high-pitch one. The only weakness inherent to this protocol is that behavioral responses are measured based on a second presentation of short samples, which may induce a different attentional focus than in the first uninstructed listening.

(3) Then, the analyses of EEG data compare the decoding results of each melody (the high or low voice, and with "richer" or "poorer" statistics), and show a similar pattern of results. However, this report leaves open the possibility of a confounding factor. In this analysis, a TRF decoding model is first trained based on the presentation of monophonic samples, and it is later used to decode the envelope of the corresponding melodies in the polyphonic scenario. The fitting scores of the training phase are not reported. If the high-pitch or richer melodies were to produce higher decoding scores during monophonic listening (due to properties of the physiological response, or to perceptual processes), a similar difference could be expected during polyphonic listening. To capture attentional biases specifically, the decoding scores in the polyphonic conditions should be compared to the scores in the monophonic conditions, and attention could be expected to increase the decoding of the attended stream or decrease the unattended one.

(4) Then, Winchester and colleagues investigate the processing of melodic information by evaluating the encoding of melodic surprise and uncertainty (Figure 3). They compare the surprise and uncertainty estimated from a monophonic or a polyphonic model (Anticipatory Music Transformer), and analyse the data with a CCA analysis. The results show a double dissociation, where the processing of melodies with a strong attentional bias (high-pitch, rich statistics) is better approximated with a monophonic model, while a polyphonic model better classifies the other melodies. While this global result is compelling, it remains a preliminary and intriguing finding, and the manuscript does not further investigate it. As it stands, the result appears more like a starting point for further exploration than a definitive finding that can support strong theoretical claims. First, it could be complemented by a comparison of the encoding of individual melodies (e.g., AMmono high-voice vs AMmono low-voice, in PolyOrig and PolyInv conditions) to highlight a more direct correspondence with the previous results (Figure 2) and allow a more precise interpretation. Second, additional analyses or experiments would be needed to unpack this result and provide greater explanatory power. Additionally, the CCA analysis is not described in the method. The statistical testing conducted on this analysis seems to be performed across the 250 repetitions of the evaluation rather than across the 40 participants, which may bias the resulting p-values. Moreover, the choice and working principle of the Anticipatory Music Transformer are not described in the method. Overall, these results seem at first glance solid, but the missing parts of the method do not allow for full evaluation or replication of them.

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

(1) Winchester and colleagues aimed at identifying the melodic stream that attracts attention during the listening of natural polyphonic music, and the underlying attentional processes. Their behavioral results confirm that high-pitched and attractive statistics increase melodic salience with a greater effect size of the former, as stated in the discussion. The TRF analyses of EEG data seem to show a similar pattern, but could also be explained by confounding factors. Next, the authors interpret the CCA results as the results of stream segregation when there is a high melodic salience, and stream integration when there are weaker attentional biases. These interpretations seem to be supported by the data, but unfortunately, no additional analyses or experiments have been conducted to further evaluate this hypothesis. The authors also acknowledge that their results do not show whether stream segregation occurs via divided attention or figure-ground separation. However, the lack of information about the music model used (Anticipatory Music Model) and the way it was set up raises some questions about its relevance and limits as a model of cognition (e.g. Is this transformer a "better" model of the listeners' expectations than the well-established IDyOM model, and why ?), and about the validity of those results.

(2) Overall, the authors achieved most of the aims presented in the introduction, although they couldn't give a more precise account of the attentional processes at stake. The interpretations are sound and not overstated, with the exception of potential confounding factors that could compromise the conclusions on the neural tracking of salient melodies (EEG results, Figure 2).

Impact of the work on the field, and the utility of the methods and data to the community:

The new uninstructed listening paradigm introduced in this paper will likely have an important impact on psychologists and neuroscientists working on music perception and auditory attention, enabling them to conduct experiments in more ecological settings. While the attentional biases towards melodies with high-pitch and attractive statistics are already known, showing their relative effect is an important step in building precise models of auditory attention, and allows future paradigms to explore more fine-grained effects. Finally, the stream segregation and integration shown with this paradigm could be important for researchers working on music perception. Future work may be necessary to identify the models (Markov chains, deep learning) and setup (data analysis, stimuli, control variables) that do or do not replicate these results.

Reviewer #1 (Public review):

Summary:

This is a well-structured and interesting manuscript that investigates how herbivorous insects, specifically whiteflies and planthoppers, utilize salivary effectors to overcome plant immunity by targeting the RLP4 receptor.

Strengths:

The authors present a strong case for the independent evolution of these effectors and provide compelling evidence for their functional roles.

Weaknesses:

Western blot evidence for effector secretion is weak. The possibility of contamination from insect tissues during the sample preparation should be avoided.

Below are some specific comments and suggestions to strengthen the manuscript.

(1) Western blot evidence for effector secretion:

The western blot evidence in Figure 1, which aims to show that the insect protein is secreted into plants, is not fully convincing. The band of the expected size (~30 kDa) in the infested tissues is very weak. Furthermore, the high and low molecular weight bands that appear in the infested tissues do not match the size of the protein in the insects themselves, and a high molecular weight band also appears in the uninfested control tissues. It is difficult to draw a definitive conclusion that this protein is secreted into the plants based on this evidence. The authors should also address the possibility of contamination from insect tissues during the sample preparation and explain how they have excluded this possibility.

(2) Inconsistent conclusion (Line 156 and Figure 3c): T

The statement in line 156 is inconsistent with the data presented in Figure 3c. The figure clearly shows that the LRR domain of the protein is the one responsible for the interaction with BtRDP, not the region mentioned in the text. This is a critical misrepresentation of the experimental findings and must be corrected. The conclusion in the text should accurately reflect the data from the figure.

(3) Role of SOBIR1 in the RLP4/SOBIR1 Complex:

The authors demonstrate that the salivary effectors destabilize the RLP4 receptor, leading to a decrease in its protein levels and a reduction in the RLP4/SOBIR1 complex. A key question remains regarding the fate of SOBIR1 within this complex. The authors should clarify what happens to the SOBIR1 protein after the destabilization of RLP4. Does SOBIR1 become unbound, targeted for degradation itself, or does it simply lose its function without RLP4? This would provide further insight into the mechanism of action of the effectors.

(4) Clarification on specificity and evolutionary claims:

The paper's most significant claim is that the effectors from both whiteflies and planthoppers "independently evolved" to target RLP4. While the functional data is compelling, this evolutionary claim would be more convincing with stronger evidence. Showing that two different effector proteins target the same host protein is a fascinating finding but without a robust phylogenetic analysis, the claim of independent evolution is not fully supported. It would be valuable to provide a more detailed evolutionary analysis, such as a phylogenetic tree of the effector proteins, showing their relationship to other known insect proteins, to definitively rule out a shared, but highly divergent, common ancestor.

(5) Role of SOBIR1 in the interaction:

The results suggest that the effectors disrupt the RLP4/SOBIR1 complex. It is not entirely clear if the effectors are specifically targeting RLP4, SOBIR1, or both. Further experiments, such as a co-immunoprecipitation assay with just RLP4 and the effector, could clarify if the effector can bind to RLP4 in the absence of SOBIR1. This would help to definitively place RLP4 as the primary target.

(6) Transcriptome analysis (Lines 130-143):

The transcriptome analysis section feels disconnected from the rest of the manuscript. The findings, or lack thereof, from this analysis do not seem to be directly linked to the other major conclusions of the paper. This section could be removed to improve the manuscript's overall focus and flow. If the authors believe this data is critical, they should more clearly and explicitly connect the conclusions of the transcriptome analysis to the core findings about the effector-RLP4 interaction.

(7) Signal peptide experiments (Lines 145 and beyond):

The experiments conducted with the signal peptide (SP) are questionable. The SP is typically cleaved before the protein reaches its final destination. As such, conducting experiments with the SP attached to the protein may have produced biased observations and could lead to unjustified conclusions about the protein's function within the plant cell. We suggest the authors remove the experiments that include the signal peptide.

(8) Overly strong conclusion and unclear evidence (Line 176):

The use of the word "must" on line 176 is very strong and presents a definitive conclusion without sufficient evidence. The authors state that the proteins must interact with SOBIR1, but they do not provide a clear justification for this claim. Is SOBIR1 the only interaction partner for NtRLP4? The authors should provide a specific reason for focusing on SOBIR1 instead of demonstrating an interaction with NtRLP4 first. Additionally, do BtRDP or NlSP694 also interact with SOBIR1 directly? The authors should either tone down their language to reflect the evidence or provide a clearer justification for this strong claim.

Reviewer #2 (Public review):

Summary:

The authors tested an interesting hypothesis that white flies and planthoppers independently evolved salivary proteins to dampen plant immunity by targeting a receptor-like protein.

Strengths:

The authors used a wide range of methods to dissect the function of the white fly protein BtRDP and identify its host target NtRLP4.

Weaknesses:

(1) Serious concerns about protein work.

I did not find the indicated protein bands for anti-BtRDP in Figures 1a and 1b in the original blot pictures shown in Figure S30. In Figure 1a, I can't get the point of showing an unspecific protein band with a size of ~190 kD as a loading control for a protein of ~ 30 kD.

The data discrepancy led me to check other Western blot pictures. Similarly, Figures 2d, 3b, 3d, and S15b (anti-Myc) do not correspond to the original blots shown. In addition, the anti-Myc blot in Figure 4i, all blot pictures in Figures 5b, 5h, and S19a appeared to be compressed vertically. These data raised concerns about the quality of the manuscript.

Blots shown in Figure 3d, 4f, 4g, and 4h appeared to be done at a different exposure rate compared to the complete blot shown in Figure S30. The undesirable connection between Western blot pictures shown in the figures and the original data might be due to the reduced quality of compressed figures during submission. Nevertheless, clarification will be necessary to support the strength of the data provided.

(2) Misinterpretation of data.

I am afraid the authors misunderstood pattern-triggered immunity through receptor-like proteins. It is true that several LRR-type RLPs constitutively associate with SOBIR1, and further recruit BAK1 or other SERKs upon ligand binding. One should not take it for granted that every RLP works this way. To test the hypothesis that NtRLP4 confers resistance to B.tabaci infestation, the author compared transcriptional profiles between an EV plant line and an RLP4 overexpression line. If I understood the methods and figure legends correctly, this was done without B. tabaci treatment. This experimental design is seriously flawed. To provide convincing genetic evidence, independent mutant lines (optionally independent overexpression lines) in combination with different treatments will be necessary. Otherwise, one can only conclude that overexpressing the RLP4 protein generated a nervous plant. In addition, ROS burst, but not H2O2 accumulation, is a common immune response in pattern-triggered immunity.

(3) Lack of logic coherence.

The written language needs substantial improvement. This impeded the readability of the work. More importantly, the logic throughout the manuscript appeared scattered. The choice of testing protein domains for protein-protein interactions, using plants overexpressing an insect protein to study its subcellular localization, switching back and forth between using proteins with signal peptides and without signal peptides, among others, lacks a clear explanation.

Reviewer #3 (Public review):

Summary:

In this study, Wang et al. investigate how herbivorous insects overcome plant receptor-mediated immunity by targeting plant receptor-like proteins. The authors identify two independently evolved salivary effectors, BtRDP in whiteflies and NlSP694 in brown planthoppers, that promote the degradation of plant RLP4 through the ubiquitin-dependent proteasome pathway. NtRLP4 from tobacco and OsRLP4 from rice are shown to confer resistance against herbivores by activating defense signaling, while BtRDP and NlSP694 suppress these defenses by destabilizing RLP4 proteins.

Strengths:

This work highlights a convergent evolutionary strategy in distinct insect lineages and advances our understanding of insect-plant coevolution at the molecular level.

Weaknesses:

(1) I found the naming of BtRDP and NlSP694 somewhat confusing. The authors defined BtRDP as "B. tabaci RLP-degrading protein," whereas NlSP694 appears to have been named after the last three digits of its GenBank accession number (MF278694, presumably). Is there a standard convention for naming newly identified proteins, for example, based on functional motifs or sequence characteristics? As it stands, the inconsistency makes it difficult for readers to clearly distinguish these proteins from those reported in other studies.

(2) Figure 2 and other figures. Transgenic experiments require at least two independent lines, because results from a single line may be confounded by position effects or unintended genomic alterations, and multiple lines provide stronger evidence for reproducibility and reliability.

(3) Figure 3e. Quantitative analysis of NtRLP4 was required. Additionally, since only one band was observed in oeRLP, were any tags included in the construct?

(4) Figure 4a. The RNAi effect appears to be well rescued in Line 1 but poorly in Line 2. Could the authors clarify the reason for this difference?

(5) ROS accumulation is shown for only a single leaf. A quantitative analysis of ROS accumulation across multiple samples would be necessary to support the conclusion. The same applies to Figure 16f.

(6) Figure 4f: NtRLP4 abundance was significantly reduced in oeBtRDP plants but not in oeBtRDP-SP. Although coexpression analysis suggests that BtRDP promotes NtRLP4 degradation in an ubiquitin-dependent manner, the reduced NtRLP4 levels may not result from a direct interaction between BtRDP and NtRLP4. It is possible that BtRDP influences other factors that indirectly affect NtRLP4 abundance. The authors should discuss this possibility.

(7) The statement in lines 335-336 that 'Overexpression of NtRLP4 or NtSOBIR1 enhances insect feeding, while silencing of either gene exerts the opposite effect' is not supported by the results shown in Figures S16-S19. The authors should revise this description to accurately reflect the data.

(8) BtRDP is reported to attach to the salivary sheath. Does the planthopper NlSP694 exhibit a similar secretion localization (e.g., attachment to the salivary sheath)? The authors should supplement this information or discuss the potential implications of any differences in secretion localization between BtRDP and NlSP694 for their respective modes of action.

Reviewer #1 (Public review):

A summary of what the authors were trying to achieve:

Zhang et al. examine connections between supramammillary (SuM) neurons and the subiculum in the context of stress-induced anxiety-like behaviors. They identify stress-activated neurons (SANs) in the SuM using Fos2A-iCreERT2 TRAP mice and show that reactivation of SANs increases anxiety-like behavior and corticosterone levels. Circuit mapping reveals inputs from glutamatergic neurons in both ventral and dorsal subiculum (Sub) to SANs. vSub neurons showing calcium dynamics correlated with open-arm exploration in the elevated zero maze (EZM), which is interpreted to indicate a link to e. Finally, chronic inhibition of vSub→SuM neurons during chronic social defeat stress (CSDS) reduces anxiety-like behaviors.

An account of the major strengths and weaknesses of the methods and results:

Strengths:

The manuscript provides compelling evidence for monosynaptic connections from the subiculum to SuM neurons activated by stress. Demonstrating that SuM neuronal activity is altered after CSDS is of particular interest, potentially linking SuM circuits to stress-related psychiatric disorders. The TRAP approach highlights a stress-responsive population of neurons, and reactivation studies suggest behavioral relevance. Together, these data contribute to an emerging literature implicating SuM in stress and anxiety regulation.

Weaknesses

As presented, the manuscript has limitations that weaken support for the central conclusions drawn by the authors. Many of the findings align with prior work on this topic, but do not extend those findings substantially.<br /> An overarching limitation is the lack of temporal resolution in the manipulations relative to the behavioral assays. This is particularly important for anxiety-like behaviors, as antecedent exposures can alter performance. In the open field and elevated zero maze assays, testing occurred 30 minutes after CNO injection. During much of this interval, the targeted neurons were likely active, making it difficult to determine whether observed behavioral changes were primary - resulting directly from SuM neuronal activity - or secondary, reflecting a stress-like state induced by prolonged activation of SuM and related circuits. This concern also applies to the chronic inhibition of ventral subiculum (vSub) neurons during 10 days of CSDS.

The combination of stressors (foot shock and CSDS) and behavioral assays further complicates interpretation. The precise role of SuM neurons, including SANs, remains unclear. Both vSub and dSub neurons responded to foot shock, but only vSub neurons showed activity differences associated with open-arm transitions in the EZM.

In light of prior studies linking SuM to locomotion (Farrell et al., Science 2021; Escobedo et al., eLife 2024), the absence of analyses connecting subpopulations to locomotor changes weakens the claim that vSub neurons selectively encode anxiety. Because open- and closed-arm transitions are inherently tied to locomotor activity, locomotion must be carefully controlled to avoid confounding interpretations.

Another limitation is the narrow behavioral scope. Beyond open field and EZM, no additional assays were used to assess how SAN reactivation affects other behaviors. Without richer behavioral analyses, interpretations about fear engrams, freezing, or broader stress-related functions of SuM remain incomplete.

In addition, small n values across several datasets reduce confidence in the strength of the conclusions.

Figure level concerns:

(1) Figure 1: In Figure 1, the acute recruitment of SuM neurons by for shock is paired with changes in neural activity induced by social defeat stress. Although interesting, the connections of changes induced by a chronic stressor to Fos induction following acute foot shock are unclear and do not establish a baseline for the studies in Figure 3 on activation of SANs by social stressors.

(2) Figure 2: The chemogenetic experiments using AAV-hSyn-Gq-DREADDs lack data or images, or hit maps showing viral spread across animals. This omission is critical given the small size of SuM, where viral spread directly determines which neurons are manipulated. Without this, it is difficult to interpret findings in the context of prior studies on SuM circuits involved in threats and rewards.

(3) Figure 3: The TRAP experiments show that the number of labeled neurons following foot shock (Figure 3F) is approximately double that of baseline home-cage animals, though y-axis scaling complicates interpretation. It is unclear whether this reflects true Fos induction, low TRAP efficiency, or baseline recombination. Overlap analyses are also limited. For example, it is not shown what proportion of foot shock SANs are reactivated by subsequent foot shock. Comparisons of Fos induction after sucrose reward are also weakened by the very low Fos signal observed. If sucrose reward does not robustly induce Fos in SuM, its utility in distinguishing reward- versus stress-activated neurons is questionable. Thus, conclusions about overlap between SANs and socially stressed neurons remain uncertain due to the missing quantification of Fos+ populations.

(4) Supplemental Figure 3: The claim that "SANs in the SuM encode anxiety but not fear memory" is not well supported. Inhibition of SANs (Gi-DREADDs) did not alter freezing behavior, but the absence of change could reflect technical issues (e.g., insufficient TRAP efficiency, low expression of Gi-DREADDs). Moreover, the manuscript does not provide a positive control showing that SuM SANs inhibition alters anxiety-like behavior, making it difficult to interpret the negative result. Prior work (Escobedo et al., eLife 2024) suggests SuM neurons drive active responses, not freezing, raising further interpretive questions.

(5) Figure 4: The statement that corticosterone concentration is "usually used to estimate whether an individual is anxious" (line 236) is an overstatement. Corticosterone fluctuates dynamically across the day and responds to a broad range of stimuli beyond anxiety.

(6) Figures 5-6: The conclusion that vSub neurons encode anxiety-like behavior is not firmly supported. Data from photo-activating terminals in SuM is shown for ex vivo recording, but not in vivo behavior, which would strengthen support for this conclusion. Both vSub and dSub neurons responded to foot shock. The key evidence comes from apparent differential recruitment during open-arm exploration. However, the timing appears to lag arm entry, no data are provided for closed-arm entry, and there is heterogeneity across animals. These limitations reduce confidence in the authors' central claim regarding vSub-specific encoding of anxiety.

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

(1) From the data presented, the authors conclude that "the SuM is the critical brain region that regulates anxiety" (line 190). This interpretation appears overstated, as it downplays well-established contributions of other brain regions and does not place SuM's role within a broader network context. The data support that SuM neurons are recruited by foot shock and, to a lesser extent, by acute social stress. However, the alterations in activity of SuM subpopulations following chronic stress reported in Figure 1 remain largely unexplored, limiting insight into their functional relevance.

(2) The limited temporal resolution of DREADD-based manipulations leaves alternative explanations untested. For example, if SANs encode signals of threat, generalized stress, or nociception, then prolonged activation could indirectly alter behavior in the open field and EZM assays, rather than reflecting direct anxiety regulation.

(3) The conclusion that "SuM store information about stress but not memory" (line 240) is not fully supported, particularly with respect to possible roles in memory. The lack of a role in memory of events, as opposed to the output of threat or stress memory, may be true, but is functionally untested in presented experiments. The data do indicate activation of the SuM neuron by foot shock, which has been previously reported(Escobedo et al eLife 2024). The changes in SuM activity following chronic stress (Figure 1) are intriguing, but their relationship to "stress information storage" is not clearly established.

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community:

The reported results align with prior studies on SuM and Sub areas' roles in stress in anxiety. There are limitations due to narrowly focused behavioral assays and the limited temporal resolution of the tools used. Overall, the study further supports a role for SuM in threat and stress responses. The reported changes in SuM neuron activity following chronic stress may offer new insights into stress-induced disorders and behavioral changes.

Reviewer #2 (Public review):

This manuscript investigates the neural mechanisms of anxiety and identifies the supramammillary nucleus (SuM) as a critical hub in mediating anxiety-related behaviors. The authors describe a population of neurons in the SuM that are activated by acute and chronic stress. While their activity is not required for fear memory recall, reactivation of these neurons after chronic stress robustly increases anxiety-like behaviors as well as physiological stress markers. Circuit analysis further shows that these stress-activated neurons are driven by inputs from the ventral, but not dorsal, subiculum, and inhibition of this pathway exerts an anxiolytic effect.

The study provides an elegant integration of techniques to link stress, neuronal ensembles, and circuit function, thereby advancing our understanding of the neural substrates of anxiety. A particularly notable point is the selective role of these stress-activated neurons in anxiety, but not in associative fear memory, which highlights functional distinctions between neural circuits underlying anxiety and fear.

Some aspects would benefit from clarification. For example, how selective is the recruitment of this population to stress compared with other aversive states, and how should one best interpret their definition as "stress-activated neurons" given the relatively modest overlap across stress exposures? In addition, the use of the term "engram" in this context raises conceptual questions. Is it appropriate to describe a neuronal ensemble encoding an emotional state as an engram, a term usually tied to specific memory recall?

Overall, this work makes a valuable contribution by identifying SuM stress-activated neurons and their ventral subiculum inputs as central elements of the circuitry underlying anxiety. These findings provide a valuable framework for future studies investigating anxiety circuitry and may inform the development of targeted interventions for stress-related disorders.

Reviewer #3 (Public review):

Summary:

The authors aim to investigate the mechanisms of anxiety. The paper focuses on the supramammillary nucleus (SuM) based on a fos screen and recordings showing that footshock and social defeat stress increase activity in this region. Using activity-dependent tagging, they show that reactivation of stress-activated neurons in SuM has an anxiety-like effect, reducing open-arm exploration in the elevated zero task. They then investigate the ventral subiculum as a potential source of anxiety-related information for SuM. They show that ventral subiculum (vSub) inputs to SuM are more strongly activated than dSub when mice explore the open arms of the elevated zero. Finally, they show that DREADD-mediated inhibition of vSub-SuM projections alleviates stress-enhanced anxiety. Overall, the results provide good evidence that SuM contains a stress-activated neuronal population whose later activity increases anxiety-like behavior. It further provides evidence that vSub projects to SuM are activated by stress, and their inhibition alleviates some effects of stress.

Strengths:

Strengths of this paper include the use of convergent methods (e.g., fos plus electrode recordings, footshock, and social defeat) to demonstrate that the SuM is activated by different forms of stress. The activity-dependent tagging experiment shows that footshock-activated SuM neurons are reactivated by social defeat but not by sucrose is also compelling because it provides evidence that SuM neurons are driven by some integrative aspect of stress rather than by a simple sensory stimulus.

Weaknesses:

The strength of some of the evidence is judged to be incomplete. The paper provides good evidence that SuM contains stress-responsive neurons, and the activity of these neurons increases some measure of anxiety-like behavior. However, the evidence that the vSub-SuM projection "encodes anxiety" and that the SuM is a key regulator of anxiety is judged to be incomplete. The claim that SuM generates an "anxiety engram" is also judged to be incompletely supported by the evidence. Namely, what is unclear is whether these cells/regions encode anxiety per se versus modulate behaviors (like exploration) that tend to correlate with anxiety. Since many brain regions respond to footshock and other stressors, the response of SuM to these stimuli is not strong evidence for a role in anxiety. I am not convinced that the identified SuM cells have a specific anxiety function. As the authors mention in the introduction, SuM regulates exploration and theta activity. Since theta potently regulates hippocampal function, there is the concern that SuM manipulations could have broad effects. As shown in Supplementary Figure 2, stimulating stress-responsive cells in SuM potently reduces general locomotor exploration. This raises concerns that the manipulation could have broader effects that go beyond just changes in anxiety-like behavior. Furthermore, the meaning of an "anxiety engram" is unclear. Would this engram encode stress, the sense of a potential threat, or the behavioral response? A more developed analysis of the behavioral correlates of SuM activity and the behavioral effects of SuM manipulations could give insight into these questions.

Reviewer #1 (Public review):

Summary:

The manuscript characterizes a functional peptidergic system in the echinoderm Apostichopus japonicus that is related to the widely conserved family of calcitonin/diuretic hormone 31 (CT/DH31) peptides in bilaterian animals. In vitro analysis of receptor-ligand interactions, using multiple receptor activation assays, identifies three cognate receptors for two CT-like peptides in the sea cucumber, which stimulate cAMP, calcium, and ERK signaling. Only one of these receptors clusters within the family of calcitonin and calcitonin-like receptors (CTR/CLR) in bilaterian animals, whereas two other receptors cluster with invertebrate pigment dispersing factor receptors (PDFRs). In addition, this study sheds light on the expression and in vivo functions of CT-like peptides in A. japonicus, by quantitative real-time PCR, immunohistochemistry, pharmacological experiments on body wall muscle and intestine preparations, and peptide injection and RNAi knockdown experiments. This reveals a conserved function of CT-like peptides as muscle relaxants and growth regulators in A. japonicus.

Strengths:

This work combines both in vitro and in vivo functional assays to identify a CT-like peptidergic system in an economically relevant echinoderm species, the sea cucumber A. japonicus. A major strength of the study is that it identifies three G protein-coupled receptors for AjCT-like peptides, one related to the CTR/CLR family and two related to the PDFR family. A similar finding was previously reported for the CT-related peptide DH31 in Drosophila melanogaster that activates both CT-type and PDF-type receptors. Here, the authors expand this observation to a deuterostomian animal, which suggests that receptor promiscuity is a more general feature of the CT/DH31 peptide family and that CT/DH31-like peptides may activate both CT-type and PDF-type receptors in other animals as well.

Besides the identification of receptor-ligand pairs, the downstream signaling pathways of AjCT receptors have been characterized, revealing broad and in some cases receptor-specific effects on cAMP, calcium, and ERK signaling.

Functional characterization of the CT-related peptide system in heterologous cells is complemented with ex vivo and in vivo experiments. First, peptide injection and RNAi knockdown experiments establish transcriptional regulation of all three identified receptors in response to changing AjCT peptide levels. Second, ex vivo experiments reveal a conserved role for the two CT-like peptides as muscle relaxants, which have differential effects on body wall muscle and intestine preparations. Finally, peptide injection and knockdown experiments uncover a growth-promoting role for one CT-like peptide (AjCT2). Injection of AjCT2 at high concentration, or long-term knockdown of the AjCT precursor, affects diverse growth-related parameters including weight gain rate, specific growth rate, and transcript levels of growth-regulating transcription factors. The authors also reveal a growth-promoting function for the PDFR-like receptor AjPDFR2, suggesting that this receptor mediates the effects of AjCT2 on growth.

Weaknesses:

Expression of CT-like peptides was investigated both at transcript and protein level, but insight into the expression of the three peptide receptors is limited. This makes it difficult to understand the mechanism underlying the (different) functions of the two CT-like peptides in vivo. The authors identify differences in signal transduction cascades activated by each peptide, which might underpin distinct functions, but these differences were established only in heterologous cells.

The authors show overlapping phenotypes for a long-term knockdown of the AjCT precursor and the AjPDFR2 receptor, suggesting that the growth-regulating functions of AjCT2 are mediated by this receptor pathway. However, it remains unclear whether this mechanism underpins the growth-regulating function of AjCT2, until further in vivo evidence for this ligand-receptor interaction is presented. For example, the authors could investigate whether knockdown of AjPDFR2 attenuates the effects of AjCT2 peptide injection. In addition, a functional PDF system in this species remains uncharacterized, and a potential role of PDF-like peptides in growth regulation has not yet been investigated in A. japonicus. Therefore, it also remains unclear whether the ability of CT-like peptides to activate PDFRs is an evolutionary ancient property of this peptide family or whether this is an example of convergent evolution in some protostomian (Drosophila) and deuterostomian (sea cucumber) species.

Reviewer #2 (Public review):

Summary:

The authors show that A. japonicus calcitonins (AjCT1 and AjCT2) activate not only the calcitonin/calcitonin-like receptor, but they also activate the two "PDF receptors", ex vivo. They also explore secondary messenger pathways that are recruited following receptor activation. They determine the source of CT1 and CT2 using qPCR and in situ hybridization and finally test the effects of these peptides on tissue contractions, feeding and growth. This study provides solid evidence that CT1 and CT2 act as ligands for calcitonin receptors; however, evidence supporting cross-talk between CT peptides and "PDF receptors" is weak.

Strengths:

This is the first study to report pharmacological characterization of CT receptors in an echinoderm. Multiple lines of evidence in cell culture (receptor internalization and secondary messenger pathways) support this conclusion.

Reviewer #1 (Public review):

Summary:

This preprint from Shaowei Zhao and colleagues presents results that suggest tumorous germline stem cells (GSCs) in the Drosophila ovary mimic the ovarian stem cell niche and inhibit the differentiation of neighboring non-mutant GSC-like cells. The authors use FRT-mediated clonal analysis driven by a germline-specific gene (nos-Gal4, UASp-flp) to induce GSC-like cells mutant for bam or bam's co-factor bgcn. Bam-mutant or bgcn-mutant germ cells produce tumors in the stem cell compartment (the germarium) of the ovary (Figure 1). These tumors contain non-mutant cells - termed SGC for single-germ cells. 75% of SGCs do not exhibit signs of differentiation (as assessed by bamP-GFP) (Figure 2). The authors demonstrate that block in differentiation in SGC is a result of suppression of bam expression (Figure 2). They present data suggesting that in 73% of SGCs, BMP signaling is low (assessed by dad-lacZ) (Figure 3) and proliferation is less in SGCs vs GSCs. They present genetic evidence that mutations in BMP pathway receptors and transcription factors suppress some of the non-autonomous effects exhibited by SGCs within bam-mutant tumors (Figure 4). They show data that bam-mutant cells secrete Dpp, but this data is not compelling (see below) (Figure 5). They provide genetic data that loss of BMP ligands (dpp and gbb) suppresses the appearance of SGCs in bam-mutant tumors (Figure 6). Taken together, their data support a model in which bam-mutant GSC-like cells produce BMPs that act on non-mutant cells (i.e., SGCs) to prevent their differentiation, similar to what is seen in the ovarian stem cell niche. This preprint from Shaowei Zhao and colleagues presents results that suggest tumorous germline stem cells (GSCs) in the Drosophila ovary mimic the ovarian stem cell niche and inhibit the differentiation of neighboring non-mutant GSC-like cells. The authors use FRT-mediated clonal analysis driven by a germline-specific gene (nos-Gal4, UASp-flp) to induce GSC-like cells mutant for bam or bam's co-factor bgcn. Bam-mutant or bgcn-mutant germ cells produce tumors in the stem cell compartment (the germarium) of the ovary (Figure 1). These tumors contain non-mutant cells - termed SGC for single-germ cells. 75% of SGCs do not exhibit signs of differentiation (as assessed by bamP-GFP) (Figure 2). The authors demonstrate that block in differentiation in SGC is a result of suppression of bam expression (Figure 2). They present data suggesting that in 73% of SGCs, BMP signaling is low (assessed by dad-lacZ) (Figure 3) and proliferation is less in SGCs vs GSCs. They present genetic evidence that mutations in BMP pathway receptors and transcription factors suppress some of the non-autonomous effects exhibited by SGCs within bam-mutant tumors (Figure 4). They show data that bam-mutant cells secrete Dpp, but this data is not compelling (see below) (Figure 5). They provide genetic data that loss of BMP ligands (dpp and gbb) suppresses the appearance of SGCs in bam-mutant tumors (Figure 6). Taken together, their data support a model in which bam-mutant GSC-like cells produce BMPs that act on non-mutant cells (i.e., SGCs) to prevent their differentiation, similar to what in seen in the ovarian stem cell niche.

Strengths:

(1) Use of an excellent and established model for tumorous cells in a stem cell microenvironment.

(2) Powerful genetics allow them to test various factors in the tumorous vs non-tumorous cells.

(3) Appropriate use of quantification and statistics.

Weaknesses:

(1) What is the frequency of SGCs in nos>flp; bam-mutant tumors? For example, are they seen in every germarium, or in some germaria, etc, or in a few germaria?

(2) Does the breakdown in clonality vary when they induce hs-flp clones in adults as opposed to in larvae/pupae?

(3) Approximately 20-25% of SGCs are bam+, dad-LacZ+. Firstly, how do the authors explain this? Secondly, of the 70-75% of SGCs that have no/low BMP signaling, the authors should perform additional characterization using markers that are expressed in GSCs (i.e., Sex lethal and nanos).

