Reviewer #1 (Public review):

Summary:

The authors investigated if/how distractor suppression derived from statistical learning may be implemented in early visual cortex. While in a scanner, participants conducted a standard additional singleton task in which one location more frequently contained a salient distractor. The results showed that activity in EVC was suppressed for the location of the salient distractor as well as for neighbouring neutral locations. This suppression was not stimulus specific - meaning it occurred equally for distractors, targets and neutral items - and it was even present in trials in which the search display was omitted. Generally, the paper was clear, the experiment was well-designed, and the data are interesting.

The authors addressed all of my concerns and the revised manuscript will make a beautiful addition to the literature.

Reviewer #2 (Public review):

The authors of this work set out to test ideas about how observers learn to ignore irrelevant visual information. Specifically, they used fMRI to scan participants who performed a visual search task. The task was designed in such a way that highly salient but irrelevant search items were more likely to appear at a given spatial location. With a region-of-interest approach, the authors found that activity in visual cortex that selectively responds to that location was generally suppressed, in response to all stimuli (search targets, salient distractors, or neutral items), as well as in the absence of an anticipated stimulus.

Strengths of the study include: A well-written and well-argued manuscript; clever application of a region of interest approach to fMRI design, which allows articulating clear tests of different hypotheses; careful application of follow-up analyses to rule out alternative, strategy-based accounts of the findings; tests of the robustness of the findings to detailed analysis parameters such as ROI size; and exclusion of the role of regional baseline differences in BOLD responses. The main findings are enhanced by supplementary analyses that distinguish between the responses of early visual areas.

The study provides an advance over previous studies, which identified enhancement or suppression in visual cortex as a function of search target/distractor predictability, but in less spatially-specific way. It also speaks to open questions about whether such suppression/enhancement is observed only in response to the arrival of visual information, or instead is preparatory, favouring the latter view. These questions have been at the heart of theoretical debates in this literature on how distractor suppression unfolds in the context of visual search.

Reviewer #1 (Public review):

Summary:

This manuscript describes the role of PRDM16 in modulating BMP response during choroid plexus (ChP) development. The authors combine PRDM16 knockout mice and cultured PRDM16 KO primary neural stem cells (NSCs) to determine the interactions between BMP signaling and PRDM16 in ChP differentiation.

They show PRDM16 KO affects ChP development in vivo and BMP4 response in vitro. They determine genes regulated by BMP and PRDM16 by ChIP-seq or CUT&TAG for PRDM16, pSMAD1/5/8, and SMAD4. They then measure gene activity in primary NSCs through H3K4me3 and find more genes are co-repressed than co-activated by BMP signaling and PRDM16. They focus on the 31 genes found to be co-repressed by BMP and PRDM16. Wnt7b is in this set and the authors then provide evidence that PRDM16 and BMP signaling together repress Wnt activity in the developing choroid plexus.

Strengths:

Understanding context-dependent responses to cell signals during development is an important problem. The authors use a powerful combination of in vivo and in vitro systems to dissect how PRDM16 may modulate BMP response in early brain development.

Main weaknesses of the experimental setup:

(1) Because the authors state that primary NSCs cultured in vitro lose endogenous Prdm16 expression, they drive expression by a constitutive promoter. However, this means the expression levels are very different from endogenous levels (as explicitly shown in Supplementary Figure 2B) and the effect of many transcription factors is strongly dose-dependent, likely creating differences between the PRDM16-dependent transcriptional response in the in vitro system and in vivo.

(2) It seems that the authors compare Prdm16_KO cells to Prdm16 WT cells overexpressing flag_Prdm16. Aside from the possible expression of endogenous Prdm16, other cell differences may have arisen between these cell lines. A properly controlled experiment would compare Prdm16_KO ctrl (possibly infected with a control vector without Prdm16) to Prdm16_KO_E (i.e. the Prdm16_KO cells with and without Prdm16 overexpression.)

Other experimental weaknesses that make the evidence less convincing:

(1) The authors show in Figure 2E that Ttr is not upregulated by BMP4 in PRDM16_KO NSCs. Does this appear inconsistent with the presence of Ttr expression in the PRDM16_KO brain in Figure1C?

(2) Figure 3: The authors use H3K4me3 to measure gene activity. This is however, very indirect, with bulk RNA-seq providing the most direct readout and polymerase binding (ChIP-seq) another more direct readout. Transcription can be regulated without expected changes in histone methylation, see e.g. papers from Josh Brickman. They verify their H3K4me3 predictions with qPCR for a select number of genes, all related to the kinetochore, but it is not clear why these genes were picked, and one could worry whether these are representative.

(3) Line 256: The overlap of 31 genes between 184 BMP-repressed genes and 240 PRDM16-repressed genes seems quite small.

(4) The Wnt7b H3K4me3 track in Fig. 3G is not discussed in the text but it shows H3K4me3 high in _KO and low in _E regardless of BMP4. This seems to contradict the heatmap of H3K4me3 in Figure 3E which shows H3K4me3 high in _E no BMP4 and low in _E BMP4 while omitting _KO no BMP4. Meanwhile CDKN1A, the other gene shown in 3G, is missing from 3E.

(5) The authors use PRDM16 CUT&TAG on dissected dorsal midline tissues to determine if their 31 identified PRDM16-BMP4 co-repressed genes are regulated directly by PRDM16 in vivo. By manual inspection, they find that "most" of these show a PRDM16 peak. How many is most? If using the same parameters for determining peaks, how many genes in an appropriately chosen negative control set of genes would show peaks? Can the authors rigorously establish the statistical significance of this observation? And why wasn't the same experiment performed on the NSCs in which the other experiments are done so one can directly compare the results? Instead, as far as I could tell, there is only ChIP-qPCR for two genes in NSCs in Supplementary Figure 4D.

(6) In comparing RNA in situ between WT and PRDM16 KO in Figure 7, the authors state they use the Wnt2b signal to identify the border between CH and neocortex. However, the Wnt2b signal is shown in grey and it is impossible for this reviewer to see clear Wnt2b expression or where the boundaries are in Figure 7A. The authors also do not show where they placed the boundaries in their analysis. Furthermore, Figure 7B only shows insets for one of the regions being compared making it difficult to see differences from the other region. Finally, the authors do not show an example of their spot segmentation to judge whether their spot counting is reliable. Overall, this makes it difficult to judge whether the quantification in Figure 7C can be trusted.

(7) The correlation between mKi67 and Axin2 in Figure 7 is interesting but does not convincingly show that Wnt downstream of PRDM16 and BMP is responsible for the increased proliferation in PRDM16 mutants.

Weaknesses of the presentation:

Overall, the manuscript is not easy to read. This can cause confusion.

Reviewer #2 (Public review):

Summary:

This article investigates the role of PRDM16 in regulating cell proliferation and differentiation during choroid plexus (ChP) development in mice. The study finds that PRDM16 acts as a corepressor in the BMP signaling pathway, which is crucial for ChP formation.

The key findings of the study are:<br /> (1) PRDM16 promotes cell cycle exit in neural epithelial cells at the ChP primordium.<br /> (2) PRDM16 and BMP signaling work together to induce neural stem cell (NSC) quiescence in vitro.<br /> (3) BMP signaling and PRDM16 cooperatively repress proliferation genes.<br /> (4) PRDM16 assists genomic binding of SMAD4 and pSMAD1/5/8.<br /> (5) Genes co-regulated by SMADs and PRDM16 in NSCs are repressed in the developing ChP.<br /> (6) PRDM16 represses Wnt7b and Wnt activity in the developing ChP.<br /> (7) Levels of Wnt activity correlate with cell proliferation in the developing ChP and CH.

In summary, this study identifies PRDM16 as a key regulator of the balance between BMP and Wnt signaling during ChP development. PRDM16 facilitates the repressive function of BMP signaling on cell proliferation while simultaneously suppressing Wnt signaling. This interplay between signaling pathways and PRDM16 is essential for the proper specification and differentiation of ChP epithelial cells. This study provides new insights into the molecular mechanisms governing ChP development and may have implications for understanding the pathogenesis of ChP tumors and other related diseases.

Strengths:

(1) Combining in vitro and in vivo experiments to provide a comprehensive understanding of PRDM16 function in ChP development.

(2) Uses of a variety of techniques, including immunostaining, RNA in situ hybridization, RT-qPCR, CUT&Tag, ChIP-seq, and SCRINSHOT.

(3) Identifying a novel role for PRDM16 in regulating the balance between BMP and Wnt signaling.

(4) Providing a mechanistic explanation for how PRDM16 enhances the repressive function of BMP signaling. The identification of SMAD palindromic motifs as preferred binding sites for the SMAD/PRDM16 complex suggests a specific mechanism for PRDM16-mediated gene repression.

(5) Highlighting the potential clinical relevance of PRDM16 in the context of ChP tumors and other related diseases. By demonstrating the crucial role of PRDM16 in controlling ChP development, the study suggests that dysregulation of PRDM16 may contribute to the pathogenesis of these conditions.

Weaknesses:

(1) Limited investigation of the mechanism controlling PRDM16 protein stability and nuclear localization in vivo. The study observed that PRDM16 protein became nearly undetectable in NSCs cultured in vitro, despite high mRNA levels. While the authors speculate that post-translational modifications might regulate PRDM16 in NSCs similar to brown adipocytes, further investigation is needed to confirm this and understand the precise mechanism controlling PRDM16 protein levels in vivo.

(2) Reliance on overexpression of PRDM16 in NSC cultures. To study PRDM16 function in vitro, the authors used a lentiviral construct to constitutively express PRDM16 in NSCs. While this approach allowed them to overcome the issue of low PRDM16 protein levels in vitro, it is important to consider that overexpressing PRDM16 may not fully recapitulate its physiological role in regulating gene expression and cell behavior.

(3) Lack of direct evidence for AP1 as the co-factor responsible for SMAD relocation in the absence of PRDM16. While the study identified the AP1 motif as enriched in SMAD binding sites in Prdm16 knockout cells, they only provided ChIP-qPCR validation for c-FOS binding at two specific loci (Wnt7b and Id3). Further investigation is needed to confirm the direct interaction between AP1 and SMAD proteins in the absence of PRDM16 and to rule out other potential co-factors.

Reviewer #3 (Public review):

Summary:

Bone morphogenetic protein (BMP) signaling instructs multiple processes during development including cell proliferation and differentiation. The authors set out to understand the role of PRDM16 in these various functions of BMP signaling. They find that PRDM16 and BMP co-operate to repress stem cell proliferation by regulating the genomic distribution of BMP pathway transcription factors. They additionally show that PRDM16 impacts choroid plexus epithelial cell specification. The authors provide evidence for a regulatory circuit (constituting of BMP, PRDM16, and Wnt) that influences stem cell proliferation/differentiation.

Strengths:

I find the topics studied by the authors in this study of general interest to the field, the experiments well-controlled and the analysis in the paper sound.

Weaknesses:

I have no major scientific concerns. I have some minor recommendations that will help improve the paper (regarding the discussion).

Reviewer #1 (Public review):

Summary:

This research focuses on C. elegans klinotaxis, a chemotactic behavior characterized by gradual turning, aiming to uncover the neural circuit mechanism responsible for the context-dependent reversal of salt concentration preference. The phenomenon observed is that the preferred salt concentration depends on the difference between the pre-assay cultivation conditions and the current environmental salt levels.

The authors propose that a synaptic-reversal plasticity mechanism at the primary sensory neuron, ASER, is critical for this memory- and context-dependent switching of preference. They build on prior findings regarding synaptic reversal between ASER and AIB, as well as the receptor composition of AIY neurons, to hypothesize that similar "plasticity" between ASER and AIY underpins salt preference behavior in klinotaxis. This plasticity differs conceptually from the classical one as it does not rely on any structural changes but rather synaptic transmission is modulated by the basal level of glutamate, and can switch from inhibitory to excitatory.

To test this hypothesis, the study employs a previously established neuroanatomically grounded model [4] and demonstrates that reversing the ASER-AIY synapse sign in the model agent reproduces the observed reversal in salt preference. The model is parameterized using a computational search technique (evolutionary algorithm) to optimize unknown electrophysiological parameters for chemotaxis performance. Experimental validity is ensured by incorporating constraints derived from published findings, confirming the plausibility of the proposed mechanism.

Finally. the circuit mechanism allowing C. elegans to switch behaviour to an exploration run when starved is also investigated. This extension highlights how internal states, such as hunger, can dynamically reshape sensory-motor programs to drive context-appropriate behaviors.

Strengths and weaknesses:

The authors' approach of integrating prior knowledge of receptor composition and synaptic reversal with the repurposing of a published neuroanatomical model [4] is a significant strength. This methodology not only ensures biological plausibility but also leverages a solid, reproducible modeling foundation to explore and test novel hypotheses effectively.

The evidence produced that the original model has been successfully reproduced is convincing.

The writing of the manuscript needs revision as it makes comprehension difficult.

One major weakness is that the model does not incorporate key findings that have emerged since the original model's publication in 2013, limiting the support for the proposed mechanism. In particular, ablation studies indicate that AIY is not critical for chemotaxis, and other interneurons may play partially overlapping roles in positive versus negative chemotaxis. These findings challenge the centrality of AIY and suggest the model oversimplifies the circuit involved in klinotaxis.

Reference [1] also shows that ASER neurons exhibit complex, memory- and context-dependent responses, which are not accounted for in the model and may have a significant impact on chemotactic model behaviour.

The hypothesis of synaptic reversal between ASER and AIY is not explicitly modeled in terms of receptor-specific dynamics or glutamate basal levels. Instead, the ASER-to-AIY connection is predefined as inhibitory or excitatory in separate models. This approach limits the model's ability to test the full range of mechanisms hypothesized to drive behavioral switching.

While the main results - such as response dependence on step inputs at different phases of the oscillator - are consistent with those observed in chemotaxis models with explicit neural dynamics (e.g., Reference [2]), the lack of richer neural dynamics could overlook critical effects. For example, the authors highlight the influence of gap junctions on turning sensitivity but do not sufficiently analyze the underlying mechanisms driving these effects. The role of gap junctions in the model may be oversimplified because, as in the original model [4], the oscillator dynamics are not intrinsically generated by an oscillator circuit but are instead externally imposed via $z_\text{osc}$. This simplification should be carefully considered when interpreting the contributions of specific connections to network dynamics. Lastly, the complex and context-dependent responses of ASER [1] might interact with circuit dynamics in ways that are not captured by the current simplified implementation. These simplifications could limit the model's ability to account for the interplay between sensory encoding and motor responses in C. elegans chemotaxis.

Appraisal:

The authors show that their model can reproduce memory-dependent reversal of preference in klinotaxis, demonstrating that the ASER-to-AIY synapse plays a key role in switching chemotactic preferences. By switching the ASER-AIY connection from excitatory to inhibitory they indeed show that salt preference reverses. They also show that the curving/turn rate underlying the preference change is gradual and depends on the weight between ASER-AIY. They further support their claim by showing that curving rates also depend on cultivated (set-point).

Thus within the constraints of the hypothesis and the framework, the model operates as expected and aligns with some experimental findings. However, significant omissions of key experimental evidence raise questions on whether the proposed neural mechanisms are sufficient for reversal in salt-preference chemotaxis.

Previous work [1] has shown that individually ablating the AIZ or AIY interneurons has essentially no effect on the Chemotactic Index (CI) toward the set point ([1] Figure 6). Furthermore, in [1] the authors report that different postsynaptic neurons are required for movement above or below the set point. The manuscript should address how this evidence fits with their model by attempting similar ablations. It is possible that the CI is rescued by klinokinesis but this needs to be tested on an extension of this model to provide a more compelling argument.

The investigation of dispersal behaviour in starved individuals is rather limited to testing by imposing inhibition of the SMB neurons. Although a circuit is proposed for how hunger states modulate taxis in the absence of food, this circuit hypothesis is not explicitly modelled to test the theory or provide novel insights.

Impact :

This research underscores the value of an embodied approach to understanding chemotaxis, addressing an important memory mechanism that enables adaptive behavior in the sensorimotor circuits supporting C. elegans chemotaxis. The principle of operation - the dependence of motor responses to sensory inputs on the phase of oscillation - appears to be a convergent solution to taxis. Similar mechanisms have been proposed in Drosophila larvae chemotaxis [2], zebrafish phototaxis [3], and other systems. Consequently, the proposed mechanism has broader implications for understanding how adaptive behaviors are embedded within sensorimotor systems and how experience shapes these circuits across species.

Although the reported reversal of synaptic connection from excitatory to inhibitory is an exciting phenomenon of broad interest, it is not entirely new, as the authors acknowledge similar reversals have been reported in ASER-to-AIB signaling for klinokinesis ( Hiroki et al., 2022). The proposed reversal of the ASER-to-AIY synaptic connection from inhibitory to excitatory is a novel contribution in the specific context of klinotaxis. While the ASER's role in gradient sensing and memory encoding has been previously identified, the current paper mechanistically models these processes, introducing a hypothesis for synaptic plasticity as the basis for bidirectional salt preference in klinotaxis.

The research also highlights how internal states, such as hunger, can dynamically reshape sensory-motor programs to drive context-appropriate behaviors.

The methodology of parameter search on a neural model of a connectome used here yielded the valuable insight that connectome information alone does not provide enough constraints to reproduce the neural circuits for behaviour. It demonstrates that additional neurophysiological constraints are required.