(4) All experiments except Figure 1I (where a single germarium with no quantification) were performed with nos-Gal4, UASp-flp. Have the authors performed any of the phenotypic characterizations (i.e., figures other than Figure 1) with hs-flp?

(5) Does the number of SGCs change with the age of the female? The experiments were all performed in 14-day-old adult females. What happens when they look at a young female (like 2-day-old). I assume that the nos>flp is working in larval and pupal stages, and so the phenotype should be present in young females. Why did the authors choose this later age? For example, is the phenotype more robust in older females? Or do you see more SGCs at later time points?

(6) Can the authors distinguish one copy of GFP versus 2 copies of GFP in germ cells of the ovary? This is not possible in the Drosophila testis. I ask because this could impact the clonal analyses diagrammed in Figure 4A and 4G and in 6A and B. Additionally, in most of the figures, the GFP is saturated, so it is not possible to discern one vs two copies of GFP.

(7) More evidence is needed to support the claim of elevated Dpp levels in bam or bgcn mutant tumors. The current results with the dpp-lacZ enhancer trap in Figure 5A, B are not convincing. First, why is the dpp-lacZ so much brighter in the mosaic analysis (A) than in the no-clone analysis (B)? It is expected that the level of dpp-lacZ in cap cells should be invariant between ovaries, and yet LacZ is very faint in Figure 5B. I think that if the settings in A matched those in B, the apparent expression of dpp-lacZ in the tumor would be much lower and likely not statistically significant. Second, they should use RNA in situ hybridization with a sensitive technique like hybridization chain reactions (HCR) - an approach that has worked well in numerous Drosophila tissues, including the ovary.

(8) In Figure 6, the authors report results obtained with the bamBG allele. Do they obtain similar data with another bam allele (i.e., bamdelta86)?

Reviewer #2 (Public review):

While the study by Zhang et al. provides valuable insights into how germline tumors can non-autonomously suppress the differentiation of neighboring wild-type germline stem cells (GSCs), several conceptual and technical issues limit the strength of the conclusions.

Major points:

(1) Naming of SGCs is confusing. In line 68, the authors state that "many wild-type germ cells located outside the niche retained a GSC-like single-germ-cell (SGC) morphology." However, bam or bgcn mutant GSCs are also referred to as "SGCs," which creates confusion when reading the text and interpreting the figures. The authors should clarify the terminology used to distinguish between wild-type SGCs and tumor (bam/bgcn mutant) SGCs, and apply consistent naming throughout the manuscript and figure legends.

a) The same confusion appears in Figure 2. It is unclear whether the analyzed SGCs are wild-type or bam mutant cells. If the SGCs analyzed are Bam mutants, then the lack of Bam expression and failure to differentiate would be expected and not informative. However, if the SGCs are wild-type GSCs located outside the niche, then the observation would suggest that Bam expression is silenced in these wild-type cells, which is a significant finding. The authors should clarify the genotype of the SGCs analyzed in Figure 2C, as this information is not currently provided.

b) In Figures 4B and 4E, the analysis of SGC composition is confusing. In the control germaria (bam mutant mosaic), the authors label GFP⁺ SGCs as "wild-type," which makes interpretation unclear. Note, this is completely different from their earlier definition shown in line 68.

c) Additionally, bam⁺/⁻ GSCs (the first bar in Figure 4E) should appear GFP⁺ and Red⁺ (i.e., yellow). It would be helpful if the authors could indicate these bam⁺/⁻ germ cells directly in the image and clarify the corresponding color representation in the main text. In Figure 2A, although a color code is shown, the legend does not explain it clearly, nor does it specify the identity of bam⁺/⁻ cells alone. Figure 4F has the same issue, and in this graph, the color does not match Figure 4A.

(2) The frequencies of bam or bgcn mutant mosaic germaria carrying [wild-type] SGCs or wild-type germ cell cysts with branched fusomes, as well as the average number of wild-type SGCs per germarium and the number of days after heat shock for the representative images, are not provided when Figure 1 is first introduced. Since this is the first time the authors describe these phenotypes, including these details is essential. Without this information, it is difficult for readers to follow and evaluate the presented observations.

(3) Without the information mentioned in point 2, it causes problems when reading through the section regarding [wild-type] SGCs induced by impairment of differentiation or dedifferentiation. In lines 90-97, the authors use the presence of midbodies between cystocytes as a criterion to determine whether the wild-type GSCs surrounded by tumor GSCs arise through dedifferentiation. However, the cited study (Mathieu et al., 2022) reports that midbodies can be detected between two germ cells within a cyst carrying a branched fusome upon USP8 loss.

a) Are wild-type germ cell cysts with branched fusomes present in the bam mutant mosaic germaria? What is the proportion of germaria containing wild-type SGCs versus those containing wild-type germ cell cysts with branched fusomes?

b) If all bam mutant mosaic germaria carry only wild-type GSCs outside the niche and no germaria contain wild-type germ cell cysts with branched fusomes, then examining midbodies as an indicator of dedifferentiation may not be appropriate.

c) If, however, some germaria do contain wild-type germ cell cysts with branched fusomes, the authors should provide representative images and quantify their proportion.

d) In line 95, although the authors state that 50 germ cell cysts were analyzed for the presence of midbodies, it would be more informative to specify how many germaria these cysts were derived from and how many biological replicates were examined.

(4) Note that both bam mutant GSCs and wild-type SGCs can undergo division to generate midbodies (double cells), as shown in Figure 4H. Therefore, the current description of the midbody analysis is confusing. The authors should clarify which cell types were examined and explain how midbodies were interpreted in distinguishing between cell division and differentiation.

(5) The data in Figure 5 showing Dpp expression in bam mutant tumorous GSCs are not convincing. The Dpp-lacZ signal appears broadly distributed throughout the germarium, including in escort cells. To support the claim more clearly, the authors should present corresponding images for Figures 5D and 5E, in which dpp expression was knocked down in the germ cells of bam or bgcn mutant mosaic germaria. Showing these images would help clarify the localization and specificity of Dpp-lacZ expression relative to the tumorous GSCs.

(6) While Figure 6 provides genetic evidence that bam mutant tumorous GSCs produce Dpp to inhibit the differentiation of wild-type SGCs, it should be noted that these analyses were performed in a dpp⁺/⁻ background. To strengthen the conclusion, the authors should include appropriate controls showing [dpp⁺/⁻; bam⁺/⁻] SGCs and [dpp⁺/⁻; bam⁺/⁻] germ cell cysts without heat shock (as referenced in Figures 6F and 6I).

(7) Previous studies have reported that bam mutant germ cells cause blunted escort cell protrusions (e.g., Kirilly et al., Development, 2011), which are known to contribute to germ cell differentiation (e.g., Chen et al., Frontiers in Cell and Developmental Biology, 2022). The authors should include these findings in the Discussion to provide a broader context and to acknowledge how alterations in escort cell morphology may further influence differentiation defects in their model.

(8) Since fusome morphology is an important readout of SGCs vs differentiation. All the clonal analysis should have fusome staining.

(9) Figure arrangement. It is somewhat difficult to identify the figure panels cited in the text due to the current panel arrangement.

(10) The number of biological replicates and germaria analyzed should be clearly stated somewhere in the manuscript-ideally in the Methods section or figure legends. Providing this information is essential for assessing data reliability and reproducibility.

Reviewer #3 (Public review):

Summary:

Zhang et al. investigated how germline tumors influence the development of neighboring wild-type (WT) germline stem cells (GSC) in the Drosophila ovary. They report that germline tumors inhibit the differentiation of neighboring WT GSCs by arresting them in an undifferentiated state, resulting from reduced expression of the differentiation-promoting factor Bam. They find that these tumor cells produce low levels of the niche-associated signaling molecules Dpp and Gbb, which suppress bam expression and consequently inhibit the differentiation of neighboring WT GSCs non-cell-autonomously. Based on these findings, the authors propose that germline tumors mimic the niche to suppress the differentiation of the neighboring stem cells.

Strengths:

This study addresses an important biological question concerning the interaction between germline tumor cells and WT germline stem cells in the Drosophila ovary. If the findings are substantiated, they could provide valuable insights applicable to other stem cell systems.

Weaknesses:

Previous work from Xie's lab demonstrated that bam and bgcn mutant GSCs can outcompete WT GSCs for niche occupancy. Furthermore, a large body of literature has established that the interactions between escort cells (ECs) and GSC daughters are essential for proper and timely germline differentiation (the differentiation niche). Disruption of these interactions leads to arrest of germline cell differentiation in a status with weak BMP signaling activation and low bam expression, a phenotype virtually identical to what is reported here.

Thus, it remains unclear whether the observed phenotype reflects "direct inhibition by tumor cells" or "arrested differentiation due to the loss of the differentiation niche". Because most data were collected at a very late stage (more than 10 days after clonal induction), when tumor cells already dominate the germarium, this question cannot be solved. To distinguish between these two possibilities, the authors could conduct a time-course analysis to examine the onset of the WT GSC-like single-germ-cell (SGC) phenotype and determine whether early-stage tumor clones with a few tumor cells can suppress the differentiation of neighboring WT GSCs with only a few tumor cells present. If tumor cells indeed produce Dpp and Gbb (as proposed here) to inhibit the differentiation of neighboring germline cells, a small cluster or probably even a single tumor cell generated at an early stage might prevent the differentiation of their neighboring germ cells.

The key evidence supporting the claim that tumor cells produce Gpp and Gbb comes from Figures 5 and 6, which suggest that tumor-derived dpp and gbb are required for this inhibition. However, interpretation of these data requires caution.

In Figure 5, the authors use dpp-lacZ to support the claim that dpp is upregulated in tumor cells (Figure 5A and 5B). However, the background expression in somatic cells (ECs and pre-follicular cells) differs noticeably between these panels. In Figure 5A, dpp-lacZ expression in somatic cells in 5A is clearly higher than in 5B, and the expression level in tumor cells appears comparable to that in somatic cells (dpp-lacZ single channel). Similarly, in Figure 5B, dpp-lacZ expression in germline cells is also comparable to that in somatic cells. Providing clear evidence of upregulated dpp and gbb expression in tumor cells (for example, through single-molecular RNA in situ) would be essential.

Most tumor data present in this study were collected from the bam[86] null allele, whereas the data in Figure 6 were derived from a weaker bam[BG] allele. This bam[BG] allele is not molecularly defined and shows some genetic interaction with dpp mutants. As shown in Figure 6E, removal of dpp from homozygous bam[BG] mutant leads to germline differentiation (evidenced by a branched fusome connecting several cystocytes, located at the right side of the white arrowhead). In Figure 6D, fusome is likely present in some GFP-negative bam[BG]/bam[BG] cells. To strengthen their claim that the tumor produces Dpp and Gbb to inhibit WT germline cell differentiation, the authors should repeat these experiments using the bam[86] null allele.

It is well established that the stem niche provides multiple functional supports for maintaining resident stem cells, including physical anchorage and signaling regulation. In Drosophila, several signaling molecules produced by the niche have been identified, each with a distinct function - some promoting stemness, while others regulate differentiation. Expression of Dpp and Gbb alone does not substantiate the claim that these tumor cells have acquired the niche-like property. To support their assertion that these tumors mimic the niche, the authors should provide additional evidence showing that these tumor cells also express other niche-associated markers. Alternatively, they could revise the manuscript title to more accurately reflect their findings.

In the Method section, the authors need to provide details on how dpp-lacZ expression levels were quantified and normalized.

Reviewer #1 (Public review):

Summary:

This study investigates the effects of transcriptional activation on chromatin dynamics and mobility. Using a breast cancer model, the authors examine the effects of estrogen receptor-a (ERa) stimulation and the resulting transcriptional activation on chromatin behavior at ERa-dependent loci during three distinct phases: unstimulated, acute stimulation, and chronic stimulation. Through live DNA and RNA imaging, the authors claim that ERa-dependent target genes display distinct bursting dynamics during periods of acute versus chronic simulation, accompanied by an overall increase in chromatin mobility. Notably, they claim that ERa-dependent loci display increased mobility during the non-bursting phase compared to the bursting phase. The study also attempts to explore the role of condensates in mediating these transcriptional and chromatin mobility changes using a single-molecule tracking assay to identify a unique population of low diffusion-coefficient molecules that appears upon E2 stimulation and is sensitive to 1,6-hexanediol.

Strengths:

While the study develops interesting tools that have the potential to provide useful insights into the relationship between transcriptional state, genomic locus mobility, and condensate formation, several major claims lack key supportive evidence, and the methods are inadequately established and described.

Weaknesses:

(1) The use of 1,6 hexanediol experiments is not suitable for drawing conclusions in live cell experiments, as this assay is now widely recognized to be plagued with artifacts and inadequate as a test for condensate formation. 1,6 hexanediol perturbs all hydrophobic interactions and has effects ranging from perturbing kinase and phosphatase activities (Düster et al, J. Biol. Chem., 2021), immobilizing and condensing chromatin in living cells (Itoh et al., Life Sci. Alliance 2021), disrupting nuclear pore complexes (Ribbeck et al., EMBO 2002), nuclear transport (Barrientos et al., Nucleus, 2023), and does not disrupt charge-mediated phase separation (Zheng et al., EMBO, 2025). There is also a discussion on these effects in a recent article: Current practices in the study of biomolecular condensates: a community comment, Alberti, Nat. Comm., 2025.

(2) The chromatin mobility is analyzed using displacement, and the differences are typically less than 50 nm. There is no discussion on the precision of this measurement and what these small differences may mean. No control loci are assessed to see if this effect is specific to the genes of interest or global.

(3) The SMT analysis is performed using Mean Square Displacement fitting of short single trajectories, which is error-prone, and no analysis is performed on the localization precision or error in estimation of the key parameters. Potential artifacts from this analysis are reflected in the distribution of alpha and diffusion coefficients that are presented in this paper, which include physically impossible values on which major claims rest.

(4) No experiment is performed to directly connect foci/cluster/condensation formation of ER at the genes of interest. Given these points alone, it is impossible to assess whether any of the claims made in the current manuscript are correct.

Reviewer #2 (Public review):

Summary:

The authors use a combination of state-of-the-art live-cell imaging techniques to track transcriptional bursting, DNA mobility, and single-molecule tracking to discern biophysical behaviours of chromatin and condensate formation in response to ER𝛼 activation. Surprisingly, the authors find that loci in estradiol-stimulated cells display enhanced mobility during the non-bursting phase. The authors attribute the reduced mobility of the loci during transcriptional bursts to condensate formation of ER𝛼 on enhancers regulating the bursting gene. Inhibition of transcription with flavopiridol shifts the loci and ER𝛼 to a non-confined state. These findings open the door to performing more complex multi-color live-cell imaging assays to fully interrogate the role of transcription factor condensates, DNA mobility, and subnuclear localization in the regulation of transcriptional bursting kinetics, and should be of great benefit to researchers studying mechanisms of gene regulation.

Strengths:

The authors presented a series of advanced multi-color live cell imaging assays used to correlate changes in DNA mobility with transcriptional bursting of a gene. By using such a defined temporal trigger associated with the addition of estroldiol to cells, the authors were also able to elegantly characterize changes in the diffusive properties of different classes of ER𝛼 during the acute (early, <2 hours) and chronic (late, >2 hours) phases of estrogen-responsive gene activation. Interestingly, one particular class of ER𝛼 that changed between acute and chronic phases was also responsive to 1,6-hexanediol treatment, suggesting that the authors are assaying ER𝛼 behaviours related to condensate formation. The authors also examined how the proximity of the NRIP1 gene to interchromatin granules impacted transcriptional bursting kinetics. There was no correlation of DNA mobility nor transcription bursting associated with localization to interchromatin granules, suggesting that other higher-order, architectural associations are regulating these processes. The imaging data were also supported by genomic GRO-seq and ChIP-seq assays showing changes in genomic occupancy of a number of transcription factors, including ER𝛼, during the pre-acute, acute, and chronic phases.

Weaknesses:

Although there are a number of compelling strengths to support the author's interpretation of the data, the paper is written in a way that lacks clarity and detail on a number of technical components. This lack of details, in particular related to how endogenous tagging of DNA, ER𝛼, and interchromatin granules (e.g. SC35) potentially impacts transcriptional bursting, makes it difficult for the reader to sufficiently judge any potential limitations of these complex engineered cell lines. Another potential weakness is the lack of any experiments directly measuring ER𝛼 diffusive properties in close proximity to the bursting gene. It is noted that this type of experiment examining transcription factor binding on a bursting gene is very technically challenging, given the different timescales of measurement of bursting (seconds-minutes) versus ER𝛼 diffusion (sub-seconds). However, these types of experiments would go a long way to supporting the authors' conclusions regarding how changes in DNA mobility and transcription bursting may be directly related to ER𝛼 condensate formation on enhancers.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors explore dynamic chromosomal mobility and transcriptional bursting events in mammalian cells, particularly focusing on ERα-dependent gene activation. The authors investigate how the physical movement of DNA loci changes during different phases of gene transcription (bursting vs. non-bursting, acute vs. chronic stimulation). Using advanced live-cell imaging techniques, including SMT of ERα and dual DNA/RNA visualization, the study reveals a multi-state model of DNA mobility linked to the formation of transcription factor condensates. The authors conclude that differential DNA kinetics serve as a reliable indicator for detecting condensate formation during gene activation, offering new insights into the mechanisms regulating gene expression within the nucleus.

Strengths:

The authors have done substantial work, and a major strength of the manuscript is being able to image both DNA and RNA from the same gene, as well as the TF that acts on that gene. This multi-pronged approach leads to complementary insights into transcription bursting mechanisms.

Weaknesses:

A major weakness of the manuscript is the lack of appropriate controls that support the specificity of the effects observed. The exclusive focus on condensates as the underlying mechanism to explain their data is also a bit limiting.

Reviewer #1 (Public review):

Summary:

The authors report the structure of the human CTF18-RFC complex bound to PCNA. Similar structures (and more) have been reported by the O'Donnell and Li labs. This study should add to our understanding of CTF18-RFC in DNA replication and clamp loaders in general. However, there are numerous major issues that I recommend the authors fix.

Strengths:

The structures reported are strong and useful for comparison with other clamp loader structures that have been reported lately.

Comments on revisions:

The revised manuscript is greatly improved. The comparison with hRFC and the addition of direct PCNA loading data from the Hedglin group are particular highlights. I think this is a strong addition to the literature.

I only have minor comments on the revised manuscript.

(1) The clamp loading kinetic data in Figure 6 would be more easily interpreted if the three graphs all had the same x axes, and if addition of RFC was t=0 rather than t=60 sec.

(2) The author's statement that "CTF18-RFC displayed a slightly faster rate than RFC" seems to me a bit misleading, even though this is technically correct. The two loaders have indistinguishable rate constants for the fast phase, and RFC is a bit slower than CTF18-RFC in the slow phase. However, the data also show that RFC is overall more efficient than CTF18-RFC at loading PCNA because much more flux through the fast phase (rel amplitudes 0.73 vs 0.36). Because the slow phase represents such a reduced fraction of loading events, the slight reduction in rate constant for the slow phase doesn't impact RFC's overall loading. And because the majority of loading events are in the fast phase, RFC has a faster halftime than CTF18-RFC. (Is it known what the different phases correspond to? If it is known, it might be interesting to discuss.)

(3) AAA+ is an acronym for "ATPases Associated with diverse cellular Activities" rather than "Adenosine Triphosphatase Associated".

Reviewer #2 (Public review):

Summary

Briola and co-authors have performed a structural analysis of the human CTF18 clamp loader bound to PCNA. The authors purified the complexes and formed a complex in solution. They used cryo-EM to determine the structure to high resolution. The complex assumed an auto-inhibited conformation, where DNA binding is blocked, which is of regulatory importance and suggests that additional factors could be required to support PCNA loading on DNA. The authors carefully analysed the structure and compared it to RFC and related structures.

Strength & Weakness

Their overall analysis is of high quality, and they identified, among other things, a human-specific beta-hairpin in Ctf18 that flexible tethers Ctf18 to Rfc2-5. Indeed, deletion of the beta-hairpin resulted in reduced complex stability and a reduction in a primer extension assay with Pol ε. Moreover, the authors identify that the Ctf18 ATP-binding domain assumes a more flexible organisation.

The data are discussed accurately and relevantly, which provides an important framework for rationalising the results.

All in all, this is a high-quality manuscript that identifies a key intermediate in CTF18-dependent clamp loading.

Comments on revisions:

The authors have done a nice job with the revision.

Reviewer #3 (Public review):

Summary:

CTF18-RFC is an alternative eukaryotic PCNA sliding clamp loader which is thought to specialize in loading PCNA on the leading strand. Eukaryotic clamp loaders (RFC complexes) have an interchangeable large subunit which is responsible for their specialized functions. The authors show that the CTF18 large subunit has several features responsible for its weaker PCNA loading activity, and that the resulting weakened stability of the complex is compensated by a novel beta hairpin backside hook. The authors show this hook is required for the optimal stability and activity of the complex.

Relevance:

The structural findings are important for understanding RFC enzymology and novel ways that the widespread class of AAA ATPases can be adapted to specialized functions. A better understanding of CTF18-RFC function will also provide clarity into aspects of DNA replication, cohesion establishment and the DNA damage response.

Strengths:

The cryo-EM structures are of high quality enabling accurate modelling of the complex and providing a strong basis for analyzing differences and similarities with other RFC complexes.

Weaknesses:

The manuscript would have benefited from a more detailed biochemical analysis using mutagenesis and assays to tease apart the differences with the canonical RFC complex. Analysis of the FRET assay could be improved.

Overall appraisal:

Overall, the work presented here is solid and important. The data is mostly sufficient to support the stated conclusions.

Comments on revisions:

While the authors addressed my previous specific concerns, they have now added a new experiment which raises new concerns.

The FRET clamp loading experiments (Fig. 6) appear to be overfitted so that the fitted values are unlikely to be robust and it is difficult to know what they mean, and this is not explained in this manuscript. Specifically, the contribution of two exponentials is floated in each experiment. By eye, CTF18-RFC looks much slower than RFC1-RFC (as also shown previously in the literature) but the kinetic constants and text suggest it is faster. This is because the contribution of the fast exponential is substantially decreased, and the rate constants then compensate for this. There is a similar change in contribution of the slow and fast rates between WT CTF18 and the variant (where the data curves look the same) and this has been balanced out by a change in the rate constants, which is then interpreted as a defect. I doubt the data are strong enough to confidently fit all these co-dependent parameters, especially for CTF18, where a fast initial phase is not visible. I would recommend either removing this figure or doing a more careful and thorough analysis.

Reviewer #1 (Public review):

This paper by Troyer et al. measures the positioning and diffusivity of RNaseE-mEos3.2 proteins in E. coli as a function of rifampicin treatment, compares RNaseE to other E. coli proteins, and measures the effect of changes in domain composition on this localization and motion. The straightforward study is thoroughly presented, including very good descriptions of the imaging parameters and the image analysis/modeling involved, which is good because the key impact of the work lies in presenting this clear methodology for determining the position and mobility of a series of proteins in living bacteria cells.

Most of my concerns in the original review were addressed in this round of revisions based on new text, experiments, and analysis, including most notably:

-A revision of the abstract to focus on the actual topic of the manuscript.<br /> -New experiments (Fig. S1) to confirm that there is no significant undercounting of the fast-moving cytoplasmic population<br /> -Removing the experiments discussion related to degradosome proteins rather than overstating results.<br /> -Improving the logical flow and writing.

One minor concern still remains:

-Though the discussion of the rifampicin-treated cells is improved, this experiment is motivated (line 196) as "To test the effect of mRNA substrates on RNE diffusion", but the conclusion of the paragraph (based on similarities with the effect on LacY) is that the observed changes are due to factors other than the concentration of mRNA substrates, such that the effect of mRNA has not been tested.

Reviewer #2 (Public review):

Summary:

Troyer and colleagues have studied the in vivo localisation and mobility of the E.coli RNaseE (a protein key for mRNA degradation in all bacteria) as well as the impact of two key protein segments (MTS and CTD) on RNase E cellular localisation and mobility. Such sequences are important to study since there is significant sequence diversity within bacteria, as well as lack of clarity about their functional effects. Using single-molecule tracking in living bacteria, the authors confirmed that >90% of RNaseE localised on the membrane, and measured its diffusion coefficient. Via a series of mutants, they also showed that MTS leads to stronger membrane association and slower diffusion compared to a transmembrane motif (despite the latter being more embedded in the membrane), and that the CTD weakens membrane binding. The study also rationalised how the interplay of MTS and CTD modulate mRNA metabolism (and hence gene expression) in different cellular contexts.

The authors have also done an excellent job addressing reviewer's concerns and improving the manuscript during revision.

Reviewer #3 (Public review):

Summary:

The manuscript by Troyer et al quantitatively measured the membrane localization and diffusion of RNase E, an essential ribonuclease for mRNA turnover as well as tRNA and rRNA processing in bacteria cells. Using single-molecule tracking in live E. coli cells, the authors investigated the impact of membrane targeting sequence (MTS) and the C-terminal domain (CTD) on the membrane localization and diffusion of RNase E under various perturbations. Finally, the authors tried to correlate the membrane localization of RNase E to its function on co- and post-transcriptional mRNA decay using lacZ mRNA as a model.

The major findings of the manuscripts include:

(1) WT RNase E is mostly membrane localized via MTS, confirming previous results. The diffusion of RNase E is increased upon removal of MTS or CTD, and more significantly increased upon removal of both regions.

(2) By tagging RNase E MTS and different lengths of LacY transmembrane domain (LacY2, LacY6 or LacY12) to mEos3.2, the results demonstrate that short LacY transmembrane sequence (LacY2 and LacY6) can increase the diffusion of mEos3.2 on the membrane compared to MTS, further supported by the molecular dynamics simulation. The similar trend was roughly observed in RNase E mutants with MTS switched to LacY transmembrane domains.

(3) The removal of RNase E MTS significantly increases the co-transcriptional degradation of lacZ mRNA, but has minimal effect on the post-transcriptional degradation of lacZ mRNA. Removal of CTD of RNase E overall decrease the mRNA decay rates, suggesting the synergistic effect of CTD on RNase E activity.

Strengths:

(1) The manuscript is clearly written with very detailed methods description and analysis parameters.

(2) The conclusions are mostly supported by the data and analysis.

(3) Some of the main conclusions are interesting and important for understanding the cellular behavior and function of RNase E.

Weaknesses:

The authors have addressed my previous concerns in the revised manuscript.

Comments on revisions:

I have one additional comment. When interpreting the small increase in the diffusion coefficient of RNase E when treating the cell with rifampicin, the authors rule out the possibility that only a small fraction of RNase E interacts with mRNA and suggest that it is more likely the mRNA-RNase E interaction is transient. However, I am wondering about an alternative possibility that RNase E prefers mRNAs with low ribosome density or even untranslated mRNAs?

Reviewer #2 (Public Review):

Here I submit my previous review and a great deal of additional information following on from the initial review and the response by the authors.

* Initial Review *

Assessment:

This manuscript is based upon the unprecedented identification of an apparently highly unusual trigeminal nuclear organization within the elephant brainstem, related to a large trigeminal nerve in these animals. The apparently highly specialized elephant trigeminal nuclear complex identified in the current study has been classified as the inferior olivary nuclear complex in four previous studies of the elephant brainstem. The entire study is predicated upon the correct identification of the trigeminal sensory nuclear complex and the inferior olivary nuclear complex in the elephant, and if this is incorrect, then the remainder of the manuscript is merely unsupported speculation. There are many reasons indicating that the trigeminal nuclear complex is misidentified in the current study, rendering the entire study, and associated speculation, inadequate at best, and damaging in terms of understanding elephant brains and behaviour at worst.

Original Public Review:

The authors describe what they assert to be a very unusual trigeminal nuclear complex in the brainstem of elephants, and based on this, follow with many speculations about how the trigeminal nuclear complex, as identified by them, might be organized in terms of the sensory capacity of the elephant trunk.<br /> The identification of the trigeminal nuclear complex/inferior olivary nuclear complex in the elephant brainstem is the central pillar of this manuscript from which everything else follows, and if this is incorrect, then the entire manuscript fails, and all the associated speculations become completely unsupported.

The authors note that what they identify as the trigeminal nuclear complex has been identified as the inferior olivary nuclear complex by other authors, citing Shoshani et al. (2006; 10.1016/j.brainresbull.2006.03.016) and Maseko et al (2013; 10.1159/000352004), but fail to cite either Verhaart and Kramer (1958; PMID 13841799) or Verhaart (1962; 10.1515/9783112519882-001). These four studies are in agreement, but the current study differs.

Let's assume for the moment that the four previous studies are all incorrect and the current study is correct. This would mean that the entire architecture and organization of the elephant brainstem is significantly rearranged in comparison to ALL other mammals, including humans, previously studied (e.g. Kappers et al. 1965, The Comparative Anatomy of the Nervous System of Vertebrates, Including Man, Volume 1 pp. 668-695) and the closely related manatee (10.1002/ar.20573). This rearrangement necessitates that the trigeminal nuclei would have had to "migrate" and shorten rostrocaudally, specifically and only, from the lateral aspect of the brainstem where these nuclei extend from the pons through to the cervical spinal cord (e.g. the Paxinos and Watson rat brain atlases), the to the spatially restricted ventromedial region of specifically and only the rostral medulla oblongata. According to the current paper the inferior olivary complex of the elephant is very small and located lateral to their trigeminal nuclear complex, and the region from where the trigeminal nuclei are located by others appears to be just "lateral nuclei" with no suggestion of what might be there instead.

Such an extraordinary rearrangement of brainstem nuclei would require a major transformation in the manner in which the mutations, patterning, and expression of genes and associated molecules during development occur. Such a major change is likely to lead to lethal phenotypes, making such a transformation extremely unlikely. Variations in mammalian brainstem anatomy are most commonly associated with quantitative changes rather than qualitative changes (10.1016/B978-0-12-804042-3.00045-2).

The impetus for the identification of the unusual brainstem trigeminal nuclei in the current study rests upon a previous study from the same laboratory (10.1016/j.cub.2021.12.051) that estimated that the number of axons contained in the infraorbital branch of the trigeminal nerve that innervate the sensory surfaces of the trunk is approximately 400 000. Is this number unusual? In a much smaller mammal with a highly specialized trigeminal system, the platypus, the number of axons innervating the sensory surface of the platypus bill skin comes to 1 344 000 (10.1159/000113185). Yet, there is no complex rearrangement of the brainstem trigeminal nuclei in the brain of the developing or adult platypus (Ashwell, 2013, Neurobiology of Monotremes), despite the brainstem trigeminal nuclei being very large in the platypus (10.1159/000067195). Even in other large-brained mammals, such as large whales that do not have a trunk, the number of axons in the trigeminal nerve ranges between 400,000 and 500,000 (10.1007/978-3-319-47829-6_988-1). The lack of comparative support for the argument forwarded in the previous and current study from this laboratory, and that the comparative data indicates that the brainstem nuclei do not change in the manner suggested in the elephant, argues against the identification of the trigeminal nuclei as outlined in the current study. Moreover, the comparative studies undermine the prior claim of the authors, informing the current study, that "the elephant trigeminal ganglion ... point to a high degree of tactile specialization in elephants" (10.1016/j.cub.2021.12.051). While clearly the elephant has tactile sensitivity in the trunk, it is questionable as to whether what has been observed in elephants is indeed "truly extraordinary".

But let's look more specifically at the justification outlined in the current study to support their identification of the unusually located trigeminal sensory nuclei of the brainstem.

(1) Intense cytochrome oxidase reactivity<br /> (2) Large size of the putative trunk module<br /> (3) Elongation of the putative trunk module<br /> (4) Arrangement of these putative modules correspond to elephant head anatomy<br /> (5) Myelin stripes within the putative trunk module that apparently match trunk folds<br /> (6) Location apparently matches other mammals<br /> (7) Repetitive modular organization apparently similar to other mammals.<br /> (8) The inferior olive described by other authors lacks the lamellated appearance of this structure in other mammals

Let's examine these justifications more closely.

(1) Cytochrome oxidase histochemistry is typically used as an indicative marker of neuronal energy metabolism. The authors indicate, based on the "truly extraordinary" somatosensory capacities of the elephant trunk, that any nuclei processing this tactile information should be highly metabolically active, and thus should react intensely when stained for cytochrome oxidase. We are told in the methods section that the protocols used are described by Purkart et al (2022) and Kaufmann et al (2022). In neither of these cited papers is there any description, nor mention, of the cytochrome oxidase histochemistry methodology, thus we have no idea of how this histochemical staining was done. In order to obtain the best results for cytochrome oxidase histochemistry, the tissue is either processed very rapidly after buffer perfusion to remove blood or in recently perfusion-fixed tissue (e.g., 10.1016/0165-0270(93)90122-8). Given: (1) the presumably long post-mortem interval between death and fixation - "it often takes days to dissect elephants"; (2) subsequent fixation of the brains in 4% paraformaldehyde for "several weeks"; (3) The intense cytochrome oxidase reactivity in the inferior olivary complex of the laboratory rat (Gonzalez-Lima, 1998, Cytochrome oxidase in neuronal metabolism and Alzheimer's diseases); and (4) The lack of any comparative images from other stained portions of the elephant brainstem; it is difficult to support the justification as forwarded by the authors. It is likely that the histochemical staining observed is background reactivity from the use of diaminobenzidine in the staining protocol. Thus, this first justification is unsupported.<br /> Justifications (2), (3), and (4) are sequelae from justification (1). In this sense, they do not count as justifications, but rather unsupported extensions.

(4) and (5) These are interesting justifications, as the paper has clear internal contradictions, and (5) is a sequelae of (4). The reader is led to the concept that the myelin tracts divide the nuclei into sub-modules that match the folding of the skin on the elephant trunk. One would then readily presume that these myelin tracts are in the incoming sensory axons from the trigeminal nerve. However, the authors note that this is not the case: "Our observations on trunk module myelin stripes are at odds with this view of myelin. Specifically, myelin stripes show no tapering (which we would expect if axons divert off into the tissue). More than that, there is no correlation between myelin stripe thickness (which presumably correlates with axon numbers) and trigeminal module neuron numbers. Thus, there are numerous myelinated axons, where we observe few or no trigeminal neurons. These observations are incompatible with the idea that myelin stripes form an axonal 'supply' system or that their prime function is to connect neurons. What do myelin stripe axons do, if they do not connect neurons? We suggest that myelin stripes serve to separate rather than connect neurons." So, we are left with the observation that the myelin stripes do not pass afferent trigeminal sensory information from the "truly extraordinary" trunk skin somatic sensory system, and rather function as units that separate neurons - but to what end? It appears that the myelin stripes are more likely to be efferent axonal bundles leaving the nuclei (to form the olivocerebellar tract). This justification is unsupported.

(6) The authors indicate that the location of these nuclei matches that of the trigeminal nuclei in other mammals. This is not supported in any way. In ALL other mammals in which the trigeminal nuclei of the brainstem have been reported they are found in the lateral aspect of the brainstem, bordered laterally by the spinal trigeminal tract. This is most readily seen and accessible in the Paxinos and Watson rat brain atlases. The authors indicate that the trigeminal nuclei are medial to the facial nerve nucleus, but in every other species, the trigeminal sensory nuclei are found lateral to the facial nerve nucleus. This is most salient when examining a close relative, the manatee (10.1002/ar.20573), where the location of the inferior olive and the trigeminal nuclei matches that described by Maseko et al (2013) for the African elephant. This justification is not supported.

(7) The dual to quadruple repetition of rostro-caudal modules within the putative trigeminal nucleus as identified by the authors relies on the fact that in the neurotypical mammal, there are several trigeminal sensory nuclei arranged in a column running from the pons to the cervical spinal cord, these include (nomenclature from Paxinos and Watson in roughly rostral to caudal order) the Pr5VL, Pr5DM, Sp5O, Sp5I, and Sp5C. But, these nuclei are all located far from the midline and lateral to the facial nerve nucleus, unlike what the authors describe in the elephants. These rostrocaudal modules are expanded upon in Figure 2, and it is apparent from what is shown that the authors are attributing other brainstem nuclei to the putative trigeminal nuclei to confirm their conclusion. For example, what they identify as the inferior olive in figure 2D is likely the lateral reticular nucleus as identified by Maseko et al (2013). This justification is not supported.

(8) In primates and related species, there is a distinct banded appearance of the inferior olive, but what has been termed the inferior olive in the elephant by other authors does not have this appearance, rather, and specifically, the largest nuclear mass in the region (termed the principal nucleus of the inferior olive by Maseko et al, 2013, but Pr5, the principal trigeminal nucleus in the current paper) overshadows the partial banded appearance of the remaining nuclei in the region (but also drawn by the authors of the current paper). Thus, what is at debate here is whether the principal nucleus of the inferior olive can take on a nuclear shape rather than evince a banded appearance. The authors of this paper use this variance as justification that this cluster of nuclei could not possibly be the inferior olive. Such a "semi-nuclear/banded" arrangement of the inferior olive is seen in, for example, giraffe (10.1016/j.jchemneu.2007.05.003), domestic dog, polar bear, and most specifically the manatee (a close relative of the elephant) (brainmuseum.org; 10.1002/ar.20573). This justification is not supported.

Thus, all the justifications forwarded by the authors are unsupported. Based on methodological concerns, prior comparative mammalian neuroanatomy, and prior studies in the elephant and closely related species, the authors fail to support their notion that what was previously termed the inferior olive in the elephant is actually the trigeminal sensory nuclei. Given this failure, the justifications provided above that are sequelae also fail. In this sense, the entire manuscript and all the sequelae are not supported.

What the authors have not done is to trace the pathway of the large trigeminal nerve in the elephant brainstem, as was done by Maseko et al (2013), which clearly shows the internal pathways of this nerve, from the branch that leads to the fifth mesencephalic nucleus adjacent to the periventricular grey matter, through to the spinal trigeminal tract that extends from the pons to the spinal cord in a manner very similar to all other mammals. Nor have they shown how the supposed trigeminal information reaches the putative trigeminal nuclei in the ventromedial rostral medulla oblongata. These are but two examples of many specific lines of evidence that would be required to support their conclusions. Clearly tract tracing methods, such as cholera toxin tracing of peripheral nerves cannot be done in elephants, thus the neuroanatomy must be done properly and with attention to detail to support the major changes indicated by the authors.

So what are these "bumps" in the elephant brainstem?

Four previous authors indicate that these bumps are the inferior olivary nuclear complex. Can this be supported?

The inferior olivary nuclear complex acts "as a relay station between the spinal cord (n.b. trigeminal input does reach the spinal cord via the spinal trigeminal tract) and the cerebellum, integrating motor and sensory information to provide feedback and training to cerebellar neurons" (https://www.ncbi.nlm.nih.gov/books/NBK542242/). The inferior olivary nuclear complex is located dorsal and medial to the pyramidal tracts (which were not labelled in the current study by the authors but are clearly present in Fig. 1C and 2A) in the ventromedial aspect of the rostral medulla oblongata. This is precisely where previous authors have identified the inferior olivary nuclear complex and what the current authors assign to their putative trigeminal nuclei. The neurons of the inferior olivary nuclei project, via the olivocerebellar tract to the cerebellum to terminate in the climbing fibres of the cerebellar cortex.

Elephants have the largest (relative and absolute) cerebellum of all mammals (10.1002/ar.22425), this cerebellum contains 257 x109 neurons (10.3389/fnana.2014.00046; three times more than the entire human brain, 10.3389/neuro.09.031.2009). Each of these neurons appears to be more structurally complex than the homologous neurons in other mammals (10.1159/000345565; 10.1007/s00429-010-0288-3). In the African elephant, the neurons of the inferior olivary nuclear complex are described by Maseko et al (2013) as being both calbindin and calretinin immunoreactive. Climbing fibres in the cerebellar cortex of the African elephant are clearly calretinin immunopositive and also are likely to contain calbindin (10.1159/000345565). Given this, would it be surprising that the inferior olivary nuclear complex of the elephant is enlarged enough to create a very distinct bump in exactly the same place where these nuclei are identified in other mammals?

What about the myelin stripes? These are most likely to be the origin of the olivocerebellar tract and probably only have a coincidental relationship to the trunk. Thus, given what we know, the inferior olivary nuclear complex as described in other studies, and the putative trigeminal nuclear complex as described in the current study, is the elephant inferior olivary nuclear complex. It is not what the authors believe it to be, and they do not provide any evidence that discounts the previous studies. The authors are quite simply put, wrong. All the speculations that flow from this major neuroanatomical error are therefore science fiction rather than useful additions to the scientific literature.

What do the authors actually have?<br /> The authors have interesting data, based on their Golgi staining and analysis, of the inferior olivary nuclear complex in the elephant.

* Review of Revised Manuscript *

Assessment:

There is a clear dichotomy between the authors and this reviewer regarding the identification of specific structures, namely the inferior olivary nuclear complex and the trigeminal nuclear complex, in the brainstem of the elephant. The authors maintain the position that in the elephant alone, irrespective of all the published data on other mammals and previously published data on the elephant brainstem, these two nuclear complexes are switched in location. The authors maintain that their interpretation is correct, but this reviewer maintains that this interpretation is erroneous. The authors expressed concern that the remainder of the paper was not addressed by the reviewer, but the reviewer maintains that these sequelae to the misidentification of nuclear complexes in the elephant brainstem render any of these speculations irrelevant as the critical structures are incorrectly identified. It is this reviewer's opinion that this paper is incorrect. I provide a lot of detail below in order to provide support to the opinion I express.

Public Review of Current Submission:

As indicated in my previous review of this manuscript (see above), it is my opinion that the authors have misidentified, and indeed switched, the inferior olivary nuclear complex (IO) and the trigeminal nuclear complex (Vsens). It is this specific point only that I will address in this second review, as this is the crucial aspect of this paper - if the identification of these nuclear complexes in the elephant brainstem by the authors is incorrect, the remainder of the paper does not have any scientific validity.

The authors, in their response to my initial review, claim that I "bend" the comparative evidence against them. They further claim that as all other mammalian species exhibit a "serrated" appearance of the inferior olive, and as the elephant does not exhibit this appearance, what was previously identified as the inferior olive is actually the trigeminal nucleus and vice versa.

For convenience, I will refer to IOM and VsensM as the identification of these structures according to Maseko et al (2013) and other authors and will use IOR and VsensR to refer to the identification forwarded in the study under review.<br /> The IOM/VsensR certainly does not have a serrated appearance in elephants. Indeed, from the plates supplied by the authors in response (Referee Fig. 2), the cytochrome oxidase image supplied and the image from Maseko et al (2013) shows a very similar appearance. There is no doubt that the authors are identifying structures that closely correspond to those provided by Maseko et al (2013). It is solely a contrast in what these nuclear complexes are called and the functional sequelae of the identification of these complexes (are they related to the trunk sensation or movement controlled by the cerebellum?) that is under debate.

Elephants are part of the Afrotheria, thus the most relevant comparative data to resolve this issue will be the identification of these nuclei in other Afrotherian species. Below I provide images of these nuclear complexes, labelled in the standard nomenclature, across several Afrotherian species.

(A) Lesser hedgehog tenrec (Echinops telfairi)

Tenrecs brains are the most intensively studied of the Afrotherian brains, these extensive neuroanatomical studies were undertaken primarily by Heinz Künzle. Below I append images (coronal sections stained with cresol violet) of the IO and Vsens (labelled in the standard mammalian manner) in the lesser hedgehog tenrec. It should be clear that the inferior olive is located in the ventral midline of the rostral medulla oblongata (just like the rat) and that this nucleus is not distinctly serrated. The Vsens is located in the lateral aspect of the medulla skirted laterally by the spinal trigeminal tract (Sp5). These images and the labels indicating structures correlate precisely with that provided by Künzle (1997, 10.1016/S0168- 0102(97)00034-5), see his Figure 1K,L. Thus, in the first case of a related species, there is no serrated appearance of the inferior olive, the location of the inferior olive is confirmed through connectivity with the superior colliculus (a standard connection in mammals) by Künzle (1997), and the location of Vsens is what is considered to be typical for mammals. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Review image 1.

(B) Giant otter shrew (Potomogale velox)

The otter shrews are close relatives of the Tenrecs. Below I append images of cresyl violet (left column) and myelin (right column) stained coronal sections through the brainstem with the IO, Vsens and Sp5 labelled as per standard mammalian anatomy. Here we see hints of the serration of the IO as defined by the authors, but we also see many myelin stripes across the IO. Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Review image 2.

(C) Four-toed sengi (Petrodromus tetradactylus)

The sengis are close relatives of the Tenrecs and otter shrews, these three groups being part of the Afroinsectiphilia, a distinct branch of the Afrotheria. Below I append images of cresyl violet (left column) and myelin (right column) stained coronal sections through the brainstem with the IO, Vsens and Sp5 labelled as per standard mammalian anatomy. Here we see vague hints of the serration of the IO (as defined by the authors), and we also see many myelin stripes across the IO. Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Review image 3.

(D) Rock hyrax (Procavia capensis)

The hyraxes, along with the sirens and elephants form the Paenungulata branch of the Afrotheria. Below I append images of cresyl violet (left column) and myelin (right column) stained coronal sections through the brainstem with the IO, Vsens and Sp5 labelled as per the standard mammalian anatomy. Here we see hints of the serration of the IO (as defined by the authors), but we also see evidence of a more "bulbous" appearance of subnuclei of the IO (particularly the principal nucleus), and we also see many myelin stripes across the IO. Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Review image 4.

(E) West Indian manatee (Trichechus manatus)

The sirens are the closest extant relatives of the elephants in the Afrotheria. Below I append images of cresyl violet (top) and myelin (bottom) stained coronal sections (taken from the University of Wisconsin-Madison Brain Collection, https://brainmuseum.org, and while quite low in magnification they do reveal the structures under debate) through the brainstem with the IO, Vsens and Sp5 labelled as per standard mammalian anatomy. Here we see the serration of the IO (as defined by the authors). Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Review image 5.

These comparisons and the structural identification, with which the authors agree as they only distinguish the elephants from the other Afrotheria, demonstrate that the appearance of the IO can be quite variable across mammalian species, including those with a close phylogenetic affinity to the elephants. Not all mammal species possess a "serrated" appearance of the IO. Thus, it is more than just theoretically possible that the IO of the elephant appears as described prior to this study.

So what about elephants? Below I append a series of images from coronal sections through the African elephant brainstem stained for Nissl, myelin, and immunostained for calretinin. These sections are labelled according to standard mammalian nomenclature. In these complete sections of the elephant brainstem, we do not see a serrated appearance of the IOM (as described previously and in the current study by the authors). Rather the principal nucleus of the IOM appears to be bulbous in nature. In the current study, no image of myelin staining in the IOM/VsensR is provided by the authors. However, in the images I provide, we do see the reported myelin stripes in all stains - agreement between the authors and reviewer on this point. The higher magnification image to the bottom left of the plate shows one of the IOM/VsensR myelin stripes immunostained for calretinin, and within the myelin stripes axons immunopositive for calretinin are seen (labelled with an arrow). The climbing fibres of the elephant cerebellar cortex are similarly calretinin immunopositive (10.1159/000345565). In contrast, although not shown at high magnification, the fibres forming the Sp5 in the elephant (in the Maseko description, unnamed in the description of the authors) show no immunoreactivity to calretinin.

Review image 6.

Peripherin Immunostaining

In their revised manuscript the authors present immunostaining of peripherin in the elephant brainstem. This is an important addition (although it does replace the only staining of myelin provided by the authors which is unusual as the word myelin is in the title of the paper) as peripherin is known to specifically label peripheral nerves. In addition, as pointed out by the authors, peripherin also immunostains climbing fibres (Errante et al., 1998). The understanding of this staining is important in determining the identification of the IO and Vsens in the elephant, although it is not ideal for this task as there is some ambiguity. Errante and colleagues (1998; Fig. 1) show that climbing fibres are peripherin-immunopositive in the rat. But what the authors do not evaluate is the extensive peripherin staining in the rat Sp5 in the same paper (Errante et al, 1998, Fig. 2). The image provided by the authors of their peripherin immunostaining (their new Figure 2) shows what I would call the Sp5 of the elephant to be strongly peripherin immunoreactive, just like the rat shown in Errant et al (1998), and moreover in the precise position of the rat Sp5! This makes sense as this is where the axons subserving the "extraordinary" tactile sensitivity of the elephant trunk would be found (in the standard model of mammalian brainstem anatomy). Interestingly, the peripherin immunostaining in the elephant is clearly lamellated...this coincides precisely with the description of the trigeminal sensory nuclei in the elephant by Maskeo et al (2013) as pointed out by the authors in their rebuttal. Errante et al (1998) also point out peripherin immunostaining in the inferior olive, but according to the authors this is only "weakly present" in the elephant IOM/VsensR. This latter point is crucial. Surely if the elephant has an extraordinary sensory innervation from the trunk, with 400,000 axons entering the brain, the VsensR/IOM should be highly peripherin-immunopositive, including the myelinated axon bundles?! In this sense, the authors argue against their own interpretation - either the elephant trunk is not a highly sensitive tactile organ, or the VsensR is not the trigeminal nuclei it is supposed to be.

Summary:

(1) Comparative data of species closely related to elephants (Afrotherians) demonstrates that not all mammals exhibit the "serrated" appearance of the principal nucleus of the inferior olive.

(2) The location of the IO and Vsens as reported in the current study (IOR and VsensR) would require a significant, and unprecedented, rearrangement of the brainstem in the elephants independently. I argue that the underlying molecular and genetic changes required to achieve this would be so extreme that it would lead to lethal phenotypes. Arguing that the "switcheroo" of the IO and Vsens does occur in the elephant (and no other mammals) and thus doesn't lead to lethal phenotypes is a circular argument that cannot be substantiated.

(3) Myelin stripes in the subnuclei of the inferior olivary nuclear complex are seen across all related mammals as shown above. Thus, the observation made in the elephant by the authors in what they call the VsensR, is similar to that seen in the IO of related mammals, especially when the IO takes on a more bulbous appearance. These myelin stripes are the origin of the olivocerebellar pathway and are indeed calretinin immunopositive in the elephant as I show.

(4) What the authors see aligns perfectly with what has been described previously, the only difference being the names that nuclear complexes are being called. But identifying these nuclei is important, as any functional sequelae, as extensively discussed by the authors, is entirely dependent upon accurately identifying these nuclei.

(4) The peripherin immunostaining scores an own goal - if peripherin is marking peripheral nerves (as the authors and I believe it is), then why is the VsensR/IOM only "weakly positive" for this stain? This either means that the "extraordinary" tactile sensitivity of the elephant trunk is non-existent, or that the authors have misinterpreted this staining. That there is extensive staining in the fibre pathway dorsal and lateral to the IOR (which I call the spinal trigeminal tract), supports the idea that the authors have misinterpreted their peripherin immunostaining.

(5) Evolutionary expediency. The authors argue that what they report is an expedient way in which to modify the organisation of the brainstem in the elephant to accommodate the "extraordinary" tactile sensitivity. I disagree. As pointed out in my first review, the elephant cerebellum is very large and comprised of huge numbers of morphologically complex neurons. The inferior olivary nuclei in all mammals studied in detail to date, give rise to the climbing fibres that terminate on the Purkinje cells of the cerebellar cortex. It is more parsimonious to argue that, in alignment with the expansion of the elephant cerebellum (for motor control of the trunk), the inferior olivary nuclei (specifically the principal nucleus) have had additional neurons added to accommodate this cerebellar expansion. Such an addition of neurons to the principal nucleus of the inferior olive could readily lead to the loss of the serrated appearance of the principal nucleus of the inferior olive and would require far less modifications in the developmental genetic program that forms these nuclei. This type of quantitative change appears to be the primary way in which structures are altered in the mammalian brainstem.

Reviewer #2 (Public Review):

Here I submit my previous review and a great deal of additional information following on from the initial review and the response by the authors.

* Initial Review *

Assessment:

This manuscript is based upon the unprecedented identification of an apparently highly unusual trigeminal nuclear organization within the elephant brainstem, related to a large trigeminal nerve in these animals. The apparently highly specialized elephant trigeminal nuclear complex identified in the current study has been classified as the inferior olivary nuclear complex in four previous studies of the elephant brainstem. The entire study is predicated upon the correct identification of the trigeminal sensory nuclear complex and the inferior olivary nuclear complex in the elephant, and if this is incorrect, then the remainder of the manuscript is merely unsupported speculation. There are many reasons indicating that the trigeminal nuclear complex is misidentified in the current study, rendering the entire study, and associated speculation, inadequate at best, and damaging in terms of understanding elephant brains and behaviour at worst.

Original Public Review:

The authors describe what they assert to be a very unusual trigeminal nuclear complex in the brainstem of elephants, and based on this, follow with many speculations about how the trigeminal nuclear complex, as identified by them, might be organized in terms of the sensory capacity of the elephant trunk.<br /> The identification of the trigeminal nuclear complex/inferior olivary nuclear complex in the elephant brainstem is the central pillar of this manuscript from which everything else follows, and if this is incorrect, then the entire manuscript fails, and all the associated speculations become completely unsupported.

The authors note that what they identify as the trigeminal nuclear complex has been identified as the inferior olivary nuclear complex by other authors, citing Shoshani et al. (2006; 10.1016/j.brainresbull.2006.03.016) and Maseko et al (2013; 10.1159/000352004), but fail to cite either Verhaart and Kramer (1958; PMID 13841799) or Verhaart (1962; 10.1515/9783112519882-001). These four studies are in agreement, the current study differs.

Let's assume for the moment that the four previous studies are all incorrect and the current study is correct. This would mean that the entire architecture and organization of the elephant brainstem is significantly rearranged in comparison to ALL other mammals, including humans, previously studied (e.g. Kappers et al. 1965, The Comparative Anatomy of the Nervous System of Vertebrates, Including Man, Volume 1 pp. 668-695) and the closely related manatee (10.1002/ar.20573). This rearrangement necessitates that the trigeminal nuclei would have had to "migrate" and shorten rostrocaudally, specifically and only, from the lateral aspect of the brainstem where these nuclei extend from the pons through to the cervical spinal cord (e.g. the Paxinos and Watson rat brain atlases), the to the spatially restricted ventromedial region of specifically and only the rostral medulla oblongata. According to the current paper the inferior olivary complex of the elephant is very small and located lateral to their trigeminal nuclear complex, and the region from where the trigeminal nuclei are located by others, appears to be just "lateral nuclei" with no suggestion of what might be there instead.

Such an extraordinary rearrangement of brainstem nuclei would require a major transformation in the manner in which the mutations, patterning, and expression of genes and associated molecules during development occurs. Such a major change is likely to lead to lethal phenotypes, making such a transformation extremely unlikely. Variations in mammalian brainstem anatomy are most commonly associated with quantitative changes rather than qualitative changes (10.1016/B978-0-12-804042-3.00045-2).

The impetus for the identification of the unusual brainstem trigeminal nuclei in the current study rests upon a previous study from the same laboratory (10.1016/j.cub.2021.12.051) that estimated that the number of axons contained in the infraorbital branch of the trigeminal nerve that innervate the sensory surfaces of the trunk is approximately 400 000. Is this number unusual? In a much smaller mammal with a highly specialized trigeminal system, the platypus, the number of axons innervating the sensory surface of the platypus bill skin comes to 1 344 000 (10.1159/000113185). Yet, there is no complex rearrangement of the brainstem trigeminal nuclei in the brain of the developing or adult platypus (Ashwell, 2013, Neurobiology of Monotremes), despite the brainstem trigeminal nuclei being very large in the platypus (10.1159/000067195). Even in other large-brained mammals, such as large whales that do not have a trunk, the number of axons in the trigeminal nerve ranges between 400 000 and 500 000 (10.1007/978-3-319-47829-6_988-1). The lack of comparative support for the argument forwarded in the previous and current study from this laboratory, and that the comparative data indicates that the brainstem nuclei do not change in the manner suggested in the elephant, argues against the identification of the trigeminal nuclei as outlined in the current study. Moreover, the comparative studies undermine the prior claim of the authors, informing the current study, that "the elephant trigeminal ganglion ... point to a high degree of tactile specialization in elephants" (10.1016/j.cub.2021.12.051). While clearly the elephant has tactile sensitivity in the trunk, it is questionable as to whether what has been observed in elephants is indeed "truly extraordinary".

But let's look more specifically at the justification outlined in the current study to support their identification of the unusual located trigeminal sensory nuclei of the brainstem.

(1) Intense cytochrome oxidase reactivity<br /> (2) Large size of the putative trunk module<br /> (3) Elongation of the putative trunk module<br /> (4) Arrangement of these putative modules correspond to elephant head anatomy<br /> (5) Myelin stripes within the putative trunk module that apparently match trunk folds<br /> (6) Location apparently matches other mammals<br /> (7) Repetitive modular organization apparently similar to other mammals.<br /> (8) The inferior olive described by other authors lacks the lamellated appearance of this structure in other mammals

Let's examine these justifications more closely.

(1) Cytochrome oxidase histochemistry is typically used as an indicative marker of neuronal energy metabolism. The authors indicate, based on the "truly extraordinary" somatosensory capacities of the elephant trunk, that any nuclei processing this tactile information should be highly metabolically active, and thus should react intensely when stained for cytochrome oxidase. We are told in the methods section that the protocols used are described by Purkart et al (2022) and Kaufmann et al (2022). In neither of these cited papers is there any description, nor mention, of the cytochrome oxidase histochemistry methodology, thus we have no idea of how this histochemical staining was done. In order to obtain the best results for cytochrome oxidase histochemistry, the tissue is either processed very rapidly after buffer perfusion to remove blood or in recently perfusion-fixed tissue (e.g., 10.1016/0165-0270(93)90122-8). Given: (1) the presumably long post-mortem interval between death and fixation - "it often takes days to dissect elephants"; (2) subsequent fixation of the brains in 4% paraformaldehyde for "several weeks"; (3) The intense cytochrome oxidase reactivity in the inferior olivary complex of the laboratory rat (Gonzalez-Lima, 1998, Cytochrome oxidase in neuronal metabolism and Alzheimer's diseases); and (4) The lack of any comparative images from other stained portions of the elephant brainstem; it is difficult to support the justification as forwarded by the authors. It is likely that the histochemical staining observed is background reactivity from the use of diaminobenzidine in the staining protocol. Thus, this first justification is unsupported.<br /> Justifications (2), (3), and (4) are sequelae from justification (1). In this sense, they do not count as justifications, but rather unsupported extensions.

(4) and (5) These are interesting justifications, as the paper has clear internal contradictions, and (5) is a sequelae of (4). The reader is led to the concept that the myelin tracts divide the nuclei into sub-modules that match the folding of the skin on the elephant trunk. One would then readily presume that these myelin tracts are in the incoming sensory axons from the trigeminal nerve. However, the authors note that this is not the case: "Our observations on trunk module myelin stripes are at odds with this view of myelin. Specifically, myelin stripes show no tapering (which we would expect if axons divert off into the tissue). More than that, there is no correlation between myelin stripe thickness (which presumably correlates with axon numbers) and trigeminal module neuron numbers. Thus, there are numerous myelinated axons, where we observe few or no trigeminal neurons. These observations are incompatible with the idea that myelin stripes form an axonal 'supply' system or that their prime function is to connect neurons. What do myelin stripe axons do, if they do not connect neurons? We suggest that myelin stripes serve to separate rather than connect neurons." So, we are left with the observation that the myelin stripes do not pass afferent trigeminal sensory information from the "truly extraordinary" trunk skin somatic sensory system, and rather function as units that separate neurons - but to what end? It appears that the myelin stripes are more likely to be efferent axonal bundles leaving the nuclei (to form the olivocerebellar tract). This justification is unsupported.

(6) The authors indicate that the location of these nuclei matches that of the trigeminal nuclei in other mammals. This is not supported in any way. In ALL other mammals in which the trigeminal nuclei of the brainstem have been reported they are found in the lateral aspect of the brainstem, bordered laterally by the spinal trigeminal tract. This is most readily seen and accessible in the Paxinos and Watson rat brain atlases. The authors indicate that the trigeminal nuclei are medial to the facial nerve nucleus, but in every other species the trigeminal sensory nuclei are found lateral to the facial nerve nucleus. This is most salient when examining a close relative, the manatee (10.1002/ar.20573), where the location of the inferior olive and the trigeminal nuclei matches that described by Maseko et al (2013) for the African elephant. This justification is not supported.

(7) The dual to quadruple repetition of rostro-caudal modules within the putative trigeminal nucleus as identified by the authors relies on the fact that in the neurotypical mammal, there are several trigeminal sensory nuclei arranged in a column running from the pons to the cervical spinal cord, these include (nomenclature from Paxinos and Watson in roughly rostral to caudal order) the Pr5VL, Pr5DM, Sp5O, Sp5I, and Sp5C. But, these nuclei are all located far from the midline and lateral to the facial nerve nucleus, unlike what the authors describe in the elephants. These rostrocaudal modules are expanded upon in Figure 2, and it is apparent from what is shown is that the authors are attributing other brainstem nuclei to the putative trigeminal nuclei to confirm their conclusion. For example, what they identify as the inferior olive in figure 2D is likely the lateral reticular nucleus as identified by Maseko et al (2013). This justification is not supported.

(8) In primates and related species, there is a distinct banded appearance of the inferior olive, but what has been termed the inferior olive in the elephant by other authors does not have this appearance, rather, and specifically, the largest nuclear mass in the region (termed the principal nucleus of the inferior olive by Maseko et al, 2013, but Pr5, the principal trigeminal nucleus in the current paper) overshadows the partial banded appearance of the remaining nuclei in the region (but also drawn by the authors of the current paper). Thus, what is at debate here is whether the principal nucleus of the inferior olive can take on a nuclear shape rather than evince a banded appearance. The authors of this paper use this variance as justification that this cluster of nuclei could not possibly be the inferior olive. Such a "semi-nuclear/banded" arrangement of the inferior olive is seen in, for example, giraffe (10.1016/j.jchemneu.2007.05.003), domestic dog, polar bear, and most specifically the manatee (a close relative of the elephant) (brainmuseum.org; 10.1002/ar.20573). This justification is not supported.

Thus, all the justifications forwarded by the authors are unsupported. Based on methodological concerns, prior comparative mammalian neuroanatomy, and prior studies in the elephant and closely related species, the authors fail to support their notion that what was previously termed the inferior olive in the elephant is actually the trigeminal sensory nuclei. Given this failure, the justifications provided above that are sequelae also fail. In this sense, the entire manuscript and all the sequelae are not supported.

What the authors have not done is to trace the pathway of the large trigeminal nerve in the elephant brainstem, as was done by Maseko et al (2013), which clearly shows the internal pathways of this nerve, from the branch that leads to the fifth mesencephalic nucleus adjacent to the periventricular grey matter, through to the spinal trigeminal tract that extends from the pons to the spinal cord in a manner very similar to all other mammals. Nor have they shown how the supposed trigeminal information reaches the putative trigeminal nuclei in the ventromedial rostral medulla oblongata. These are but two examples of many specific lines of evidence that would be required to support their conclusions. Clearly tract tracing methods, such as cholera toxin tracing of peripheral nerves cannot be done in elephants, thus the neuroanatomy must be done properly and with attention to details to support the major changes indicated by the authors.

So what are these "bumps" in the elephant brainstem?

Four previous authors indicate that these bumps are the inferior olivary nuclear complex. Can this be supported?

The inferior olivary nuclear complex acts "as a relay station between the spinal cord (n.b. trigeminal input does reach the spinal cord via the spinal trigeminal tract) and the cerebellum, integrating motor and sensory information to provide feedback and training to cerebellar neurons" (https://www.ncbi.nlm.nih.gov/books/NBK542242/). The inferior olivary nuclear complex is located dorsal and medial to the pyramidal tracts (which were not labelled in the current study by the authors but are clearly present in Fig. 1C and 2A) in the ventromedial aspect of the rostral medulla oblongata. This is precisely where previous authors have identified the inferior olivary nuclear complex and what the current authors assign to their putative trigeminal nuclei. The neurons of the inferior olivary nuclei project, via the olivocerebellar tract to the cerebellum to terminate in the climbing fibres of the cerebellar cortex.

Elephants have the largest (relative and absolute) cerebellum of all mammals (10.1002/ar.22425), this cerebellum contains 257 x109 neurons (10.3389/fnana.2014.00046; three times more than the entire human brain, 10.3389/neuro.09.031.2009). Each of these neurons appears to be more structurally complex than the homologous neurons in other mammals (10.1159/000345565; 10.1007/s00429-010-0288-3). In the African elephant, the neurons of the inferior olivary nuclear complex are described by Maseko et al (2013) as being both calbindin and calretinin immunoreactive. Climbing fibres in the cerebellar cortex of the African elephant are clearly calretinin immunopositive and also are likely to contain calbindin (10.1159/000345565). Given this, would it be surprising that the inferior olivary nuclear complex of the elephant is enlarged enough to create a very distinct bump in exactly the same place where these nuclei are identified in other mammals?

What about the myelin stripes? These are most likely to be the origin of the olivocerebellar tract and probably only have a coincidental relationship to the trunk. Thus, given what we know, the inferior olivary nuclear complex as described in other studies, and the putative trigeminal nuclear complex as described in the current study, is the elephant inferior olivary nuclear complex. It is not what the authors believe it to be, and they do not provide any evidence that discounts the previous studies. The authors are quite simply put, wrong. All the speculations that flow from this major neuroanatomical error are therefore science fiction rather than useful additions to the scientific literature.

What do the authors actually have?<br /> The authors have interesting data, based on their Golgi staining and analysis, of the inferior olivary nuclear complex in the elephant.

* Review of Revised Manuscript *

Assessment:

There is a clear dichotomy between the authors and this reviewer regarding the identification of specific structures, namely the inferior olivary nuclear complex and the trigeminal nuclear complex, in the brainstem of the elephant. The authors maintain the position that in the elephant alone, irrespective of all the published data on other mammals and previously published data on the elephant brainstem, these two nuclear complexes are switched in location. The authors maintain that their interpretation is correct, this reviewer maintains that this interpretation is erroneous. The authors expressed concern that the remainder of the paper was not addressed by the reviewer, but the reviewer maintains that these sequelae to the misidentification of nuclear complexes in the elephant brainstem renders any of these speculations irrelevant as the critical structures are incorrectly identified. It is this reviewer's opinion that this paper is incorrect. I provide a lot of detail below in order to provide support to the opinion I express.