Additional Context

Oscillators with stimulus-driven perturbations appear to be a convergent solution for taxis and navigation across species. Similar mechanisms have been studied in zebrafish phototaxis [3], Drosophila larvae chemotaxis [2], and have even been proposed to underlie search runs in ants. The modulation of taxis by context and memory is a ubiquitous requirement, with parallels across species. For example, Drosophila larvae modulate taxis based on current food availability and predicted rewards associated with odors, though the underlying mechanism remains elusive. The synaptic reversal mechanism highlighted in this study offers a compelling framework for understanding how taxis circuits integrate context-related memory retrieval more broadly.

As a side note, an interesting difference emerges when comparing C. elegans and Drosophila larvae chemotaxis. In Drosophila larvae, oscillatory mechanisms are hypothesized to underlie all chemotactic reorientations, ranging from large turns to smaller directional biases (weathervaning). By contrast, in C. elegans, weathervaning and pirouettes are treated as distinct strategies, often attributed to separate neural mechanisms. This raises the possibility that their motor execution could share a common oscillator-based framework. Re-examining their overlap might reveal deeper insights into the neural principles underlying these maneuvers.

(1) Luo, L., Wen, Q., Ren, J., Hendricks, M., Gershow, M., Qin, Y., Greenwood, J., Soucy, E.R., Klein, M., Smith-Parker, H.K., & Calvo, A.C. (2014). Dynamic encoding of perception, memory, and movement in a C. elegans chemotaxis circuit. Neuron, 82(5), 1115-1128.

(2) Antoine Wystrach, Konstantinos Lagogiannis, Barbara Webb (2016) Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae eLife 5:e15504.

(3) Wolf, S., Dubreuil, A.M., Bertoni, T. et al. Sensorimotor computation underlying phototaxis in zebrafish. Nat Commun 8, 651 (2017).

(4) Izquierdo, E.J. and Beer, R.D., 2013. Connecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxis. PLoS computational biology, 9(2), p.e1002890.

Reviewer #2 (Public review):

Summary:

This study explores how a simple sensorimotor circuit in the nematode C. elegans enables it to navigate salt gradients based on past experiences. Using computational simulations and previously described neural connections, the study demonstrates how a single neuron, ASER, can change its signaling behavior in response to different salt conditions, with which the worm is able to "remember" prior environments and adjust its navigation toward "preferred" salinity accordingly.

Strengths:

The key novelty and strength of this paper is the explicit demonstration of computational neurobehavioral modeling and evolutionary algorithms to elucidate the synaptic plasticity in a minimal neural circuit that is sufficient to replicate memory-based chemotaxis. In particular, with changes in ASER's glutamate release and sensitivity of downstream neurons, the ASER neuron adjusts its output to be either excitatory or inhibitory depending on ambient salt concentration, enabling the worm to navigate toward or away from salt gradients based on prior exposure to salt concentration.

Weaknesses:

While the model successfully replicates some behaviors observed in previous experiments, many key assumptions lack direct biological validation. As to the model output readouts, the model considers only endpoint behaviors (chemotaxis index) rather than the full dynamics of navigation, which limits its predictive power. Moreover, some results presented in the paper lack interpretation, and many descriptions in the main text are overly technical and require clearer definitions.

Reviewer #1 (Public review):

Summary:

The study investigates the role of vascular mural cells, specifically pericytes and vascular smooth muscle cells (vSMCs), in maintaining blood-brain barrier (BBB) integrity and regulating vascular patterning. Analyzing zebrafish pdgfrb mutants that lack brain pericytes and vSMCs, they show that mural cell deficiency does not impair BBB establishment or maintenance during larval and early juvenile stages. However, mural cells seem to be crucial for preventing vascular aneurysms and hemorrhage in adulthood as focal leakage, basement membrane disruption, and increased caveolae formation are observed in adult zebrafish at aneurysm hotspots. The authors challenge the paradigm that mural cells are essential for BBB regulation in early development while highlighting their importance for long-term vascular stability.

Strengths:

Previous studies have established that the zebrafish BBB shares molecular and morphological homology with e.g. the mammalian BBB and therefore represents a suitable model. By examining mural cell roles across different life stages - from larval to adult zebrafish - the study provides an unprecedented comprehensive developmental analysis of brain vascular development and of how mural cells influence BBB integrity and vascular stability over time. The use of live imaging, whole-brain clearing, and electron microscopy offers high-resolution insights into cerebrovascular patterning, aneurysm development, and structural changes in endothelial cells and basement membranes. By analyzing "leakage hotspots" and their association with structural endothelial defects in adults the presented findings add novel insights into how mural cell loss may lead to vascular instability.

Weaknesses:

The study uses quantitative tracer assays with multiple molecular weight dyes to evaluate blood-brain barrier (BBB) permeability. The study normalizes the intensity of tracer signals (e.g., 10 kDa, 70 kDa dextrans) in the brain parenchyma to the vascular signal of a 2000 kDa dextran tracer (assumed to remain within vessels). Intensity normalization is used to control for variations in tracer injection efficiency or vascular density. This method doesn't directly assess the absolute amount of tracer present in the parenchyma, potentially underestimating leakage severity. As the lack of BBB impairment is a "negative" finding, more rigorous controls or other methods might be needed to corroborate it.

Reviewer #2 (Public review):

Summary:

The authors generated a zebrafish mutant of the pdgfrb gene. The presented analyses and data confirm previous studies demonstrating that Pdgfrb signaling is necessary for mural cell development in zebrafish. In addition, the data support previously published studies in zebrafish showing that mural cell deficiency leads to hemorrhages later in life. The authors presented quantified data on vessel density and branching, assessed tracer extravasation, and investigated the vasculature of adult mice using electron microscopy.

Strengths:

The strength of this article is that it provides independent confirmation of the important role of Pdgfrb signaling for the development of mural cells in the zebrafish brain. In addition, it confirms previous literature on zebrafish that provides evidence that, in the absence of pericytes/VSMC, hemorrhages appear (Wang et al, 2014, PMID: 24306108 and Ando et al 2021, PMID: 3431092). The study by Ando et al, 2021 did not report experiments assessing BBB leakage in pdgfrb mutants but in the review article by Ando et al (PMID: 34685412) it is stated that "indicating that endothelial cells can produce basic barrier integrity without pericytes in zebrafish".

Weaknesses:

(1) The authors should avoid using violin plots, which show distribution. Instead, they should replace all violin plots in the figures with graphs showing individual data points and standard deviation. For Figure 2f specifically, the standard deviation in the analyzed cohort should be shown.

(2) The authors have not shown the reduced PDGFRB protein or the effect of mutation on mRNA level in their zebrafish mutant.

(3) Statistical data analysis: Did the authors perform analyses to investigate whether the data has a normal distribution (e.g., Figures 1d, e)?

(4) Analysis of tracer extravasation. The use of 2000 kDa dextran intensity as an internal reference is problematic because the authors have not provided data demonstrating that the 2000 kDa dextran signal remains consistent across the entire vasculature. The authors have not provided data demonstrating that the 2000 kDa dextran signal in vessels exhibits acceptable variance across the vasculature to serve as a reliable internal reference. The variability of this signal within a single animal remains unknown. The presented data do not address this aspect.

Additionally, it's intriguing that the signal intensity in the parenchyma of the tested tracers presents a substantial range, varying by 20-30% in the analysed cohort (Figure 1g, Extended Figure 1e). Such large variability raises the question of its origin. Could it be a consequence of the normalization to 2000 kDa dextran intensity which differs between different fish? Or is it due to the differences in the parenchymal signal intensity while the baseline 2000 kDa intensity is stable? Or is the situation mixed?

An alternative and potentially more effective approach would be to cross the pdgfrb mutant line with a line where endothelial cells are genetically labeled to define vessels (e.g. the line kdrl used in acquiring data presented in Figure 2a). Non-injected controls could then be used as a baseline to assess tracer extravasation into the parenchyma.

How is the data presented in Figure 3e generated? How was the dextran intensity calculated? It looks like the authors have used the kdrl line to define vessels. Was the 2000 kDa still used as in previous figures? If not, please describe this in the Materials and Methods section.

(5) The authors state that both controls and mutants show extravasation of 1 kDa NHS-ester into the parenchyma. However, the presented images do not illustrate this; it is not obvious from these images (Extended Data Figure 1c). Additionally, the presented quantification data (Extended Data Figure 1e) do not show that, at 7 dpf, the vasculature is permeable to this tracer. Note that the range of signal intensity of the 1 kDa NHS-ester is similar to the 70 kDa dextran (Figure 1g and Extended Figure 1e). Would one expect an increase in the ratio in case of extravasation, considering that the 2000 kDa dextran has the same intensity in all experiments? Please explain.

(6) The study would be strengthened by a more detailed temporal analysis of the phenotype. When do the aneurysms appear? Is there an additional loss of VSMC?

(7) The authors intended to analyze the BBB at later stages (line 128), but there is not a significant time difference between 2 months (Figure 2) and 3 months (Figure 3) considering that zebrafish live on average 3 years. Therefore, the selection of only two time-points, 2 and 3 months, to analyze BBB changes does not provide a comprehensive overview of temporal changes throughout the zebrafish's lifespan. How long do the pdgfb mutants live?

(8) Why is there a difference in tracer permeability between 2 and 3 months (Figures 2 and 3)? Are hemorrhages not detected in 2-month-old zebrafish?

(9) Figure 3: The capillary bed should be presented in magnified images as it is not clearly visible. Figure 3e shows that in the pdgfb mutant the dextran intensity is higher also in regions 6-10. How do the authors explain this?

(10) In general, the manuscript would benefit from a more detailed description of the performed experiments. How long did the tracer circulate in the experiments presented in Figures 2, 3, and 4?

(11) How do the authors explain the poor signal of the 70 kDa dextran from the vasculature of 5-month-old zebrafish presented in Extended Data Figure 3?

(12) The study would benefit from a clear separation of the phenotypes caused by the loss of VSMC. The title eludes that also capillaries present hemorrhages which is not the case. How do vascular mural cells differ from mural cells? Are there any other mural cells?

(13) I have a few comments about how the authors have interpreted the literature and why, in my opinion, they should revise their strong statements (e.g., the last sentence in the abstract).

Scientists have their own insights and interpretations of data. However, when citing published data, it should be clearly indicated whether the statement is a direct quote from the original publication or an interpretation. In the current manuscript, the authors have not correctly cited the data presented in the two published papers (references 5 and 6). These papers do not propose a model where pericytes suppress "adsorptive transcytosis" (lines 73-76). While increased transcytosis is observed in pericyte-deficient mice, the specific type of vesicular transport that is increased or induced remains unknown.

Similarly, lines 151-152 refer to references 5 and 6 and use the term "adsorptive transcytosis," but the authors of both papers did not use this term. Attributing this term to the original authors is inaccurate. Additionally, lines 152-153 do not accurately represent the findings of references 5 and 6. These papers do not state that there is an induction of "caveolae" in endothelial cells in pericyte-deficient mice. In the absence of pericytes, many vesicles can be observed in endothelial cells, but these vesicles are relatively large. It is more likely that there is some form of uncontrolled transcytosis, perhaps micropinocytosis. Please refer to the original papers accurately.

Also, the authors have missed the fact that in mice, the extent of pericyte loss correlates with the extent of BBB leakage. To a certain extent, the remaining pericytes, can compensate for the loss by making longer processes and so ensure the full longitudinal coverage of the endothelium. This was shown in the initial work of Armulik et al (reference 5) and later in other studies.

The bold assertion on lines 183 -187 that a lack of specific BBB phenotype in pdgfrb zebrafish mutant invalidates mouse model findings is unfounded. Despite the notion that zebrafish endothelium possesses a BBB, I present a few examples highlighting the differences in brain vascular development and why the authors' expectation of a straightforward extrapolation of mouse BBB phenotypes to zebrafish is untenable.

In mice Pdgfrb knockout is lethal, but in zebrafish, this is not the case. In marked contrast to mice, however, zebrafish pdgfrb null mutants reach adulthood despite extensive cerebral vascular anomalies and hemorrhage. Following the authors' argumentation about the unlikely divergence of zebrafish and mice evolution, does it mean that the described mouse phenotype warrants a revisit and that the Pdgfrb knockout in mice perhaps is not lethal? Another example where the role of a gene product is not one-to-one, which relates to pericyte development, is Notch3. Notch3-null mice do not show significant changes in pericyte numbers or distribution, suggesting a less prominent role in pericyte development compared to zebrafish.

Although many aspects of development are conserved between species, there are significant differences during brain vascular development between zebrafish and mice. These differences could reveal why the BBB is not impaired in zebrafish pdgfrb mutants. There is a difference in the temporal aspect when various cellular players emerge. The timing of microglia colonization in the brain differs. In mice, microglia colonization starts before the first vessel sprouts enter the brain, while in zebrafish, microglia enter after. Additionally, microglia in zebrafish and mice have a different ontogeny. In mice, astrocytes specialize postnatally and form astrocyte endfeet postnatally. In zebrafish, radial glia/astrocytes form at 48 hpf, and as early as 3 dpf, gfap+ cells have a close relationship with blood vessels. Thus, these radial glia/astrocyte-like cells could play an important role in BBB induction in zebrafish. It's worth noting that in Drosophila, the blood-brain barrier is located in glial cells. While speculative, these cells might still play a role in zebrafish, while the role of pericytes does not seem to be crucial. Pericytes enter the brain and contact with developing vasculature (endothelium) relatively late in zebrafish (60 hpf). In mice, the situation is different, as there is no such lag between endothelium and pericyte entry into the brain. I suggest that the authors approach the observed data with curiosity and ask: Why are these differences present? Are all aspects of the BBB induced by neural tissue in zebrafish? What is the contribution of microglia and astrocytes?"

Another interesting aspect to consider is the endothelial-pericyte ratio and longitudinal coverage of pericytes in the zebrafish brain, and how this relates to what is observed in mice. How similar is the zebrafish vasculature to the mouse vasculature when it comes to the average length of pericytes in the zebrafish brain? Does the longitudinal coverage of pericytes in the zebrafish brain reach nearly 100%, as it does in mice?

Based on the preceding arguments, it is recommended that the authors present a balanced discussion that provides insightful discussion and situates their work within a broader framework.

Reviewer #3 (Public review):

This manuscript examines the role of pdgfrb-positive pericytes in the establishment and maintenance of the blood-brain barrier (BBB) in the zebrafish. Previous studies in PDGFB- or PDGFRB-deficient mice have suggested that loss of pericytes results in disruption of the BBB. The authors show that zebrafish pdgfrb mutant larvae have an intact BBB and that pdgfrb mutant adult fish show large vessel defects and hemorrhage but do not exhibit substantial leakage from brain capillaries, suggesting loss of pericytes is not sufficient to "open" the BBB. The authors use beautiful and compelling images and rigorous quantification to back up most of their conclusions. The imaging of the adult brain is particularly nice. The authors rigorously document the lack of BBB leakage in pdgfrbuq30bh mutant larvae and large vessel phenotypes (eg, enlargement and rupture) in pdgfrbuq30bh mutant adults. A few points would help the authors to further strengthen their findings contradicting the current dogma from rodent models.

Major point:

The authors document pericyte loss using a single TgBAC(pdgfrb:egfp)ncv22 transgenic line driven by the promoter of the same gene mutated in their pdgfrbuq30bh mutants. Given their findings on the consequences of pericyte loss directly contradict current dogma from rodent studies, it would be useful to further validate the absence of brain pericytes in these mutants using one of several other transgenic lines marking pericytes currently available in the zebrafish. This could be done using pdgfrb crispants, which the authors show nicely phenocopy the germline mutants, at least in larvae. This would help nail down the absence of any currently identifiable pericyte population or sub-population in the loss of pdgfrb animals and substantially strengthen the authors' conclusions.

Other issues:

The authors should provide more information about the pdgfrbuq30bh mutant and how it was generated (including a diagram in a supplemental figure would be useful).

It would be helpful to show some data on whether mutants show morphological phenotypes or developmental delay at 7 and 14 dpf, to provide some context to better assess the reduced branching and vessel length vascular phenotypes (see Figures 1c-e).

If available, it would be helpful to have a positive control for the tracer leakage experiments - a genetic manipulation that does cause disruption of the BBB and leakage at 2 hours post-tracer injection (see Figures 1f and g).

Quantification of the findings in Figure 4c,d would be useful, as would the use of germline fish for these experiments if these are now available. If this is not possible, it would be helpful to document that the crispants used in these experiments lack pdgfrb:egfp pericytes at adult stages (this is only shown for 5 dpf larvae, in Extended Data Figure 4b).

Adult mutants clearly show less dye leakage in the more superficial capillary regions than WT siblings, but dextran intensity is a bit higher, although this could well be diffusion from more central brain regions where overt hemorrhage is occurring. Along similar lines though, the authors' TEM data in Extended Data Figure 4d hints that there may be more caveolae in mutant brain capillaries, although the N number was lower here than for the measurements from TEM of larger central vessels (Figure 4g). It would be useful to carry out additional measurements to increase the N number in Figure 4d to see whether the difference between wild-type sibling and mutant capillary caveolae numbers remains as not significant.