Public Review of Current Submission:

As indicated in my previous review of this manuscript (see above), it is my opinion that the authors have misidentified, and indeed switched, the inferior olivary nuclear complex (IO) and the trigeminal nuclear complex (Vsens). It is this specific point only that I will address in this second review, as this is the crucial aspect of this paper - if the identification of these nuclear complexes in the elephant brainstem by the authors is incorrect, the remainder of the paper does not have any scientific validity.

The authors, in their response to my initial review, claim that I "bend" the comparative evidence against them. They further claim that as all other mammalian species exhibit a "serrated" appearance of the inferior olive, and as the elephant does not exhibit this appearance, that what was previously identified as the inferior olive is actually the trigeminal nucleus and vice versa.

For convenience, I will refer to IOM and VsensM as the identification of these structures according to Maseko et al (2013) and other authors and will use IOR and VsensR to refer to the identification forwarded in the study under review.<br /> The IOM/VsensR certainly does not have a serrated appearance in elephants. Indeed, from the plates supplied by the authors in response (Referee Fig. 2), the cytochrome oxidase image supplied and the image from Maseko et al (2013) shows a very similar appearance. There is no doubt that the authors are identifying structures that closely correspond to those provided by Maseko et al (2013). It is solely a contrast in what these nuclear complexes are called and the functional sequelae of the identification of these complexes (are they related to the trunk sensation or movement controlled by the cerebellum?) that is under debate.

Elephants are part of the Afrotheria, thus the most relevant comparative data to resolve this issue will be the identification of these nuclei in other Afrotherian species. Below I provide images of these nuclear complexes, labelled in the standard nomenclature, across several Afrotherian species.

(A) Lesser hedgehog tenrec (Echinops telfairi)

Tenrecs brains are the most intensively studied of the Afrotherian brains, these extensive neuroanatomical studies undertaken primarily by Heinz Künzle. Below I append images (coronal sections stained with cresol violet) of the IO and Vsens (labelled in the standard mammalian manner) in the lesser hedgehog tenrec. It should be clear that the inferior olive is located in the ventral midline of the rostral medulla oblongata (just like the rat) and that this nucleus is not distinctly serrated. The Vsens is located in the lateral aspect of the medulla skirted laterally by the spinal trigeminal tract (Sp5). These images and the labels indicating structures correlate precisely with that provide by Künzle (1997, 10.1016/S0168- 0102(97)00034-5), see his Figure 1K,L. Thus, in the first case of a related species, there is no serrated appearance of the inferior olive, the location of the inferior olive is confirmed through connectivity with the superior colliculus (a standard connection in mammals) by Künzle (1997), and the location of Vsens is what is considered to be typical for mammals. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Review image 1.

(B) Giant otter shrew (Potomogale velox)

The otter shrews are close relatives of the Tenrecs. Below I append images of cresyl violet (left column) and myelin (right column) stained coronal sections through the brainstem with the IO, Vsens and Sp5 labelled as per standard mammalian anatomy. Here we see hints of the serration of the IO as defined by the authors, but we also see many myelin stripes across the IO. Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Review image 2.

(C) Four-toed sengi (Petrodromus tetradactylus)

The sengis are close relatives of the Tenrecs and otter shrews, these three groups being part of the Afroinsectiphilia, a distinct branch of the Afrotheria. Below I append images of cresyl violet (left column) and myelin (right column) stained coronal sections through the brainstem with the IO, Vsens and Sp5 labelled as per standard mammalian anatomy. Here we see vague hints of the serration of the IO (as defined by the authors), and we also see many myelin stripes across the IO. Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Review image 3.

(D) Rock hyrax (Procavia capensis)

The hyraxes, along with the sirens and elephants form the Paenungulata branch of the Afrotheria. Below I append images of cresyl violet (left column) and myelin (right column) stained coronal sections through the brainstem with the IO, Vsens and Sp5 labelled as per the standard mammalian anatomy. Here we see hints of the serration of the IO (as defined by the authors), but we also see evidence of a more "bulbous" appearance of subnuclei of the IO (particularly the principal nucleus), and we also see many myelin stripes across the IO. Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Review image 4.

(E) West Indian manatee (Trichechus manatus)

The sirens are the closest extant relatives of the elephants in the Afrotheria. Below I append images of cresyl violet (top) and myelin (bottom) stained coronal sections (taken from the University of Wisconsin-Madison Brain Collection, https://brainmuseum.org, and while quite low in magnification they do reveal the structures under debate) through the brainstem with the IO, Vsens and Sp5 labelled as per standard mammalian anatomy. Here we see the serration of the IO (as defined by the authors). Vsens is located laterally and skirted by the Sp5. This is in agreement with the authors, as they propose that ONLY the elephants show the variations they report.

Review image 5.

These comparisons and the structural identification, with which the authors agree as they only distinguish the elephants from the other Afrotheria, demonstrate that the appearance of the IO can be quite variable across mammalian species, including those with a close phylogenetic affinity to the elephants. Not all mammal species possess a "serrated" appearance of the IO. Thus, it is more than just theoretically possible that the IO of the elephant appears as described prior to this study.

So what about elephants? Below I append a series of images from coronal sections through the African elephant brainstem stained for Nissl, myelin, and immunostained for calretinin. These sections are labelled according to standard mammalian nomenclature. In these complete sections of the elephant brainstem, we do not see a serrated appearance of the IOM (as described previously and in the current study by the authors). Rather the principal nucleus of the IOM appears to be bulbous in nature. In the current study, no image of myelin staining in the IOM/VsensR is provided by the authors. However, in the images I provide, we do see the reported myelin stripes in all stains - agreement between the authors and reviewer on this point. The higher magnification image to the bottom left of the plate shows one of the IOM/VsensR myelin stripes immunostained for calretinin, and within the myelin stripes axons immunopositive for calretinin are seen (labelled with an arrow). The climbing fibres of the elephant cerebellar cortex are similarly calretinin immunopositive (10.1159/000345565). In contrast, although not shown at high magnification, the fibres forming the Sp5 in the elephant (in the Maseko description, unnamed in the description of the authors) show no immunoreactivity to calretinin.

Review image 6.

Peripherin Immunostaining

In their revised manuscript the authors present immunostaining of peripherin in the elephant brainstem. This is an important addition (although it does replace the only staining of myelin provided by the authors which is unusual as the word myelin is in the title of the paper) as peripherin is known to specifically label peripheral nerves. In addition, as pointed out by the authors, peripherin also immunostains climbing fibres (Errante et al., 1998). The understanding of this staining is important in determining the identification of the IO and Vsens in the elephant, although it is not ideal for this task as there is some ambiguity. Errante and colleagues (1998; Fig. 1) show that climbing fibres are peripherin-immunopositive in the rat. But what the authors do not evaluate is the extensive peripherin staining in the rat Sp5 in the same paper (Errante et al, 1998, Fig. 2). The image provided by the authors of their peripherin immunostaining (their new Figure 2) shows what I would call the Sp5 of the elephant to be strongly peripherin immunoreactive, just like the rat shown in Errant et al (1998), and more over in the precise position of the rat Sp5! This makes sense as this is where the axons subserving the "extraordinary" tactile sensitivity of the elephant trunk would be found (in the standard model of mammalian brainstem anatomy). Interestingly, the peripherin immunostaining in the elephant is clearly lamellated...this coincides precisely with the description of the trigeminal sensory nuclei in the elephant by Maskeo et al (2013) as pointed out by the authors in their rebuttal. Errante et al (1998) also point out peripherin immunostaining in the inferior olive, but according to the authors this is only "weakly present" in the elephant IOM/VsensR. This latter point is crucial. Surely if the elephant has an extraordinary sensory innervation from the trunk, with 400 000 axons entering the brain, the VsensR/IOM should be highly peripherin-immunopositive, including the myelinated axon bundles?! In this sense, the authors argue against their own interpretation - either the elephant trunk is not a highly sensitive tactile organ, or the VsensR is not the trigeminal nuclei it is supposed to be.

Summary:

(1) Comparative data of species closely related to elephants (Afrotherians) demonstrates that not all mammals exhibit the "serrated" appearance of the principal nucleus of the inferior olive.

(2) The location of the IO and Vsens as reported in the current study (IOR and VsensR) would require a significant, and unprecedented, rearrangement of the brainstem in the elephants independently. I argue that the underlying molecular and genetic changes required to achieve this would be so extreme that it would lead to lethal phenotypes. Arguing that the "switcheroo" of the IO and Vsens does occur in the elephant (and no other mammals) and thus doesn't lead to lethal phenotypes is a circular argument that cannot be substantiated.

(3) Myelin stripes in the subnuclei of the inferior olivary nuclear complex are seen across all related mammals as shown above. Thus, the observation made in the elephant by the authors in what they call the VsensR, is similar to that seen in the IO of related mammals, especially when the IO takes on a more bulbous appearance. These myelin stripes are the origin of the olivocerebellar pathway, and are indeed calretinin immunopositive in the elephant as I show.

(4) What the authors see aligns perfectly with what has been described previously, the only difference being the names that nuclear complexes are being called. But identifying these nuclei is important, as any functional sequelae, as extensively discussed by the authors, is entirely dependent upon accurately identifying these nuclei.

(4) The peripherin immunostaining scores an own goal - if peripherin is marking peripheral nerves (as the authors and I believe it is), then why is the VsensR/IOM only "weakly positive" for this stain? This either means that the "extraordinary" tactile sensitivity of the elephant trunk is non-existent, or that the authors have misinterpreted this staining. That there is extensive staining in the fibre pathway dorsal and lateral to the IOR (which I call the spinal trigeminal tract), supports the idea that the authors have misinterpreted their peripherin immunostaining.

(5) Evolutionary expediency. The authors argue that what they report is an expedient way in which to modify the organisation of the brainstem in the elephant to accommodate the "extraordinary" tactile sensitivity. I disagree. As pointed out in my first review, the elephant cerebellum is very large and comprised of huge numbers of morphologically complex neurons. The inferior olivary nuclei in all mammals studied in detail to date, give rise to the climbing fibres that terminate on the Purkinje cells of the cerebellar cortex. It is more parsimonious to argue that, in alignment with the expansion of the elephant cerebellum (for motor control of the trunk), the inferior olivary nuclei (specifically the principal nucleus) have had additional neurons added to accommodate this cerebellar expansion. Such an addition of neurons to the principal nucleus of the inferior olive could readily lead to the loss of the serrated appearance of the principal nucleus of the inferior olive, and would require far less modifications in the developmental genetic program that forms these nuclei. This type of quantitative change appears to be the primary way in which structures are altered in the mammalian brainstem.

Reviewer #2 (Public Review):

Assessment

This study develops a potentially useful metric for quantifying codon usage adaptation – the Codon Adaptation Index of Species (CAIS) – that is intended to allow for more direct comparisons of the strength of selection at the molecular level across species by controlling for interspecies variation in amino acid usage and GC content. As evidence to support there claim CAIS better controls for GC content and amino acid usage across species, they note that CAIS has only a weak positive correlation with GC% (that does not stand up to multiple hypothesis testing correction) while CAI has a clear negative correlation with GC%. Using CAIS, they find better adapted species have more disordered protein domains; however, excitement about these findings is dampened due to (1) this result is also observed using the effective number of codons (ENC) and

(2) concerns over the interpretation of CAIS as a proxy for the effectiveness of selection.

Public Review

Summary

The goal of the authors in this study is to develop a more reliable approach for quantifying codon usage such that it is more comparable across species. Specifically, the authors wish to estimate the degree of adaptive codon usage, which is potentially a general proxy for the strength of selection at the molecular level. To this end, the authors created the Codon Adaptation Index for Species (CAIS) that attempts to control for differences in amino acid usage and GC% across species. Using their new metric, the authors observe a positive relationship between CAIS and the overall “disorderedness” of a species protein domains. I think CAIS has the potential to be a valuable tool for those interested in comparing codon adaptation across species in certain situations. However, I have certain theoretical concerns about CAIS as a direct proxy for the efficiency of selection sNe when mutation bias changes across species.

Strengths

(1) I appreciate that the authors recognize the potential issues of comparing CAI when amino acid usage varies and correct for this in CAIS. I think this is sometimes an under-appreciated point in the codon usage literature, as CAI is a relative measure of codon usage bias (i.e. only considers synonyms). However, the strength of natural selection on codon usage can potentially vary across amino acids, such that comparing mean CAI between protein regions with different amino acid biases may result in spurious signals of statistical significance.

(2) The CAIS metric presented here is generally applicable to any species that has an annotated genome with protein-coding sequences. A significant improvement over the previous version is the implementation of software tool for applying this method.

(3) The authors do a better job of putting their results in the context of the underlying theory of CAIS compared to the previous version.

(4) The paper is generally well-written.

Weaknesses

(1) The previously observed correlation between CAIS and body size was due to a bug when calculating phylogenetic independent contrasts. I commend the authors for acknowledging this mistake and updating the manuscript accordingly. I feel that the unobserved correlation between CAIS and body size should remain in the final version of the manuscript. Although it is disappointing that it is not statistically significant, the corrected results are consistent with previous findings (Kessler and Dean 2014).

(2) I appreciate the authors for providing a more detailed explanation of the theoretical basis model. However, I remain skeptical that shifts in CAIS across species indicates shifts in the strength of selection. I am leaving the math from my previous review here for completeness.

As in my previous review, let’s take a closer look at the ratio of observed codon frequencies vs. expected codon frequencies under mutation alone, which was previously notated as RSCUS in the original formulation. In this review, I will keep using the RSCUS notation, even though it has been dropped from the updated version. The key point is this is the ratio of observed and expected codon frequencies. If this ratio is 1 for all codons, then CAIS would be 0 based on equation 7 in the manuscript – consistent with the complete absence of selection on codon usage. From here on out, subscripts will only be used to denote the codon and it will be assumed that we are only considering the case of r = genome for some species s.

I think what the authors are attempting to do is “divide out” the effects of mutation bias (as given by Ei), such that only the effects of natural selection remain, i.e. deviations from the expected frequency based on mutation bias alone represents adaptive codon usage. Consider Gilchrist et al. GBE 2015, which says that the expected frequency of codon i at selection-mutation-drift equilibrium in gene g for an amino acid with Na synonymous codons is

where ∆M is the mutation bias, ∆η is the strength of selection scaled by the strength of drift, and φg is the gene expression level of gene g. In this case, ∆M and ∆η reflect the strength and direction of mutation bias and natural selection relative to a reference codon, for which ∆M,∆η = 0. Assuming the selection-mutation-drift equilibrium model is generally adequate to model of the true codon usage patterns in a genome (as I do and I think the authors do, too), the Ei,g could be considered the expected observed frequency codon i in gene g

E[Oi,g].

Let’s re-write the  in the form of Gilchrist et al., such that it is a function of mutation bias ∆M. For simplicity we will consider just the two codon case and assume the amino acid sequence is fixed. Assuming GC% is at equilibrium, the term gr and 1 − gr can be written as

where µx→y is the mutation rate from nucleotides x to y. As described in Gilchrist et al. MBE 2015 and Shah and Gilchrist PNAS 2011, the mutation bias . This can be expressed in terms of the equilibrium GC content by recognizing that

As we are assuming the amino acid sequence is fixed, the probability of observing a synonymous codon i at an amino acid becomes just a Bernoulli process.

If we do this, then

Recall that in the Gilchrist et al. framework, the reference codon has ∆MNNG,NNG \= 0 =⇒ e−∆MNNG,NNG \=

(1) Thus, we have recovered the Gilchrist et al. model from the formulation of Ei under the assumption that natural selection has no impact on codon usage and codon NNG is the pre-defined reference codon. To see this, plug in 0 for ∆η in equation (1).

We can then calculate the expected RSCUS using equation (1) (using notation E[Oi]) and equation (6) for the two codon case. For simplicity assume, we are only considering a gene of average expression (defined as ). Assume in this case that NNG is the reference codon (∆MNNG,∆ηNNG \= 0).

This shows that the expected value of RSCUS for a two codon amino acid is expected to increase as the strength of selection ∆η increases, which is desired. Note that ∆η in Gilchrist et al. is formulated in terms of selection against a codon relative to the reference, such that a negative value represents that a codon is favored relative to the reference. If ∆η = 0 (i.e. selection does not favor either codon), then E[RSCUS] = 1. Also note that the expected RSCUS does not remain independent of the mutation bias. This means that even if sNe (i.e. the strength of natural selection) does not change between species, changes to the strength and direction of mutation bias across species could impact RSCUS. Assuming my math is right, I think one needs to be cautious when interpreting CAIS as representative of the differences in the efficiency of selection across species except under very particular circumstances.

Consider our 2-codon amino acid scenario. You can see how changing GC content without changing selection can alter the CAIS values calculated from these two codons. Particularly problematic appears to be cases of extreme mutation biases, where CAIS tends toward 0 even for higher absolute values of the selection parameter. Codon usage for the majority of the genome will be primarily determined by mutation biases,

with selection being generally strongest in a relatively few highly-expressed genes. Strong enough mutation biases ultimately can overwhelm selection, even in highly-expressed genes, reducing the fraction of sites subject to codon adaptation.

Peer review image 1.

Peer review image 2.

CAIS (Low Expression)

Peer review image 3.

CAIS (Average Expression)

Peer review image 4.

CAIS (High Expression)

If we treat the expected codon frequencies as genome-wide frequencies, then we are basically assuming this genome made up entirely of a single 2-codon amino acid with selection on codon usage being uniform across all genes. This is obviously not true, but I think it shows some of the potential limitations of the CAIS approach. Based on these simulations, CAIS seems best employed under specific scenarios. One such case could be when it is known that mutation bias varies little across the species of interest. Looking at the species used in this manuscript, most of them have a GC content around 0.41, so I suspect their results are okay (assuming things like GC-biased gene conversion are not an issue). Outliers in GC content probably are best excluded from the analysis.

Although I have not done so, I am sure this could be extended to the 4 and 6 codon amino acids. One potential challenge to CAIS is the non-monotonic changes in codon frequencies observed in some species (again, see Shah and Gilchrist 2011 and Gilchrist et al. 2015).

Reviewer #3 (Public review):

Summary:

The authors examine the role of the medial frontal cortex of mice in exploiting statistical structure in tasks. They claim that mice are "proactive": they predict upcoming changes, rather than responding in a "model-free" way to environmental changes. Further, they speculate that the estimation of future change (i.e., prediction of upcoming events, based on learning temporal regularities) might be "a main ... function of dorsal medial frontal cortex (dmFC)." Unfortunately, the current manuscript contains flaws such that the evidence supporting these claims is inadequate.

Strengths:

Understanding the neural mechanisms by which we learn about statistical structure in the world is an important goal. The authors developed an interesting task and used model-based techniques to try to understand the mechanisms by which perturbation of dmFC influenced behavior. They demonstrate that lesions and optogenetic silencing of dmFC influence behavior, showing that this region has a causal influence on the task.

Weaknesses:

I was concerned that the main behavioral effects shown in Figure 1F were a statistical artifact. By requiring the Geometric block length to be preceded by a performance-based block, the authors introduce a dependence that can generate the phenomena they describe as anticipation.

To demonstrate this, I simulated their task with an agent that does not have any anticipation of the change point (Reviewer image 1). The agent repeats the previous action with probability `p(repeat)` (similar to the choice kernel in the author's models). If the agent doesn't repeat then the next choice depends on the previous outcome. If the previous choice was rewarded, it stays with `P(WS)` and chooses randomly with `1-P(WS)`. If the previous choice was unrewarded, it switches with `P(LS)` and chooses randomly with `1-P(LS)`.

Review image 1.

An agent with `P(WS)=P(LS)=P(repeat)=0.85` shows the same phenomena as the mice: a difference in performance before the block switch and "earlier" crossing of the midpoint after the switch. https://imgdrop.io/image/aHn6y. The phenomena go away in the simulations when a fixed block length of 20 trials is followed by a Geometric block length.

The authors did not completely rely on the phenomena of Figure 1F for their conclusions. They did a model comparison to provide evidence that animals are anticipating the switch. Unfortunately, the authors did not use state-of-the-art methods in this section of the paper. In particular, they failed to show that under a range of generative parameters for each model class, the model selection process chooses the correct model class (i.e. a confusion matrix). A more minor point, they used BIC instead of a more robust cross-validated metric for model selection. Finally, instead of comparing their "best" anticipating model to their 2nd best model (without anticipation), they compared their best to their 4th best (Supp Fig 3.5). This seems misleading.

Given all of the the above issues, it is hard to critically evaluate the model-based analysis of the effects of lesions/optogenetics.

Reviewer #2 (Public Review):

Patterns scored into or painted on durable media have long been considered important markers of the cognitive capabilities of hominins. More specifically, the association of such markers with Homo sapiens has been used to argue that our evolutionary success was in part shaped by our unique ability to code, store and convey information through abstract conventions.

That singularity of association has been cast into doubt in the last decade with finds of designs apparently painted or carved by Neanderthals, and potentially by even earlier hominins. Even allowing for these developments, however, extending the capability to generate putatively abstract designs to a relatively small-brained hominin like Homo naledi is contentious. The evidential bar for such claims is necessarily high, and I don't believe that it has been cleared here.

The central issue is that the engravings themselves are not dated. As the authors themselves note, the minimum age constraint provided by U/Th on flowstone does not necessarily relate to the last occupation of the Dinaledi cave system, as the earlier ESR age on teeth does not necessarily document first use of the cave. The authors state that "At present we have no evidence limiting the time period across which H. naledi was active in the cave system". On those grounds though, assigning the age range of presently dated material within the cave system to the engravings - as the current title unambiguously does - is not justifiable.

Because we don't know when they were made, the association between the engravings and Homo naledi rests on the assertion that no humans entered and made alterations to the cave system between its last occupation by Homo naledi, and its recent scientific recording. This is argued on page 6 with the statement that "No physical or cultural evidence of any other hominin population occurs within this part of the cave system".

There is an important contrast between the quotes I have referred to in the last two paragraphs. In the earlier quote, the absence of evidence for Homo naledi in the cave system >335 ka and <241 ka is not considered evidence for their absence before or after these ages. Just because we have no evidence that Homo naledi was in the cave at 200 ka doesn't mean they weren't there, which is an argument I think most archaeologists would accept. When it comes to other kinds of humans, though - per the latter quote - the opposite approach is taken. Specifically, the present lack of physical evidence of more recent humans in the cave is considered evidence that no such humans visited the cave until its exploration by cavers 40 years ago. I don't think many archaeologists would consider that argument compelling. I can see why the authors would be drawn to make that assertion, but an absence of evidence cannot be used to argue in one way for use of the cave by Homo naledi and in another way for use of the cave by all other humans.

A second problem is with what Homo naledi might have made engravings. The authors state that "The lines appear to have been made by repeatedly and carefully passing a pointed or sharp lithic fragment or tool into the grooves". The authors then describe one rock with superficial similarities to a flake from the more recent site of Blombos to suggest that sharp-edge stones with which to make the engravings were available to Homo naledi. Blombos is considered relevant here presumably because it has evidence for Middle Stone Age engravings. The authors do not, however, demonstrate any usewear on that stone object such as might be expected if it was used to carve dolomite. Given that it is presented as the only such find in the cave system so far, this seems important.

My greater concern is that the authors did not compare the profile morphology of the Dinaledi engravings with the extensive literature on the morphology of scored lines caused by sharp-edge stone implements (e.g., Braun et al. 2016, Pante et al. 2017). I appreciate that the research group is reticent to undertake any invasive work until necessary, but non-destructive techniques could have been used to produce profiles with which to test the proposition that the engravings were made with a sharp edge stone.

One thing I noticed in this respect is that the engravings seem very wide, both in absolute terms and relative to their depth. The data I collected from the Middle Stone Age engraved ochre from Klein Kliphuis suggested average line widths typically around 0.1-0.2 mm (Mackay and Welz 2008). The engraved lines at Dinaledi appear to be much wider, perhaps 2-5 mm. This doesn't discount the possibility that the engravings in the Dinaledi system were carved with a sharp edge stone - the range of outcomes for such engravings in soft rock can be quite variable (Hodgskiss 2010) - only that detailed analysis should precede rather than follow any assertion about their mode of formation.

None of this is to say that the arguments mounted here are wrong. It should be considered possible that Homo naledi made the engravings in the Dinaledi cave system. The problem is that other explanations are not precluded.

As an example, the western end of the Dinaledi subsystem has a particular geometry to the intersection of its passages, with three dominant orientations, one vertical (which is to say, north-south), and two diagonal (Figure 1). The major lines on Panel A have one repeated vertical orientation and two repeated diagonal orientations (Figure 16), particularly in the upper area not impacted by stromatolites. The lines in both the cave system and engravings in Panel A appear to intersect at similar angles. Several of the cave features appear, superficially at least, to be replicated. In fact, scaled, rotated, and super-imposed, Figure 16 is a plausible 'mud map' of the western end of the Dinaledi system carved incrementally by people exploring the caves. A figure showing this is included here:

Of course, there are problems with this suggestion. The choice of the upper part of Panel A is selective, the similarity is superficial, and the scales are not necessarily comparable. (Note, btw, that all of those caveats hold equally well for the comparison the authors make between the unmodified rock from Dinaledi and the flake from Blombos in Figure 19). However, the point is that such a 'mud map hypothesis' is, as with the arguments mounted in this paper, both plausible and hard to prove.

Having read this paper a few times, I am intrigued by the engravings in the Dinaledi system and look forward to learning more about them as this research unfolds. Based on the evidence presently available, however, I feel that we have no robust grounds for asserting when these engravings were made, by whom they were made, or for what reason they were made.

References:

• Braun, D. R., et al. (2016). "Cut marks on bone surfaces: influences on variation in the form of traces of ancient behaviour." Interface Focus 6: 20160006.

• Hodgskiss, T. (2010). "Identifying grinding, scoring and rubbing use-wear on experimental ochre pieces." Journal of Archaeological Science 37: 3344-3358.

• Mackay, A. & A. Welz (2008). "Engraved ochre from a Middle Stone Age context at Klein Kliphuis in the Western Cape of South Africa." Journal of Archaeological Science 35: 1521-1532.

• Pante, M. C., et al. (2017). "A new high-resolution 3-D quantitative method for identifying bone surface modifications with implications for the Early Stone Age archaeological record." J Hum Evol 102: 1-11.

Reviewer #2 (Public Review):

The goal of the present study is to better understand the 'control objectives' that subjects adopt in a video-game-like virtual-balancing task. In this task, the hand must move in the opposite direction from a cursor. For example, if the cursor is 2 cm to the right, the subject must move their hand 2 cm to the left to 'balance' the cursor. Any imperfection in that opposition causes the cursor to move. E.g., if the subject were to move only 1.8 cm, that would be insufficient, and the cursor would continue to move to the right. If they were to move 2.2 cm, the cursor would move back toward the center of the screen. This return to center might actually be 'good' from the subject's perspective, depending on whether their objective is to keep the cursor still or keep it near the screen's center. Both are reasonable 'objectives' because the trial fails if the cursor moves too far from the screen's center during each six-second trial.

This task was recently developed for use in monkeys (Quick et al., 2018), with the intention of being used for the study of the cortical control of movement, and also as a task that might be used to evaluate BMI control algorithms. The purpose of the present study is to better characterize how this task is performed. What sort of control policies are used. Perhaps more deeply, what kind of errors are those policies trying to minimize? To address these questions, the authors simulate control-theory style models and compare with behavior. They do in both in monkeys and in humans.

These goals make sense as a precursor to future recording or BMI experiments. The primate motor-control field has long been dominated by variants of reaching tasks, so introducing this new task will likely be beneficial. This is not the first non-reaching task, but it is an interesting one and it makes sense to expand the presently limited repertoire of tasks. The present task is very different from any prior task I know of. Thus, it makes sense to quantify behavior as thoroughly as possible in advance of recordings. Understanding how behavior is controlled is, as the authors note, likely to be critical to interpreting neural data.

From this perspective - providing a basis for interpreting future neural results - the present study is fairly successful. Monkeys seem to understand the task properly, and to use control policies that are not dissimilar from humans. Also reassuring is the fact that behavior remains sensible even when task-difficulty become high. By 'sensible' I simply mean that behavior can be understood as seeking to minimize error: position, velocity, or (possibly) both, and that this remains true across a broad range of task difficulties. The authors document why minimizing position and minimizing velocity are both reasonable objectives. Minimizing velocity is reasonable, because a near-stationary cursor can't move far in six seconds. Minimizing position error is reasonable, because the trial won't fail if the cursor doesn't stray far from the center. This is formally demonstrated by simulating control policies: both objectives lead to control policies that can perform the task and produce realistic single-trial behavior. The authors also demonstrate that, via verbal instruction, they can induce human subjects to favor one objective over the other. These all seem like things that are on the 'need to know' list, and it is commendable that this amount of care is being taken before recordings begin, as it will surely aid interpretation.

Yet as a stand-alone study, the contribution to our understanding of motor control is more limited. The task allows two different objectives (minimize velocity, minimize position) to be equally compatible with the overall goal (don't fail the trial). Or more precisely, there exists a range of objectives with those two at the extreme. So it makes sense that different subjects might choose to favor different objectives, and also that they can do so when instructed. But has this taught us something about motor control, or simply that there is a natural ambiguity built into the task? If I ask you to play a game, but don't fully specify the rules, should I be surprised that different people think the rules are slightly different?

The most interesting scientific claim of this study is not the subject-to-subject variability; the task design makes that quite likely and natural. Rather, the central scientific result is the claim that individual subjects are constantly switching objectives (and thus control policies), such that the policy guiding behavior differs dramatically even on a single-trial basis. This scientific claim is supported by a technical claim: that the authors' methods can distinguish which objective is in use, even on single trials. I am uncertain of both claims.

Consider Figure 8B, which reprises a point made in Figure 1&3 and gives the best evidence for trial-to-trial variability in objective/policy. For every subject, there are two example trials. The top row of trials shows oscillations around the center, which could be consistent with position-error minimization. The bottom row shows tolerance of position errors so long as drift is slow, which could be consistent with velocity-error minimization. But is this really evidence that subjects were switching objectives (and thus control policies) from trial to trial? A simpler alternative would be a single control policy that does not switch, but still generates this range of behaviors. The authors don't really consider this possibility, and I'm not sure why. One can think of a variety of ways in which a unified policy could produce this variation, given noise and the natural instability of the system.

Indeed, I found that it was remarkably easy to produce a range of reasonably realistic behaviors, including the patterns that the authors interpret as evidence for switching objectives, based on a simple fixed controller. To run the simulations, I made the simple assumption that subjects simply attempt to match their hand position to oppose the cursor position. Because subjects cannot see their hand, I assumed modest variability in the gain, with a range from -1 to -1.05. I assumed a small amount of motor noise in the outgoing motor command. The resulting (very simple) controller naturally displayed the basic range of behaviors observed across trials (see Image 1)

Peer review image 1.

Some trials had oscillations around the screen center (zero), which is the pattern the authors suggest reflects position control. In other trials the cursor was allowed to drift slowly away from the center, which is the pattern the authors suggest reflects velocity control. This is true even though the controller was the same on every trial. Trial-to-trial differences were driven both by motor noise and by the modest variability in gain. In an unstable system, small differences can lead to (seemingly) qualitatively different behavior on different trials.

This simple controller is also compatible with the ability of subjects to adapt their strategy when instructed. Anyone experienced with this task likely understands (or has learned) that moving the hand slightly more than 'one should' will tend to shepherd the cursor back to center, at the cost of briefly high velocity. Using this strategy more sparingly will tend to minimize velocity even if position errors persist. Thus, any subject using this control policy would be able to adapt their strategy via a modest change in gain (the gain linking visible cursor position to intended hand position).

This model is simple, and there may be reasons to dislike it. But it is presumably a reasonable model. The nature of the task is that you should move your hand opposite where the cursor is. Because you can't see your hand, you will make small mistakes. Due to the instability of the system, those small mistakes have large and variable effects. This feature is likely common to other controllers as well; many may explicitly or implicitly blend position and velocity control, with different trials appearing more dominated by one versus the other. Given this, I think the study presents only weak evidence that individual subjects are switching their objective on individual trials. Indeed, the more parsimonious explanation may be that they aren't. While the study certainly does demonstrate that the control policy can be influenced by verbal instructions, this might be a small adjustment as noted above.

I thus don't feel convinced that the authors can conclusively tell us the true control policy being used by human and monkey subjects, nor whether that policy is mostly fixed or constantly switching. The data are potentially compatible with any of these interpretations, depending on which control-style model one prefers.

I see a few paths that the authors might take if they chose.<br /> --First, my reasoning above might be faulty, or there might be additional analyses that could rule out the possibility of a unified policy underlying variable behavior. If so, the authors may be able to reject the above concerns and retain the present conclusions. The main scientifically novel conclusion of the present study is that subjects are using a highly variable control policy, and switching on individual trials. If this is indeed the case, there may be additional analyses that could reveal that.<br /> --Second, additional trial types (e.g., with various perturbations) might be used as a probe of the control policy. As noted below, there is a long history of doing this in the pursuit system. That additional data might better disambiguate control policies both in general, and across trials.<br /> --Third, the authors might find that a unified controller is actually a good (and more parsimonious) explanation. Which might actually be a good thing from the standpoint of future experiments. Interpretation of neural data is likely to be much easier if the control policy being instantiated isn't in constant flux.