It might be helpful to include some orienting labels and/or additional descriptions in the figure legends to help readers who are not used to looking at zebrafish brain vessels have an easier time figuring out what they are looking at and where it is in the brain.

Reviewer #1 (Public review):

Summary:

In this study, the authors employed Saturated Transposon Analysis in Yeast (SATAY) in the model yeast Saccharomyces cerevisiae to uncover mutations conferring resistance to 20 different antifungal compounds. These screens revealed novel resistance mechanisms and the modes of action for the antifungal compounds Chitosan and HTI-2307. The authors discovered that Chitosan electrostatically interacts with cell wall mannosylphosphate and identified Hol1 as the transporter of HTI-2307.

Strengths:

The study highlights the power of SATAY in uncovering drug-resistance mechanisms, modes of action, and cellular processes influencing fungal responses to drugs. Identifying novel resistance mechanisms and modes of action for various compounds in this model yeast provides valuable insights for further investigating these compounds in fungal pathogens and developing antifungal strategies. This study thus represents a significant resource for exploring cellular responses to chemical stresses.

The manuscript is well-written and highly clear.

Weaknesses:

As the study was conducted using highly modified non-pathogenic laboratory yeast strains, verification of the findings in fungal pathogens would greatly enhance its relevance and applicability.

Reviewer #2 (Public review):

The study begins by exposing wild-type yeast libraries to some well-understood antifungals (amphotericin B, caspofungin, myriocin) to illustrate the complexity and power of the analytical method. These toxins are positively selected for loss-of-function transposon (CDS) insertions in many of the genes identified previously in earlier studies. The outlier genes were visually evident in scatter plots (Figure 1A, 1B, 1C) but the magnitude and statistical significance of the effects were not presented in tables. There were some unexplained and unexpected findings as well. For example, caspofungin targets the product of the GSC2 gene, and yet transposon insertions in this gene were positively selected rather than negatively selected (seemingly discordant from other studies).

Interestingly, transposon insertions immediately upstream of toxin targets (Figure 1D) and toxin efflux transporters or their regulators (Figure 1E) were visibly selected by exposure to the toxins, suggesting gain-of-expression. Most of these findings are convincing, even without statistical tests. However, some were not (for example, Soraphen A on YOR1). A relevant question emerges here: Do both ends of the transposon confer the same degree of cryptic enhancer/promoter activity? If one end contains strong activity on downstream gene expression while the other does not, the effects of one may be obscured by the other. The directionality of transposon insertions (not provided) would then be important to consider when interpreting the raw data.

A masterful rationalization of transposon insertion selection in the YAP1 and FLR1 genes was presented wherein loss of C-terminal auto-inhibitory domain of the Yap1 transcription factor resulted in FLR1 overexpression and resistance to Cerulenin. Transposon insertions in the CDS of YAP1 and FLR1 were negatively selected in Chlorothalonil while the gain-of-function and -expression insertions (enriched in Cerulenin) were not. The rationalization of these findings - that Chlorothalonil activates Yap1 while Cerulenin does not - was much less convincing and should be tested directly with a simple experiment such as Q-PCR.

Moving to specially engineered yeast strains (Figure 2) where multiple efflux transporters were eliminated (for Prochloraz testing) or new drug targets were inserted (for Fludioxonil and Iprodione), numerous interesting observations were obtained. For instance, transposon insertions in totally different sets of genes were enriched by prochloraz depending on the strain background. Conversely, almost the exact same genes were selected in Fludioxonil and Iprodione, including genes in the well-known HOG pathway. Because several candidate receptors of these compounds were not significant in the Tn-seq dataset, the authors add new evidence to the field suggesting that the introduced gene (BdDRK1) represents the direct, or near-direct, target of these compounds.

Chitosan effectiveness was studied by Tn-seq in yet another specialized strain of yeast that is uniquely susceptible to the toxin. Once again, the authors masterfully rationalize the complex effects, leading to a simple model where chitosan interacts with mannosyl-phosphate in the cell wall and membrane, which is deposited by Mnn4 and Mnn6 and masked by Mnn1 enzymes in the Golgi complex (themselves regulated or dependent on a number of additional gene products such as YND1. This research compellingly adds to our understanding of an industrial antifungal.

Finally, the effects of a preclinical antifungal ATI-2307 were studied for the first time. Remarkably, ATI-2307 efficacy greatly depended on HOL1 coding sequences and an upstream enhancer (Figure 4). After engineering hol1∆ strains, uptake of the compound and sensitivity to the compound were lost and then restored by heterologous expression of CaHOL1 from a pathogenic yeast. HOL1 also conferred susceptibility to polyamines with related structures (Pentamidine, Iminoctadine). Remarkably, separation-of-function mutations were obtained in HOL1 that abolished the uptake of the toxins while preserving the uptake of nutrient polyamines in low nitrogen conditions, which strongly suggests that HOL1 encodes a direct transporter of the toxins. The implications are important for ATI-2307 efficacy in patients, where resistance mutations could arise spontaneously and produce poor clinical outcomes.

Additional comments:

The experiments presented here are often convincing and serve to illustrate the power of Tn-seq approaches in elucidating drug resistance mechanisms in eukaryotic microbes. The gain-of-expression effects (upstream of CDS), gain-of-function effects (elimination of auto-inhibitory domains), and loss-of-function effects were all carefully exposed and discussed, leading to numerous new insights on the action of diverse toxins.

On the other hand, several deficiencies and weaknesses (in addition to the minor ones described above) limit the utility of the data that has been generated.

(1) There was no summary table of Tn-seq data for different genes in the different conditions, so readers could not easily access data for genes and pathways not mentioned in the text. This is especially important because transposon insertions that were negatively selected (of great interest to the community) were barely mentioned. Additionally, the statistical significance of outlier genes was not reported. The same is true for insertions within the DNA segments upstream of CDSs. Users of these data are therefore restricted to visually inspecting insertion sites on a genome browser.

(2) Only one dose of each toxin was studied, which therefore produces a limited perspective on the genetic mechanisms of resistance in each case.

(3) No Tn-seq experiments were performed in diploid yeast strains. The gain-of-expression and gain-of-function insertions under positive selection in haploid strains in the different conditions are expected to be dominant in diploid strains as well, while loss-of-function insertions in CDS are expected to be recessive. Do these expectations hold? Could such experiments potentially confirm the models for Cerulenin and Chlorothalonil effects on YAP1 and FLR1? Pathogenic Candida species are usually diploid where gain-of-function/expression mutants most frequently lead to poor clinical outcomes. Resistance to ATI-2307 through loss of HOL1 may not be as significant for diploid C. albicans with two functional copies of all genes. On a related note, is it possible that transposon insertions in the 3' untranslated region produce anti-sense transcripts that lowers the expression of the upstream gene from both alleles in diploids, thereby producing a strong selective advantage in ATI-2307? This study already touches on exciting new applications of the Tn-seq method but could easily go a bit further.

Reviewer #3 (Public review):

Summary:

This manuscript describes an extensive application of the Yeast (SATAY) transposon mutagenesis and sequencing method to explore loss- and gain-of-function mutations conferring resistance to 20 different antifungal compounds. Impressively, the authors demonstrate that SATAY can be used to identify mutations that lead to antifungal resistance, including promoter mutations that include the direct targets of antifungal compounds and drug efflux pumps. Because SATAY is not tied to a specific genetic background, the sensitivity of an S. cerevisiae strain, AD1-8, that specifically displays Chitosan susceptibility was examined in detail, and the results suggest that Chitosan acts through interactions with the fungal cell wall. Through a series of experiments that expand upon SATAY analysis, the novel antifungal ATI-2307, the authors clearly show that the transporter Hol1 concentrates this compound within yeast.

General Comments:

This is a very impressive application of SATAY, highlighting many different strategies for exploring the mechanism of action of various antifungal compounds. It's clear from the findings presented that SATAY is a powerful and potentially highly productive approach for chemical-genetic analysis.

Reviewer #1 (Public review):

Summary:

This study investigates whether pupil dilation reflects prediction error signals during associative learning, defined formally by Kullback-Leibler (KL) divergence, an information-theoretic measure of information gain. Two independent tasks with different entropy dynamics (decreasing and increasing uncertainty) were analyzed: the cue-target 2AFC task and the letter-color 2AFC task. Results revealed that pupil responses scaled with KL divergence shortly after feedback onset, but the direction of this relationship depended on whether uncertainty (entropy) increased or decreased across trials. Furthermore, signed prediction errors (interaction between frequency and accuracy) emerged at different time windows across tasks, suggesting task-specific temporal components of model updating. Overall, the findings highlight that pupil dilation reflects information-theoretic processes in a complex, context-dependent manner.

Strengths:

This study provides a novel and convincing contribution by linking pupil dilation to information-theoretic measures, such as KL divergence, supporting Zénon's hypothesis that pupil responses reflect information gained during learning. The robust methodology, including two independent datasets with distinct entropy dynamics, enhances the reliability and generalisability of the findings. By carefully analysing early and late time windows, the authors capture the temporal dynamics of prediction error signals, offering new insights into the timing of model updates. The use of an ideal learner model to quantify prediction errors, surprise, and entropy provides a principled framework for understanding the computational processes underlying pupil responses. Furthermore, the study highlights the critical role of task context - specifically increasing versus decreasing entropy - in shaping the directionality and magnitude of these effects, revealing the adaptability of predictive processing mechanisms.

Weaknesses:

While this study offers important insights, several limitations remain. The two tasks differ significantly in design (e.g., sensory modality and learning type), complicating direct comparisons and limiting the interpretation of differences in pupil dynamics. Importantly, the apparent context-dependent reversal between pupil constriction and dilation in response to feedback raises concerns about how these opposing effects might confound the observed correlations with KL divergence. Finally, subjective factors such as participants' confidence and internal belief states were not measured, despite their potential influence on prediction errors and pupil responses.

Reviewer #2 (Public review):

Summary:

The authors proposed that variability in post-feedback pupillary responses during the associative learning tasks can be explained by information gain, which is measured as KL divergence. They analysed pupil responses in a later time window (2.5s-3s after feedback onset) and correlated them with information-theory-based estimates from an ideal learner model (i.e., information gain-KL divergence, surprise-subjective probability, and entropy-average uncertainty) in two different associative decision-making tasks.

Strength:

The exploration of task-evoked pupil dynamics beyond the immediate response/feedback period and then associating them with model estimates was interesting and inspiring. This offered a new perspective on the relationship between pupil dilation and information processing.

Weakness:

However, disentangling these later effects from noise needs caution. Noise in pupillometry can arise from variations in stimuli and task engagement, as well as artefacts from earlier pupil dynamics. The increasing variance in the time series of pupillary responses (e.g., as shown in Figure 2D) highlights this concern.

It's also unclear what this complicated association between information gain and pupil dynamics actually means. The complexity of the two different tasks reported made the interpretation more difficult in the present manuscript.

Reviewer #3 (Public review):

Summary:

This study examines prediction errors, information gain (Kullback-Leibler [KL] divergence), and uncertainty (entropy) from an information-theory perspective using two experimental tasks and pupillometry. The authors aim to test a theoretical proposal by Zénon (2019) that the pupil response reflects information gain (KL divergence). In particular, the study defines the prediction error in terms of KL divergence and speculates that changes in pupil size associated with KL divergence depend on entropy. Moreover, the authors examine the temporal characteristics of pupil correlates of prediction errors, which differed considerably across previous studies that employed different experimental paradigms. In my opinion, the study does not achieve these aims due to several methodological and theoretical issues.

Strengths:

(1) Use of an established Bayesian model to compute KL divergence and entropy.

(2) Pupillometry data preprocessing, including deconvolution.

Weaknesses:

(1) Definition of the prediction error in terms of KL divergence:

I'm concerned about the authors' theoretical assumption that the prediction error is defined in terms of KL divergence. The authors primarily refer to a review article by Zénon (2019): "Eye pupil signals information gain". It is my understanding that Zénon argues that KL divergence quantifies the update of a belief, not the prediction error: "In short, updates of the brain's internal model, quantified formally as the Kullback-Leibler (KL) divergence between prior and posterior beliefs, would be the common denominator to all these instances of pupillary dilation to cognition." (Zénon, 2019).

From my perspective, the update differs from the prediction error. Prediction error refers to the difference between outcome and expectation, while update refers to the difference between the prior and the posterior. The prediction error can drive the update, but the update is typically smaller, for example, because the prediction error is weighted by the learning rate to compute the update. My interpretation of Zénon (2019) is that they explicitly argue that KL divergence defines the update in terms of the described difference between prior and posterior, not the prediction error.

The authors also cite a few other papers, including Friston (2010), where I also could not find a definition of the prediction error in terms of KL divergence. For example [KL divergence:] "A non-commutative measure of the non-negative difference between two probability distributions." Similarly, Friston (2010) states: Bayesian Surprise - "A measure of salience based on the Kullback-Leibler divergence between the recognition density (which encodes posterior beliefs) and the prior density. It measures the information that can be recognized in the data." Finally, also in O'Reilly (2013), KL divergence is used to define the update of the internal model, not the prediction error.

The authors seem to mix up this common definition of the model update in terms of KL divergence and their definition of prediction error along the same lines. For example, on page 4: "KL divergence is a measure of the difference between two probability distributions. In the context of predictive processing, KL divergence can be used to quantify the mismatch between the probability distributions corresponding to the brain's expectations about incoming sensory input and the actual sensory input received, in other words, the prediction error (Friston, 2010; Spratling, 2017)."

Similarly (page 23): "In the current study, we investigated whether the pupil's response to decision outcome (i.e., feedback) in the context of associative learning reflects a prediction error as defined by KL divergence."

This is problematic because the results might actually have limited implications for the authors' main perspective (i.e., that the pupil encodes prediction errors) and could be better interpreted in terms of model updating. In my opinion, there are two potential ways to deal with this issue:

a) Cite work that unambiguously supports the perspective that it is reasonable to define the prediction error in terms of KL divergence and that this has a link to pupillometry. In this case, it would be necessary to clearly explain the definition of the prediction error in terms of KL divergence and dissociate it from the definition in terms of model updating.

b) If there is no prior work supporting the authors' current perspective on the prediction error, it might be necessary to revise the entire paper substantially and focus on the definition in terms of model updating.

(2) Operationalization of prediction errors based on frequency, accuracy, and their interaction:

The authors also rely on a more model-agnostic definition of the prediction error in terms of stimulus frequency ("unsigned prediction error"), accuracy, and their interaction ("signed prediction error"). While I see the point here, I would argue that this approach offers a simple approximation to the prediction error, but it is possible that factors like difficulty and effort can influence the pupil signal at the same time, which the current approach does not take into account. I recommend computing prediction errors (defined in terms of the difference between outcome and expectation) based on a simple reinforcement-learning model and analyzing the data using a pupillometry regression model in which nuisance regressors are controlled, and results are corrected for multiple comparisons.

(3) The link between model-based (KL divergence) and model-agnostic (frequency- and accuracy-based) prediction errors:

I was expecting a validation analysis showing that KL divergence and model-agnostic prediction errors are correlated (in the behavioral data). This would be useful to validate the theoretical assumptions empirically.

(4) Model-based analyses of pupil data:

I'm concerned about the authors' model-based analyses of the pupil data. The current approach is to simply compute a correlation for each model term separately (i.e., KL divergence, surprise, entropy). While the authors do show low correlations between these terms, single correlational analyses do not allow them to control for additional variables like outcome valence, prediction error (defined in terms of the difference between outcome and expectation), and additional nuisance variables like reaction time, as well as x and y coordinates of gaze.

Moreover, including entropy and KL divergence in the same regression model could, at least within each task, provide some insights into whether the pupil response to KL divergence depends on entropy. This could be achieved by including an interaction term between KL divergence and entropy in the model.

(5) Major differences between experimental tasks:

More generally, I'm not convinced that the authors' conclusion that the pupil response to KL divergence depends on entropy is sufficiently supported by the current design. The two tasks differ on different levels (stimuli, contingencies, when learning takes place), not just in terms of entropy. In my opinion, it would be necessary to rely on a common task with two conditions that differ primarily in terms of entropy while controlling for other potentially confounding factors. I'm afraid that seemingly minor task details can dramatically change pupil responses. The positive/negative difference in the correlation with KL divergence that the authors interpret to be driven by entropy may depend on another potentially confounding factor currently not controlled.

(6) Model validation:

My impression is that the ideal learner model should work well in this case. However, the authors don't directly compare model behavior to participant behavior ("posterior predictive checks") to validate the model. Therefore, it is currently unclear if the model-derived terms like KL divergence and entropy provide reasonable estimates for the participant data.

(7) Discussion:

The authors interpret the directional effect of the pupil response w.r.t. KL divergence in terms of differences in entropy. However, I did not find a normative/computational explanation supporting this interpretation. Why should the pupil (or the central arousal system) respond differently to KL divergence depending on differences in entropy?

The current suggestion (page 24) that might go in this direction is that pupil responses are driven by uncertainty (entropy) rather than learning (quoting O'Reilly et al. (2013)). However, this might be inconsistent with the authors' overarching perspective based on Zénon (2019) stating that pupil responses reflect updating, which seems to imply learning, in my opinion. To go beyond the suggestion that the relationship between KL divergence and pupil size "needs more context" than previously assumed, I would recommend a deeper discussion of the computational underpinnings of the result.

Reviewer #1 (Public review):

Summary:

The investigators in this study analyzed the dataset assembly from 540 Salmonella isolates, and those from 45 recent isolates from Zhejiang University of China. The analysis and comparison of the resistome and mobilome of these isolates identified a significantly higher rate of cross-region dissemination compared to localized propagation. This study highlights the key role of the resistome in driving the transition and evolutionary history of S. Gallinarum.

Strengths:

The isolates included in this study were from 16 countries in the past century (1920 to 2023). While the study uses S. Gallinarun as the prototype, the conclusion from this work will likely apply to other Salmonella serotypes and other pathogens.

Reviewer #2 (Public review):

Summary:

The authors sequence 45 new samples of S. Gallinarum, a commensal Salmonella found in chickens, which can sometimes cause disease. They combine these sequences with around 500 from public databases, determine the population structure of the pathogen, and coarse relationships of lineages with geography. The authors further investigate known anti-microbial genes found in these genomes, how they associate with each other, whether they have been horizontally transferred, and date the emergence of clades.

Strengths:

- It doesn't seem that much is known about this serovar, so publicly available new sequences from a high burden region are a valuable addition to the literature.<br /> - Combining these sequences with publicly available sequences is a good way to better contextualise any findings.<br /> - The genomic analyses have been greatly improved since the first version of the manuscript, and appropriately analyse the population and date emergence of clades.<br /> - The SNP thresholds are contextualised in terms of evolutionary time.<br /> - The importance and context of the findings are fairly well described.

Reviewer #2 (Public review):

Summary:

Translation of CGG repeats leads to accumulation of poly G, which is associated with neurological disorders. This is an important paper in which the authors sought out proteins that modulate RAN translation. They determined which proteins in Hela cells were enriched on CGG repeats and affected levels of polyG encoded in the 5'UTR of the FMR1 mRNA. They then showed that siRNA depletion of ribosomal protein RPS26 results in less production of FMR1polyG than in control. Experiments were performed in several cell lines and with several reporters with differences in repeats and transfection methods to increase confidence that changes were occurring. New data and details of the methods increase confidence that reporter translation but not global translation is diminished by RPS26 knockdown as concluded. The manuscript has been improved by data showing that new proteins are being synthesized in cells following RPS26 knockdown, and that near-cognate start codon usage is diminished in lines when RPS26 is knocked down, but the mechanism by which RPS26 depletion affects translation is still unclear.

Strengths:

- The authors have proteomics data that show enrichment of a set of proteins on FMR1-polyG RNA but not a related RNA.<br /> - Knockdown of RPS26, which was enriched on the FMR1 RNA, led to decreases in cell growth, but surprisingly did not strongly affect global translation, as assessed by puromycin incorporation<br /> - There is some new evidence that near-cognate start codon selection is affected by RPS26 knockdown

Weaknesses:

- The mechanism for RPS26 knockdown affecting translation of the polyG sequences is unclear, whether knockdown is affecting ribosome levels, extra ribosomal RPS26 or ribosome composition is not known.

Reviewer #3 (Public review):

Tutak et al provide intriguing findings demonstrating that insufficiency of RPS26 and related proteins, such as TSR2 and RPS25, downregulates RAN translation from CGG repeat RNA in fragile X-associated conditions. Using RNA-tagging system and mass spectrometry-based screening, the authors identified RPS26 as a potential regulator of RAN translation. They further confirmed its regulatory effects on RAN translation by siRNA-based knockdown experiments in multiple cellular disease models. Quantitative mass spectrometry analysis revealed that the expression of some ribosomal proteins is sensitive to RPS26 depletion, while approximately 80% of proteins, including FMRP, were not influenced. Given the limited understanding of the roles of ribosomal proteins in RAN translation regulation, this study provides novel insights into this research field. However, certain data do not fully support the authors' critical conclusions.

(1) While the authors substituted the ACG near-cognate initiation codon with other near-cognate codons, such as GTG and CTG, in the luciferase assay (Figure 4F), substitution of the ACG codon with an ATG codon should also be performed. Although they evaluated RPS26 knockdown effect on AUG-dependent FMRP translation in Figure 3C, investigating its effect on AUG-dependent repeat-associated translation (e.g., AUG-CGG-repeat) is necessary to substantiate their claim that ACG codon selection is important for RAN translation downregulation by RPS26 knockdown.

(2) The results of the ASO-based ACG codon-blocking experiment in Figure 4G are difficult to interpret. While RPS knockdown reduces FMRpolyG expression, the effect appears attenuated by the ASO-ACG treatment compared to the control. However, this does not conclusively demonstrate that the regulatory effect is directly due to ACG codon selection during translation initiation for some reasons. For example, ASO-ACG treatment possibly interferes with ribosomal scanning rather than ACG-codon selection, or alters the expression of template CGG repeat RNA. To validate the effect of RPS26 knockdown on ACG codon selection, experiments using the ACG-to-ATG substituted CGG repeat reporter are recommended, as suggested in comment 1.

(3) The regulatory effects of RPS26 and other molecules on RAN translation have been investigated as effects on the expression levels of FMRpolyG proteins upon knockdown of these molecules in disease model cells expressing CGG repeat sequences (Figures 1C, 1D, 3B, 3C, 3E, 4F, 4G, 5A, 5C, 6A, 6D）. However, FMRpolyG expression levels can be influenced by factors other than RAN translation in these cellular experiments, such as template RNA level, template RNA localization, and FMRpolyG protein degradation. Although the authors evaluated the effect on the expression levels of template CGG repeat RNA, it would be better to confirm the direct effect of these regulators on RAN translation by other experiments. In vitro translation assay that can directly evaluate RAN translation is preferable, but experiments using the ACG-to-ATG substituted CGG repeat reporter, as suggested in comment 1, would also provide valuable insights.

Reviewer #2 (Public review):

Summary

The authors use a tree biodiversity experiment to evaluate the effects of tree community and canopy cover on communities of cavity-nesting Hymenoptera and their parasitoids and the interactions between these two guilds. They find that multiple measures of tree diversity influence the hosts, parasitoids, and their interactions. In addition, host-parasitoid interactions show a phylogenetic signal.

Strength

The authors use a massive, long-term data set, meaningful community descriptors, and a solid set of analyses to explore the impacts of tree communities on host-parasitoid networks. It is rare to have such detailed data from multiple different trophic levels.

Weakness

Even though the data expands over several seasons, this is not considered in the analyses, but communities sampled at different years are pooled at the plot level. A more detailed analysis of the variations between years could reveal underlaying patterns as currently the differences in the communities and their structure between the years are ignored (e.g., when estimating the phylogenetic compositions not all the species pooled together actually coexist in time).<br /> Also, the precision of the writing should be improved as it was not always easy to follow the text and the thoughts.

Reviewer #1 (Public review):

Summary:

In this work, Noorman and colleagues test the predictions of the "four-stage model" of consciousness by combining psychophysics and scalp EEG in humans. The study relies on an elegant experimental design to investigate the respective impact of attentional and perceptual blindness on visual processing.

The study is very well summarised, the text is clear and the methods seem sound. Overall, a very solid piece of work. I haven't identified any major weaknesses. Below I raise a few questions of interpretation that may possibly be the subject of a revision of the text.

(1) The perceptual performance on Fig1D appears to show huge variation across participants, with some participants at chance levels and others with performance > 90% in the attentional blink and/or masked conditions. This seems to reveal that the procedure to match performance across participants was not very successful. Could this impact the results? The authors highlight the fact that they did not resort to post-selection or exclusion of participants, but at the same time do not discuss this equally important point.

(2) In the analysis on collinearity and illusion-specific processing, the authors conclude that the absence of a significant effect of training set demonstrates collinearity-only processing. I don't think that this conclusion is warranted: as the illusory and non-illusory share the same shape, so more elaborate object processing could also be occuring. Please discuss.

(3) Discussion, lines 426-429: It is stated that the results align with the notion that processes of perceptual segmentation and organization represent the mechanism of conscious experience. My interpretation of the results is that they show the contrary: for the same visibility level in the attentional blind or masking conditions, these processes can be implicated or not, which suggests a role during unconscious processing instead.

(4) The two paradigms developed here could be used jointly to highlight non-idiosyncratic NCCs, i.e. EEG markers of visibility or confidence that generalise regardless of the method used. Have the authors attempted to train the classifier on one method and apply it to another (e.g. AB to masking and vice versa)? What perceptual level is assumed to transfer?

(4) How can the results be integrated with the attentional literature showing that attentional filters can be applied early in the processing hierarchy?

Comments on revisions:

I'm very pleased with the responses to my previous comments, and congratulate the authors on this excellent piece of work.

Reviewer #2 (Public review):

Summary:

This is a very elegant and important EEG study that unifies within a single set of behaviorally equated experimental conditions conscious access (and therefore also conscious access failures) during visual masking and attentional blink (AB) paradigms in humans. By a systematic and clever use of multivariate pattern classifiers across conditions, they could dissect, confirm, and extend a key distinction (initially framed within the GNWT framework) between 'subliminal' and 'pre-conscious' unconscious levels of processing. In particular, the authors could provide strong evidence to distinguish here within the same paradigm these two levels of unconscious processing that precede conscious access : (i) an early (< 80ms) bottom-up and local (in brain) stage of perceptual processing ('local contrast processing') that was preserved in both unconscious conditions, (ii) a later stage and more integrated processing (200-250ms) that was impaired by masking but preserved during AB. On the basis of preexisting studies and theoretical arguments, they suggest that this later stage could correspond to lateral and local recurrent feedback processes. Then, the late conscious access stage appeared as a P3b-like event.

Strengths:

The methodology and analyses are strong and valid. This work adds an important piece in the current scientific debate about levels of unconscious processing and specificities of conscious access in relation to feed-forward, lateral, and late brain-scale top-down recurrent processing.

Comments on revisions:

I congratulate the authors for the quality of their revised ms. They convincingly addressed each of the issues raised in my previous review.

Reviewer #3 (Public review):

Summary:

This work aims to investigate how perceptual and attentional processes affect conscious access in humans. By using multivariate decoding analysis of electroencephalography (EEG) data, the authors explored the neural temporal dynamics of visual processing across different levels of complexity (local contrast, collinearity, and illusory perception). This is achieved by comparing the decidability of an illusory percept in matched conditions of perceptual (i.e., degrading the strength of sensory input using visual masking) and attentional impairment (i.e., impairing top-down attention using attentional blink, AB). The decoding results reveal three distinct temporal responses associated with the three levels of visual processing. Interestingly, the early stage of local contrast processing remains unaffected by both masking and AB. However, the later stage of collinearity and illusory percept processing are impaired by the perceptual manipulation but remained unaffected by the attentional manipulation. These findings contribute to the understanding of the unique neural dynamics of perceptual and attentional functions and how they interact with the different stages of conscious access.

Strengths:

The study investigates perceptual and attentional impairments across multiple levels of visual processing in a single experiment. Local contrast, collinearity, and illusory perception were manipulated using different configurations of the same visual stimuli. This clever design allows for the investigation of different levels of visual processing under similar low-level conditions.

Moreover, behavioural performance was matched between perceptual and attentional manipulations. One of the main problems when comparing perceptual and attentional manipulations on conscious access is that they tend to impact performance at different levels, with perceptual manipulations like masking producing larger effects. The study utilizes a staircasing procedure to find the optimal contrast of the mask stimuli to produce a performance impairment to the illusory perception comparable to the attentional condition, both in terms of perceptual performance (i.e., indicating whether the target contained the Kanizsa illusion) and metacognition (i.e., confidence in the response).

The results show a clear dissociation between the three levels of visual processing in terms of temporal dynamics. Local contrast was represented at an early stage (~80 ms), while collinearity and illusory perception were associated with later stages (~200-250 ms). Furthermore, the results provide clear evidence in support of a dissociation between the effects of perceptual and attentional processes on conscious access: while the former affected both neuronal correlates of collinearity and illusory perception, the latter did not have any effect on the processing of the more complex visual features involved in the illusion perception.

Weaknesses:

The design of the study and the results presented are very similar to those in Fahrenfort et al. (2017), reducing its novelty. Similar to the current study, Fahrenfort et al. (2017) tested the idea that if both masking and AB impact perceptual integration, they should affect the neural markers of perceptual integration in a similar way. They found that behavioural performance (hit/false alarm rate) was affected by both masking and AB, even though only the latter was significant in the unmasked condition. In contrast, an early classification peak was exclusively affected by masking. A later classification peak mirrored the behavioural findings, with classification performance impacted by both masking and AB.

The interpretation of the results primarily relies on the recurrent processing theory of consciousness (Lamme, 2020), which lead to the assumption that local contrast and illusory perception reflect feedforward and (lateral and feedback) recurrent connections, respectively. It should be mentioned, however, that this theoretical prediction is not directly tested in the study. Moreover, the evidence for the dissociation between illusion and collinearity in terms of lateral and feedback connections seems at least limited. For instance, Kok et al. (2016) found that, whereas bottom-up stimulation activated all cortical layers, feedback activity induced by illusory figures led to a selective activation of the deep layers. Lee & Nguyen (2001), instead, found that V1 neurons respond to illusory contours of the Kanizsa figures, particularly in the superficial layers. Although both studies reference feedback connections, neither provides clear evidence for the involvement of lateral connections.

The evidence in favour of primarily lateral connections driving collinearity seems mixed as well. On one hand, Liang et al. (2017) showed that feedback and lateral connections closely interact to mediate image grouping and segmentation. On the other hand, Stettler et al. (2002) showed that, whereas the intrinsic connections link similarly oriented domains in V1, V2 to V1 feedback displays no such specificity. Additionally, the other studies cited in the manuscript focused solely on lateral connections without examining feedback pathways, making it challenging to draw definitive conclusions.

Comments on revisions:

The authors have thoroughly addressed all my comments and provided comprehensive responses to each point raised.

Reviewer #1 (Public review):

Summary:

In this study, Kroll et al. conduct an in-depth behavioral analysis of F0 knockouts of 4 genes associated with late-onset Alzheimer's Disease (AD), together with 3 genes associated with early-onset AD. Kroll and colleagues developed a web application (ZOLTAR) to compare sleep-associated traits between genetic mutants with those obtained from a panel of small molecules to promote identification of affected pathways and potential therapeutic interventions. The authors make a set of potentially important findings vis-à-vis the relationship between AD-associated genes and sleep. First, they find that loss-of-function in late-onset AD genes universally result in nighttime sleep loss, consistent with the well-supported hypothesis that sleep disruption contributes to Alzheimer's-related pathologies. psen-1, an early-onset associated AD gene, which the authors find is principally responsible for the generation of AB40 and AB42 in zebrafish, also shows a slight increase in activity at night and slight decreases in nighttime sleep. Conversely, psen-2 mutations increase daytime sleep, while appa/appb mutations have no impact on sleep. Finally, using ZOLTAR, the authors identify serotonin receptor activity as potentially disrupted in sorl1 mutants, while betamethasone is identified as a potential therapeutic to promote reversal of psen2 knockout-associated phenotypes.

This is a highly innovative and thorough study, yet a handful of key questions remain. First, are the nighttime sleep loss phenotypes observed in all knockouts for late-onset AD genes in the larval zebrafish a valid proxy for AD risk? Can 5-HT reuptake inhibitors reverse other AD-related pathologies in zebrafish? Can compounds be identified which have a common behavioral fingerprint across all or multiple AD risk genes? Do these modify sleep phenotypes? Finally, the authors propose but do not test the hypothesis that sorl1 might regulate localization/surface expression of 5-HT2 receptors. This could provide exciting / more convincing mechanistic support for the assertion that serotonin signaling is disrupted upon loss of AD-associated genes. Despite these important considerations, this study provides a valuable platform for high-throughput analysis of sleep phenotypes and correlation with small-molecule induced sleep phenotypes. The platform could also be expanded to facilitate comparison of other behavioral phenotypes, including stimulus-evoked behaviors. Moreover, the new analyses looking for pathways that might be co-regulated by AD risk genes and discussion of cholinergic signaling as a potentially meaningful target downstream of 5/7 knockouts are valuable.

Strengths:<br /> - Provides a useful platform for comparison of sleep phenotypes across genotypes/drug manipulations.<br /> - Presents convincing evidence that nighttime sleep is disrupted in mutants for multiple late-onset AD-related genes.<br /> - Provides potential mechanistic insights for how AD-related genes might impact sleep and identifies a few drugs that modify their identified phenotypes.

Weaknesses:<br /> - Exploration of potential mechanisms for serotonin disruption in sorl1 mutants is limited<br /> - The pipeline developed is only used to examine sleep-related / spontaneous movement phenotypes. Stimulus-evoked behaviors are not examined.

Reviewer #2 (Public review):

Summary:

This work delineates the larval zebrafish behavioral phenotypes caused by F0 knockout of several important genes that increase risk for Alzheimer's disease. Using behavioral pharmacology, comparing the behavioral fingerprint of previously assayed molecules to the newly generated knockout data, compounds were discovered that impacted larval movement in ways that suggest interaction with or recovery of disrupted mechanisms.