In any case, I would recommend altering the strength of some conclusions, particularly the conclusion that the presented methods can reliably discriminate amongst objectives/policies on individual trials. This is mentioned as a major motivation on multiple occasions, but in most of these instances, the subsequent analysis infers the objective only across trial (e.g., one must observe a scatterplot of many trials). By Figure 7, they do introduce a method for inferring the control policy on individual trials, and while this seems to work considerably better than chance, it hardly appears reliable.

In this same vein I would suggest toning down aspects of the Introduction and Discussion. The Introduction in particular is overly long, and tries to position the present study as unique in ways that seem strained. Other studies have built links between human behavior, monkey behavior, and monkey neural data (for just one example, consider the corpus of work from the Scott lab that includes Pruszynski et al. 2008 and 2011). Other studies have used highly quantitative methods to infer the objective function used by subjects (e.g. Kording and Wolpert 2004). The very issue that is of interest in the present study - velocity-error-minimization versus position-error-minimization - has been extensively addressed in the smooth pursuit system. That field has long combined quantitative analyses of behavior in humans and monkeys, along with neural recordings. Many pursuit experiments used strategies that could be fruitfully employed to address the central questions of the present study. For example, error stabilization was important for dissecting the control policy used by the pursuit system. By artificially stabilizing the error (position or velocity) at zero, or at some other value, one can determine the system's response. The classic Rashbass step (1961) put position and velocity errors in opposition, to see which dominates the response. Step and sinusoidal perturbations were useful in distinguishing between models, as was the imposition of artificially imposed delays. The authors note the 'richness' of the behavior in the present task, and while one could say the same of pursuit, it was still the case that specific and well-thought through experimental manipulations were pretty critical. It would be better if the Introduction considered at least some of the above-mentioned work (or other work in a similar vein). While most would agree with the motivations outlined by the authors - they are logical and make sense - the present Introduction runs the risk of overselling the present conclusions while underselling prior work.

Reviewer #2 (Public Review):

Summary:

The goal of the authors in this study is to develop a more reliable approach for quantifying codon usage such that it is more comparable across species. Specifically, the authors wish to estimate the degree of adaptive codon usage, which is potentially a general proxy for the strength of selection at the molecular level. To this end, the authors created the Codon Adaptation Index for Species (CAIS) that controls for differences in amino acid usage and GC% across species. Using their new metric, the authors find a previously unobserved negative correlation between the overall adaptiveness of codon usage and body size across 118 vertebrates. As body size is negatively correlated with effective population size and thus the general strength of natural selection, the negative correlation between CAIS and body size is expected. The authors argue this was previously unobserved due to failures of other popular metrics such as Codon Adaptation Index (CAI) and the Effective Number of Codons (ENC) to adequately control for differences in amino acid usage and GC content across species. Most surprisingly, the authors also find a positive relationship between CAIS and the overall "disorderedness" of a species protein domains. As some of these results are unexpected, which is acknowledged by the authors, I think it would be particularly beneficial to work with some simulated datasets. I think CAIS has the potential to be a valuable tool for those interested in comparing codon adaptation across species in certain situations. However, I have certain theoretical concerns about CAIS as a direct proxy for the efficiency of selection when the mutation bias changes across species.

Strengths:

(1) I appreciate that the authors recognize the potential issues of comparing CAI when amino acid usage varies and correct for this in CAIS. I think this is sometimes an under-appreciated point in the codon usage literature, as CAI is a relative measure of codon usage bias (i.e. only considers synonyms). However, the strength of natural selection on codon usage can potentially vary across amino acids, such that comparing mean CAI between protein regions with different amino acid biases may result in spurious signals of statistical significance (see Cope et al. Biochemica et Biophysica Acta - Biomembranes 2018 for a clear example of this).

(2) The authors present numerous analysis using both ENC and mean CAI as a comparison to CAIS, helping given a sense of how CAIS corrects for some of the issues with these other metrics. I also enjoyed that they examined the previously unobserved relationship between codon usage bias and body size, which has bugged me ever since I saw Kessler and Dean 2014. The result comparing protein disorder to CAIS was particularly interesting and unexpected.

(3) The CAIS metric presented here is generally applicable to any species that has an annotated genome with protein-coding sequences.

Weaknesses:

(1) The main weakness of this work is that it lacks simulated data to confirm that it works as expected. This would be particularly useful for assessing the relationship between CAIS and the overall effect of protein structure disorder, which the authors acknowledge is an unexpected result. I think simulations could also allow the authors to assess how their metric performs in situations where mutation bias and natural selection act in the same direction vs. opposite directions. Additionally, although I appreciate their comparisons to ENC and mean CAI, the lack of comparison to other popular codon metrics for calculating the overall adaptiveness of a genome (e.g. dos Reis et al.'s statistic, which is a function of tRNA Adaptation Index (tAI) and ENC) may be more appropriate. Even if results are similar to , CAIS has a noted advantage that it doesn't require identifying tRNA gene copy numbers or abundances, which I think are generally less readily available than genomic GC% and protein-coding sequences.

The authors mention the selection-mutation-drift equilibrium model, which underlies the basic ideas of this work (e.g. higher results in stronger selection on codon usage), but a more in-depth framing of CAIS in terms of this model is not given. I think this could be valuable, particularly in addressing the question "are we really estimating what we think we're estimating?"

Let's take a closer look at the formulation for RSCUS. From here on out, subscripts will only be used to denote the codon and it will be assumed that we are only considering the case of for some species

I think what the authors are attempting to do is "divide out" the effects of mutation bias (as given by , such that only the effects of natural selection remain, i.e. deviations from the expected frequency based on mutation bias alone represent adaptive codon usage. Consider Gilchrist et al. MBE 2015, which says that the expected frequency of codon at selection-mutation-drift equilibrium in gene for an amino acid with synonymous codons is

where is the mutation bias, is the strength of selection scaled by the strength of drift, and is the gene expression level of gene \(g\). In this case, \ and reflect the strength and direction of mutation bias and natural selection relative to a reference codon, for which . Assuming the selection-mutation-drift equilibrium model is generally adequate to model the true codon usage patterns in a genome (as I do and I think the authors do, too), the could be considered the expected observed frequency codon in gene .

Let's re-write the in the form of Gilchrist et al., such that it is a function of mutation bias . For simplicity, we will consider just the two-codon case and assume the amino acid sequence is fixed. Assuming GC% is at equilibrium, the term and can be written as

where is the mutation rate from nucleotides to. As described in Gilchrist et al. MBE 2015 and Shah and Gilchrist PNAS 2011, the mutation bias . This can be expressed in terms of the equilibrium GC content by recognizing that

As we are assuming the amino acid sequence is fixed, the probability of observing a synonymous codon at an amino acid becomes just a Bernoulli process.

If we do this, then

Recall that in the Gilchrist et al. framework, the reference codon has . Thus, we have recovered the Gilchrist et al. model from the formulation of under the assumption that natural selection has no impact on codon usage and codon NNG is the pre-defined reference codon. To see this, plug in 0 for in equation (1).

We can then calculate the expected RSCUS using equation (1) (using notation and equation (6) for the two codon case. For simplicity assume, we are only considering a gene of average expression (defined as . Assume in this case that NNG is the reference codon .

This shows that the expected value of RSCUS for a two-codon amino acid is expected to increase as the strength of selection increases, which is desired. Note that in Gilchrist et al. is formulated in terms of selection against a codon relative to the reference, such that a negative value represents that a codon is favored relative to the reference. If (i.e. selection does not favor either codon), then . Also note that the expected RSCUS does not remain independent of the mutation bias. This means that even if (i.e. the strength of natural selection) does not change between species, changes to the strength and direction of mutation bias across species could impact RSCUS. Assuming my math is right, I think one needs to be cautious when interpreting CAIS as representative of the differences in the efficiency of selection across species except under very particular circumstances. One such case could be when it is known that mutation bias varies little across the species of interest. Looking at the species used in this manuscript, most of them have a GC content ranging around 0.41, so I suspect their results are okay.

Although I have not done so, I am sure this could be extended to the 4 and 6 codon amino acids.

Another minor weakness of this work is that although the method is generally applicable to any species with an annotated genome and the code is publicly available, the code itself contains hard-coded values for GC% and amino acid frequencies across the 118 vertebrates. The lack of a more flexible tool may make it difficult for less computationally-experienced researchers to take advantage of this method.

Reviewer #3 (Public Review):

The manuscript presents an intriguing explanation for why grid cell firing fields do {\em not} lie on a lattice whose axes aligned to the walls of a square arena. This observation, by itself, merits the manuscript's dissemination to the journals audience.

The presentation is quirky (but keep the quirkiness!).

But let me recast the problem presented by the authors as one of combinatorics. Given repeating, spatially separated firing fields across cells, one obtains temporal sequences of grid cells firing. Label these cells by integers from $[n]$. Any two cells firing in succession should uniquely identify one of six directions (from the hexagonal lattice) in which the agent is currently moving.

Now, take the symmetric group $\Sigma$ of cyclic permutations on $n$ elements.<br /> We ask whether there are cyclic permutations of $[n]$ such that

So, for instance, $(4,2,3,1)$ would not be counted as a valid permutation of $(1,2,3,4)$, as $(2,3)$ and $(1,4)$ are adjacent.

Furthermore, given $[n]$, are there two distinct cyclic permutations such that {\em no} adjacencies are preserved when considering any pair of permutations (among the triple of the original ordered sequence and the two permutations)? In other words, if we consider the permutation required to take the first permutation into the second, that permutation should not preserve any adjacencies.

{\bf Key question}: is there any difference between the solution to the combinatorics problem sketched above and the result in the manuscript? Specifically, the text argues that for $n=7$ there is only {\em one} solution.

Ideally, one would strive to obtain a closed-form solution for the number of such permutations as a function of $n$.

Reviewer #1 (Public review):

In this work, Neiswender and colleagues test the hypothesis that mutations in BicD2 that are associated with SMALED alter BicD2-cargo interactions. To do this, they first establish the WT BicD2 cargo interactome (using a proximity-dependent biotin ligase screen with Turbo-ID on the BicD2 C-terminus). In addition to known cargo interactors, they also identified many proteins in the HOPs complex. Interestingly, they find that the HOPs complex may interact with BicD2 in a different manner than other known cargos. The authors also show that while BicD2 is required for the HOPs complex localization, on average, depletion of BicD2 from HeLa and Cos7 cells causes HOPs and Lysosome mislocalization that is consistent with Kinesin-1 trafficking defects, rather than dynein. The authors also use proximity biotin ligase approaches to define the cargo interactome of three BicD2 variants associated with SMALED. One variant (R747C) has the most altered cargo interactome. The authors highlight one protein, in particular, GRAMD1A, that is only found in the R747C dataset and mislocalizes specifically when R747C is expressed.

The work in this manuscript is of a very high quality and contributes important findings to the field.

Comments on revisions:

The authors did a great job addressing the points I brought up!

Reviewer #2 (Public review):

Neiswender et al. investigated the interactomes between wild-type BICD2 and BICD2 mutants that are associated with Spinal Muscular Atrophy with Lower Extremity Predominance (SMALED2). Although BICD2 has previously been implicated in SMALED2, it is unclear how mutations in BICD2 may contribute to disease symptoms. In this study, the authors characterize the interactome of wild-type BICD2 and identify potential new cargos including the HOPS complex. The authors then chose three SMALED2-associated BICD2 mutants and compared each mutant interactome to that of wild-type BICD2. Each mutant had a change in the interactome, with the most drastic being BICD2_R747C, a mutation in the cargo binding domain of BICD2. This mutant displayed less interaction with a potential new BICD2 cargo, the HOPS complex. Additionally, it displayed more interaction with an ER protein, GRAMD1A.

The data in the paper is generally strong but the major conclusions of this paper need more evidence to be better supported.

(1) The authors use cells that have been engineered to express the different BICD2 constructs. As shown in Figure 4B, the authors see wide expression of BICD2_WT throughout the cell. However, WT BICD2 usually localizes to the TGN. This widespread localization introduces some uncertainty about the interactome data. The authors should either try to verify the interaction data (specifically with the HOPS complex and GRAMD1A) by immunoprecipitating endogenous BICD2 or by repeating their interactome experiment in Figure 1 using BICD2 knockout cells that express the BICD2_WT construct. This should also be done to verify the immunoprecipitation and microscopy data shown in Figure 7.

(2) The authors conclude that cargo transport defects resulting from BICD2 mutations may contribute to SMALED2 symptoms. However, the authors are unable to determine if BICD2 directly binds to the potential new cargo, the HOPS complex. To address this, the authors could purify full-length WT BICD2 and perform in vitro experiments. Furthermore, the authors were unable to identify the minimal region of BICD2 needed for HOPS interaction. The authors could expand on the experiment attempted with the extended BICD2 C-terminal using a deltaCC1 construct, which could also be used for in vitro experiments.

(3) Again, the authors conclude that BICD2 mutants cause cargo transport defects that are likely to lead to SMALED2 symptoms. This would be better supported if the authors are able to find a protein relevant to SMALED2 and examine if/how its localization is changed under expression of the BICD2 mutants. The authors currently use the HOPS complex and GRAMD1A as indicators of cargo transport defects, but it is unclear if these are relevant to SMALED2 symptoms.

Comments on revisions:

The investigators did a good job in responding to our initial concerns (see below). We appreciate that they used siRNA to address our first comment because they do not have a BICD2 KO cell line. We appreciated that they added a new section in the Discussion to address the limitations of the study.

In regards to our first comment about the BICD2 WT construct localization, since they use KD to validate the interaction between their BICD2 WT construct and VPS41, it would be nice to see localization of this construct under the KD condition. However, the binding they presented in Sup. Fig 1B does look convincing, so this may not be necessary.

Overall, I believe this revision has satisfied our previous concerns.

Reviewer #3 (Public review):

Summary:

BicD2 is a motor adapter protein that facilitates cellular transport pathways, which are impacted by human disease mutations of BicD2 causing spinal muscular atrophy with lower extremity dominance (SMALED2). The authors provide evidence that some of these mutations result in interactome changes, which may be the underlying cause of the disease. This is supported by proximity biotin ligation screens, immunoprecipitation and cell biology assays. The authors identify several novel BicD2 interactions such as the HOPS complex that participates in the fusion of late endosomes and autophagosomes with lysosomes, which could have important functions. Three BicD2 disease mutants studied had changes in the interactome, which could be an underlying cause for SMALED2. The study extends our understanding of the BicD2 interactome under physiological conditions, as well as of the changes of cellular transport pathways that result in SMALED2. It will be of great interest for the BicD2 and dynein fields.

Strengths:

Extensive interactomes are presented for both WT BicD2 as well as the disease mutants, which will be valuable for the community. The HOPS complex was identified as a novel interactor of BicD2, which is important for fusion of late endosomes and lysosomes, which is of interest, since some of the BicD2 disease mutations result in Golgi-fragmentation phenotypes. The interaction with the HOPS complex is affected by the R747C mutation, which also results in a gain of function interaction with GRAMD1A.

Weaknesses:

The manuscript should be strengthened by further evidence of the BicD2/HOPS complex interaction and the functional implications for spinal muscular atrophy by changes in the interactome through mutations. Which functional implications does the loss of the BicD2/HOPS complex interaction and the gain of function interaction with GRAMD1A have in the context of the R747C mutant?

Major points:

(1) In the biotin proximity ligation assay, a large number of targets were identified, but it is not clear why only the HOPS complex was chosen for further verification. Immunoprecipitation was used for target verification, but due to the very high number of targets identified in the screen, and the fact that the HOPS complex is a membrane protein that could potentially be immunoprecipitated along with lysosomes or dynein, additional experiments to verify the interaction of BicD2 with the HOPS complex (reconstitution of a complex in vitro, GST-pull down of a complex from cell extracts or other approaches) are needed to strengthen the manuscript.<br /> (2) In the biotin proximity ligation assay, a large number of BicD2 interactions were identified that are distinct between the mutant and the WT, but it was not clear why particularly GRAMD1A was chosen as gain of function interaction, and what the functional role of a BicD2/GRAMD1A interaction may be. A Western blot shows a strengthened interaction with the R747C mutant but GRAMD1A also interacts with WT BicD2.<br /> (3) Furthermore, functional implications of changed interactions with HOPS and GRAMD1A in the R747C mutant are unclear. Additional experiments are needed to establish the functional implication of the loss of the BicD2/HOPS interaction in the BicD2/R747C mutant. For the GRAMD1A gain of function interaction, according to the authors a significant amount of the protein localized with BicD2/R747C at the centrosomal region. This changed localization is not very clear from the presented images (no centrosomal or other markers were used, and the changed localization could also be an effect of dynein hyper activation in the mutant). Furthermore, the functional implication of a changed localization of GRAMD1A is unclear from the presented data.

Comments on revisions:

After a major revision, the manuscript is much improved. Additional evidence for the HOPS complex/BicD2 interaction was provided (the interaction was identified in multiple independent screens), and while the authors unfortunately were not able to confirm a direct interaction between BicD2 and the HOPS complex, additional caveats were added in the result section, which clearly state these limitations. The authors also included a very nice discussion of potential physiological roles of the GRAMD1A mislocalization in the disease mutant, which could potentially affect cholesterol transport and homostatis. Limitations of the presented approaches were clearly described as caveats.

Reviewer #1 (Public review):

Summary:

Silbaugh, Koster, and Hansel investigated how the cerebellar climbing fiber (CF) signals influence neuronal activity and plasticity in mouse primary somatosensory (S1) cortex. They found that optogenetic activation of CFs in the cerebellum modulates responses of cortical neurons to whisker stimulation in a cell-type-specific manner and suppresses potentiation of layer 2/3 pyramidal neurons induced by repeated whisker stimulation. This suppression of plasticity by CF activation is mediated through modulation of VIP- and SST-positive interneurons. Using transsynaptic tracing and chemogenetic approaches, the authors identified a pathway from the cerebellum through the zona incerta and the thalamic posterior medial (POm) nucleus to the S1 cortex, which underlies this functional modulation.

Strengths:

This study employed a combination of modern neuroscientific techniques, including two-photon imaging, opto- and chemo-genetic approaches, and transsynaptic tracing. The experiments were thoroughly conducted, and the results were clearly and systematically described. The interplay between the cerebellum and other brain regions - and its functional implications - is one of the major topics in this field. This study provides solid evidence for an instructive role of the cerebellum in experience-dependent plasticity in the S1 cortex.

Weaknesses:

There may be some methodological limitations, and the physiological relevance of the CF-induced plasticity modulation in the S1 cortex remains unclear. In particular, it has not been elucidated how CF activity influences the firing patterns of downstream neurons along the pathway to the S1 cortex during stimulation.

(1) Optogenetic stimulation may have activated a large population of CFs synchronously, potentially leading to strong suppression followed by massive activation in numerous cerebellar nuclear (CN) neurons. Given that there is no quantitative estimation of the stimulated area or number of activated CFs, observed effects are difficult to interpret directly. The authors should at least provide the basic stimulation parameters (coordinates of stim location, power density, spot size, estimated number of Purkinje cells included, etc.).

(2) There are CF collaterals directly innervating CN (PMID:10982464). Therefore, antidromic spikes induced by optogenetic stimulation may directly activate CN neurons. On the other hand, a previous study reported that CN neurons exhibit only weak responses to CF collateral inputs (PMID: 27047344). The authors should discuss these possibilities and the potential influence of CF collaterals on the interpretation of the results.

(3) The rationale behind the plasticity induction protocol for RWS+CF (50 ms light pulses at 1 Hz during 5 min of RWS, with a 45 ms delay relative to the onset of whisker stimulation) is unclear.

a) The authors state that 1 Hz was chosen to match the spontaneous CF firing rate (line 107); however, they also introduced a delay to mimic the CF response to whisker stimulation (line 108). This is confusing, and requires further clarification, specifically, whether the protocol was designed to reproduce spontaneous or sensory-evoked CF activity.

b) Was the timing of delivering light pulses constant or random? Given the stochastic nature of CF firing, randomly timed light pulses with an average rate of 1Hz would be more physiologically relevant. At the very least, the authors should provide a clear explanation of how the stimulation timing was implemented.

(4) CF activation modulates inhibitory interneurons in the S1 cortex (Figure 2): responses of interneurons in S1 to whisker stimulation were enhanced upon CF coactivation (Figure 2C), and these neurons were predominantly SST- and PV-positive interneurons (Figure 2H, I). In contrast, VIP-positive neurons were suppressed only in the late time window of 650-850 ms (Figure 2G). If the authors' hypothesis-that the activity of VIP neurons regulates SST- and PV-neuron activity during RWS+CF-is correct, then the activity of SST- and PV-neurons should also be increased during this late time window. The authors should clarify whether such temporal dynamics were observed or could be inferred from their data.

(5) Transsynaptic tracing from CN nicely identified zona incerta (ZI) neurons and their axon terminals in both POm and S1 (Figure 6 and Figure S7).

a) Which part of the CN (medial, interposed, or lateral) is involved in this pathway is unclear.

b) Were the electrophysiological properties of these ZI neurons consistent with those of PV neurons?

c) There appears to be a considerable number of axons of these ZI neurons projecting to the S1 cortex (Figure S7C). Would it be possible to estimate the relative density of axons projecting to the POm versus those projecting to S1? In addition, the authors should discuss the potential functional role of this direct pathway from the ZI to the S1 cortex.

Reviewer #2 (Public review):

Summary:

The authors examined long-distance influence of climbing fiber (CF) signaling in the somatosensory cortex by manipulating whiskers through stimulation. Also, they examined CF signaling using two-photon imaging and mapped projections from the cerebellum to the somatosensory cortex using transsynaptic tracing. As a final manipulation, they used chemogenetics to perturb parvalbumin-positive neurons in the zona incerta and recorded from climbing fibers.

Strengths:

There are several strengths to this paper. The recordings were carefully performed, and AAVs used were selective and specific for the cell types and pathways being analyzed. In addition, the authors used multiple approaches that support climbing fiber pathways to distal regions of the brain. This work will impact the field and describes nice methods to target difficult-to-reach brain regions, such as the inferior olive.

Weaknesses:

There are some details in the methods that could be explained further. The discussion was very short and could connect the findings in a broader way.

Reviewer #3 (Public review):

Summary:

The authors developed an interesting novel paradigm to probe the effects of cerebellar climbing fiber activation on short-term adaptation of somatosensory neocortical activity during repetitive whisker stimulation. Normally, RWS potentiated whisker responses in pyramidal cells and weakly suppressed them in interneurons, lasting for at least 1h. Crusii Optogenetic climbing fiber activation during RWS reduced or inverted these adaptive changes. This effect was generally mimicked or blocked with chemogenetic SST or VIP activation/suppression as predicted based on their "sign" in the circuit.

Strengths:

The central finding about CF modulation of S1 response adaptation is interesting, important, and convincing, and provides a jumping-off point for the field to start to think carefully about cerebellar modulation of neocortical plasticity.

Weaknesses:

The SST and VIP results appeared slightly weaker statistically, but I do not personally think this detracts from the importance of the initial finding (if there are multiple underlying mechanisms, modulating one may reproduce only a fraction of the effect size). I found the suggestion that zona incerta may be responsible for the cerebellar effects on S1 to be a more speculative result (it is not so easy with existing technology to effectively modulate this type of polysynaptic pathway), but this may be an interesting topic for the authors to follow up on in more detail in the future.

Reviewer #1 (Public review):

The manuscript by Chiu et al describes the modification of the Zwitch strategy to efficiently generate conditional knockouts of zebrafish betapix. They leverage this system to identify a surprising glia-exclusive function of betapix in mediating vascular integrity and angiogenesis. Betapix has been previously associated with vascular integrity and angiogenesis in zebrafish, and betapix function in glia has also been proposed. However, this study identifies glial betapix in vascular stability and angiogenesis for the first time.

The study derives its strength from the modified CRISPR-based Zwitch approach to identify the specific role of glial betapix (and not neuronal, mural or endothelial). Using RNA-in situ hybridisation and analysis of scRNA-Seq data, they also identify delayed maturation of neurons and glia and implicate a reduction in stathmin levels in the glial knockouts in mediating vascular homeostasis and angiogenesis. The study also implicates a betapix-zfhx3/4-vegfa axis in mediating cerebral angiogenesis.

There is both technical (the generation of conditional KOs) and knowledge-related (the exclusive role of glial betapix in vascular stability/angiogenesis) novelty in this work that is going to benefit the community significantly.

However, the study has the following major weaknesses:

(1) The lack of glia-specific rescue of betapix in the global KOs/mutants prevents the study from making a compelling case for the unexpected glial-specific function in vascular development and stability.

(2) Given the known splice-isoform specific function of betapix in haemorrhaging (Liu et al, 2007), at least an expression profile of the isoforms in glia at the relevant timepoints would have further underscored betapix function.

(3) Direct evidence of the status of endothelial cell proliferation/survival deficits, if any, in the glial betapix KOs would have provided a key mechanistic handle. It becomes all the more relevant as Liu et al, 2012 have demonstrated reduced proliferation of endothelial cells in bbh fish and linked it to deficits in angiogenesis.

Reviewer #1 (Public review):

This paper describes a number of patterns of epistasis in a large fitness landscape dataset recently published by Papkou et al. The paper is motivated by an important goal in the field of evolutionary biology to understand the statistical structure of epistasis in protein fitness landscapes, and it capitalizes on the unique opportunities presented by this new dataset to address this problem.

The paper reports some interesting previously unobserved patterns that may have implications for our understanding of fitness landscapes and protein evolution. In particular, Figure 5 is very intriguing. However, I have two major concerns detailed below. First, I found the paper rather descriptive (it makes little attempt to gain deeper insights into the origins of the observed patterns) and unfocused (it reports what appears to be a disjointed collection of various statistics without a clear narrative. Second, I have concerns with the statistical rigor of the work.

(1) I think Figures 5 and 7 are the main, most interesting, and novel results of the paper. However, I don't think that the statement "Only a small fraction of mutations exhibit global epistasis" accurately describes what we see in Figure 5. To me, the most striking feature of this figure is that the effects of most mutations at all sites appear to be a mixture of three patterns. The most interesting pattern noted by the authors is of course the "strong" global epistasis, i.e., when the effect of a mutation is highly negatively correlated with the fitness of the background genotype. The second pattern is a "weak" global epistasis, where the correlation with background fitness is much weaker or non-existent. The third pattern is the vertically spread-out cluster at low-fitness backgrounds, i.e., a mutation has a wide range of mostly positive effects that are clearly not correlated with fitness. What is very interesting to me is that all background genotypes fall into these three groups with respect to almost every mutation, but the proportions of the three groups are different for different mutations. In contrast to the authors' statement, it seems to me that almost all mutations display strong global epistasis in at least a subset of backgrounds. A clear example is C>A mutation at site 3.

1a. I think the authors ought to try to dissect these patterns and investigate them separately rather than lumping them all together and declaring that global epistasis is rare. For example, I would like to know whether those backgrounds in which mutations exhibit strong global epistasis are the same for all mutations or whether they are mutation- or perhaps position-specific. Both answers could be potentially very interesting, either pointing to some specific site-site interactions or, alternatively, suggesting that the statistical patterns are conserved despite variation in the underlying interactions.

1b. Another rather remarkable feature of this plot is that the slopes of the strong global epistasis patterns seem to be very similar across mutations. Is this the case? Is there anything special about this slope? For example, does this slope simply reflect the fact that a given mutation becomes essentially lethal (i.e., produces the same minimal fitness) in a certain set of background genotypes?

1c. Finally, how consistent are these patterns with some null expectations? Specifically, would one expect the same distribution of global epistasis slopes on an uncorrelated landscape? Are the pivot points unusually clustered relative to an expectation on an uncorrelated landscape?

1d. The shapes of the DFE shown in Figure 7 are also quite interesting, particularly the bimodal nature of the DFE in high-fitness (HF) backgrounds. I think this bimodality must be a reflection of the clustering of mutation-background combinations mentioned above. I think the authors ought to draw this connection explicitly. Do all HF backgrounds have a bimodal DFE? What mutations occupy the "moving" peak?

1e. In several figures, the authors compare the patterns for HF and low-fitness (LF) genotypes. In some cases, there are some stark differences between these two groups, most notably in the shape of the DFE (Figure 7B, C). But there is no discussion about what could underlie these differences. Why are the statistics of epistasis different for HF and LF genotypes? Can the authors at least speculate about possible reasons? Why do HF and LF genotypes have qualitatively different DFEs? I actually don't quite understand why the transition between bimodal DFE in Figure 7B and unimodal DFE in Figure 7C is so abrupt. Is there something biologically special about the threshold that separates LF and HF genotypes? My understanding was that this was just a statistical cutoff. Perhaps the authors can plot the DFEs for all backgrounds on the same plot and just draw a line that separates HF and LF backgrounds so that the reader can better see whether the DFE shape changes gradually or abruptly.

1f. The analysis of the synonymous mutations is also interesting. However I think a few additional analyses are necessary to clarify what is happening here. I would like to know the extent to which synonymous mutations are more often neutral compared to non-synonymous ones. Then, synonymous pairs interact in the same way as non-synonymous pair (i.e., plot Figure 1 for synonymous pairs)? Do synonymous or non-synonymous mutations that are neutral exhibit less epistasis than non-neutral ones? Finally, do non-synonymous mutations alter epistasis among other mutations more often than synonymous mutations do? What about synonymous-neutral versus synonymous-non-neutral. Basically, I'd like to understand the extent to which a mutation that is neutral in a given background is more or less likely to alter epistasis between other mutations than a non-neutral mutation in the same background.

(2) I have two related methodological concerns. First, in several analyses, the authors employ thresholds that appear to be arbitrary. And second, I did not see any account of measurement errors. For example, the authors chose the 0.05 threshold to distinguish between epistasis and no epistasis, but why this particular threshold was chosen is not justified. Another example: is whether the product s12 × (s1 + s2) is greater or smaller than zero for any given mutation is uncertain due to measurement errors. Presumably, how to classify each pair of mutations should depend on the precision with which the fitness of mutants is measured. These thresholds could well be different across mutants. We know, for example, that low-fitness mutants typically have noisier fitness estimates than high-fitness mutants. I think the authors should use a statistically rigorous procedure to categorize mutations and their epistatic interactions. I think it is very important to address this issue. I got very concerned about it when I saw on LL 383-388 that synonymous stop codon mutations appear to modulate epistasis among other mutations. This seems very strange to me and makes me quite worried that this is a result of noise in LF genotypes.

Reviewer #2 (Public review):

Significance:

This paper reanalyzes an experimental fitness landscape generated by Papkou et al., who assayed the fitness of all possible combinations of 4 nucleotide states at 9 sites in the E. coli DHFR gene, which confers antibiotic resistance. The 9 nucleotide sites make up 3 amino acid sites in the protein, of which one was shown to be the primary determinant of fitness by Papkou et al. This paper sought to assess whether pairwise epistatic interactions differ among genetic backgrounds at other sites and whether there are major patterns in any such differences. They use a "double mutant cycle" approach to quantify pairwise epistasis, where the epistatic interaction between two mutations is the difference between the measured fitness of the double-mutant and its predicted fitness in the absence of epistasis (which equals the sum of individual effects of each mutation observed in the single mutants relative to the reference genotype). The paper claims that epistasis is "fluid," because pairwise epistatic effects often differs depending on the genetic state at the other site. It also claims that this fluidity is "binary," because pairwise effects depend strongly on the state at nucleotide positions 5 and 6 but weakly on those at other sites. Finally, they compare the distribution of fitness effects (DFE) of single mutations for starting genotypes with similar fitness and find that despite the apparent "fluidity" of interactions this distribution is well-predicted by the fitness of the starting genotype.

The paper addresses an important question for genetics and evolution: how complex and unpredictable are the effects and interactions among mutations in a protein? Epistasis can make the phenotype hard to predict from the genotype and also affect the evolutionary navigability of a genotype landscape. Whether pairwise epistatic interactions depend on genetic background - that is, whether there are important high-order interactions -- is important because interactions of order greater than pairwise would make phenotypes especially idiosyncratic and difficult to predict from the genotype (or by extrapolating from experimentally measured phenotypes of genotypes randomly sampled from the huge space of possible genotypes). Another interesting question is the sparsity of such high-order interactions: if they exist but mostly depend on a small number of identifiable sequence sites in the background, then this would drastically reduce the complexity and idiosyncrasy relative to a landscape on which "fluidity" involves interactions among groups of all sites in the protein. A number of papers in the recent literature have addressed the topics of high-order epistasis and sparsity and have come to conflicting conclusions. This paper contributes to that body of literature with a case study of one published experimental dataset of high quality. The findings are therefore potentially significant if convincingly supported.

Validity:

In my judgment, the major conclusions of this paper are not well supported by the data. There are three major problems with the analysis.

(1) Lack of statistical tests. The authors conclude that pairwise interactions differ among backgrounds, but no statistical analysis is provided to establish that the observed differences are statistically significant, rather than being attributable to error and noise in the assay measurements. It has been established previously that the methods the authors use to estimate high-order interactions can result in inflated inferences of epistasis because of the propagation of measurement noise (see PMID 31527666 and 39261454). Error propagation can be extreme because first-order mutation effects are calculated as the difference between the measured phenotype of a single-mutant variant and the reference genotype; pairwise effects are then calculated as the difference between the measured phenotype of a double mutant and the sum of the differences described above for the single mutants. This paper claims fluidity when this latter difference itself differs when assessed in two different backgrounds. At each step of these calculations, measurement noise propagates. Because no statistical analysis is provided to evaluate whether these observed differences are greater than expected because of propagated error, the paper has not convincingly established or quantified "fluidity" in epistatic effects.

(2) Arbitrary cutoffs. Many of the analyses involve assigning pairwise interactions into discrete categories, based on the magnitude and direction of the difference between the predicted and observed phenotypes for a pairwise mutant. For example, the authors categorize as a positive pairwise interaction if the apparent deviation of phenotype from prediction is >0.05, negative if the deviation is <-0.05, and no interaction if the deviation is between these cutoffs. Fluidity is diagnosed when the category for a pairwise interaction differs among backgrounds. These cutoffs are essentially arbitrary, and the effects are assigned to categories without assessing statistical significance. For example, an interaction of 0.06 in one background and 0.04 in another would be classified as fluid, but it is very plausible that such a difference would arise due to error alone. The frequency of epistatic interactions in each category as claimed in the paper, as well as the extent of fluidity across backgrounds, could therefore be systematically overestimated or underestimated, affecting the major conclusions of the study.

(3) Global nonlinearities. The analyses do not consider the fact that apparent fluidity could be attributable to the fact that fitness measurements are bounded by a minimum (the fitness of cells carrying proteins in which DHFR is essentially nonfunctional) and a maximum (the fitness of cells in which some biological factor other than DHFR function is limiting for fitness). The data are clearly bounded; the original Papkou et al. paper states that 93% of genotypes are at the low-fitness limit at which deleterious effects no longer influence fitness. Because of this bounding, mutations that are strongly deleterious to DHFR function will therefore have an apparently smaller effect when introduced in combination with other deleterious mutations, leading to apparent epistatic interactions; moreover, these apparent interactions will have different magnitudes if they are introduced into backgrounds that themselves differ in DHFR function/fitness, leading to apparent "fluidity" of these interactions. This is a well-established issue in the literature (see PMIDs 30037990, 28100592, 39261454). It is therefore important to adjust for these global nonlinearities before assessing interactions, but the authors have not done this.

This global nonlinearity could explain much of the fluidity claimed in this paper. It could explain the observation that epistasis does not seem to depend as much on genetic background for low-fitness backgrounds, and the latter is constant (Figure 2B and 2C): these patterns would arise simply because the effects of deleterious mutations are all epistatically masked in backgrounds that are already near the fitness minimum. It would also explain the observations in Figure 7. For background genotypes with relatively high fitness, there are two distinct peaks of fitness effects, which likely correspond to neutral mutations and deleterious mutations that bring fitness to the lower bound of measurement; as the fitness of the background declines, the deleterious mutations have a smaller effect, so the two peaks draw closer to each other, and in the lowest-fitness backgrounds, they collapse into a single unimodal distribution in which all mutations are approximately neutral (with the distribution reflecting only noise).<br /> Global nonlinearity could also explain the apparent "binary" nature of epistasis. Sites 4 and 5 change the second amino acid, and the Papkou paper shows that only 3 amino acid states (C, D, and E) are compatible with function; all others abolish function and yield lower-bound fitness, while mutations at other sites have much weaker effects. The apparent binary nature of epistasis in Figure 5 corresponds to these effects given the nonlinearity of the fitness assay. Most mutations are close to neutral irrespective of the fitness of the background into which they are introduced: these are the "non-epistatic" mutations in the binary scheme. For the mutations at sites 4 and 5 that abolish one of the beneficial mutations, however, these have a strong background-dependence: they are very deleterious when introduced into a high-fitness background but their impact shrinks as they are introduced into backgrounds with progressively lower fitness. The apparent "binary" nature of global epistasis is likely to be a simple artifact of bounding and the bimodal distribution of functional effects: neutral mutations are insensitive to background, while the magnitude of the fitness effect of deleterious mutations declines with background fitness because they are masked by the lower bound. The authors' statement is that "global epistasis often does not hold." This is not established. A more plausible conclusion is that global epistasis imposed by the phenotype limits affects all mutations, but it does so in a nonlinear fashion.

In conclusion, most of the major claims in the paper could be artifactual. Much of the claimed pairwise epistasis could be caused by measurement noise, the use of arbitrary cutoffs, and the lack of adjustment for global nonlinearity. Much of the fluidity or higher-order epistasis could be attributable to the same issues. And the apparently binary nature of global epistasis is also the expected result of this nonlinearity.

Reviewer #3 (Public review):

Summary:

The authors have studied a previously published large dataset on the fitness landscape of a 9 base-pair region of the folA gene. The objective of the paper is to understand various aspects of epistasis in this system, which the authors have achieved through detailed and computationally expensive exploration of the landscape. The authors describe epistasis in this system as "fluid", meaning that it depends sensitively on the genetic background, thereby reducing the predictability of evolution at the genetic level. However, the study also finds two robust patterns. The first is the existence of a "pivot point" for a majority of mutations, which is a fixed growth rate at which the effect of mutations switches from beneficial to deleterious (consistent with a previous study on the topic). The second is the observation that the distribution of fitness effects (DFE) of mutations is predicted quite well by the fitness of the genotype, especially for high-fitness genotypes. While the work does not offer a synthesis of the multitude of reported results, the information provided here raises interesting questions for future studies in this field.

Strengths:

A major strength of the study is its detailed and multifaceted approach, which has helped the authors tease out a number of interesting epistatic properties. The study makes a timely contribution by focusing on topical issues like the prevalence of global epistasis, the existence of pivot points, and the dependence of DFE on the background genotype and its fitness. The methodology is presented in a largely transparent manner, which makes it easy to interpret and evaluate the results.

The authors have classified pairwise epistasis into six types and found that the type of epistasis changes depending on background mutations. Switches happen more frequently for mutations at functionally important sites. Interestingly, the authors find that even synonymous mutations in stop codons can alter the epistatic interaction between mutations in other codons. Consistent with these observations of "fluidity", the study reports limited instances of global epistasis (which predicts a simple linear relationship between the size of a mutational effect and the fitness of the genetic background in which it occurs). Overall, the work presents some evidence for the genetic context-dependent nature of epistasis in this system.

Weaknesses:

Despite the wealth of information provided by the study, there are some shortcomings of the paper which must be mentioned.

(1) In the Significance Statement, the authors say that the "fluid" nature of epistasis is a previously unknown property. This is not accurate. What the authors describe as "fluidity" is essentially the prevalence of certain forms of higher-order epistasis (i.e., epistasis beyond pairwise mutational interactions). The existence of higher-order epistasis is a well-known feature of many landscapes. For example, in an early work, (Szendro et. al., J. Stat. Mech., 2013), the presence of a significant degree of higher-order epistasis was reported for a number of empirical fitness landscapes. Likewise, (Weinreich et. al., Curr. Opin. Genet. Dev., 2013) analysed several fitness landscapes and found that higher-order epistatic terms were on average larger than the pairwise term in nearly all cases. They further showed that ignoring higher-order epistasis leads to a significant overestimate of accessible evolutionary paths. The literature on higher-order epistasis has grown substantially since these early works. Any future versions of the present preprint will benefit from a more thorough contextual discussion of the literature on higher-order epistasis.

(2) In the paper, the term 'sign epistasis' is used in a way that is different from its well-established meaning. (Pairwise) sign epistasis, in its standard usage, is said to occur when the effect of a mutation switches from beneficial to deleterious (or vice versa) when a mutation occurs at a different locus. The authors require a stronger condition, namely that the sum of the individual effects of two mutations should have the opposite sign from their joint effect. This is a sufficient condition for sign epistasis, but not a necessary one. The property studied by the authors is important in its own right, but it is not equivalent to sign epistasis.

(3) The authors have looked for global epistasis in all 108 (9x12) mutations, out of which only 16 showed a correlation of R^2 > 0.4. 14 out of these 16 mutations were in the functionally important nucleotide positions. Based on this, the authors conclude that global epistasis is rare in this landscape, and further, that mutations in this landscape can be classified into one of two binary states - those that exhibit global epistasis (a small minority) and those that do not (the majority). I suspect, however, that a biologically significant binary classification based on these data may be premature. Unsurprisingly, mutational effects are stronger at the functional sites as seen in Figure 5 and Figure 2, which means that even if global epistasis is present for all mutations, a statistical signal will be more easily detected for the functionally important sites. Indeed, the authors show that the means of DFEs decrease linearly with background fitness, which hints at the possibility that a weak global epistatic effect may be present (though hard to detect) in the individual mutations. Given the high importance of the phenomenon of global epistasis, it pays to be cautious in interpreting these results.

(4) The study reports that synonymous mutations frequently change the nature of epistasis between mutations in other codons. However, it is unclear whether this should be surprising, because, as the authors have already noted, synonymous mutations can have an impact on cellular functions. The reader may wonder if the synonymous mutations that cause changes in epistatic interactions in a certain background also tend to be non-neutral in that background. Unfortunately, the fitness effect of synonymous mutations has not been reported in the paper.

(5) The authors find that DFEs of high-fitness genotypes tend to depend only on fitness and not on genetic composition. This is an intriguing observation, but unfortunately, the authors do not provide any possible explanation or connect it to theoretical literature. I am reminded of work by (Agarwala and Fisher, Theor. Popul. Biol., 2019) as well as (Reddy and Desai, eLife, 2023) where conditions under which the DFE depends only on the fitness have been derived. Any discussion of possible connections to these works could be a useful addition.

Reviewer #1 (Public review):

Summary:

The idea is appealing, but the authors have not sufficiently demonstrated the utility of this approach.

Strengths:

Novelty of the approach, potential implications for discovering novel interactions

Comments on revisions:

The authors have adequately addressed most of my concerns in this improved version of the manuscript

Reviewer #2 (Public review):

Summary:

The membrane mimetic thermal proteome profiling (MM-TPP) presented by Jandu et al. promises a useful way to minimize the interference of detergents in efficient mass spectrometry analysis of membrane proteins. Thermal proteome profiling is a mass spectrometric method that measures binding of a drug to different proteins in a cell lysate by monitoring thermal stabilization of the proteins because of the interaction with the ligands that are being studied. This method has been underexplored for membrane proteome because of the inefficient mass spectrometric detection of membrane proteins and because of the interference from detergents that are used often for membrane protein solubilization.

Strengths:

In this report the binding of ligands to membrane protein targets has been monitored in crude membrane lysates or tissue homogenates exalting the efficacy of the method to detect both intended and off-target binding events in a complex physiologically relevant sample setting. The manuscript is lucidly written and the data presented seems clear. Kudos to the authors. This methodology shows immense potential for identifying membrane protein binders (small-molecule or protein) in a near-native environment, and as a result promises to be a great tool for drug discovery campaigns.

Weaknesses:

While this is a solid report and a promising tool for analyzing membrane protein drug interactions in a detergent-free environment, it is crucial to bear in mind that the process of reconstitution begins with detergent solubilization of the proteome and does not completely circumvent structural perturbations invoked by detergents.

Reviewer #2 (Public review):

Summary:

This study characterized the function of SLC35G3, a putative transmembrane UDP-N-acetylglucosamine transporter, in spermatogenesis. They showed that SLC35G3 is testis-specific and expressed in round spermatids. Slc35g3-null males were sterile but females were fertile. Slc35g3-null males produced normal sperm count but sperm showed subtle head morphology. Sperm from Slc35g3-null males have defects in uterotubal junction passage, ZP binding, and oocyte fusion. Loss of SLC35G3 causes abnormal processing and glycosylation of a number sperm proteins in testis and sperm. They demonstrated that SLC35G3 functions as a UDP-GlcNAc transporter in cell lines. Two human SLC35G3 variants impaired its transporter activity, implicating these variants in human infertility.

Strengths:

This study is thorough. The mutant phenotype is strong and interesting. The major conclusions are supported by the data. This study demonstrated SLC35G3 as a new and essential factor for male fertility in mice, which is likely conserved in humans.

Weaknesses:

Some data interpretations needed to be revised. These have been adequately addressed in the revised manuscript.

Reviewer #1 (Public review):

Summary:

Zhang et al. used a conditional knockout mouse model to re-examine the role of the RNA-binding protein PTBP1 in the transdifferentiation of astroglial cells into neurons. Several earlier studies reported that PTBP1 knockdown can efficiently induce the transdifferentiation of rodent glial cells into neurons, suggesting potential therapeutic applications for neurodegenerative diseases. However, these findings have been contested by subsequent studies, which in turn have been challenged by more recent publications. In their current work, Zhang et al. deleted exon 2 of the Ptbp1 gene using an astrocyte-specific, tamoxifen-inducible Cre line and investigated - using fluorescence imaging and bulk and single-cell RNA-sequencing - whether this manipulation promotes the transdifferentiation of astrocytes into neurons across various brain regions. The data strongly indicate that genetic ablation of PTBP1 is not sufficient to drive efficient conversion of astrocytes into neurons. Interestingly, while PTBP1 loss alters splicing patterns in numerous genes, these changes do not shift the astroglial transcriptome toward a neuronal profile.

Strengths:

Although this is not the first report of PTBP1 ablation in mouse astrocytes in vivo, this study utilizes a distinct knockout strategy and provides novel insights into PTBP1-regulated splicing events in astrocytes. The manuscript is well written, and the experiments are technically sound and properly controlled. I believe this study will be of considerable interest to the broad readership of eLife.

Original weaknesses:

(1) The primary point that needs to be addressed is a better understanding of the effect of exon 2 deletion on PTBP1 expression. Figure 4D shows successful deletion of exon 2 in knockout astrocytes. However - assuming that the coverage plots are CPM-normalized - the overall PTBP1 mRNA expression level appears unchanged. Figure 6A further supports this observation. This is surprising, as one would expect that the loss of exon 2 would shift the open reading frame and trigger nonsense-mediated decay of the PTBP1 transcript. Given this uncertainty, the authors should confirm the successful elimination of PTBP1 protein in cKO astrocytes using an orthogonal approach, such as Western blotting, in addition to immunofluorescence. They should also discuss possible reasons why PTBP1 mRNA abundance is not detectably affected by the frameshift.

(2) The authors should analyze PTBP1 expression in WT and cKO substantia nigra samples shown in Figure 3 or justify why this analysis is not necessary.

(3) Lines 236-238 and Figure 4E: The authors report an enrichment of CU-rich sequences near PTBP1-regulated exons. To better compare this with previous studies on position-specific splicing regulation by PTBP1, it would be helpful to assess whether the position of such motifs differs between PTBP1-activated and PTBP1-repressed exons.

(4) The analyses in Figure 5 and its supplement strongly suggest that the splicing changes in PTBP1-depleted astrocytes are distinct from those occurring during neuronal differentiation. However, the authors should ensure that these comparisons are not confounded by transcriptome-wide differences in gene expression levels between astrocytes and developing neurons. One way to address this concern would be to compare the new PTBP1 cKO data with publicly available RNA-seq datasets of astrocytes induced to transdifferentiate into neurons using proneural transcription factors (e.g., PMID: 38956165).

Point 1 has been successfully addressed in the revision by providing relevant references/discussion. Points 2-4 were addressed by including additional data/analyses.

Reviewer #2 (Public review):

Summary:

The manuscript by Zhang and colleagues describes a study that investigated if deletion of PTBP1 in adult astrocytes in mice led to an astrocyte-to-neuron conversion. The study revisited the hypothesis that reduced PTBP1 expression reprogrammed astrocytes to neurons. More than 10 studies have been published on this subject, with contradicting results. Half of the studies supported the hypothesis while the other half did not. The question being addressed is an important one because if the hypothesis is correct, it can lead to exciting therapeutic applications for treating neurodegenerative diseases such as Parkinson's disease.

In this study, Zhang and colleagues conducted a conditional mouse knockout study to address the question. They used the Cre-LoxP system to specifically delete PTBP1 in adult astrocytes. Through a series of carefully controlled experiments including cell lineage tracing, the authors found no evidence for the astrocyte-to-neuron conversion.

The authors then carried out a key experiment that none of previous studies on the subject did: investigating alternative splicing pattern changes in PTBP1-depleted cells using RNA-seq analysis. The idea is to compare the splicing pattern change caused by PTBP1 deletion in astrocytes to what occurs during neurodevelopment. This is an important experiment that will help illuminate if the astrocyte-to-neuron transition occurred in the system. The result was consistent with that of the cell staining experiments: no significant transition being detected.

These experiments demonstrate that, in this experiment setting, PTBT1 deletion in adult astrocytes did not convert the cells to neurons.

Strengths:

This is a well-designed, elegantly conducted, and clearly described study that addresses an important question. The conclusions provide important information to the field.<br /> To this reviewer, this study provided convincing and solid experimental evidence to support the authors' conclusions.

My concerns in the previous review have been addressed satisfactorily.

Reviewer #2 (Public review):

Summary:

In this paper, the authors use numerical simulations to try to understand better a major experimental discovery in songbird neuroscience from 2002 by Richard Hahnloser and collaborators. The 2002 paper found that a certain class of projection neurons in the premotor nucleus HVC of adult male zebra finch songbirds, the neurons that project to another premotor nucleus RA, fired sparsely (once per song motif) and precisely (to about 1 ms accuracy) during singing.

The experimental discovery is important to understand since it initially suggested that the sparsely firing RA-projecting neurons acted as a simple clock that was localized to HVC and that controlled all details of the temporal hierarchy of singing: notes, syllables, gaps, and motifs. Later experiments suggested that the initial interpretation might be incomplete: that the temporal structure of adult male zebra finch songs instead emerged in a more complicated and distributed way, still not well understood, from the interaction of HVC with multiple other nuclei, including auditory and brainstem areas. So at least two major questions remain unanswered more than two decades after the 2002 experiment: What is the neurobiological mechanism that produces the sparse precise bursting: is it a local circuit in HVC or is it some combination of external input to HVC and local circuitry? And how is the sparse precise bursting in HVC related to a songbird's vocalizations?

The authors only investigate part of the first question, whether the mechanism for sparse precise bursts is local to HVC. They do so indirectly, by using conductance-based Hodgkin-Huxley-like equations to simulate the spiking dynamics of a simplified network that includes three known major classes of HVC neurons and such that all neurons within a class are assumed to be identical. A strength of the calculations is that the authors include known biophysically deduced details of the different conductances of the three majors classes of HVC neurons, and they take into account what is known, based on sparse paired recordings in slices, about how the three classes connect to one another. One weakness of the paper is that the authors make arbitrary and not-well-motivated assumptions about the network geometry, and they do not use the flexibility of their simulations to study how their results depend on their network assumptions. A second weakness is that they ignore many known experimental details such as projections into HVC from other nuclei, dendritic computations (the somas and dendrites are treated by the authors as point-like isopotential objects), the role of neuromodulators, and known heterogeneity of the interneurons. These weaknesses make it difficult for readers to know the relevance of the simulations for experiments and for advancing theoretical understanding.

Strengths:

The authors use conductance-based Hodgkin-Huxley-like equations to simulate spiking activity in a network of neurons intended to model more accurately songbird nucleus HVC of adult male zebra finches. Spiking models are much closer to experiments than models based on firing rates or on 2-state neurons.

The authors include information deduced from modeling experimental current-clamp data such as the types and properties of conductances. They also take into account how neurons in one class connect to neurons in other classes via excitatory or inhibitory synapses, based on sparse paired recordings in slices by other researchers.

The authors obtain some new results of modest interest such as how changes in the maximum conductances of four key channels (e.g., A-type K+ currents or Ca-dependent K+ currents) influence the structure and propagation of bursts, while simultaneously being able to mimic accurately current-clamp voltage measurements.

Weaknesses:

One weakness of this paper is the lack of a clearly stated, interesting, and relevant scientific question to try to answer. The authors do not discuss adequately in their introduction what questions have recent experimental and theoretical work failed to explain adequately concerning HVC neural dynamics and its role in producing vocalizations. The authors do not discuss adequately why they chose the approach of their paper and how their results address some of these questions.

For example, the authors need to explain in more detail how their calculations relate to the works of Daou et al, J. Neurophys. 2013 (which already fitted spiking models to neuronal data and identified certain conductances), to Jin et al J. Comput. Neurosci. 2007 (which already discussed how to get bursts using some experimental details), and to the rather similar paper by E. Armstrong and H. Abarbanel, J. Neurophys 2016, which already postulated and studied sequences of microcircuits in HVC. This last paper is not even cited by the authors.

The authors' main achievement is to show that simulations of a certain simplified and idealized network of spiking neurons, that includes some experimental details but ignores many others, can match some experimental results like current-clamp-derived voltage time series for the three classes of HVC neurons (although this was already reported in earlier work by Daou and collaborators in 2013), and simultaneously the robust propagation of bursts with properties similar to those observed in experiments. The authors also present results about how certain neuronal details and burst propagation change when certain key maximum conductances are varied.

But these are weak conclusions for two reasons. First, the authors did not do enough calculations to allow the reader to understand how many parameters were needed to obtain these fits and whether simpler circuits, say with fewer parameters and simpler network topology, could do just as well. Second, many previous researchers have demonstrated robust burst propagation in a variety of feed-forward models. So what is new and important about the authors' results compared to the previous computational papers?

Also missing is a discussion, or at least an acknowledgement, of the fact that not all of the fine experimental details of undershoots, latencies, spike structure, spike accommodation, etc may be relevant for understanding vocalization. While it is nice to know that some model can match these experimental details and produce realistic bursts, that does not mean that all of these details are relevant for the function of producing precise vocalizations. Scientific insights in biology often require exploring which of the many observed details can be ignored, and especially identifying the few that are essential for answering some questions. As one example, if HVC-X neurons are completely removed from the authors' model, does one still get robust and reasonable burst propagation of HVC-RA neurons? While part of nucleus HVC acts as a premotor circuit that drives nucleus RA, part of HVC is also related to learning. It is not clear that HVC-X neurons, which carry out some unknown calculation and transmit information to area X in a learning pathway, are relevant for burst production and propagation of HVC-RA neurons, and so relevant for vocalization. Simulations provide a convenient and direct way to explore questions of this kind.

One key question to answer is whether the bursting of HVC-RA projection neurons is based on a mechanism local to HVC or is some combination of external driving (say from auditory nuclei) and local circuitry. The authors do not contribute to answering this question because they ignore external driving and assume that the mechanism is some kind of intrinsic feed-forward circuit, which they put in by hand in a rather arbitrary and poorly justified way, by assuming the existence of small microcircuits consisting of a few HVC-RA, HVC-X, and HVC-I neurons that somehow correspond to "sub-syllabic segments". To my knowledge, experiments do not suggest the existence of such microcircuits nor does theory suggest the need for such microcircuits.

Another weakness of this paper is an unsatisfactory discussion of how the model was obtained, validated, and simulated. The authors should state as clearly as possible, in one location such as an appendix, what is the total number of independent parameters for the entire network and how parameter values were deduced from data or assigned by hand. With enough parameters and variables, many details can be fit arbitrarily accurately so researchers have to be careful to avoid overfitting. If parameter values were obtained by fitting to data, the authors should state clearly what was the fitting algorithm (some iterative nonlinear method, whose results can depend on the initial choice of parameters), what was the error function used for fitting (sum of least squares?), and what data were used for the fitting.

The authors should also state clearly what is the dynamical state of the network, the vector of quantities that evolve over time. (What is the dimension of that vector, which is also the number of ordinary differential equations that have to be integrated?) The authors do not mention what initial state was used to start the numerical integrations, whether transient dynamics were observed and what were their properties, or how the results depend on the choice of initial state. The authors do not discuss how they determined that their model was programmed correctly (it is difficult to avoid typing errors when writing several pages or more of a code in any language) or how they determined the accuracy of the numerical integration method beyond fitting to experimental data, say by varying the time step size over some range or by comparing two different integration algorithms.

Also disappointing is that the authors do not make any predictions to test, except rather weak ones such as that varying a maximum conductance sufficiently (which might be possible by using dynamic clamps) might cause burst propagation to stop or change its properties. Based on their results, the authors do not make suggestions for further experiments or calculations, but they should.

Comments on revised version:

The second version, unfortunately, did not address most of the substantive comments so that, while some parts of the discussion were expanded, most of the serious scientific weaknesses mentioned in the first round of review remain. The revised preprint is not a substantive improvement over the first.

Joint Public Review:

Summary:

The authors previously published a study of RGC boutons in the dLGN in developing wild-type mice and developing mutant mice with disrupted spontaneous activity. In the current manuscript, they have broken down their analysis of RGC boutons according to the number of Homer/Bassoon puncta associated with each vGlut3 cluster.

The authors find that, in the first post-natal week, RGC boutons with multiple active zones (mAZs) are about a third as common as boutons with a single active zone (sAZ). The size of the vGluT2 cluster associated with each bouton was proportional to the number of active zones present in each bouton. Within the author's ability to estimate these values (n=3 per group, 95% of results expected to be within ~2.5 standard deviations), these results are consistent across groups: 1) dominant eye vs. non-dominant eye, 2) wild-type mice vs. mice with activity blocked, and at 3) ages P2, P4, and P8. The authors also found that mAZs and sAZs also have roughly the same number (about 1.5) of sAZs clustered around them (within 1.5 um).

There has been much discussion with the reviewers through multiple versions of this paper. of how to interpret these findings. Based on a large number of tests for statistical significance, the authors interpreted the presence of a statistical significance difference as evidence that "Eye-specific active zone clustering underlies synaptic competition in the developing visual system (title of previous version of manuscript)". The reviewers have focused on the small effect size as indicating that the small differences observed are not informative regarding this biological question. The authors have now tempered this interpretation.

Strengths:

The source dataset is high resolution data showing the colocalization of multiple synaptic proteins across development. Added to this data is labeling that distinguishes axons from the right eye from axons from the left eye. The first order analysis of this data showing changes in synapse density and in the occurrence of multi-active zone synapses is useful information about the development of an important model for activity dependent synaptic remodeling.

Reviewing Editor's comment on the latest revision (without sending the paper back to the individual reviewers):

In their latest revision, the authors have moderated earlier causal claims, incorporated additional statistical controls, and largely maintained their original interpretation of the data. While these changes address some prior concerns, the underlying issues remain. The previous review emphasized that the reported effect sizes were small and therefore hard to link to biological relevance. The authors argue that the effect sizes are large. Given the lack of a biological argument for this effect size, this point is really semantic. We would like to point out that the effect size measurement the authors used is likely a standard effect size calculation (the difference between groups is divided by the standard deviation of the groups). With only three experiments and irregular variance, it is likely that their estimates of standard deviation-and therefore effect size-are unreliable. Overall, the revisions improve presentation but do not substantively resolve the difficulty in drawing strong conclusions from the data set raised earlier.

Reviewer #1 (Public review):

Summary:

The previous evidence for NMDARs containing N1, N2, and N3 subunits (t-NMDARs) was weak. All previous results could be explained by mixtures of di-heteromeric receptors. The authors here set out to identify t-NMDARs both in vitro and in the brain.

Strengths:

The single-channel recording is quite convincing because the authors could reproduce previous results in their system, but could also then add new observations. It is quite hard (if not impossible) to obtain the N1-N2A-N3A result at 100 µM Glu/Gly from a mixture, because the N1-N2A diheteromer has such a high open probability. Therefore, any idea that this might be, in fact, two receptors (GluN1-N2A and GluN1-N3A) is trivially falsified. The authors might prefer to make this argument based on the reduction of open probability, which cannot be achieved from a mixture masquerading as a single channel.

With regard to crosslinker usage in brain tissue, these are very impressive attempts, which I applaud. The fluorescence images of the brain sections look convincing. But the bands corresponding to N2-N3 crosslinked subunits from neurons or the brain are faint. I would want more information to be convinced that these faint bands come from GluN2-N3 dimers.

Weaknesses:

In the first part of the paper, where the CryoEM structure is determined, it's not really clear to me the extent to which Fab binding might bias the position of the ATDs (and even then the arrangement of each subunit within the whole complex). Then, much later at the end of the results, there is a structural analysis that claims to be integrative (Figure 7) but does not obviously rely on any other data than the structures, but does mention this point about the Fabs. The results could be rearranged to make these points clearer.

I have my biggest doubts about the crosslinking of native receptors. For the biochemistry from neurons or brain tissue, this is a very ambitious idea that has been hard to execute over the past 15-20 years. The authors use AzF for the obvious reason that this was done before in NMDARs. The constructs that have been assembled are neat. But AzF is a really bad crosslinker. The authors attribute the weak bands to subunit mobility, but the minor abundance is more likely due to the strong constraints on AzF crosslinking and its unsuitable photochemistry in general (very easily activated with room light, for example).

There is no information at all given about the wavelength, intensity, duration of UV exposure, and how, for example, the right exposure was determined. How were the samples protected in between?

Reviewer #2 (Public review):

Summary:

The authors purified and solved by cryo-EM a structure of tri-heteromeric GluN1/GluN2A/GluN3A NMDA receptors, whose existence has long been contentious. Using patch-clamp electrophysiology on GluN1/GluN2/GluN3A NMDARs reconstituted into liposomes, they characterized the function of this NMDAR subtype. Finally, thanks to site-targeted crosslinking using unnatural amino acid incorporation, they show that the GluN2A subunit can crosslink with the GluN3A subunit in a cellular context, both in recombinant systems (HEK cells) and neuronal cultures and in vivo.

Strengths:

The NMDAR GluN3 subunit is a glycine-binding subunit that was long thought to assemble into GluN1/GluN2/GluN3 tri-heteromeric receptors during development, acting as a brake for synaptic development. However, several studies based on single subunit counting (Ulbrich et al., PNAS 2008) and ex vivo/in vivo electrophysiology have challenged the existence of these tri-heteromers (see Bossi, Pizzamiglio et al., Trends Neurosci. 2023). A large part of the controversy stems from the difficulty in isolating the tri-heteromeric population from their di-heteromeric counterparts, which led to a lack of knowledge on the biophysical and pharmacological properties of putative GluN1/GluN2/GluN3 receptors. To counteract this problem, the authors used a two-step purification method - first with a strep-tag attached to the GluN3 subunit, then with a His tag attached to the GluN2 subunit - to isolate GluN1/GluN2/GluN3 tri-heteromers from GluN1/GluN2A and GluN1/GluN3 di-heteromers, and they did observe these entities in Western blot and FSEC. They solved a cryo-EM structure of this NMDAR subtype using specific FAbs to identify the GluN1 and GluN2A subunits, showing an asymmetrical, splayed architecture. Then, they reconstituted the purified receptors in lipid vesicles to perform single-channel electrophysiological recordings. Finally, in order to validate the tri-heteromeric arrangement in a cellular system, they performed photocrosslinking experiments between the GluN2A and GluN3 subunits. For this purpose, a photoactivatable unnatural amino acid (AzF) was incorporated at the bottom of GluN2A NTD, a region embedded within the receptor complex that is predicted to be in close proximity to the GluN3 subunit. This is an elegant approach to validate the existence of GluN1/GluN2/GluN3 tri-hets, since at the chosen AzF incorporation position, crosslinking between GluN2A and GluN3 is more likely to reflect interaction of subunits within the same receptor complex than between two receptors. They show crosslinking between GluN2A and GluN3 in the presence of AzF and UV light, but not if UV light or AzF were not provided, suggesting that GluN2A and GluN3 can indeed be incorporated in the same complex. In a further attempt to demonstrate the physiological relevance of these tri-heteromers, they performed the same crosslinking experiments in cultured neurons and even native brain samples. While unnatural amino acid incorporation is now a well-established technique in vitro, such an approach is very difficult to implement in vivo. The technical effort put into the validation of the presence of these tri-heteromers in vivo should thus be commended.

Overall, all the strategies used by this paper to prove the existence of GluN1/GluN2/GluN3 tri-heteromers, and investigate their structure and function, are well-thought-out and very elegant. But the current data do not fully support the conclusions of the paper.

Weaknesses:

All the experiments aiming at proving the existence of GluN1/GluN2/GluN3 tri-heteromers rely on the purification of these receptors from whole cell extracts. There is therefore no proof that these receptors are expressed at the membrane and are functional. This is a limitation that has been overlooked and should be discussed in the manuscript. In addition, in the current manuscript state, each demonstration suffers from caveats that do not allow for a firm conclusion about the existence and the properties of this receptor subtype.

(1) In Cryo-EM images of GluN1/GluN2A/GluN3A receptors, the GluN3 subunit is identified as the subunit having no Fab bound to it. How can the authors be sure that this is indeed the GluN3A subunit and not a GluN2A subunit that has not bound the Fab? Does the GluN3A subunit carry features that would allow distinguishing it independently of Fab binding? In addition, it is surprising that the authors did not incubate the tri-heteromers with a Fab against GluN3A, since Extended Figure 3 shows that such a Fab is available.

(2) Whether the single-channel recordings reflect the activity of GluN1/GluN2/GluN3 tri-heteromers is not convincing. Indeed, currents from liposomes containing these tri-heteromers have two conductance levels that correspond to the conductances of the corresponding di-heteromers. There is therefore a need for additional proof that the measured currents do not reflect a mixture of currents from N1/2A di-heteromers on one side, and N1/3A di-heteromers on the other side. What is the purity of the N1/3A sample? Indeed, given the high open probability and high conductance of N1/2A tri-heteromers, even a small fraction of them could significantly contribute to the single-channel currents. Additionally, although the authors show no current induced by 3uM glycine alone on proteoliposomes with the N1/2A/3A prep (no stats provided, though), given the sharp dependence of N1/3A currents on glycine concentration, this control alone cannot rule out the presence of contaminant N1/3A dihets in the preparation.