Strengths:

This is a well-written manuscript that uses newly developed analysis methods to present the findings in a clear, high-quality way. The addition of an extensive behavioral analysis pipeline is of value to the field of zebrafish neuroscience and will be particularly helpful for researchers who prefer the R programming language. Even the behavioral profiling of these AD risk genes, regardless of the pharmacology aspect, is an important contribution. The recovery of most behavioral parameters in the psen2 knockout with betamethasone, predicted by comparing fingerprints, is an exciting demonstration of the approach. The hypotheses generated by this work are important stepping stones to future studies uncovering the molecular basis of the proposed gene-drug interactions and discovering novel therapeutics to treat AD or co-occurring conditions such as sleep disturbance. Most concerns are sufficiently addressed in the revised manuscript or response to reviewers.

Weaknesses:

- The overarching concept of the work is that comparing behavioral fingerprints can align genes and molecules with similarly disrupted molecular pathways. While the recovery of the psen2 phenotypes by one molecule with the opposite phenotype is interesting, as are previous studies that show similar behaviorally-based recoveries, the underlying assumption that normalizing the larval movement normalizes the mechanism still lacks substantial support. While I agree with the authors detailed response that rescuing most behavioral parameters is a good indication that the underlying mechanism is normalized, I disagree that high-throughput larval behavior kinematics is a sufficient enough representation of most behavioral parameters to be indicative of molecular mechanism normalization. There are many instances of mutants with completely normal kinetics at baseline, but a behavioral difference that emerges during stimulation or in a new paradigm such as hunting. Without testing far more behavioral paradigms than are possible in the multi-well plate format, as well as possibly multiple life stages, I remain unconvinced that this approach will yield valuable therapeutic insights. I do agree that it can yield insight for future investigation, such as in the case of cntnap2a/cntnap2b and GABA receptor agonists, but even in that instance is it not clear that such an agonist would rescue abnormalities in a meaningful way. In the case of a disorder such as autism, the early locomotor phenotypes may be disconnected from the molecular mechanisms underlying later social deficits, and it is far more challenging to screen on juvenile behaviors that would be a more appropriate target for a behavior-first approach. The added experiment of testing fluvoxamine, a second SSRI, yielded very different behavioral responses to the SSRI citalopram, supporting my assertion that this approach and the disrupted underlying mechanisms are more complicated than suggested by the authors. I disagree that the connection between sorl1 and serotonin is strengthened by this experiment. The authors suggest that since the knockout larvae react differently than control siblings to both SSRIs, it indicates that serotonin is disrupted. There is no negative control included, where a pathway that is clearly not indicated to be important is pharmacologically manipulated. It is possible that the mutants would also behave differently compared to siblings when other pathways are perturbed. The authors acknowledge in the reviewers that they may not have identified the underlying molecular disruption in this mutant, but they did not substantially alter the Discussion section on this point. I agree with the authors that using a different wild-type strain in a different lab could lead to discrepancies, but these issues could have been experimentally mitigated or more clearly highlighted in the manuscript itself.

Reviewer #1 (Public review):

Summary:

This study aims to investigate the links between social behaviors observed in free-moving situations and behavioral performances measured in well-controlled, laboratory settings. The authors assessed general social tendencies and dyadic relationships among four monkeys in a group by scoring agonistic (aggression) and affiliative (grooming and proximity) behaviors in each pair. By measuring the saccadic reaction time in a classic social interference task, the authors reported that the monkeys with higher SEIs (i.e., more social individuals) were less distracted by the faces of other monkeys. These effects were enhanced when the distractors were out-group monkey faces rather than in-group ones. Lastly, oxytocin administration increased the impact of the out-group monkey faces in the social interference task, while reducing the magnitude of general social tendencies measured with SEI.

Strengths:

(1) The combination of behavioral data obtained in a colony room and in a laboratory environment is rare and important.<br /> (2) The evaluation of social interactions were successfully performed based on an automated target detection algorithm. The resulting multi-dimensional, complicated social interactions were summarized into simple indices (SEI and IEI). These indices provide a good measure for the social tendencies of each monkey.<br /> (3) Well-designed and robust experiments in the laboratory environment that are linked nicely with the general social tendencies observed in spontaneous behaviors.

Weaknesses:

(1) While the overall results are interesting, I am somewhat left confused about how to interpret the difference in the scores derived from different conditions. For example, the authors stated "Comparing the weights for in-group and out-group distractors, the effect of proximity was larger than that of aggression and grooming" in p.8. Does this mean that the proximity is indeed the type of behavior most affected in the out-group condition compared to the in-group condition? The out-group effects are difficult to examine with actual behavioral data, but some in-group effects such as those involving OT can be tested, which possibly provides good insights into interpreting the differences of the weights observed across the experimental conditions.

(2) I think it is important to provide how variable spontaneous social interactions were across sessions and how impactful the variability of the interactions is on the SEI and IEI, as it helps to understand how meaningful the differences of weights are across the conditions, but such data are missing. In line with this point, although the conclusions still hold as those data were obtained during the same experimental periods, shouldn't the weights in Fig. 3f and Figs. 4g and 4h (saline) be expected to be similar, if not the same?

Comments on revisions: I do not have further comments.

Reviewer #2 (Public review):

Summary:

The study presents significant findings that elucidate the relationship between multi-dimensional social relationships and social attention in rhesus macaques. By integrating advanced computational methods, behavioral analyses, and neuroendocrine manipulation, the authors provide strong evidence for how oxytocin modulates attention within social networks. The results are robust and address critical gaps in understanding the dynamics of social attention in primates.

Strengths:

(1) The use of YOLOv5 for automatic behavioral detection is an exceptional methodological advance. The combination of automated analyses with manual validation enhances confidence in the data.<br /> (2) The study's focus on three distinct dimensions of social interaction (aggression, grooming, and proximity) is comprehensive and provides nuanced insights into the complexity of primate social networks.<br /> (3) The investigation of oxytocin's role adds a compelling neuroendocrine dimension to the findings, providing a bridge between behavioral and neural mechanisms.

Weaknesses:

(1) The study's conclusions are based on observations of only four monkeys, which limits the generalizability of the findings. Larger sample sizes could strengthen the validity of the results.<br /> (2) The limited set of stimulus images (in-group and out-group faces) may introduce unintended biases. This could be addressed by increasing the diversity of stimuli or incorporating a broader range of out-group members.

Comments on revisions: I have no further comments!

Reviewer #1 (Public review):

Summary:

Shows a new mechanism of GS regulation in the archaean Methanosarcina maze and clarifies the direct activation of GS activity by 2-oxoglutarate, thus featuring an other way, how 2-oxoglutarate acts as a central status reporter of C/N sensing.

Strengths:

mass photometry reveals a a dynamic mode the effect of 2-OG on the oligomerization state of GS. Single particle Cryo-EM reveals the mechanism of 2-OG mediated dodecamer formation.

Weaknesses:

Not entirely clear, how very high 2-OG concentrations activate GS beyond dodecamer formation.

In the revised version, most of my concerns were adequately addressed. In the summary it is stated that glutamine acts as allosteric inhibitor of dodecameric GS. This is not correct: glutamine binds to the active site and is therefore not allosteric. This way of feedback inhibition is a type of product inhibition

Reviewer #2 (Public review):

Summary:

Herdering et al. introduced research on an archaeal glutamine synthetase (GS) from Methanosarcina mazei, which exhibits sensitivity to the environmental presence of 2-oxoglutarate (2-OG). While previous studies have indicated 2-OG's ability to enhance GS activity, the precise underlying mechanism remains unclear. Initially, the authors utilized biophysical characterization, primarily employing a nanomolar-scale detection method called mass photometry, to explore the molecular assembly of Methanosarcina mazei GS (M. mazei GS) in the absence or presence of 2-OG. Similar to other GS enzymes, the target M. mazei GS forms a stable dodecamer, with two hexameric rings stacked in tail-to-tail interactions. Despite approximately 40% of M. mazei GS existing as monomeric or dimeric entities in the detectable solution, the majority spontaneously assemble into a dodecameric state. Upon mixing 2-OG with M. mazei GS, the population of the dodecameric form increases proportionally with the concentration of 2-OG, indicating that 2-OG either promotes or stabilizes the assembly process. The cryo-electron microscopy (cryo-EM) structure reveals that 2-OG is positioned near the interface of two hexameric rings. At a resolution of 2.39 Å, the cryo-EM map vividly illustrates 2-OG forming hydrogen bonds with two individual GS subunits as well as with solvent water molecules. Moreover, local sidechain reorientation and conformational changes of loops in response to 2-OG further delineate the 2-OG-stabilized assembly of M. mazei GS.

Strengths & Weaknesses:

The investigation studies into the impact of 2-oxoglutarate (2-OG) on the assembly of Methanosarcina mazei glutamine synthetase (M mazei GS). Utilizing cutting-edge mass photometry, the authors scrutinized the population dynamics of GS assembly in response to varying concentrations of 2-OG. Notably, the findings demonstrate a promising and straightforward correlation, revealing that dodecamer formation can be stimulated by 2-OG concentrations of up to 10 mM, although GS assembly never reaches 100% dodecamerization in this study. Furthermore, catalytic activities showed a remarkable enhancement, escalating from 0.0 U/mg to 7.8 U/mg with increasing concentrations of 2-OG, peaking at 12.5 mM. However, an intriguing gap arises between the incomplete dodecameric formation observed at 10 mM 2-OG, as revealed by mass photometry, and the continued increase in activity from 5 mM to 10 mM 2-OG for M mazei GS. This prompts questions regarding the inability of M mazei GS to achieve complete dodecamer formation and the underlying factors that further enhance GS activity within this concentration range of 2-OG.

Moreover, the cryo-electron microscopy (cryo-EM) analysis provides additional support for the biophysical and biochemical characterization, elucidating the precise localization of 2-OG at the interface of two GS subunits within two hexameric rings. The observed correlation between GS assembly facilitated by 2-OG and its catalytic activity is substantiated by structural reorientations at the GS-GS interface, confirming the previously reported phenomenon of "funnel activation" in GS. However, the authors did not present the cryo-EM structure of M. mazei GS in complex with ATP and glutamate in the presence of 2-OG, which could have shed light on the differences in glutamine biosynthesis between previously reported GS enzymes and the 2-OG-bound M. mazei GS.

Furthermore, besides revealing the cryo-EM structure of 2-OG-bound GS, the study also observed the filamentous form of GS, suggesting that filament formation may be a universal stacking mechanism across archaeal and bacterial species. However, efforts to enhance resolution to investigate whether the stacked polymer is induced by 2-OG or other factors such as ions or metabolites were not undertaken by the authors, leaving room for further exploration into the mechanisms underlying filament formation in GS.

Comments on revisions:

My comments have been addressed adequately.

I recognize that determining the structure of the GS complex bound to ATP and/or other ligands would enhance this study by offering a more comprehensive understanding of 2-oxoglutarate-mediated dodecameric assembly and activation. However, I accept the authors' explanation for not including this aspect in the current work.

Reviewer #3 (Public review):

The current manuscript investigates the effect of 2-oxoglutarate (2OG) as modulator of glutamine synthetase (GS). To do this, the authors rely of mass photometry, specific activity measurements and single particle cryo-EM data.<br /> From the results, the authors conclude that the GS from Methanosarcina mazei shifts from a dimeric, non-active state under low concentrations of 2OG, to a dodecameric and fully active complex at saturating concentrations of 2OG.

GS is a crucial enzyme in all domains of life. The dodecameric fold of GS is recurrent amongst prokaryotic and archaea organisms but the enzyme activity can be regulated in distinct ways. This is a very interesting work combining protein biochemistry with structural biology.

A novel role for 2OG is presented for this mesophilic methanoarchaeon, as a crucial effector for the enzyme oligomerization and full reactivity.

The conclusions of this paper are mostly well supported by data, but some aspects of this GS regulation and interaction with known partners like Glnk1 and sp26 need to be clarified and extended.

Reviewer #1 (Public review):

Summary:

In their manuscript entitled 'The domesticated transposon protein L1TD1 associates with its ancestor L1 ORF1p to promote LINE-1 retrotransposition', Kavaklıoğlu and colleagues delve into the role of L1TD1, an RNA binding protein (RBP) derived from a LINE1 transposon. L1TD1 proves crucial for maintaining pluripotency in embryonic stem cells and is linked to cancer progression in germ cell tumors, yet its precise molecular function remains elusive. Here, the authors uncover an intriguing interaction between L1TD1 and its ancestral LINE-1 retrotransposon.

The authors delete the DNA methyltransferase DNMT1 in a haploid human cell line (HAP1), inducing widespread DNA hypo-methylation. This hypomethylation prompts abnormal expression of L1TD1. To scrutinize L1TD1's function in a DNMT1 knock-out setting, the authors create DNMT1/L1TD1 double knock-out cell lines (DKO). Curiously, while the loss of global DNA methylation doesn't impede proliferation, additional depletion of L1TD1 leads to DNA damage and apoptosis.

To unravel the molecular mechanism underpinning L1TD1's protective role in the absence of DNA methylation, the authors dissect L1TD1 complexes in terms of protein and RNA composition. They unveil an association with the LINE-1 transposon protein L1-ORF1 and LINE-1 transcripts, among others.

Surprisingly, the authors note fewer LINE-1 retro-transposition events in DKO cells compared to DNMT1 KO alone.

Strengths:

The authors present compelling data suggesting the interplay of a transposon-derived human RNA binding protein with its ancestral transposable element. Their findings spur interesting questions for cancer types, where LINE1 and L1TD1 are aberrantly expressed.

Weaknesses:

The finding that L1TD1/DNMT1 DKO cells exhibit increased apoptosis and DNA damage but decreased L1 retro-transposition is unexpected. Considering the DNA damage associated with retro-transposition and the DNA damage and apoptosis observed in L1TD1/DNMT1 DKO cells, one would anticipate the opposite outcome. Could it be that the observation of fewer transposition-positive colonies stems from the demise of the most transposition-positive colonies? Future studies are bound to further explore this intriguing phenomenon.

Reviewer #2 (Public review):

In this study, Kavaklıoğlu et al. investigated and presented evidence for a role for domesticated transposon protein L1TD1 in enabling its ancestral relative, L1 ORF1p, to retrotranspose in HAP1 human tumor cells. The authors provided insight into the molecular function of L1TD1 and shed some clarifying light on previous studies that showed somewhat contradictory outcomes surrounding L1TD1 expression. Here, L1TD1 expression was correlated with L1 activation in a hypomethylation dependent manner, due to DNMT1 deletion in HAP1 cell line. The authors then identified L1TD1 associated RNAs using RIP-Seq, which display a disconnect between transcript and protein abundance (via Tandem Mass Tag multiplex mass spectrometry analysis). The one exception was for L1TD1 itself, is consistent with a model in which the RNA transcripts associated with L1TD1 are not directly regulated at the translation level. Instead, the authors found L1TD1 protein associated with L1-RNPs and this interaction is associated with increased L1 retrotransposition, at least in the contexts of HAP1 cells. Overall, these results support a model in which L1TD1 is restrained by DNA methylation, but in the absence of this repressive mark, L1TD1 is expression, and collaborates with L1 ORF1p (either directly or through interaction with L1 RNA, which remains unclear based on current results), leads to enhances L1 retrotransposition. These results establish feasibility of this relationship existing in vivo in either development or disease, or both.

Comments on revised version:

Thank you for this revised manuscript and for addressing our concerns and suggestions. These improvements have significantly enhanced the quality and reliability of the results presented and have addressed all our questions.

Reviewer #1 (Public review):

Wang et al., recorded concurrent EEG-fMRI in 107 participants during nocturnal NREM sleep to investigate brain activity and connectivity related to slow oscillations (SO), sleep spindles, and in particular their co-occurrence. The authors found SO-spindle coupling to be correlated with increased thalamic and hippocampal activity, and with increased functional connectivity from the hippocampus to the thalamus and from the thalamus to the neocortex, especially the medial prefrontal cortex (mPFC). They concluded the brain-wide activation pattern to resemble episodic memory processing, but to be dissociated from task-related processing and suggest that the thalamus plays a crucial role in coordinating the hippocampal-cortical dialogue during sleep.

The paper offers an impressively large and highly valuable dataset that provides the opportunity for gaining important new insights into the network substrate involved in SOs, spindles, and their coupling. However, the paper does unfortunately not exploit the full potential of this dataset with the analyses currently provided, and the interpretation of the results is often not backed up by the results presented.

I have the following specific comments.

(1) The introduction is lacking sufficient review of the already existing literature on EEG-fMRI during sleep and the BOLD-correlates of slow oscillations and spindles in particular (Laufs et al., 2007; Schabus et al., 2007; Horovitz et al., 2008; Laufs, 2008; Czisch et al., 2009; Picchioni et al., 2010; Spoormaker et al., 2010; Caporro et al., 2011; Bergmann et al., 2012; Hale et al., 2016; Fogel et al., 2017; Moehlman et al., 2018; Ilhan-Bayrakci et al., 2022). The few studies mentioned are not discussed in terms of the methods used or insights gained.

(2) The paper falls short in discussing the specific insights gained into the neurobiological substrate of the investigated slow oscillations, spindles, and their interactions. The validity of the inverse inference approach ("Open ended cognitive state decoding"), assuming certain cognitive functions to be related to these oscillations because of the brain regions/networks activated in temporal association with these events, is debatable at best. It is also unclear why eventually only episodic memory processing-like brain-wide activation is discussed further, despite the activity of 16 of 50 feature terms from the NeuroSynth v3 dataset were significant (episodic memory, declarative memory, working memory, task representation, language, learning, faces, visuospatial processing, category recognition, cognitive control, reading, cued attention, inhibition, and action).