Finally, pharmacological characterization of these tri-heteromers is lacking. In vivo, the presence of tri-heteromeric GluN1/GluN2/GluN3 tri-heteromers was inferred from recordings of NMDARs activated by glutamate but with low magnesium sensitivity. What is the effect of magnesium on N1/2A/3A currents? Does APV, the classical NMDAR antagonist acting at the glutamate site, inhibit the tri-heteromers? What is the effect of CGP-78608, which inhibits GluN1/GluN2 NMDARs but potentiates GluN1/GluN3 NMDARs? Such pharmacological characterization is critical to validate that the measured currents are indeed carried by a tri-heteromeric population, and would also be very important to identify such tri-heteromers in native tissues.

(3) Validation of GluN1/GluN2/GluN3 tri-heteromer expression by photocrosslinking: The mixture of constructions used (full-length or CTD-truncated constructs, with or without tags) is confusing, and it is difficult to track the correct molecular weight of the different constructs. In Figure 6, the band corresponding to a putative GluN3/GluN2A dimer is very weak. In addition, given the differences in molecular weights between the GluN2 subunits and GluN3, we would expect the band corresponding to a GluN2A/GluN2B to migrate differently from the GluN2A/GluN3 dimer, but all high molecular weight bands seem to be a the same level in the blot. Finally, in the source data, the blots display additional bands that were not dismissed by the authors without justification. In short, better clarification of the constructs and more careful interpretation of the blots are necessary to support the conclusions claimed by the authors.

Reviewer #1 (Public review):

Summary:

The study shows that childhood malaria can weaken the antibody response to other vaccines and infections. This suggests that early exposure to P. falciparum may have a long-lasting effect on immunity, with implications for vaccine efficacy in endemic areas.

Strengths:

This study stands out for its longitudinal design, the use of robust immunological techniques, and the comparison between areas with different levels of malaria exposure. Its findings reveal that early malaria can weaken the response to childhood vaccines, with important implications for public health in endemic regions.

Weaknesses:

One of the study's main limitations is the lack of functional data confirming the clinical impact of the low antibody levels. Furthermore, although multiple immune responses were measured, other important components, such as cellular immunity, were not assessed. Furthermore, the results may not be generalizable to other regions.

Reviewer #2 (Public review):

Summary:

The authors investigated whether early-life malaria exposure has long-term effects on immune responses to unrelated antigens. They leveraged a natural experiment in coastal Kenya where two adjacent communities (Junju and Ngerenya) experienced divergent malaria transmission patterns after 2004. Using 15 years of longitudinal data from 123 children with weekly malaria surveillance and annual serological sampling, they measured antibody responses to multiple pathogens using a protein microarray technology and ELISA.

Strengths:

(1) Extensive longitudinal data collection with weekly malaria surveillance, enabling precise exposure classification.

(2) Use of a natural experiment design that allows for causal inference about malaria's immunological effects.

(3) Broad panel of antigens tested, demonstrating generalized rather than antigen-specific effects.

(4) Within-cohort analysis in Ngerenya controls for geographic and environmental factors.

(5) Validation of key findings using both serologic microarray and ELISA.

(6) Important public health implications for vaccine strategies in malaria-endemic regions.

Weaknesses:

(1) Lack of participants' characteristics (socio-economic, nutritional, physical).

(2) Somewhat limited sample size (longitudinal analysis of 123 children total), with further subdivision reducing statistical power for some analyses.

(3) Potential confounding by unmeasured socioeconomic, nutritional, or environmental factors between communities.

(4) Lack of ability to determine the direction of the associations found between malaria exposure and other IgG levels to unrelated pathogens.

(5) Despite good longitudinal data, the main analysis was conducted as a cross-sectional analysis at age 10 for many comparisons, which limits the understanding of temporal dynamics.

(6) Statistical analysis is limited to univariable comparisons without consideration for confounders or adjusting for multiple comparisons.

(7) No mechanistic understanding of how early malaria exposure creates lasting immunosuppression.

(8) No understanding of the clinical Implications of the reduced IgG levels observed in the area with high malaria exposure.

Assessment of Claims:

The data appear to support the authors' primary claims, but the strength of the evidence is limited, and the results should be interpreted with caution. Together with the currently available evidence of P. falciparum's impact on the host's immune function, this natural experiment design provides further evidence for a relationship between early malaria exposure and reduced antibody responses. The within-Ngerenya analysis controls for geographic factors and thus enhances the quality of the evidence; however, it still fails to account for the physical, nutritional, and socio-economic factors that may have driven the observed changes. Additionally, the mechanism underlying this effect remains unclear, and the clinical significance of reduced antibody levels is not established.

Impact and Utility:

This work has fundamental implications for understanding vaccine effectiveness in malaria-endemic regions and may contribute to informing vaccination strategies. The findings, if strengthened, would suggest that children in areas of high malaria transmission may require modified immunization approaches. The dataset provides a valuable resource for future studies of malaria's immunological legacy.

Context:

This study builds on prior work showing acute immunosuppressive effects of malaria but uniquely attempts to demonstrate the durability of these effects years after exposure. The natural experiment design addresses limitations of previous observational studies by providing a more controlled comparison.

Reviewer #1 (Public review):

Summary:

This report demonstrates that the gene expression output of the Wnt pathway, when controlled precisely by a synthetic light-based input, depends substantially on the frequency of stimulation. The particular frequency-dependent trend that is observed - anti-resonance, a suppression of target gene expression at intermediate frequencies given a constant duty cycle - is a novel aspect that has not been clearly shown before for this or other signaling pathways. The paper provides both clear experimental evidence of the phenomenon with engineered cellular systems and a model-based analysis of how the pairing of rate constants in pathway activation/deactivation could result in such a trend.

Strengths:

This report couples in vitro experimental data with an abstracted mathematical model. Both of these approaches appear to be technically sound and to provide consistent and strong support for the main conclusion. The experimental data are particularly clear, and the demonstration that Brachyury expression is subject to anti-resonance in ESCs is particularly compelling. The modeling approach is reasonably scaled for the system at the level of detail that is needed in this case, and the hidden variable analysis provides some insight into how the anti-resonance works.

Weaknesses:

(1) The anti-resonance phenomenon has not been demonstrated using physiological Wnt ligands; however, I view this as only a minor weakness for an initial report of the phenomenon. The potential significance of the phenomenon for Wnt outweighs the amount of effort it would take to carry the demonstration further - testing different frequencies/duty cycles at the level of ligand stimulus using microfluidics could get quite involved, and would likely take quite some time. Adding some more discussion about how the time scales of ligand-receptor binding could play into the reduced model would further ameliorate this issue.

(2) While the model is fully consistent with the data, it has not been validated using experimental manipulations to establish that the mechanisms of the cell system and the model are the same. There may be some ways to make such modifications, for example, using a proteasome inhibitor. An alternative would be to more explicitly mention the need to validate the model's mechanism with experiments.

(3) I think the manuscript misses an opportunity to discuss the potential of the phenomenon in other pathways. The hedgehog pathway, for example, involves GSK3-mediated partial proteolysis of a transcription factor, which could conceivably be subject to similar behaviors, and there are certainly other examples as well.

(4) Some aspects of the modeling and hidden variable analysis are not optimally presented in the main text, although when considered together with the Supplemental Data, there are no significant deficiencies.

Reviewer #2 (Public review):

Summary:

By combining optogenetics with theoretical modelling, the authors identify an anti-resonance behavior in the WnT signaling pathway. This behavior is manifested as a minimal response at a certain stimulation frequency. Using an abstracted hidden variable model, the authors explain their findings by a competition of timescales. Furthermore, they experimentally show that this anti-resonance influences the cell fate decision involved in human gastrulation.

Strengths:

(1) This interdisciplinary study combines precise optogenetic manipulation with advanced modelling.

(2) The results are directly tested in two different systems: HEK293T cells and H9 human embryonic stem cells.

(3) The model is implemented based on previous literature and has two levels of detail: i) a detailed biochemical model and ii) an abstract model with a hidden parameter.

Weaknesses:

(1) While the experiments provide both single-cell data and population data, the model only considers population data.

(2) Although the model captures the experimental data for TopFlash very well, the beta-Cat curves (Figure 2B) are only described qualitatively. This discrepancy is not discussed.

Overall Assessment:

The authors convincingly identified an anti-resonance behavior in a signaling pathway that is involved in cell fate decisions. The focus on a dynamic signal and the identification of such a behavior is important. I believe that the model approach of abstracting a complicated pathway with a hidden variable is an important tool to obtain an intuitive understanding of complicated dependencies in biology. Such a combination of precise ontogenetic manipulation with effective models will provide a new perspective on causal dependencies in signaling pathways and should not be limited only to the system that the authors study.

Reviewer #1 (Public review):

Summary:

This manuscript presents a compelling and innovative approach that combines Track2p neuronal tracking with advanced analytical methods to investigate early postnatal brain development. The work provides a powerful framework for exploring complex developmental processes such as the emergence of sensory representations, cognitive functions, and activity-dependent circuit formation. By enabling the tracking of the same neurons over extended developmental periods, this methodology sets the stage for mechanistic insights that were previously inaccessible.

Strengths:

(1) Innovative Methodology:

The integration of Track2p with longitudinal calcium imaging offers a unique capability to follow individual neurons across critical developmental windows.

(2) High Conceptual Impact:

The manuscript outlines a clear path for using this approach to study foundational developmental questions, such as how early neuronal activity shapes later functional properties and network assembly.

(3) Future Experimental Potential:

The authors convincingly argue for the feasibility of extending this tracking into adulthood and combining it with targeted manipulations, which could significantly advance our understanding of causality in developmental processes.

(4) Broad Applicability:

The proposed framework can be adapted to a wide range of experimental designs and questions, making it a valuable resource for the field.

Weaknesses:

None major. The manuscript is conceptually strong and methodologically sound. Future studies will need to address potential technical limitations of long-term tracking, but this does not detract from the current work's significance and clarity of vision

Comments on revisions:

I have no further requests. I think this is an excellent manuscript

Reviewer #2 (Public review):

Summary:

The manuscript by Majnik and colleagues introduces "Track2p", a new tool designed to track neurons across imaging sessions of two-photon calcium imaging in developing mice. The method addresses the challenge of tracking cells in the growing brain of developing mice. The authors showed that "Track2p" successfully tracks hundreds of neurons in the barrel cortex across multiple days during the second postnatal week. This enabled identification of the emergence of behavioral state modulation and desynchronization of spontaneous network activity around postnatal day 11.

Strengths

The authors have satisfactorily addressed the majority of our questions and comments, and the revisions substantially improve the manuscript. The expansion of Track2p to accept general NumPy array inputs makes the tool more accessible to researchers using different analysis pipelines. While the absence of benchmarking standards remains a limitation across the field, the release of the ground-truth dataset is an important step forward that will allow other researchers to evaluate and compare algorithms.

Minor point

(1) The authors tested the robustness of the algorithm across non-consecutive days. As expected, performance drops significantly under these conditions. We agree that this limitation reflects biological constraints due to brain growth rather than shortcomings of the algorithm itself. This is relevant for researchers planning to use Track2p for longitudinal imaging or benchmarking new algorithms, and we recommend including some of this information in the Supplementary Information along with a brief discussion.

Comments on revisions:

We acknowledge the extended documentation for using Track2p and converting between Suite2p outputs and NumPy arrays. This addition is of great utility. We would also suggest further expanding the documentation for the NumPy array implementation, as we ran into some errors when testing this feature using NumPy arrays generated from deltaF traces, TIFF FOVs, and Cellpose masks.

Reviewer #3 (Public review):

Summary:

In this manuscript Majnik et al. developed a computational algorithm to track individual developing interneurons in the rodent cortex at postnatal stages. Considerable development in cortical networks takes place during the first postnatal weeks, however, tools to study them longitudinally at a single cell level are scarce. This paper provides a valuable approach to study both single cell dynamics across days and state-drive network changes. The authors used Gad67Cre mice together with virally introduced TdTom to track interneurons based on their anatomical location in the FOV and AAVSynGCaMP8m to follow their activity across the second postnatal week, a period during which the cortex is known to undergo marked decorrelation in spontaneous activity. Using Track2P, the authors show feasibility to track populations of neurons in the same mice capturing with their analysis previously described developmental decorrelation and uncovering stable representations of neuronal activity, coincident with the onset of spontaneous active movement. The quality of the imaging data is compelling, and the computational analysis is thorough, providing a widely applicable tool for the analysis of emerging neuronal activity in the cortex. Below are some points for the authors to consider.

Major points

The authors use a viral approach to label cortical interneurons. It is unclear how Track2P will perform in dense networks of excitatory cells using GCaMP transgenic mice.

The authors used 20 neurons to generate a ground truth data set. The rational for this sample size is unclear. Figure 1 indicates capability to track ~728 neurons. A larger ground truth data set will increase the robustness of the conclusions.

It is unclear how movement was scored in the analysis shown in Fig 5A. Was the time that the mouse spent moving scored after visual inspection of the videos? Were whisker and muscle twitches scored as movement or was movement quantified as amount of time in which the treadmill was displaced?

The rational for binning the data analysis in early P11 is unclear. As the authors acknowledged, it is likely that the decoder captured active states from P11 onwards. Because active whisking begins around P14, it is unlikely to drive this change in network dynamics at P11. Does pupil dilation in the pups change during locomotor and resting states? Does the arousal state of the pups abruptly change at P11?

Comments on revisions:

The authors have addressed carefully all my comments. This is an interesting paper.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors explore the role of the conserved transcription factor POU4-2 in planarian maintenance and regeneration of mechanosensory neurons. The authors explore the role of this transcription factor and identify potential targets of this transcription factor. Importantly, many genes discovered in this work are deeply conserved, with roles in mechanosensation and hearing, indicating that planarians may be a useful model with which to study the roles of these key molecules. This work is important within the field of regenerative neurobiology, but also impactful for those studying evolution of the machinery that is important for human hearing.

Strengths:

The paper is rigorous and thorough, with convincing support for the conclusions of the work.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigate the role of the transcription factor Smed-pou4-2 in the maintenance, regeneration and function of mechanosensory neurons in the freshwater planarian Schmidtea mediterranea. First, they characterize the expression of pou4-2 in mechanosensory neurons during both homeostasis and regeneration, and examine how its expression is affected by the knockdown of soxB1, 2, a previously identified transcription factor essential for the maintenance and regeneration of these neurons. Second, the authors assess whether pou4-2 is functionally required for the maintenance and regeneration of mechanosensory neurons.

Strengths:

The study provides some new insights into the regulatory role of pou4-2 in the differentiation, maintenance, and regeneration of ciliated mechanosensory neurons in planarians.

Reviewer #1 (Public review):

This is an interesting and valuable paper by Gil-Lievana, Arroyo et al. that presents an open-source method (the "Crunchometer") for quantifying biting and chewing behavior in mice using audio detection. The work addresses an important and unmet need in the field: quantitative measures of feeding behavior with solid foods, since most prior approaches have been limited to liquids. The authors make a clear and compelling case for why this problem is important, and I fully agree with their motivation.

The system is carefully validated against human-scored video data and is shown to be at least as accurate, and in some cases more accurate, than human observers. This is a major strength of the study. I also particularly appreciate the demonstration of the technology in the context of LHA circuitry, which nicely illustrates its utility and importance for mechanistic studies of feeding. I also appreciate the ability to readily time-lock neural data to individual crunches. Overall, the manuscript is well-executed and represents a useful contribution to the field.

The comments I have are largely minor and should be straightforward to address:

(1) The authors should report sample sizes for all mouse cohorts, either alongside the statistics or in the figure legends for mean data.

(2) Clarification is needed as to whether crunch detection fidelity is influenced by the hardness or softness of the food. The focus here is on standard pellets, with some additional high-fat pellet data, but it would be useful to know how generalizable the method is across different textures.

(3) The authors should comment on how susceptible the Crunchometer is to background noise. For example, how well does it perform in the presence of white noise, experimenter movement, or other task-related sounds?

(4) Chemogenetic activation of LHA GABAergic neurons is used. DREADD-based activation may strongly drive these neurons in a way that is not directly comparable to optogenetic or more physiological manipulations. While I do not think additional experiments are required, it would strengthen the discussion to briefly acknowledge this limitation.

Reviewer #2 (Public review):

Summary:

This manuscript introduces the Crunchometer, a low-cost, open-source acoustic platform for monitoring the microstructure of solid food intake in mice. The Crunchometer is designed to overcome the limitations of existing methods for studying feeding behavior in rodents. The goal was to provide a tool that could precisely capture the microstructure of solid food intake, something often overlooked in favor of liquid-based assays, while being affordable, scalable, and compatible with neural recording techniques. By doing so, the authors aimed to enable detailed analysis of how physiological states, drugs, and specific neural circuits shape naturalistic feeding behaviors.

Strengths:

The study's strengths lie in its clear innovation, methodological rigor in validation against human annotation, and demonstration of broad utility across behavioral and neuroscience paradigms. The approach addresses a significant methodological gap in the field by moving beyond liquid-based feeding assays and provides an accessible tool for precisely dissecting ingestive behavior. The system is validated across multiple contexts, including physiological state (fed vs. fasted), pharmacological manipulation (semaglutide), and circuit-level interventions (chemogenetic activation of LH neurons), and is further shown to integrate seamlessly with both electrophysiology and calcium imaging.

(1) Introduces a low-cost, open-source acoustic tool for measuring solid food intake, filling a critical gap left by expensive and proprietary systems.

(2) Makes the method easily adoptable across labs with detailed setup instructions and shared benchmark datasets.

(3) Provides high temporal precision for detecting bite events compared to human observers.

(4) Successfully distinguishes feeding microstructure (bites, bouts, IBIs, gnawing vs. consumption) with greater objectivity than manual annotation.

(5) Demonstrates compatibility with electrophysiology and calcium imaging, enabling fine-scale alignment of neural activity with feeding behavior.

(6) Effectively discriminates between fed vs. fasted states, validating physiological sensitivity.

(7) Captures the pharmacological effects of semaglutide, although this is really just reduced feeding and associated readouts (bouts, latency, etc).

(8) Has potential to distinguish consummatory vs. non-consummatory behaviors (e.g., food spillage, gnawing); however, the current SVM model struggles to separate biting from gnawing due to similar acoustic profiles, and manual validation is still required.

(9) Provides potential for closed-loop experiments.

Weaknesses:

Several limitations temper the strength of the conclusions: the supervised classifier still requires manual correction for gnawing, generalizability across different setups is limited, and the neuroscience findings, particularly calcium imaging of GABAergic and glutamatergic neurons, are based on small pilot samples. These issues do not undermine the value of the tool, but mean that the neural circuit findings should be interpreted as preliminary.

(1) Some neuroscience findings (calcium imaging of GABAergic vs. glutamatergic neurons) are based on small pilot samples (n=2 mice per condition), limiting generalizability.

(2) Chemogenetic and pharmacological experiments used small cohorts, raising statistical power concerns.

(3) Correlation with actual food intake is modest and sometimes less accurate than human observers.

(4) Sensitive to hoarding behavior, which can reduce detection accuracy and requires manual correction for misclassifications (e.g., tail movements, non-food noises). However, these limitations are discussed and not ignored.

Conclusion:

Overall, this is an exciting and impactful methodological advance that will likely be widely adopted in the field. I recommend minor revisions to clarify the limits of classifier generalizability, better contextualize the small-sample neuroscience findings as pilot data, and discuss future directions (e.g., real-time closed-loop applications).

Reviewer #3 (Public review):

Summary:

The manuscript provides detailed information on the construction of open-source systems to monitor ingestive behavior with low-cost equipment. Overall, this is a welcome addition to the arsenal of equipment that could be used to make measurements. The authors show interesting applications with data that reveal important neurophysiological properties of neurons in the lateral hypothalamus. The identification of previously unknown "meal-related" neurons in the LH highlights the utility of the device and is a novel insight that should spark further investigation on the LH. This manuscript and videos provide a wealth of useful information that should be a must-read for anyone in the ingestive behavior or hypothalamus fields.

A scholarly introduction to the history and utility of various ways feeding is measured in rodents is provided. One point - the microstructure of eating solid food - has been studied extensively (for one of many studies, see https://doi.org/10.1371/journal.pone.0246569 ). However, I agree that the crunchometer will allow for more people to access recordings during food intake and temporally lock consummatory behavior to neural activity.

Questions on results:

(1) It is unclear why 10% sucrose solution was used as a liquid instead of water, given that the study is focusing on the solid food source.

(2) It is unclear how essential the human verification is in the pipeline - results for Figure 1 keep referring to the verification as essential. Is that dispensable once the ML algorithms have been trained?

(3) The ability to extrapolate food quantity consumed is limited, with high variability. This limitation does not undercut the utility of the crunchometer, but should be highlighted as one of the parameters that are not suitable for this system. This limitation should be added to the limitations section.

(4) The ability to discriminate between gnawing and consummatory behavior is a strength (Figure 5), and these findings are important. However, it is unclear what can be made of mice that have 'gnawing' behavior in the fasted state (like in Figure 3). It seems they would need to be eliminated from the analysis with this tool?

(5) Why is there a post-semaglutide fed group and not a fasted group in Figure 4? It seems both would have been interesting, as one could expect an effect on feeding even 24h after semaglutide treatment. This would help parse the preference better because the animals eat such a small amount on semaglutide, that it is hard to compare to the fasted condition with saline treatment.

(6) The identification of 'meal-related' neurons in the LH is another strength of the manuscript. Although there is currently insufficient data, could similar recordings be used to give a neurophysiological definition of a 'meal' duration/size? Typically, these were somewhat arbitrarily defined behaviorally. Having a neural correlate to a 'meal' would be a powerful tool for understanding how meals are involved in overall caloric intake.

(7) The conclusion in the title of Figure 8 is premature, given the pilot nature and small number of neurons and mice sampled.

Conclusion:

Overall, this report on the Crunchometer is well done and provides a valuable tool for all who study food intake and the behaviors around food intake. Clarification or answers to the points above will only further the utility and understanding of the tool for the research community. I am excited to see the future utility of this tool in emerging research.

Reviewer #1 (Public review):

This paper is a relevant overview of the currently published literature on low-intensity focused ultrasound stimulation (TUS) in humans, with a meta-analysis of this literature that explores which stimulation parameters might predict the directionality of the physiological stimulation effects.

The pool of papers to draw from is small, which is not surprising given the nascent technology. It seems, nevertheless, relevant to summarise the current field in the way done here, not least to mitigate and prevent some of the mistakes that other non-invasive brain stimulation techniques have suffered from, most notably the theory- and data free permutation of the parameter space.

A database summarising the literature and allowing for quantitative assessment of these studies is a key contribution of the paper. If curated well, it can become a valuable community resource.

Comments on revisions:

The paper is much improved. There remain a few caveats the authors may want to address.

I'm not going to dwell on this if the authors don't agree, but remain critical about the inclusion of TPS in the discussion. It's comparing apples and oranges, and unless there's a personal interest the authors have in TPS, it remains puzzling why it is included in the first place. As per my previous review, the literature on TPS, and especially the main example cited, has been highly criticised, including national patient and medical associations. A mere disclaimer that more work is needed isn't enough, in this reviewer's opinion - I simply don't understand why the authors go out on a limb here when the rest of the paper is done so well and thoroughly.

Reviewer #1 (Public review):

Summary:

The manuscript "Lifestyles shape genome size and gene content in fungal pathogens" by Fijarczyk et al. presents a comprehensive analyses of a large dataset of fungal genomes to investigate what genomic features correlate with pathogenicity and insect associations. The authors focus on a single class of fungi, due to the diversity of life styles and availability of genomes. They analyze a set of 12 genomic features for correlations with either pathogenicity or insect association and find that, contrary to previous assertions, repeat content does not associate with pathogenicity. They discover that the number of protein coding genes, including total size of non-repetitive DNA does correlate with pathogenicity. However, unique features are associated to insect associations. This work represents an important contribution to the attempts to understand what features of genomic architecture impact the evolution of pathogenicity in fungi.

Strengths:

The statistical methods appear to be properly employed and analyses thoroughly conducted. The size of the dataset is impressive and likely makes the conclusions robust. The manuscript is well written and the information, while dense, is generally presented in a clear manner.

Weaknesses:

My main concerns all involve the genomic data, how they were annotated, and the biases this could impart to the downstream analyses. The three main features I'm concerned with are sequencing technology, gene annotation, and repeat annotation. The authors have done an excellent investigation into these issues, but these show concerning trends, and my concerns are not as assuaged as the authors.

The collection of genomes is diverse and includes assemblies generated from multiple sequencing technologies including both short- and long-read technologies. From the number of scaffolds its clear that the quality of the assemblies varies dramatically, even within categories of long- and short-read. This is going to impact many of the values important for this study, as the authors show.

I have considerable worries that the gene annotation methods could impart biases that significantly effect the main conclusions. Only 5 reference training sets were used for the Sordariomycetes and these are unequally distributed across the phylogeny. Augusts obviously performed less than ideally, as the authors observe in their extended analysis. While the authors are not concerned about phylogenetic distance from the training species, due to prevailing trends, I am not as convinced. In figure S12, the Augustus features appear to have considerably more variation in values for the H2 set and possible the microascales. It is unclear how this would effect the conclusions in this study.

Unfortunately, the genomes available from NCBI will vary greatly in the quality of their repeat masking. While some will have been masked using custom libraries generated with software like Repeatmodeler, others will probably have been masked with public databases like repbase. As public databases are again biased towards certain species (Fusarium is well represented in repbase for example), this could have significant impacts on estimating repeat content. Additionally, even custom libraries can be problematic as some software (like RepeatModeler) will included multicopy host genes leading to bona fide genes being masked if proper filtering is not employed. A more consistent repeat masking pipeline would add to the robustness of the conclusions. The authors show that there is a significant bias in their set.

To a lesser degree I wonder what impact the use of representative genomes for a species has on the analyses. Some species vary greatly in genome size, repeat content and architecture among strains. I understand that it is difficult to address in this type of analysis, but it could be discussed.

Reviewer #2 (Public review):

Summary:

In this paper, the authors report on the genomic correlates of the transition to the pathogenic lifestyle in Sordariomycetes. The pathogenic lifestyle was found to be better explained by the number of genes, and in particular effectors and tRNAs, but this was modulated by the type of interacting host (insect or not insect) and the ability to be vectored by insects.

Strengths:

The main strengths of this study lie in (i) the size of the dataset, and the potentially high number of lifestyle transitions in Sordariomycetes, (ii) the quality of the analyses and the quality of the presentation of the results, (iii) the importance of the authors' findings.

Weaknesses:

The weakness is a common issue in most comparative genomics studies in fungi, but it remains important and valid to highlight it. Defining lifestyles is complex because many fungi go through different lifestyles during their life cycles (for instance, symbiotic phases interspersed with saprotrophic phases). In many fungi, the lifestyle referenced in the literature is merely the sampling substrate (such as wood or dung), which does not necessarily mean that this substrate is a key part of the life cycle. The authors discuss this issue, but they do not eliminate the underlying uncertainties.

Reviewer #1 (Public review):

Chaiyasitdhi et al. set out to investigate the detailed ultrastructure of the scolopidia in the locust Müller's organ, the geometry of the forces delivered to these scolopidia during natural stimulation, and the direction of forces that are most effective at eliciting transduction currents. To study the ultrastructure, they used the FIB-SEM technique, to study the geometry of natural stimulation, they used OCT vibrometry and high-speed light microscopy, and to study transduction currents, they used patch clamp physiology.

Strengths:

I believe that the ultrastructural description of the locust scolopidium is excellent and the first of its kind in any insect system. In particular, the finding of the bend in the dendritic cilium and the position of the ciliary dilation are interesting, and it would be interesting to see whether these are common features within the huge diversity of insect chordotonal organs.

I believe the use of OCT to measure organ movements is a significant strength of this paper; however, using ex vivo preparations undermines any conclusions drawn about the system's in vivo mechanics.

The choice of Group III scolopidia is also good. Research on the mechanics of locust tympana has shown that travelling waves are formed on the tympanum and waves of different frequencies show highest amplitudes at different positions on the tympanum, and therefore also on different groups of scolopidia within the Müller's organ (Windmill et al, 2005; 2008, and Malkin et al, 2013). The lowest frequency modal waves (F0) observed by Windmill et al 2008 were at about 4.4 kHz, which are slightly higher than the ~3 kHz frequencies studied in this paper but do show large deflections where these group III scolopidia attach at the styliform body (Windmill et al, 2005).

This should be mentioned in the paper since the electrophysiology justification to use group III neurons is less convincing, given that Jacobs et al 1999 clearly point out that group III neurons are very variable and some of them are tuned much higher to 10 kHz, and others even higher to 20-30 kHz.

Weaknesses:

Specifically, it is understandable that the authors decided to use excised ears for the light microscopy, where Müller's organ would not be accessible in situ. However, it is very likely that excision will change the system's mechanics, especially since any tension or support to Müller's organ will be ablated. OCT enables in vivo measurements in fully undissected systems (Mhatre et al, Biorxiv, 2021) or in systems with minimal dissection where the mechanics have not been compromised (Vavakou et al, 2021). The choice to entirely dissect out the membrane is difficult to understand here.

My main concern with this paper, however, is the use of light microscopy very close to the Nyquist limit to study scolopidial motion, and the fact that the OCT data contradict and do not match the light microscopy data.

The light microscopy data is collected at ~8 kHz, and hence the Nyquist limit is ~4 kHz. It is possible to measure frequencies reliably this close to the limit, but the amplitude of motion is quite likely to be underestimated, given that the technique only provides 2 sample points per cycle at 4 kHz and approximately 2.66 sample points at 3 kHz. At that temporal resolution, the samples are much more likely to miss the peak of the wave than not, and therefore, amplitudes will be misestimated. A much more reasonable sample rate for amplitude estimation is generally about 10 samples per cycle. I do not believe the data from the microscopy is reliable for what the authors wish to use them for.

Using the light microscopy data, the authors claim that the strains experienced by the group III scolopidia at 3 kHz are greater along the AP axis than the ML axis (Figure 4). However, this is contradicted by the OCT data, which show very low strain along the AP axis (black traces) at and around 3 kHz (Figure 3c and extended data Figure 2f) and show some movement along the ML axis (red traces, same figures). The phase at low amplitudes of motion cannot be considered very reliable either, and hence phase variations at these frequencies in the OCT cannot be considered reliable indicators of AP motion; hence, I'm unclear whether the vector difference in the OCT is a reliable indicator of movement.

The OCT data are significantly more reliable as they are acquired at an appropriate sampling rate of 90 kHz. The authors do not mention what microphone they use to monitor or calibrate their sound field and phase measurements in OCT, but I presume this was done since it is the norm. Thus, the OCT data show that the movement within the Müller's organ is complex, probably traces an ellipse at some frequencies as observed in bushcrickets (Vavkou et al, 2021) and also thought to be the case in tree crickets based on the known attachment points of the TO (Mhatre et al, 2021). The OCT data shows relatively low AP motion at frequencies near 3 kHz, and higher ML motion, which contradicts the less reliable light microscopy data. Given that the locust membrane shows peaks in motion at ~4.5 kHz, ~11 kHz, and also at ~20 kHz (Windmill et al, 2008), I am surprised that the authors limited their OCT experiments and analyses to 5 kHz.

In summary for this section, I am not convinced of the conclusion drawn by the authors that group III scolopidia receive significantly higher stimulation along the AP axis in their native configuration, if indeed they were studied in the appropriate force regime (altered due to excision).

In the scolopidial patch clamp data, the authors study transduction currents in response to steady state stimulation along the AP axis and the ML axis. The responses to steady state and periodic forces may well be different, and the authors do not offer us a way to clearly relate the two and therefore, to interpret the data.

In addition, both stimulation types, along the AP axis and the ML, elicit clear transduction responses. Stimulation along the AP axis might be slightly higher, but there is over 40% variation around the mean in one case (pull: 26.22 {plus minus} 10.99 pA) and close to 80% variation in the other (push: 10.96 {plus minus} 8.59 pA). These data are indeed from a very high displacement range (2000 nm), which is very high compared to the native displacement levels, which are in the 1-10 nm range.

The factor change from sample to sample is not reported, and is small even overall. The statistical analyses of these data are not clearly reported, and I don't see the results of the overall ANOVA in the results section. I also find the dip in the reported transduction currents between 10 and 100 nm quite odd (Figure 5 j-m) and would like to know what the authors' interpretation of this behaviour is. It seems to me that those currents increase continuously linearly after ~50-100 nm and that the data below that range are in the noise. Thus, the transduction currents observed at the relevant displacement range (1-10 nm) may not actually be reliable. How were these small displacements achieved, and how closely were the actual levels monitored? Is it possible to reliably deliver 1-10 nm displacements using a micromanipulator?

What is clear, despite the difficulty in interpreting this data, is that both AP and ML stimulation evoke transduction currents, and their relative differences are small. Additionally, in Müller's organ itself, in the excised organ, the scolopidia are stimulated along both axes. Thus, in my opinion, it is not possible to say that axial stretch along the cilium is 'the key mechanical input that activates mechano-electrical transduction'.