(3) Hippocampal activation during SO-spindles is stated as a main hypothesis of the paper - for good reasons - however, other regions (e.g., several cortical as well as thalamic) would be equally expected given the known origin of both oscillations and the existing sleep-EEG-fMRI literature. However, this focus on the hippocampus contrasts with the focus on investigating the key role of the thalamus instead in the Results section.

(4) The study included an impressive number of 107 subjects. It is surprising though that only 31 subjects had to be excluded under these difficult recording conditions, especially since no adaptation night was performed. Since only subjects were excluded who slept less than 10 min (or had excessive head movements) there are likely several datasets included with comparably short durations and only a small number of SOs and spindles and even less combined SO-spindle events. A comprehensive table should be provided (supplement) including for each subject (included and excluded) the duration of included NREM sleep, number of SOs, spindles, and SO+spindle events. Also, some descriptive statistics (mean/SD/range) would be helpful.

(5) Was the 20-channel head coil dedicated for EEG-fMRI measurements? How were the electrode cables guided through/out of the head coil? Usually, the 64-channel head coil is used for EEG-fMRI measurements in a Siemens PRISMA 3T scanner, which has a cable duct at the back that allows to guide the cables straight out of the head coil (to minimize MR-related artifacts). The choice for the 20-channel head coil should be motivated. Photos of the recording setup would also be helpful.

(6) Was the EEG sampling synchronized to the MR scanner (gradient system) clock (the 10 MHz signal; not referring to the volume TTL triggers here)? This is a requirement for stable gradient artifact shape over time and thus accurate gradient noise removal.

(7) The TR is quite long and the voxel size is quite large in comparison to state-of-the-art EPI sequences. What was the rationale behind choosing a sequence with relatively low temporal and spatial resolution?

(8) The anatomically defined ROIs are quite large. It should be elaborated on how this might reduce sensitivity to sleep rhythm-specific activity within sub-regions, especially for the thalamus, which has distinct nuclei involved in sleep functions.

(9) The study reports SO & spindle amplitudes & densities, as well as SO+spindle coupling, to be larger during N2/3 sleep compared to N1 and REM sleep, which is trivial but can be seen as a sanity check of the data. However, the amount of SOs and spindles reported for N1 and REM sleep is concerning, as per definition there should be hardly any (if SOs or spindles occur in N1 it becomes by definition N2, and the interval between spindles has to be considerably large in REM to still be scored as such). Thus, on the one hand, the report of these comparisons takes too much space in the main manuscript as it is trivial, but on the other hand, it raises concerns about the validity of the scoring.

(10) Why was electrode F3 used to quantify the occurrence of SOs and spindles? Why not a midline frontal electrode like Fz (or a number of frontal electrodes for SOs) and Cz (or a number of centroparietal electrodes) for spindles to be closer to their maximum topography?

(11) Functional connectivity (hippocampus -> thalamus -> cortex (mPFC)) is reported to be increased during SO-spindle coupling and interpreted as evidence for coordination of hippocampo-neocortical communication likely by thalamic spindles. However, functional connectivity was only analysed during coupled SO+spindle events, not during isolated SOs or isolated spindles. Without the direct comparison of the connectivity patterns between these three events, it remains unclear whether this is specific for coupled SO+spindle events or rather associated with one or both of the other isolated events. The PPIs need to be conducted for those isolated events as well and compared statistically to the coupled events.

(12) The limited temporal resolution of fMRI does indeed not allow for easily distinguishing between fMRI activation patterns related to SO-up- vs. SO-down-states. For this, one could try to extract the amplitudes of SO-up- and SO-down-states separately for each SO event and model them as two separate parametric modulators (with the risk of collinearity as they are likely correlated).

(13) L327: "It is likely that our findings of diminished DMN activity reflect brain activity during the SO DOWN-state, as this state consistently shows higher amplitude compared to the UP-state within subjects, which is why we modelled the SO trough as its onset in the fMRI analysis." This conclusion is not justified as the fact that SO down-states are larger in amplitude does not mean their impact on the BOLD response is larger.

(14) Line 77: "In the current study, while directly capturing hippocampal ripples with scalp EEG or fMRI is difficult, we expect to observe hippocampal activation in fMRI whenever SOs-spindles coupling is detected by EEG, if SOs- spindles-ripples triple coupling occurs during human NREM sleep". Not all SO-spindle events are associated with ripples (Staresina et al., 2015), but hippocampal activation may also be expected based on the occurrence of spindles alone (Bergmann et al., 2012).

References:

Bergmann TO, Molle M, Diedrichs J, Born J, Siebner HR (2012) Sleep spindle-related reactivation of category-specific cortical regions after learning face-scene associations. Neuroimage 59:2733-2742.<br /> Caporro M, Haneef Z, Yeh HJ, Lenartowicz A, Buttinelli C, Parvizi J, Stern JM (2011) Functional MRI of sleep spindles and K-complexes. Clin Neurophysiol.<br /> Czisch M, Wehrle R, Stiegler A, Peters H, Andrade K, Holsboer F, Samann PG (2009) Acoustic oddball during NREM sleep: a combined EEG/fMRI study. PLoS One 4:e6749.<br /> Fogel S, Albouy G, King BR, Lungu O, Vien C, Bore A, Pinsard B, Benali H, Carrier J, Doyon J (2017) Reactivation or transformation? Motor memory consolidation associated with cerebral activation time-locked to sleep spindles. PLoS One 12:e0174755.<br /> Hale JR, White TP, Mayhew SD, Wilson RS, Rollings DT, Khalsa S, Arvanitis TN, Bagshaw AP (2016) Altered thalamocortical and intra-thalamic functional connectivity during light sleep compared with wake. Neuroimage 125:657-667.<br /> Horovitz SG, Fukunaga M, de Zwart JA, van Gelderen P, Fulton SC, Balkin TJ, Duyn JH (2008) Low frequency BOLD fluctuations during resting wakefulness and light sleep: a simultaneous EEG-fMRI study. Hum Brain Mapp 29:671-682.<br /> Ilhan-Bayrakci M, Cabral-Calderin Y, Bergmann TO, Tuscher O, Stroh A (2022) Individual slow wave events give rise to macroscopic fMRI signatures and drive the strength of the BOLD signal in human resting-state EEG-fMRI recordings. Cereb Cortex 32:4782-4796.<br /> Laufs H (2008) Endogenous brain oscillations and related networks detected by surface EEG-combined fMRI. Hum Brain Mapp 29:762-769.<br /> Laufs H, Walker MC, Lund TE (2007) 'Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study'--its limitations and an alternative approach. Brain 130:e75; author reply e76.<br /> Moehlman TM, de Zwart JA, Chappel-Farley MG, Liu X, McClain IB, Chang C, Mandelkow H, Ozbay PS, Johnson NL, Bieber RE, Fernandez KA, King KA, Zalewski CK, Brewer CC, van Gelderen P, Duyn JH, Picchioni D (2018) All-Night Functional Magnetic Resonance Imaging Sleep Studies. J Neurosci Methods.<br /> Picchioni D, Horovitz SG, Fukunaga M, Carr WS, Meltzer JA, Balkin TJ, Duyn JH, Braun AR (2010) Infraslow EEG oscillations organize large-scale cortical-subcortical interactions during sleep: A combined EEG/fMRI study. Brain Res.<br /> Schabus M, Dang-Vu TT, Albouy G, Balteau E, Boly M, Carrier J, Darsaud A, Degueldre C, Desseilles M, Gais S, Phillips C, Rauchs G, Schnakers C, Sterpenich V, Vandewalle G, Luxen A, Maquet P (2007) Hemodynamic cerebral correlates of sleep spindles during human non-rapid eye movement sleep. Proc Natl Acad Sci U S A 104:13164-13169.<br /> Spoormaker VI, Schroter MS, Gleiser PM, Andrade KC, Dresler M, Wehrle R, Samann PG, Czisch M (2010) Development of a large-scale functional brain network during human non-rapid eye movement sleep. J Neurosci 30:11379-11387.<br /> Staresina BP, Bergmann TO, Bonnefond M, van der Meij R, Jensen O, Deuker L, Elger CE, Axmacher N, Fell J (2015) Hierarchical nesting of slow oscillations, spindles and ripples in the human hippocampus during sleep. Nat Neurosci 18:1679-1686.

Reviewer #2 (Public review):

In this study, Wang and colleagues aimed to explore brain-wide activation patterns associated with NREM sleep oscillations, including slow oscillations (SOs), spindles, and SO-spindle coupling events. Their findings reveal that SO-spindle events corresponded with increased activation in both the thalamus and hippocampus. Additionally, they observed that SO-spindle coupling was linked to heightened functional connectivity from the hippocampus to the thalamus, and from the thalamus to the medial prefrontal cortex-three key regions involved in memory consolidation and episodic memory processes.

This study's findings are timely and highly relevant to the field. The authors' extensive data collection, involving 107 participants sleeping in an fMRI while undergoing simultaneous EEG recording, deserves special recognition. If shared, this unique dataset could lead to further valuable insights. While the conclusions of the data seem overall well supported by the data, some aspects with regard to the detection of sleep oscillations need clarification.

The authors report that coupled SO-spindle events were most frequent during NREM sleep (2.46 {plus minus} 0.06 events/min), but they also observed a surprisingly high occurrence of these events during N1 and REM sleep (2.23 {plus minus} 0.09 and 2.32 {plus minus} 0.09 events/min, respectively), where SO-spindle coupling would not typically be expected. Combined with the relatively modest SO amplitudes reported (~25 µV, whereas >75 µV would be expected when using mastoids as reference electrodes), this raises the possibility that the parameters used for event detection may not have been conservative enough - or that sleep staging was inaccurately performed. This issue could present a significant challenge, as the fMRI findings are largely dependent on the reliability of these detected events.

Reviewer #3 (Public review):

Summary:

Wang et al., examined the brain activity patterns during sleep, especially when locked to those canonical sleep rhythms such as SO, spindle, and their coupling. Analyzing data from a large sample, the authors found significant coupling between spindles and SOs, particularly during the upstate of the SO. Moreover, the authors examined the patterns of whole-brain activity locked to these sleep rhythms. To understand the functional significance of these brain activities, the authors further conducted open-ended cognitive state decoding and found a variety of cognitive processing may be involved during SO-spindle coupling and during other sleep events. The authors next investigated the functional connectivity analyses and found enhanced connectivity between the hippocampus, the thalamus, and the medial PFC. These results reinforced the theoretical model of sleep-dependent memory consolidation, such that SO-spindle coupling is conducive to systems-level memory reactivation and consolidation.

Strengths:

There are obvious strengths in this work, including the large sample size, state-of-the-art neuroimaging and neural oscillation analyses, and the richness of results.

Weaknesses:

Despite these strengths and the insights gained, there are weaknesses in the design, the analyses, and inferences.

A repeating statement in the manuscript is that brain activity could indicate memory reactivation and thus consolidation. This is indeed a highly relevant question that could be informed by the current data/results. However, an inherent weakness of the design is that there is no memory task before and after sleep. Thus, it is difficult (if not impossible) to make a strong argument linking SO/spindle/coupling-locked brain activity with memory reactivation or consolidation.

Relatedly, to understand the functional implications of the sleep rhythm-locked brain activity, the authors employed the "open-ended cognitive state decoding" method. While this method is interesting, it is rather indirect given that there were no behavioral indices in the manuscript. Thus, discussions based on these analyses are speculative at best. Please either tone down the language or find additional evidence to support these claims.

Moreover, the results from this method are difficult to understand. Figure 3e showed that for all three types of sleep events (SO, spindle, SO-spindle), the same mental states (e.g., working memory, episodic memory, declarative memory) showed opposite directions of activation (left and right panels showed negative and positive activation, respectively). How to interpret these conflicting results? This ambiguity is also reflected by the term used: declarative memory and episodic memories are both indexed in the results. Yet these two processes can be largely overlapped. So which specific memory processes do these brain activity patterns reflect? The Discussion shall discuss these results and the limitations of this method.

The coupling strength is somehow inconsistent with prior results (Hahn et al., 2020, eLife, Helfrich et al., 2018, Neuron). Specifically, Helfrich et al. showed that among young adults, the spindle is coupled to the peak of the SO. Here, the authors reported that the spindles were coupled to down-to-up transitions of SO and before the SO peak. It is possible that participants' age may influence the coupling (see Helfrich et al., 2018). Please discuss the findings in the context of previous research on SO-spindle coupling.

The discussion is rather superficial with only two pages, without delving into many important arguments regarding the possible functional significance of these results. For example, the author wrote, "This internal processing contrasts with the brain patterns associated with external tasks, such as working memory." Without any references to working memory, and without delineating why WM is considered as an external task even working memory operations can be internal. Similarly, for the interesting results on SO and reduced DMN activity, the authors wrote "The DMN is typically active during wakeful rest and is associated with self-referential processes like mind-wandering, daydreaming, and task representation (Yeshurun, Nguyen, & Hasson, 2021). Its reduced activity during SOs may signal a shift towards endogenous processes such as memory consolidation." This argument is flawed. DMN is active during self-referential processing and mind-wandering, i.e., when the brain shifts from external stimuli processing to internal mental processing. During sleep, endogenous memory reactivation and consolidation are also part of the internal mental processing given the lack of external environmental stimulation. So why during SO or during memory consolidation, the DMN activity would be reduced? Were there differences in DMN activity between SO and SO-spindle coupling events?

Reviewer #1 (Public review):

Summary:

Howard et al. performed deep mutational scanning on the MC4R gene, using a reporter assay to investigate two distinct downstream pathways across multiple experimental conditions. They validated their findings with ClinVar data and previous studies. Additionally, they provided insights into the application of DMS results for personalized drug therapy and differential ligand responses across variant types.

Strengths:

They captured over 99% of variants with robust signals and investigated subtle functionalities, such as pathway-specific activities and interactions with different ligands, by refining both the experimental design and analytical methods.

They provided additional details regarding the quality of the library, including the even composition of variants, sufficient readout from tested cells, and adequate sequencing depth. Additionally, they clarified the underlying assay mechanisms, effectively demonstrating the robustness of their results.

Reviewer #2 (Public review):

Overview

In this manuscript the authors use deep mutational scanning to assess the effect of ~6,600 protein-coding variants in MC4R, a G protein-coupled receptor associated with obesity. Reasoning that current deep mutational scanning approaches are insufficiently precise for some drug development applications, they focus on articulating new, more precise approaches. These approaches, which include a new statistical model and innovative reporter assay, enable them to probe molecular phenotypes directly relevant to the development of drugs that target this receptor with high precision and statistical rigor.

They use the resulting data for a variety of purposes, including probing the relationship between MC4R's sequence and structure, analyzing the effect of clinically important variants, identifying variants that disrupt downstream MC4R signaling via one but not both pathways, identifying loss of function variants are amenable to a corrector drug and exploring how deep mutational scanning data could guide small molecule drug optimization.

Strengths

The analysis and statistical framework developed by the authors represent a significant advance. In particular, it makes use of barcode-level internally replicated measurements to more accurately estimate measurement noise.<br /> The framework allows variant effects to be compared across experimental conditions, a task which is currently hard to do with rigor. Thus, this framework will be applicable to a large number of existing and future deep mutational scanning experiments.

The authors refine their existing barcode transcription-based assay for GPCR signaling, and develop a clever "relay" new reporter system to boost signaling in a particular pathway. They show that these reporters can be used to measure both gain of function and loss of function effects, which many deep mutational scanning approaches cannot do.

The use of systematic approaches to integrate and then interrogate high-dimensional deep mutational scanning data is a big strength. For example, the authors applied PCA to the variant effect results from reporters for two different MC4R signaling pathways and were able to discover variants that biased signaling through one or the other pathway. This approach paves the way for analyses of higher dimensional deep mutational scans.

The authors use the deep mutational scanning data they collect to map how different variants impact small molecule agonists activate MC4R signaling. This is an exciting idea because developing small-molecule protein-targeting therapeutics is difficult, and this manuscript suggests a new way to map small molecule-protein interactions.

Weaknesses

The authors derive insights into the relationship between MC4R signaling through different pathways and its structure. While these make sense based on what is already known, the manuscript would be stronger if some of these insights were validated using methods other than deep mutational scanning.

Likewise, the authors use their data to identify positions where variants disrupt MC4R activation by one small molecule agonist but not another. They hypothesize these effects point to positions that are more or less important for the binding of different small molecule agonists. The manuscript would be stronger if some of these insights were explored further.

Impact

In this manuscript the authors present new methods, including a statistical framework for analyzing deep mutational scanning data that will have a broad impact. They also generate MC4R variant effect data that is of interest to the GPCR community.

Comments on revisions:

I do not have additional comments, and feel that the authors addressed most of my concerns!