Reviewer #2 (Public review):

Summary of strengths and weaknesses:

Using several techniques-FIB-SEM, OCT, high-speed light microscopy, and electrophysiology-Chaiyasitdhi et al. provide evidence that chordotonal receptors in the locust ear (Müller's organ) sense the stretch of the scolapale cell, primarily of its cilium. Careful measurements certainly show cell stretch, albeit with some inconsistencies regarding best frequencies and amplitudes. The weakest argument concerns the electrophysiological recordings, because the authors do not show directly that the stimulus stretches the cells. If this latter point can be clarified, then our confidence that ciliary stretch is the proximal stimulus for mechanotransduction will be increased. This conclusion will not come as a surprise for workers in the field, as the chordotonal organ is known as a stretch-receptor organ (e.g., Wikipedia). But it is a useful contribution to the field and allows the authors to suggest transduction mechanisms whereby ciliary stretch is transduced into channel opening.

Reviewer #3 (Public review):

Summary:

The paper 'A stretching mechanism evokes mechano-electrical transduction in auditory chordotonal neurons' by Chaiyasitdhi et al. presents a study that aims to address the mechanical model for scolopidia in Schistocerca gregaria Müller's organ, the basic mechanosensory units in insect chordotonal organs. The authors combine high-resolution ultrastructural analysis (FIB-SEM), sound-evoked motion tracking (OCT and high-speed light microscopy), and electrophysiological recordings of transduction currents during direct mechanical stimulation of individual scolopidia. They conclude that axial stretching along the ciliary axis is an adequate mechanical stimulus for activating mechanotransduction channels.

Strengths/Highlights:

(1) The 3D FIB-SEM reconstruction provides high resolution of scolopidial architecture, including the newly described "scolopale lid" and the full extent of the cilium.

(2) High-speed microscopy clearly demonstrates axial stretch as the dominant motion component in the auditory receptors, which confirms a long-standing question of what the actual motion of a stretch receptor is upon auditory stimulation.

(3) Patch-clamp recordings directly link mechanical stretch to transduction currents, a major advance over previous indirect models.

Weaknesses/Limitations:

(1) The text is conceptually unclear or written in an unclear manner in some places, for example, when using the proposed model to explain the sensitivity of Nanchung-Inactive in the discussion.

(2) The proposed mechanistic models (direct-stretch, stretch-compression, stretch-deformation, stretch-tilt) are compelling but remain speculative without direct molecular or biophysical validation. For example, examining whether the organ is pre-stretched and identifying the mechanical components of cells (tissues), such as the extracellular matrix and cytoskeleton, would help establish the mechanical model and strengthen the conclusion.

(3) To some extent, the weaknesses of the paper are part of its strengths and vice versa. For example, the direct push/pull and up/down stimulations are a great experimental advance to approach an answer to the question of how the underlying cellular components are deformed and how the underlying ion channels are forced. However, as the authors clearly state, neither of their stimulations can limit all forces to only one direction, and both orthogonal forces evoke responses in the neurons. The question of which of the two orthogonal forces 'causes' the response cannot be answered with these experiments and has not been answered by this manuscript. But the study has brought the field a considerable step closer to answering the question. The answer, however, might be that both longitudinal ('stretch') and perpendicular ('compression') forces act together to open the ion channels and that both dendritic extension via stretch and bending can provide forces for ion channel gating. The current paper has identified major components (longitudinal stretch components) for the neurons they analysed, but these will surely have been chosen according to their accessibility, and as such, the variety of mechanical responses in Müller's organ might be greater. In light of these considerations, the authors might acknowledge such uncertainties more clearly in their paper. The paper is an impressive methodological progress and breakthrough, but it simply does not "demonstrate that axial stretch along the cilium is the adequate stimulus or the key mechanical input that activates mechano-electrical transduction" as the authors write at the start of their discussion. They do show that axial stretch dominates for the neurons they looked at, which is important information. The same applies to the end of the discussion: The authors write, "This relative motion within the organ then drives an axial stretch of the scolopidium, which in turn evokes the mechano-electrical transduction current." Reading the manuscript, the certainty and display of confidence are not substantiated by the data provided. But they are also not necessary. The study has paved the road to answer these questions. Instead, the authors are encouraged to make suggestions on how the remaining uncertainties could be removed (and what experiments or model might be used).

Reviewer #1 (Public review):

Summary:

This study shows a novel role for SCoR2 in regulating metabolic pathways in the heart to prevent injury following ischemia/reperfusion. It combines a new multi-omics method to determine SCoR2 mediated metabolic pathways in the heart. This paper would be of interest to cardiovascular researchers working on cardioprotective strategies following ischemic injury in the heart.

Strengths:

(1) Use of SCoR2KO mice subjected to I/R injury.

(2) Identification of multiple metabolic pathways in the heart by a novel multi-omics approach.

Comments on revisions:

Authors have addressed all concerns raised in the previous round of review. Substantial modifications have been made in response to those concerns. There are no further comments.

Reviewer #2 (Public review):

Summary:

This manuscript addresses the gap in knowledge related to the cardiac function of the S-denitrosylase SNO-CoA Reductase 2 (SCoR2; product of the Akr1a1 gene). Genetic variants in SCoR2 have been linked to cardiovascular disease, yet its exact role in heart remains unclear. This paper demonstrates that mice deficient in SCoR2 show significant protection in a myocardial infarction (MI) model. SCoR2 influenced ketolytic energy production, antioxidant levels, and polyol balance through the S-nitrosylation of crucial metabolic regulators.

Strengths:

Addresses a well-defined gap in knowledge related to the cardiac function of SNO-CoA Reductase 2. Besides the in-depth case for this specific player, the manuscripts sheds more light on the links between S-nytrosylation and metabolic reprogramming in heart.

Rigorous proof of requirement through the combination of gene knockout and in vivo myocardial ischemia/reperfusion

Identification of precise Cys residue for SNO-modification of BDH1 as SCoR2 target in cardiac ketolysis

Weaknesses:

The experiments with BDH1 stability were performed in mutant 293 cells. Was there a difference in BDH1 stability in myocardial tissue or primary cardiomyocytes from SCoR2-null vs -WT mice? Same question extends to PKM2.

In the absence of tracing experiments, the cross-sectional changes in ketolysis, glycolysis or polyol intermediates presented in Figures 4 and 5 are suggestive at best. This needs to be stressed while describing and interpreting these results.

The findings from human samples with ischemic and non-ischemic cardiomyopathy do not seem immediately or linearly in line with each other and with the model proposed from the KO mice. While the correlation holds up in the non-ischemic cardiomyopathy (increased SNO-BDH1, SNO-PKM2 with decreased SCoR2 expression), how do the Authors explain the decreased SNO-BDH1 with preserved SCoR2 expression in ischemic cardiomyopathy? This seems counterintuitive as activation of ketolysis is a quite established myocardial response to the ischemic stress. It may help the overall message clarity to focus the human data part on only NICM patients.

(partially linked to the point above) an important proof that is lacking at present is the proof of sufficiency for SCoR2 in S-Nytrosylation of targets and cardiac remodeling. Does SCoR2 overexpression in heart or isolated cardiomyocytes reduce S-nitrosylation of BDH1 and other targets, undermining heart function at baseline or under stress?

Comments on revisions:

Some of my points have been addressed. However, the points related to 1) BDH1 stability effect in cardiomyocytes; 2) human relevance of SNO-BDH1; 3) SCoR2 sufficiency remain unclear. That said, this manuscript will provide useful information to the field as such.

Reviewer #3 (Public review):

Summary:

This manuscript demonstrates that mice lacking the denitrosylase enzyme SCoR2/AKR1A1 demonstrate a robust cardioprotection resulting from reprogramming of multiple metabolic pathways, revealing<br /> widespread, coordinated metabolic regulation by SCoR2.

Strengths:

The extensive experimental evidence provided the use of the knockout model

Weaknesses:

No direct evidence for the underlying mechanism.

The mouse model used is not a tissue-specific knock-out.

Reviewer #1 (Public review):

Summary:

The authors identify small-molecule compounds modulating the stability of the mitochondrial transcription factor A (TFAM) using a high-throughput CETSA screen and subsequent secondary assays. The identified compounds increased the protein levels of TFAM without affecting its RNA levels and led to an increase in mtDNA levels. As a read-out for dose-dependent action of the identified compounds, the authors investigated cGAS-STING and ISG activation in cellular inflammation models in the presence or absence of their compounds. The addition of TFAM modulators led to a decrease in cGAS-STING/ISG activation and decreased mtDNA release. Furthermore, beneficial effects could be determined in models of mtDNA disease (rescue of ATP rates), sclerotic fibroblasts (decreased fibrosis), and regulatory T cells (decreased activation of effector T cells). The study thus proposes novel first-in-class regulators of TFAM as a therapeutic option in conditions of mitochondrial dysfunction.

Strengths:

The authors identified TFAM as a promising target in conditions of mitochondrial dysfunction, as it is a key regulator of mitochondrial function, serving both as a transcription and packaging factor of mtDNA. Importantly, TFAM is a key regulator of mtDNA copy number, and a moderate increase in TFAM/mtDNA levels has been shown to be beneficial in a number of pathological conditions. Furthermore, mtDNA release leading to activation of inflammatory responses has been linked to a variety of pathological conditions in the last decade. Thus, the identification of small molecule modulators of TFAM that have the potential to increase mtDNA copy number and decrease inflammatory signaling is of great importance. Furthermore, the authors highlight potential applications in the field of mitochondrial disease, fibrosis, and autoimmune disease.

Weaknesses:

The central weakness of the study is the fact that the authors propose compounds as modulators or even activators of TFAM without sufficiently proving a direct effect on TFAM itself. There are no data indicating a direct effect on TFAM activity (e.g., mtDNA transcription, replication, packaging), and it is not sufficiently ruled out that other proteins (e.g., LONP1) mediate the effect. Additionally, important information on the performed screen is not provided. Thus, the data presented is currently incomplete to support the described findings. Furthermore, the introduction and discussion are lacking key references.

Reviewer #2 (Public review):

Summary:

The present paper aims to identify small molecules that could possibly affect mitochondrial DNA (mtDNA) stability, limiting cytosolic mtDNA abundance and activation of interferon signaling. The authors developed a high-throughput screen incorporating HiBiT technology to identify possible target compounds affecting mitochondrial transcription factor A (TFAM) content, a compound known to impact mtDNA stability. Cells were subsequently exposed to target compounds to investigate the impact on TNFα-stimulated interferon signaling, a process activated by cytosolic mtDNA abundance. Compound 2, an analog of arylsulfonamide, was highlighted as a possible mitochondrial transcription factor A (TFAM)-activator, and emphasized as a small molecule that could stabilize mtDNA and prevent stress-induced interferon signaling.

Strengths:

Identifying compounds that positively affect mitochondrial biology has diverse implications. The combination of high-throughput screening and assay development to connect identified compounds with cellular interferon signalling events is a strength of the current approach, and the authors should be commended for identifying compounds that broadly impact interferon signalling. The authors have incorporated diverse measurements, including TFAM content, mtDNA content, interferon signaling, and ATP content, as well as verified the necessity of TFAM in mediating the beneficial effects of the emphasized small molecule (Compound 2).

Weaknesses:

(1) While the identified compound clearly works through TFAM, Compound 2 was identified as an arylsulfonamide, which would be expected to affect voltage-gated sodium channels (e.g. PMID: 31316182). Alterations in cellular sodium content and membrane polarization could affect metabolism to indirectly influence mtDNA and TFAM content. It remains unclear if this compound directly or indirectly affects TFAM content, especially as the authors have utilized various cancer cell lines, which could have aberrant sodium channels.

(2) TFAM is nuclear encoded - if this compound directly functions to 'activate TFAM', why/how would TFAM content increase independent of nuclear transcription?

(3) While a listed strength is the incorporation of diverse readouts, this is also a weakness, as there is a lack of consistency between approaches. For instance, data is not provided to show compound 2 increases TFAM or mtDNA content following TNFα stimulation, and extrapolating between cell lines may not be appropriate. The authors are encouraged to directly report TFAM and mtDNA for target compounds 2 and 15 to support their data reported in Figure 2. Ideally, the authors would also report for compound 1 as a control.

(4) While the authors indicate compound 11 displayed the strongest effect on ISRE activity, this appears not to be identified in Figure 1B as a compound affecting TFAM content? Can the authors identify various Compounds in Figure 1B to better highlight the relationship between compounds and TFAM content?

(5) The authors suggest Compound 2 increases cellular ATP - but they are encouraged to normalize luminescence to cellular protein and OXPHOS content to better interpret this data. Additionally, the authors are encouraged to report cellular ATP content following TNFα stimulation/stress (the key emphasis of the present data) and test compound 11, which the authors have implicated as a more sensitive compound.

The discussion is really a perspective, theorizing the diverse implications of small molecule activation of TFAM. The authors are encouraged to provide a balanced discussion, including a critical evaluation of their own work, including an acknowledgement that evidence is not provided that Compound 2 directly activates TFAM or decreases mtDNA cytosolic leakage.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors aimed to clarify the transcriptional changes across murine postnatal small intestinal development (0 days to 1 month) in both the duodenum and ileum, a period that shows morphological similarity to 20-30 week old fetal humans. This is an especially critical stage in human intestinal development, as necrotizing enterocolitis (NEC) usually manifests during these stages.

Strengths:

The authors assessed numerous timepoints between 0 days and 1 month in the postnatal mouse duodenum and ileum using bulk RNA transcriptomics of bulk-isolated tissues. Cellular deconvolution, based on relative marker expression, was used to clarify immune cell proportions in the bulk RNA sequencing data. They confirmed some transcriptional targets found in vivo primarily in mouse via qrtPCR and immunohistochemistry, but also in human fetal tissues and isolated organoids, and are of decent quality.

Weaknesses:

The overall weakness of this study, as mentioned by the authors themselves, is that the bulk transcriptomic data generated for the study were isolated from non-fractionated bulk intestinal tissue. This makes it difficult to interpret much of this data regarding cellular fractions found across developmental time. It is difficult to rationalize the approach here, as even isolation protocols of epithelial-only or mesenchyme-only tissues for bulk RNA sequencing are well established. The authors address some of these concerns using cellular deconvolution for immune cell populations, which I think might be helpful if they expanded this analysis to other cell types (mesenchyme, endothelium, glia). However, I would assume that bulk isolations across developmental time are going to be influenced primarily by the bulk of tissue-type found at each time point - primarily epithelium. But this is also confirmed by the immune transcripts becoming more apparent later in their time series, as this system becomes more established during weaning. This study might also be strengthened by comparison with data that is publicly available for early fetal stage development in humans. Comparisons between the duodenum and ileum could be strengthened by what we already know from adult data, from both epithelial- and mesenchyme-isolated fractions. The rationale of using the postnatal mouse as a comparison to NEC is also a little unclear- perhaps some of the developmental processes are similar, however, the environments are completely different. For example, even in early postnatal mouse development, you would find microbial activity and milk.

Reviewer #2 (Public review):

Summary:

This work presents a valuable resource by generating a comprehensive bulk RNA sequencing catalogue of gene expression in the mouse duodenum and ileum during the first postnatal month. The central findings of this work are based on an analysis of this dataset. Specifically, the authors characterized molecular shifts that occur as the intestine matures from an immature to an adult-like state, investigating both temporal changes and regional differences between the proximal and distal small intestine. A key objective was to identify gene expression patterns relevant to understanding the region-specific susceptibility and resistance to necrotizing enterocolitis (NEC) observed in humans during the postnatal period. They also sought to validate key findings through complementary methods and to provide comparative context with human intestinal samples. This study will provide a solid reference dataset for the community of researchers studying postnatal gastrointestinal development and diseases that arise during these stages. However, the study lacks functional validation of the interpretations.

Strengths:

(1) The inclusion of numerous time points (day 0 through 4 weeks) and comparative analyses throughout the first postnatal month.

(2) Validation of key interpretations of RNA-seq data by other methods.

(3) Linking mouse postnatal development to human premature infant development, enhancing its clinical relevance, particularly for NEC research. The inclusion of human intestinal biopsy and organoid data for comparison further strengthens this link.

(4) The investigation covers a wide array of developmental gene categories with known significance, including epithelial differentiation markers (e.g., Vil1, Muc2, Lyz1), intestinal stem cell markers (e.g., Lgr5, Olfm4, Ascl2), mesenchymal markers (e.g., Pdgfra, Vim), Wnt signaling components (e.g., Wnt3, Wnt5a, Ctnnb1), and various immune genes (e.g., defensins, T cell, B cell, ILC, macrophage markers).

Weaknesses:

(1) The primary limitation is that there is no functional validation. The study primarily focuses on the interpretation of RNA expression. This is a common limitation of transcriptomic "atlas" studies, but the functional and mechanistic relevance of these interpretations remains to be determined.

(2) The data are derived from bulk RNA-Seq of full-thickness intestinal tissue. While this approach helps capture rare cell types and both epithelial and mesenchymal components simultaneously, it does not provide cell-type-specific gene expression profiles, which might obscure important nuances. Future investigations using single-cell sequencing would be a logical follow-up.

(3) The day 4 samples were omitted due to quality issues, which might have led to missing some dynamic changes, especially given that some ISC genes show dynamic changes around day 6.

Reviewer #3 (Public review):

Summary:

This study uses bulk mRNA sequencing to profile transcriptional changes in intestinal cells during the early postnatal period in mice - a developmental window that has received relatively little attention despite its importance. This developmental stage is particularly significant because it parallels late gestation in humans, a time when premature infants are highly vulnerable to necrotizing enterocolitis (NEC). By sampling closely spaced timepoints from birth through postnatal week four, the authors generate a resource that helps define transcriptional trajectories during this phase. Although the primary focus is on murine tissue, the authors also present limited data from human fetal intestinal biopsy samples and organoids. In addition, they discuss potential links between observed gene expression changes and factors that may contribute to NEC.

Strengths:

The close temporal sampling in mice offers a detailed view of dynamic transcriptional changes across the first four weeks after birth. The authors leverage these close timepoints to perform hierarchical clustering to define relationships between developmental stages. This is a useful approach, as it highlights when transcriptional states shift most dramatically and allows for functional predictions about classes of genes that vary over time. This high-level analysis provides an effective entry point into the dataset and will be useful for future investigations. The inclusion of human fetal intestinal samples, although limited, is especially notable given the scarcity of data from late fetal timepoints. The authors are generally careful in their presentation of results, acknowledging the limitations of their approach and avoiding over-interpretation. As they note, this dataset is intended as a foundation for their lab and others, with secondary approaches required to more fully explore the biological questions raised.

Weaknesses:

One limitation of the study is the use of bulk mRNA sequencing to draw conclusions about individual cell types. It has been documented that a few genes are exclusively expressed in single cell types. For instance, markers such as Lgr5 and Olfm4 are enriched in intestinal stem cells (ISCs), but they are also expressed at lower levels in other lineages and in differentiating cells. Using these markers as proxies for specific cell populations lowers confidence in the conclusions, particularly without complementary validation to confirm cell type-specific dynamics.

Validation of the sequencing data was itself limited, relying primarily on qPCR, which measures expression at the same modality rather than providing orthogonal support. It is unclear how the authors selected the subset of genes for validation; many key genes highlighted in the sequencing data were not assessed. Moreover, the regional differences reported in Lgr5, Olfm4, and Ascl2, appearing much higher in proximal samples than in distal ones, were not recapitulated by qPCR validation of Olfm4, and this discrepancy was not addressed. Resolving such inconsistencies will be important for interpreting the dataset.

The basis for linking particular gene sets to NEC susceptibility rests largely on their spatial restriction to the distal intestine and their temporal regulation between early (day 0-14) and later (weeks 3-4) developmental stages. While this is a reasonable approach for generating hypotheses, the correlations have limited interpretive power without experimental validation, which is not provided here. Many factors beyond NEC may drive regional and temporal differences in intestinal development.

Finally, the contribution of human fetal biopsy samples is minimal. The central figure presenting these data (Figure 4A) shows immunofluorescence for LGR5, a single stem cell marker. The staining at day 35 is not convincing, and the conclusions that can be drawn are limited to confirming the localization of LGR5-positive cells to crypts as early as 26 weeks.

Joint Public Review:

In this study, the authors sought to characterize the relationship between the timescales of evidence integration in an auditory change detection task and neural activity dynamics in the rat posterior parietal cortex (PPC), an area that has been implicated in the accumulation of sensory evidence. Using the state-of-the-art Neuropixel recording techniques, they identified two subpopulations of neurons whose firing rates were positively and negatively modulated by auditory clicks. The timescale of click-related response was similar to the behaviorally measured timescale for evidence evaluation. The click-related response of positively modulated neurons also depended on when the clicks were presented, which the authors hypothesized to reflect a time-dependent gain change to implement an urgency signal. Using muscimol injections to inactivate the PPC, they showed that PPC inactivation affected the rats' choices and reaction times.

There are several strengths of this study, including:

(1) Compelling evidence for short temporal integration in behavioral and neural data for this task.

(2) Well-executed and interpretable comparisons of psychophysical reverse correlation with single-trial, click-triggered neuronal analyses to relate behavior and neural activity.

(3) Inactivation experiments to test for causality.

(4) Characterization of neural subpopulations that allows for complex relationships between a brain region and behavior.

(5) Experimental evidence for an interesting way to use sensory gain change to implement urgency signals.

There are also some concerns, including:

(1) The work could be better contextualized. From a normative Bayesian perspective, the observed adaptation of timescales and gain aligns closely with optimal strategies for change detection in noisy streams: placing greater weight on recent sensory samples and lowering evidence requirements as decision urgency grows. However, the manuscript could go further in explicitly connecting the experimental findings to normative models, such as leaky accumulator or dynamic belief-updating frameworks. This would strengthen the broader impact of the work by making clear how the observed PPC dynamics instantiate computationally optimal strategies.

(2) It is unclear how the rats are performing the task, both in terms of the quality of performance (they only show hit rates, but the rats also seem to have high false alarm rates), and in terms of the underlying strategy that they seem to be using.

(3) A major conceptual weakness lies in the claim that PPC "dynamically modulates evidence evaluation in a time-adaptive manner to suit the behavioral demands of a free-response change detection task." To support this claim, it would require direct comparison of neural activity between two task demands, either in two tasks or in one task with manipulations that promote the adoption of different timescales.

(4) Some analyses of neural data are lacking or seem incomplete, without considering alternative interpretations.

(5) The muscimol inactivation results did not provide a clear interpretation about the link between PPC activity and decision performance.

Reviewer #1 (Public review):

Summary:

(1) Introduction Hybridogenesis involves one genome being clonally transmitted while the other is replaced by backcrossing. It results in high heterozygosity and balanced ancestry proportions in hybrids. Distinguishing it from other hybrid systems requires a combination of nuclear, mitochondrial, and population-genetic evidence. Hybridogenesis has been identified in only a few taxa (e.g., some fish, frogs, and stick insects), but no new cases have been reported in over a decade. Advancements in high-throughput sequencing now allow for the detection of high individual heterozygosity, which can indicate hybridization, but it is difficult to distinguish hybridogenesis from other similar asexual systems based solely on genome-wide data. To differentiate these systems, researchers look at several key indicators: Presence of pure-species offspring from hybrids (possible only in hybridogenesis); sex ratio (male presence in hybridogenetic systems); nuclear and mitochondrial haplotype sharing with co-distributed parental species; geographic distribution patterns, especially the lack of both parental species in hybrid populations.

(2) What the authors were trying to achieve The paper studies Quasipaa Frogs. Q. robertingeri (narrowly endemic) and Q. boulengeri (widespread), which are morphologically similar and found sympatrically in parts of China. Preliminary RAD-seq data revealed bimodal heterozygosity in Q. boulengeri samples. Some individuals had extremely high heterozygosity, consistent across loci and suggestive of F1 hybrids. These high-heterozygosity individuals had one haplotype from each species. The study investigates the high heterozygosity observed in Quasipaa frogs, particularly in individuals morphologically resembling Q. boulengeri but genetically appearing to be F1 hybrids with Q. robertingeri. The goal is to determine whether these patterns are consistent with hybridogenesis, rather than other atypical reproductive modes. The authors also suggest the hypothesis that hybridogenesis could enable range expansion of an endemic species through hybridization with a widespread relative.

(3) Methods A total of 107 individuals from 53 localities were collected for the study. This sample included 58 sexed adults-27 males and 31 females-as well as a majority of tadpoles. Of these individuals, 31 had previously determined karyotypes. DNA was extracted and sequenced. Individual heterozygosity and ancestry were estimated using bioinformatics tools. F1 hybrids were compared to one of the parental species to examine patterns of fixed heterozygous loci. Mitochondrial DNA was also extracted from sequencing data, and phylogenetic trees were constructed

(4) Results Two groups of individuals were detected based on heterozygosity: one group exhibited high heterozygosity and consisted of F1 hybrids, while the other group showed low heterozygosity, representing pure-species types. The F1 hybrids demonstrated approximately equal ancestry from Q. robertingeri and Q. boulengeri, consistently maintaining a high proportion of heterozygous loci at around 16.7%. In contrast, pure individuals had much lower heterozygosity, approximately 2.9%. F1 hybrids were found across 21 different sites, including both male and female individuals. The presence of numerous fixed heterozygous loci in F1 hybrids confirmed their hybrid origin, and these loci were absent in pure Q. boulengeri samples. F1 individuals typically carried one haplotype from each parental species. There was minimal haplotype sharing between the two pure species, but extensive sharing was observed between F1 hybrids and co-occurring pure-species individuals. In fact, F1 types shared haplotypes with local Q. boulengeri in over 90% of cases, which supports the occurrence of local backcrossing and parental contribution. In terms of mitochondrial DNA, F1 hybrids possessed mitochondrial haplotypes that clustered with Q. boulengeri and often shared these haplotypes directly. Genetic structure and phylogenetic analyses, revealed three distinct genetic clusters corresponding to F1 hybrids, Q. boulengeri, and Q. robertingeri. The F1 hybrids positioned themselves intermediate between the two pure species. Neighbor-joining trees and TreeMix analyses confirmed a strong separation between pure-species types, with F1 hybrids clustering alongside local Q. boulengeri subpopulations, indicating local formation of hybrids.

(5) Discussion In summary, the study reveals hybridogenesis (a reproductive system where hybrids clonally transmit one parental genome) in Quasipaa boulengeri and Q. robertingeri. Hybrids show high genetic heterozygosity and coexist with parental species, ruling out other reproductive modes like parthenogenesis or kleptogenesis. Evidence suggests hybridogenesis enables Q. robertingeri genomes to appear far outside their normal range, possibly aiding range expansion. Chromosomal abnormalities are linked to hybrid hybrids, supporting clonal genome transmission. The genetic divergence between parental species fits patterns seen in other hybridogenetic systems, highlighting a unique, understudied case in East Asia.

Strengths:

Overall, the authors carefully interpret their genetic data to support hybridogenesis as the reproductive mode in this system and propose that this mechanism may aid range expansion. They also appropriately acknowledge the need for further cytogenetic and ecological studies, demonstrating scientific caution. In summary, the discussion reasonably follows from the results, offering cautious interpretation where necessary.

Weaknesses:

Direct reproductive or cytological evidence is still lacking. While alternative reproductive modes are discussed and mostly ruled out logically, some require further empirical testing. The authors maintain a cautious interpretation, appropriately suggesting further research. Some outstanding questions remain.

(1) The elevated heterozygosity and presence of fixed heterozygous loci in hybrids compared to parental species strongly indicate hybridogenesis. However, alternative explanations such as repeated F1 hybridization or some form of balanced polymorphism, while less likely, are not fully excluded.

(2) The coexistence of hybrids and parental species, along with high nuclear and mitochondrial haplotype sharing between hybrids and Q. boulengeri, argues against reproductive modes like parthenogenesis, gynogenesis, or kleptogenesis. However, the assumption that hybrid sterility or multiple local hybrid origins are unlikely could be challenged if undetected local variation or cryptic reproductive strategies exist.

(3) The presence of Q. robertingeri nuclear genomes far outside their known geographic range, genetically linked to nearby populations, fits a hybridogenetic-mediated dispersal model. Although the authors dismiss human-mediated or accidental transport as explanations, these scenarios are not necessarily unlikley.

Reviewer #2 (Public review):

This study describes F1 hybrid frog lineages that use an "unusual" form of reproduction, perhaps hybridogenesis. Identifying such species is important for understanding the biodiversity of reproduction in animals, and animals that do not reproduce via "canonical" sex can be useful model systems in ecology and evolution. The conclusion of the study are based on reduced representation sequencing (RAD-seq with a de-novo assembly of loci) of 107 wild-caught individuals from 53 localities (plus 4 outgroup individuals), including 27 males, 31 females, and 49 juveniles of unknown sex. Conclusive inferences of unusual forms of reproduction typically require breeding studies and parent-offspring genotype comparisons but such information is not available (and perhaps impossible to generate) for the focal frog lineages.

(1) Conclusion 1: there are two pure species and F1 hybrids

The authors infer that there are two lineages RR and BB (corresponding to two named species), and F1 interspecific hybrids RB. This inference is based on the results presented in Figure 1 (PCA, admixture, and heterozygosity analyses) as well as analyses of fixed SNP differences between R and B. I think that this conclusion is well supported; my only comment on this part is that it would be useful to have the admixture plots & cross-validation for the 107 samples with other k values (not only k=2) as a supplemental figure. The plots in the supplemental file S1 are for the subset of 55 inds inferred to be BB only.

(2) Conclusion 2: F1 hybrids most likely reproduce via hybridogenesis

This conclusion is based on the sex ratio of hybrids and haplotype sharing between species and lineages at different, ~150 bp long loci. Parthenogenesis (including sperm-dependent parthenogenesis) is unlikely to generate males, yet sexed F1 hybrid individuals include 18 females and 10 males which prompts the exclusion of parthenogenesis in the present paper. Specific haplotype-sharing patterns are also discussed in the study and used as further support, but these arguments (and the related main and supplementary figures) are difficult to read/interpret. To clarify the arguments related to haplotype sharing and haplotype diversities, I suggest that the authors phase the R and B haplotypes from all their hybrids by using their pure (RR and BB individuals) as references. The concatenated lineage-specific haplotypes can then be used to reconstruct a single phylogenetic tree for all loci (easier to visualize and interpret that the separate haplotype networks for the loci). The authors can then draw cartoon phylogenies for what would be the expected pattern for haplotype clustering and diversity for different reproductive modes, and discuss their observed phylogenies in this regard. Similarly, the migration weights (represented in Figure 4) can then also be computed for separate haplotypes in the hybrids.

However, independently of the outcome of the phasing, it is important to note that there is no a priori reason why all F1 hybrid individuals would reproduce via the same reproductive mode. Notably, work by Barbara Mantovani and Valerio Scali on stick insects has shown that different F1 hybrid lineages involving the same parental species reproduce via hybridogenesis or parthenogenesis. I don't see how the presented data can allow excluding that some F1 hybrid frogs are parthenogenetic while others are hybridogenetic for example.

(3) Conclusion 3: Crosses between hybridogenetic RB males and hybridogenetic RB females gave rise to a new population of RR individuals outside of the RR species range (this new population would correspond to location 30 from Figure 1).

It is not entirely clear to me which data this conclusion is based on, I believe it is the combination of known species ranges for the species R (location 30 being outside of this) and the relatively low heterozygosity of RR individuals at location 30.

However, as the authors point out, the study focuses on an understudied geographic range. Isolated or rare populations of the R species may easily have been overlooked in the past, especially since the R and B species are morphologically difficult to distinguish. Furthermore, an isolated, perhaps vestigial population may also likely be inbred/feature low diversity. It seems most appropriate to discuss different (equally likely) scenarios for the RR population at location 30 rather than implying a hybridogenetic origin of RR individuals. I would also choose a title that does not directly imply this scenario but reflects the solid (not speculative) findings of the study.

Reviewer #3 (Public review):

Summary:

This work reports a new case of hybridogenetic reproduction in the frog genus Quasipaa. Only one other example of this peculiar reproductive mode is known in amphibians, and fewer than a dozen across the tree of life. Interestingly, a population of one of the parental species (Q. robertingeri) was found away from the core of its distribution, within the distribution of the hybridogens. This range expansion might have been mediated by hybridogenesis, whereby two copies of the same parental genome came together again after many generations of hybridogenesis.

Strengths:

Evidence for hybridogenesis is solid. The state of the art would be to genotype parents and offspring, but other known alternative scenarios have been considered carefully and can be ruled out convincingly. In addition, the authors are very careful in their phrasing and made sure to never overinterpret their data.

The explicit predictions under different reproductive modes (and Table 1) are a useful resource for future studies and could inspire new findings of unusual reproductive modes in other taxa.

The sampling is very impressive, with over 50 populations sampled across a very large area.

The comparison of p-distances between pairs of species involved in hybridogenesis is interesting.

Weaknesses:

The current phylogenetic reconstruction with the F1s does not enable to infer the number of origins of hybridogenesis, nor whether the population of Q. robertingeri that was found far from the core of the species' distribution indeed derives from hybridogenesis. This is because some of the signal is driven by the Q. boulengeri haplome, which is replaced every generation and therefore does not reflect the evolutionary history of the lineage.

All known reproductive modes except hybridogenesis can be excluded, but without genotyping parents and offspring, it is impossible to rule out another, yet undescribed reproductive mode.