Reviewer #1 (Public review):

This study presents Jyvaskylavirus, a new member of the Marseilleviridae family, infecting Acanthamoeba castellanii. The study provides a detailed and comprehensive genomic and structural analysis of Jyvaskylavirus. The authors identified ORF142 as the capsid penton protein and additional structural proteins that comprise the virion. Using a combination of imaging techniques the authors provide new insights into the giant virus architecture and lifecycle. The study could be improved by providing atomic coordinates and refinement statistics, comparisons with available giant virus structures could be expanded, and the novelty in terms of the first isolated example of a giant virus from Finland could be expounded upon.

The study contributes new structural and genomic diversity to the Marseilleviridae family, hinting at a broader distribution and ecological significance of giant viruses than previously thought.

Comments on revisions: I'm satisfied with the authors' responses to the review, and request no further changes.

Reviewer #2 (Public review):

This paper describes the molecular characterisation of a new isolate of the giant virus Jyvaskylavirus, a member of the Marseilleviridae family infecting Acanthamoeba castellanii. The isolate comes from a boreal environment in Finland, showcasing that giant viruses can thrive in this ecological niche. The authors came up with a non-trivial isolation procedure that can be applied to characterise other members of the family and will be beneficial for the virology field. The genome shows typical Marseilleviridae features and phylogenetically belongs to their clade B. The structural characterisation was performed on the level of isolated virion morphology by negative stain EM, virions associated with cells either during the attachment or release by helium microscopy, the visualisation of the virus assembly inside cells using stained thin sections, and lastly on the protein secondary structure level by reconstructing ~6 A icosahedral map of the massive virion using cryoEM. The cryoEM density combined with gene product structure prediction enabled the identification and functional assessment of various virion proteins. The visualisation of ongoing virus assembly inside virus factories brings interesting hypotheses about the process that; however, needs to be verified in the next studies.

Strengths:

The detailed description of the virus isolation protocol is the largest strength of the paper and I believe it can be modified for isolating various viruses infecting small eukaryotes. The cryoEM map allows us to understand how exceptionally large virions of these viruses are stabilised by minor capsid proteins and nicely demonstrates the integration of medium-resolution cryoEM with protein structure prediction in deciphering virion protein function.

Weaknesses:

No mass spectrometry data are presented to supplement and confirm the identity of virion proteins which predicted models were fitted into the cryoEM density.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors provide strong evidence that the cell surface E3 ubiquitin ligases RNF43 and ZNRF3, which are well known for their role in regulating cell surface levels of WNT receptors encoded by FZD genes, also target EGFR for degradation. This is newly identified function for these ubiquitin ligases beyond their role in regulating WNT signaling. Loss of RNF43/ZNRF3 expression leads to elevated EGFR levels and signaling, suggesting a potential new axis to drive tumorigenesis, whereas overexpression of RNF43 or ZNRF3 decreases EGFR levels and signaling. Furthermore, RNF43 and ZNRF3 directly interact with EGFR through their extracellular domains.

Strengths:

The data showing that RNF43 and ZNRF3 interact with EGFR and regulate its levels and activity are thorough and convincing, and the conclusions are largely supported.

Weaknesses:

Prior work established a clear role for RNF43 and ZNRF3 in regulating cell surface levels of FZD, a class of WNT receptors. These new findings that these E3 ubiquitin ligases also target EGFR add a new layer of complexity, and it remains unclear to what extent WNT signaling versus EGFR signaling are impacted in cancer settings. The authors acknowledge this gap in our understanding, which will likely be the topic of follow-up studies.

Comments on revisions:

The authors addressed my main concerns in this revised version and in their rebuttal comments. I have no further critiques to add.

Reviewer #2 (Public review):

1st Public review:<br /> Using proteogenomic analysis of human cancer datasets, Yu et al, found that EGFR protein levels negatively correlate with ZNFR3/RNF43 expression across multiple cancers. Interestingly, they found that CRC harbouring the frequent RNF43 G659Vfs*41 mutation exhibit higher levels of EGFR when compared to RNF43 wild-type tumors. This is highly interesting since this mutation is generally not thought to influence Frizzled levels and Wnt-bcatenin pathway activity. Using CRISPR knockouts and overexpression experiments, the authors show that EGFR levels are modulated by ZNRF3/RNF43. Supporting these findings modulation of ZNRF3/RNF43 activity using Rspondin also leads to increased EGFR levels. Mechanistically, the authors, show that ZNRF3/RNF43 ubiquitinate EGFR and lead to degradation. Finally, the authors present functional evidence that loss of ZNRF3/RNF43 unleashes EGFR-mediated cell growth in 2D culture and organoids and promote tumor growth in vivo.

Overall, the conclusions of the manuscript are well supported by the data presented, but some aspects of the mechanism presented need to be re-enforced to fully support the claims made by the authors. Additionally, the title of the paper suggests that ZNRF3 and RNF43 loss leads to hyperactivity of EGFR and that its signalling activity contribute to cancer initiation/progression. I don't think the authors convincingly showed this in their study.

Major points:

(1) EGFR ubiquitination. All of the experiments supporting that ZNFR3/RNF43 mediate EGFR ubiquitination are performed under overexpression conditions. A major caveat is also that none of the ubiquitination experiments are performed under denaturing conditions. Therefore, it is impossible to claim that the ubiquitin immunoreactivity observed on the western blots presented in Fig.4 corresponds to ubiquitinated-EGFR species.

Another issue is that in Figure 4A, the experiments suggest that the RNF43-dependent ubiquitination of EGFR is promoted by EGF. However, there is no control showing the ubiquitination of EGFR in the absence of EGF but under RNF43 overexpression. According to the other experiments presented in Figures 4B, 4C and 4F, there seems to be a constitutive ubiquitination of EGFR upon overexpression. How do the authors reconcile the role of ZNRF3/RNF43 vs c-cbl?

(2) EGFR degradation vs internalization. In Figure 3C, the authors show experiments that demonstrate that RNF43 KO increases steady state levels of EGFR and prevents its EGF-dependent proteolysis. Using flow cytometry they then present evidence that the reduction in cell surface levels of EGFR mediated by EGF is inhibited in the absence of RNF43. The authors conclude that this is due to inhibition of EGF-induced internalization of surface EGF. However, the experiments are not designed to study internalization and rather merely examine steady state levels of surface EGFR pre and post treatment. These changes are an integration of many things (retrograde and anterograde transport mechanisms presumable modulated by EGF). What process(es) is/are specifically affected by ZNFR3/RNF43? Are these processes differently regulated by c-cbl? If the authors are specifically interested in internalization/recycling, the use of cell surface biotinylation experiments and time courses are needed to examine the effect of EGF in the presence or absence of the E3 ligases.

(3) RNF43 G659fs*41. The authors make a point in Figure 1D that this mutant leads to elevated EGFR in cancers but do not present evidence that this mutant is ineffective in mediated ubiquitination and degradation of EGFR. As this mutant maintains its ability to promote Frizzled ubiquitination and degradation, it would be important to show side by side that it does not affect EGFR. This would perhaps imply differential mechanisms for these two substrates.

(4) "Unleashing EGFR activity". The title of the paper implies that ZNRF3/RNF43 loss leads to increased EGFR expression and hence increased activity that underlies cancer. However, I could find only one direct evidence showing that increased proliferation of the HT29 cell line mutant for RNF43 could be inhibited by the EGFR inhibitor Erlotinib. All the other evidence presented that I could find is correlative or indirect (e.g. RPPA showing increased phosphorylation of pathway members upon RNF43 KO, increased proliferation of a cell line upon ZNRF3/ RNF43 KO, decreased proliferation of a cell line upon ZNRF3/RNF43 OE in vitro or in xeno...). Importantly, the authors claim that cancer initiation/ progression in ZNRF3/RNF43 mutant may in some contexts be independent of their regulation of Wnt-bcatenin signaling and relying on EGFR activity upregulation. However, this has not been tested directly. Could the authors leverage their znrf3/RNF43 prostate cancer model to test whether EGFR inhibition could lead to reduced cancer burden whereas a Frizzled or Wnt inhibitor does not?

More broadly, if EGFR signaling were to be unleashed in cancer, then one prediction would be that these cells would be more sensitive to EGFR pathway inhibition. Could the authors provide evidence that this is the case? Perhaps using isogenic cell lines or a panel of patient derived organoids (with known genotypes).

Comments on revisions:

The most important criticism of this manuscript that I raised in my original review has not been addressed. Indeed, the authors claim that EGFR is a direct substrate of the RNF43/ZNFR3 E3 ligase. This has not been directly demonstrated. Indeed, showing increased detection of ubiquitinated species in an immunoprecipitate could mean that a protein is directly modified. However, an alternative explanation is that a protein that is co-immunoprecipitated with the target protein is ubiquitinated (such as several EGFR adapters and interacting partners). Performing these experiments under denaturing conditions is one way to determine that EGFR is the substrate. Alternatively, a quantitative MS approach to quantify an increase in ubiquitinated peptides would also enable the authors to conclude that EGFR is indeed a substrate.

In addition, one of the main conclusions of the authors is that EGFR activity is unleashed in cancer following ZNRF3 and/or RNF43 loss (as the title suggests). There is still no direct evidence in the manuscript that this is the case. I appreciate the new data showing that MEF with knockout of RNF43/ZNRF3 are sensitive to EGFR inhibitor (and not porcupine inhibitor) but what is the data supporting that EGFR activity is "unleashed" in cancer? The authors still claim that ZNRF3 and RNF43 loss could impact cancer initiation/development in a Wnt-independent fashion (see lines 341-343). I believe this conclusion is based on correlative staining of nuclear bcatenin (which is in itself not a reliable readout of active sginaling) and not on functional data.... I suggested in my original review that the authors should test the efficacy of EGFR inhibitor and Wnt inhibitor in the prostate cancer model that they present in Figure 7 that would have enabled them to firmly conclude about their relative contribution. This was largely handwaved in their rebuttal letter... Doing experiment in WT cells is not the same as addressing this question in the context of cancer.

Finally, the authors use CRISPR KO experiments, without assessing editing or KO efficiencies throughout the manuscript and simply assume that the gRNA work. In my opinion this is an unacceptable practice.

Reviewer #1 (Public review):

Summary:

Prior research indicates that NaV1.2 and NaV1.6 have different compartmental distributions, expression timelines in development, and roles in neuron function. The lack of subtype-specific tools to control Nav1.2 and Nav1.6 activity however has hampered efforts to define the role of each channel in neuronal behavior. The authors attempt to address the problem of subtype specificity here by using aryl sulfonamides (ASCs) to stabilize channels in the inactivated state in combination with mice carrying a mutation that renders NaV1.2 and/or NaV1.6 genetically resistant to the drug. Using this innovative approach, the authors find that action potential initiation is controlled by NaV1.6 while both NaV1.2 and NaV1.6 are involved in backpropagation of the action potential to the soma, corroborating previous findings. Additionally, NaV1.2 inhibition paradoxically increases the firing rate, as has also been observed in genetic knockout models. Finally, the potential anticonvulsant properties of ASCs were tested. NaV1.6 inhibition but not NaV1.2 inhibition was found to decrease action potential firing in prefrontal cortex layer 5b pyramidal neurons in response to current injections designed to mimic inputs during seizure. This result is consistent with studies of loss-of-function Nav1.6 models and knockdown studies showing that these animals are resistant to certain seizure types. These results lend further support for the therapeutic promise of activity-dependent, NaV1.6-selective, inhibitors for epilepsy.

Strengths:

(1) The chemogenetic approaches used to achieve selective inhibition of NaV1.2 and NaV1.6 are innovative and help resolve long-standing questions regarding the role of Nav1.2 and Nav1.6 in neuronal electrogenesis.

(2) The experimental design is overall rigorous, with appropriate controls included.

(3) The assays to elucidate the effects of channel inactivation on typical and seizure-like activity were well selected.

Weaknesses:

(1) The potential impact of the YW->SR mutation in the voltage sensor does not appear to have been sufficiently assessed. The activation/inactivation curves in Figure 1E show differences in both activation and inactivation at physiologically relevant membrane voltages, which may be significant even though the V1/2 and slope factors are roughly similar.

(2) Additional discussion of the fact that channels are only partially blocked by the ASC and that ASCs act in a use-dependent manner would improve the manuscript and help readers interpret these results.

(3) NaV1.6 was described as being exclusively responsible for the change in action potential threshold, but when NaV1.6 alone was inactivated, the effect was significantly reduced from the condition in which both channels were inactivated (Figure 4E). Similarly, Figure 6C shows that blockade of both channels causes threshold depolarization prior to the seizure-like event, but selective inactivation of NaV1.6 does not. As NaV1.2 does not appear to be involved in action potential initiation and threshold change, what is the mechanism of this dissimilarity between the NaV1.6 inactivation and combined NaV1.6/ NaV1.2 inactivation?

(4) The idea that use-dependent VGSC-acting drugs may be effective antiseizure medications is well established. Additional discussion or at least acknowledgement of the existing, widely used, use-dependent VGSC drugs should be included (e.g. Carbamazepine, Lamotrigine, Phenytoin). Also, the idea that targeting NaV1.6 may be effective for seizures is established by studies using genetic models, knockdown, and partially selective pharmacology (e.g. NBI-921352). Additional discussion of how the results reported here are consistent with or differ from studies using these alternative approaches would improve the discussion

Reviewer #2 (Public review):

The authors used a clever and powerful approach to explore how Nav1.2 and Nav1.6 channels, which are both present in neocortical pyramidal neurons, differentially control firing properties of the neurons. Overall, the approach worked very well, and the results show very interesting differences when one or the other channel is partially inhibited. The experimental data is solid and the experimental data is very nicely complemented by a computational model incorporating the different localization of the two types of sodium channels.

In my opinion the presentation and interpretation of the results could be improved by a more thorough discussion of the fact that only incomplete inhibition of the channels can be achieved by the inhibitor under physiological recording conditions and I thought the paper could be easier to digest if the figures were re-organized. However, the key results are well-documented.

Reviewer #3 (Public review):

Summary:

The authors used powerful and novel reagents to carefully assess the roles of the voltage gated sodium channel (NaV) isoforms in regulating the neural excitability of principal neurons of the cerebral cortex. Using this approach, they were able to confirm that two different isoforms, NaV1.2 and NaV1.6 have distinct roles in electrogenesis of neocortical pyramidal neurons.

Strengths:

Development of very powerful transgenic mice in which NaV1.2 and/or NaV1.6 were modified to be insensitive to ASCs, a particular class of NaV blocker. This allowed them to test for roles of the two isoforms in an acute setting, without concerns of genetic or functional compensation that might result from a NaV channel knockout.

Careful biophysical analysis of ASC effects on different NaV isoforms.

Extensive and rigorous analysis of electrogenesis - action potential production - under conditions of blockade of either NaV1.2 or NaV1 or both.

Weaknesses:

Some results are overstated in that the representative example records provided do not directly support the conclusions.

Results from a computational model are provided to make predictions of outcomes, but the computational approach is highly underdeveloped.

Reviewer #1 (Public review):

Summary:

Howard-Spink et al. investigated how older chimpanzees changed their behavior regarding stone tool use for nutcracking over a period of 17 years, from late adulthood to old age. This behavior is cognitively demanding, and it is a good target for understanding aging in wild primates. They used several factors to follow the aging process of five individuals, from attendance at the nut-cracking outdoor laboratory site to time to select tools and efficiency in nut-cracking to check if older chimpanzee changed their behavior.

Indeed, older chimpanzees reduced their visits to the outdoor lab, which was not observed in the younger adults. The authors discuss several reasons for that; the main ones being physiological changes, cognitive and physical constraints, and changes in social associations. Much of the discussion is hypothetical, but a good starting point, as there is not much information about senescence in wild chimpanzees.

The efficiency for nut-cracking was variable, with some individuals taking a long time to crack nuts while others showed little variance. As this is not compared with the younger individuals and the sample is small (only five individuals), it is difficult to be sure if this is also partly a normal variance caused by other factors (ecology) or is only related to senescence.

Strengths:

(1) 17 years of longitudinal data in the same setting, following the same individuals.

(2) Using stone tool use, a cognitively demanding behavior, to understand the aging process.

Weaknesses:

A lack of comparison of the stone tool use behavior with younger individuals in the same period, to check if the changes observed are only related to age or if it is an overall variance. The comparison with younger chimpanzees was only done for one of the variables (attendance).

Reviewer #2 (Public review):

Summary:

Primates are a particularly important and oft-applied model for understanding the evolution of, e.g., life history and senescence in humans. Although there is a growing body of work on aging in primates, there are three components of primate senescence research that have been underutilized or understudied: (1) longitudinal datasets, (2) wild populations, and (3) (stone) tool-use behaviors. Therefore, the goal of this study was to (1) use a 17-year longitudinal dataset (2) of wild chimpanzees in the Bossou forest, (3) visiting a site for field experiments on nut-cracking. They sampled and analyzed data from five field seasons for five chimpanzees of old age. From this sample, Howard-Spink and colleagues noted a decline in tool-use and tool-use efficiency in some individuals, but not in others. The authors then conclude that there is a measurable effect of senescence on chimpanzee behavior, but that it varies individually. The study has major intellectual value as a building block for future research, but there are several major caveats.

Strengths:

With this study, Howard-Spink and colleagues make a foray into a neglected topic of research: the impact of the physiological and cognitive changes due to senescence on stone tool use in chimpanzees. Based on novelty alone, this is a valuable study. The authors cleverly make use of a longitudinal record covering 17 years of field data, which provides a window into long-term changes in the behavior of wild chimpanzees, which I agree cannot be understood through cross-sectional comparisons.

The metrics of 'efficiency' (see caveats below) are suitable for measuring changes in technological behavior over time, as specifically tailored to the nut-cracking (e.g., time, number of actions, number of strikes, tool changes). The ethogram and the coding protocol are also suitable for studying the target questions and objectives. I would recommend, however, the inclusion of further variables that will assist in improving the amount of valid data that can be extrapolated (see also below).

With this pilot, Howard-Spink and colleagues have established a foundation upon which future research can be designed, including further investigation with the Bossou dataset and other existing video archives, but especially future targeted data collection, which can be designed to overcome some of the limits and confounds that can be identified in the current study.

Weaknesses:

Although I agree with the reasoning behind conducting this research and understand that, as the authors state, there are logistical considerations that have to be made when planning and executing such a study, there are a number of methodological and theoretical shortcomings that either need to be more explicitly stated by the authors or would require additional data collection and analysis.

One of the main limitations of this study is the small sample size. There are only 5 of the old-aged individuals, which is not enough to draw any inferences about aging for chimpanzees more generally. Howard-Spink and colleagues also study data from only five of the 17 years of recorded data at Bossou. The selection of this subset of data requires clarification: why were these intervals chosen, why this number of data points, and how do we know that it provides a representative picture of the age-related changes of the full 17 years?

With measuring and interpreting the 'efficiency' of behaviors, there are in-built assumptions about the goals of the agents and how we can define efficiency. First, it may be that efficiency is not an intentional goal for nut-cracking at all, but rather, e.g., productivity as far as the number of uncrushed kernels (cf. Putt 2015). Second, what is 'efficient' for the human observer might not be efficient for the chimpanzee who is performing the behavior. More instances of tool-switching may be considered inefficient, but it might also be a valid strategy for extracting more from the nuts, etc. Understanding the goals of chimpanzees may be a difficult proposition, but these are uncertainties that must be kept in mind when interpreting and discussing 'decline' or any change in technological behaviors over time.

For the study of the physiological impact of senescence of tool use (i.e., on strength and coordination), the study would benefit from the inclusion of variables like grip type and (approximate) stone size (Neufuss et al., 2016). The size and shape of stones for nut-cracking have been shown to influence the efficacy and 'efficiency' of tool use (i.e., the same metrics of 'efficiency' implemented by Howard-Spink et al. in the current study), meaning raw material properties are a potential confound that the authors have not evaluated.

Similarly, inter- and intraspecific variation in the properties of nuts being processed is another confound (Falótico et al., 2022; Proffitt et al., 2022). If oil palm nuts were varying year-to-year, for example, this would theoretically have an effect on the behavioral forms and strategies employed by the chimpanzees, and thus, any metric of efficiency being collected and analyzed. Further, it is perplexing that the authors analyze only one year where the coula nuts were provided at the test site, but these were provided during multiple field seasons. It would be more useful to compare data from a similar number of field seasons with both species if we are to study age-related changes in nut processing over time (one season of coula nut-cracking certainly does not achieve this).

Both individual personality (especially neophilia versus neophobia; e.g., Forss & Willems, 2022) and motivation factors (Tennie & Call, 2023) are further confounds that can contribute to a more valid interpretation of the patterns found. To draw any conclusions about age-related changes in diet and food preferences, we would need to have data on the overall food intake/preferences of the individuals and the food availability in the home range. The authors refer briefly to this limitation, but the implications for the interpretation of the data are not sufficiently underlined (e.g., for the relevance of age-related decline in stone tool-use ability for individual survival).

Generally speaking, there is a lack of consideration for temporal variation in ecological factors. As a control for these, Howard-Spink and colleagues have examined behavioral data for younger individuals from Bossou in the same years, to ostensibly show that patterns in older adults are different from patterns in younger adults, which is fair given the available data. Nonetheless, they seem to focus mostly on the start and end points and not patterns that occur in between. For example, there is a curious drop in attendance rate for all individuals in the 2008 season, the implications of which are not discussed by the authors.

As far as attendance, Howard-Spink and colleagues also discuss how this might be explained by changes in social standing in later life (i.e., chimpanzees move to the fringes of the social network and become less likely to visit gathering sites). This is not senescence in the sense of physiological and cognitive decline with older age. Instead, the reduced attendance due to changes in social standing seems rather to exacerbate signs of aging rather than be an indicator of it itself. The authors also mention a flu-like epidemic that caused the death of 5 individuals; the subsequent population decline and related changes in demography also warrant more discussion and characterization in the manuscript.

Understandably, some of these issues cannot be evaluated or corrected with the presented dataset. Nonetheless, these undermine how certain and/or deterministic their conclusions can really be considered. Howard-Spink et al. have not strongly 'demonstrated' the validity of relationships between the variables of the study. If anything, their cursory observations provide us with methods to apply and hypotheses to test in future studies. It is likely that with higher-resolution datasets, the individual variability in age-related decline in tool-use abilities will be replicated. For now, this can be considered a starting point, which will hopefully inspire future attempts to research these questions.

Falótico, T., Valença, T., Verderane, M. & Fogaça, M. D. Stone tools differences across three capuchin monkey populations: food's physical properties, ecology, and culture. Sci. Rep. 12, 14365 (2022).<br /> Forss, S. & Willems, E. The curious case of great ape curiosity and how it is shaped by sociality. Ethology 128, 552-563 (2022).<br /> Neufuss, J., Humle, T., Cremaschi, A. & Kivell, T. L. Nut-cracking behaviour in wild-born, rehabilitated bonobos (Pan paniscus): a comprehensive study of hand-preference, hand grips and efficiency. Am. J. Primatol. 79, e22589 (2016).<br /> Proffitt, T., Reeves, J. S., Pacome, S. S. & Luncz, L. V. Identifying functional and regional differences in chimpanzee stone tool technology. R. Soc. Open Sci. 9, 220826 (2022).<br /> Putt, S. S. The origins of stone tool reduction and the transition to knapping: An experimental approach. J. Archaeol. Sci.: Rep. 2, 51-60 (2015).<br /> Tennie, C. & Call, J. Unmotivated subjects cannot provide interpretable data and tasks with sensitive learning periods require appropriately aged subjects: A Commentary on Koops et al. (2022) "Field experiments find no evidence that chimpanzee nut cracking can be independently innovated". ABC 10, 89-94 (2023).

Reviewer #1 (Public review):

Ejdrup, Gether, and colleagues present a sophisticated simulation of dopamine (DA) dynamics based on a substantial volume of striatum with many DA release sites. The key observation is that a reduced DA uptake rate in the ventral striatum (VS) compared to the dorsal striatum (DS) can produce an appreciable "tonic" level of DA in VS and not DS. In both areas they find that a large proportion of D2 receptors are occupied at "baseline"; this proportion increases with simulated DA cell phasic bursts but has little sensitivity to simulated DA cell pauses. They also examine, in a separate model, the effects of clustering dopamine transporters (DAT) into nanoclusters and say this may be a way of regulating tonic DA levels in VS. I found this work of interest and I think it will be useful to the community. At the same time, there are a number of weaknesses that should be addressed, and the authors need to more carefully explain how their conclusions are distinct from those based on prior models.

(1) The conclusion that even an unrealistically long (1s) and complete pause in DA firing has little effect on DA receptor occupancy is potentially important. The ability to respond to DA pauses has been thought to be a key reason why D2 receptors (may) have high affinity. This simulation instead finds evidence that DA pauses may be useless. This result should be highlighted in the abstract and discussed more.

(2) The claim of "DAT nanoclustering as a way to shape tonic levels of DA" is not very well supported at present. None of the panels in Figure 4 simply show mean steady-state extracellular DA as a function of clustering. Perhaps mean DA is not the relevant measure, but then the authors need to better define what is and why. This issue may be linked to the fact that DAT clustering is modeled separately (Figure 4) to the main model of DA dynamics (Figures 1-3) which per the Methods assumes even distribution of uptake. Presumably, this is because the spatial resolution of the main model is too coarse to incorporate DAT nanoclusters, but it is still a limitation. As it stands it is convincing (but too obvious) that DAT clustering will increase DA away from clusters, while decreasing it near clusters. I.e. clustering increases heterogeneity, but how this could be relevant to striatal function is not made clear, especially given the different spatial scales of the models.

(3) I question how reasonable the "12/40" simulated burst firing condition is, since to my knowledge this is well outside the range of firing patterns actually observed for dopamine cells. It would be better to base key results on more realistic values (in particular, fewer action potentials than 12).

(4) There is a need to better explain why "focality" is important, and justify the measure used.

(5) Line 191: " D1 receptors (-Rs) were assumed to have a half maximal effective concentration (EC50) of 1000 nM"<br /> The assumptions about receptor EC50s are critical to this work and need to be better justified. It would also be good to show what happens if these EC50 numbers are changed by an order of magnitude up or down.

(6) Line 459: "we based our receptor kinetics on newer pharmacological experiments in live cells (Agren et al., 2021) and properties of the recently developed DA receptor-based biosensors (Labouesse & Patriarchi, 2021). Indeed, these sensors are mutated receptors but only on the intracellular domains with no changes of the binding site (Labouesse & Patriarchi, 2021)"<br /> This argument is diminished by the observation that different sensors based on the same binding site have different affinities (e.g. in Patriarchi et al. 2018, dLight1.1 has Kd of 330nM while dlight1.3b has Kd of 1600nM).

(7) Estimates of Vmax for DA uptake are entirely based on prior fast-scan voltammetry studies (Table S2). But FSCV likely produces distorted measures of uptake rate due to the kinetics of DA adsorption and release on the carbon fiber surface.

(8) It is assumed that tortuosity is the same in DS and VS - is this a safe assumption?

(9) More discussion is needed about how the conclusions derived from this more elaborate model of DA dynamics are the same, and different, to conclusions drawn from prior relevant models (including those cited, e.g. from Hunger et al. 2020, etc).

Reviewer #2 (Public review):

The work presents a model of dopamine release, diffusion, and reuptake in a small (100 micrometer^2 maximum) volume of striatum. This extends previous work by this group and others by comparing dopamine dynamics in the dorsal and ventral striatum and by using a model of immediate dopamine-receptor activation inferred from recent dopamine sensor data. From their simulations, the authors report two main conclusions. The first is that the dorsal striatum does not appear to have a sustained, relatively uniform concentration of dopamine driven by the constant 4Hz firing of dopamine neurons; rather that constant firing appears to create hotspots of dopamine. By contrast, the lower density of release sites and lower rate of reuptake in the ventral striatum creates a sustained concentration of dopamine. The second main conclusion is that D1 receptor (D1R) activation is able to track dopamine concentration changes at short delays but D2 receptor activation cannot.

The simulations of the dorsal striatum will be of interest to dopamine aficionados as they throw some doubt on the classic model of "tonic" and "phasic" dopamine actions, further show the disconnect between dopamine neuron firing and consequent release, and thus raise issues for the reward-prediction error theory of dopamine.

There is some careful work here checking the dependence of results on the spatial volume and its discretisation. The simulations of dopamine concentration are checked over a range of values for key parameters. The model is good, the simulations are well done, and the evidence for robust differences between dorsal and ventral striatum dopamine concentration is good.

However, the main weakness here is that neither of the main conclusions is strongly evidenced as yet. The claim that the dorsal striatum has no "tonic" dopamine concentration is based on the single example simulation of Figure 1 not the extensive simulations over a range of parameters. Some of those later simulations seem to show that the dorsal striatum can have a "tonic" dopamine concentration, though the measurement of this is indirect. It is not clear why the reader should believe the example simulation over those in the robustness checks, for example by identifying which range of parameter values is more realistic.

The claim that D1Rs can track rapid changes in dopamine is not well supported. It is based on a single simulation in Figure 1 (DS) and 2 (VS) by visual inspection of simulated dopamine concentration traces - and even then it is unclear that D1Rs actually track dynamics because they clearly do not track rapid changes in dopamine that are almost as large as those driven by bursts (cf Figure 1i). The claim also depends on two things that are poorly explained. First, the model of binding here is missing from the text. It seems to be a simple bound-fraction model, simulating a single D1 or D2 receptor. It is unclear whether more complex models would show the same thing. Second, crucial to the receptor model here is the inference that D1 receptor unbinding is rapid; but this inference is made based on the kinetics of dopamine sensors and is superficially explained - it is unclear why sensor kinetics should let us extrapolate to receptor kinetics, and unclear how safe is the extrapolation of the linear regression by an order of magnitude to get the D1 unbinding rate.

Reviewer #1 (Public review):

Summary:

This study by Xu et al. focuses on the impact of clathrin-independent endocytosis in cancer cells on T cell activation. In particular, by using a combination of biochemical approaches and imaging, the authors identify ICAM1, the ligand for T cell-expressed integrin LFA-1, as a novel cargo for EndoA3-mediated endocytosis. Subsequently, the authors aim to identify functional implications for T cell activation, using a combination of cytokine assays and imaging experiments.

They find that the absence of EndoA3 leads to a reduction in T cell-produced cytokine levels. Additionally, they observe slightly reduced levels of ICAM1 at the immunological synapse and an enlarged contact area between T cells and cancer cells. Taken together, the authors propose a mechanism where EndoA3-mediated endocytosis of ICAM1, followed by retrograde transport, supplies the immunological synapse with ICAM1. In the absence of EndoA3, T cells attempt to compensate for suboptimal ICAM1 levels at the synapse by enlarging their contact area, which proves insufficient and leads to lower levels of T cell activation.

Strengths:

The authors utilize a rigorous and innovative experimental approach that convincingly identifies ICAM1 as a novel cargo for Endo3A-mediated endocytosis.

Weaknesses:

The characterization of the effects of Endo3A absence on T cell activation appears incomplete. Key aspects, such as surface marker upregulation, T cell proliferation, integrin signalling and most importantly, the killing of cancer cells, are not comprehensively investigated.

As Endo- and exocytosis are intricately linked with the biophysical properties of the cellular membrane (e.g. membrane tension), which can significantly impact T-cell activation and cytotoxicity, the authors should address this possibility and ideally address it experimentally to some degree.

Crucially, key literature relevant to this research, addressing the role of ICAM1 endocytosis in antigen-presenting cells, has not been taken into consideration.

Reviewer #2 (Public review):

Summary:

The manuscript by Xu et al. studies the relevance of endophilin A3-dependent endocytosis and retrograde transport of immune synapse components and in the activation of cytotoxic CD8 T cells. First, the authors show that ICAM1 and ALCAM, known components of immune synapses, are endocytosed via endoA3-dependent endocytosis and retrogradely transported to the Golgi. The authors then show that blocking internalization or retrograde trafficking reduces the activation of CD8 T cells. Moreover, this diminished CD8 T cell activation resulted in the formation of an enlarged immune synapse with reduced ICAM1 recruitment.

Strengths:

The authors show a novel EndoA3-dependent endocytic cargo and provide strong evidence linking EndoA3 endocytosis to the retrograde transport of ALCAM and ICAM1.

Weaknesses:

The role of EndoA3 in the process of T cell activation is shown in a cell that requires exogenous expression of this gene. Moreover, the authors claim that their findings are important for polarized redistribution of cargoes, but failed to show convincingly that the cargoes they are studying are polarized in their experimental system. The statistics of the manuscript also require some refinement.

Reviewer #3 (Public review):

Summary:

Shiqiang Xu and colleagues have examined the importance of ICAM-1 and ALCAM internalization and retrograde transport in cancer cells on the formation of a polarized immunological synapse with cytotoxic CD8+ T cells. They find that internalization is mediated by Endophilin A3 (EndoA3) while retrograde transport to the Golgi apparatus is mediated by the retromer complex. The paper is building on previous findings from corresponding author Henri-François Renard showing that ALCAM is an EndoA3-dependent cargo in clathrin-independent endocytosis.

Strengths:

The work is interesting as it describes a novel mechanism by which cancer cells might influence CD8+ T cell activation and immunological synapse formation, and the authors have used a variety of cell biology and immunology methods to study this. However, there are some aspects of the paper that should be addressed more thoroughly to substantiate the conclusions made by the authors.

Weaknesses:

In Figure 2A-B, the authors show micrographs from live TIRF movies of HeLa and LB33-MEL cells stably expressing EndoA3-GFP and transiently expressing ICAM-1-mScarlet. The ICAM-1 signal appears diffuse across the plasma membrane while the EndoA3 signal is partially punctate and partially lining the edge of membrane patches. Previous studies of EndoA3-mediated endocytosis have indicated that this can be observed as transient cargo-enriched puncta on the cell surface. In the present study, there is only one example of such an ICAM-1 and EndoA3 positive punctate event. Other examples of overlapping signals between ICAM-1 and EndoA3 are shown, but these either show retracting ICAM-1 positive membrane protrusions or large membrane patches encircled by EndoA3. While these might represent different modes of EndoA3-mediated ICAM-1 internalization, any conclusion on this would require further investigation.

Moreover, in Figure 2C-E, uptake of the previously established EndoA3 endocytic cargo ALCAM is analyzed by quantifying total internal fluorescence in LB33-MEL cells of antibody labelled ALCAM following both overexpression and siRNA-mediated knockdown of EndoA3, showing increased and decreased uptake respectively. Why has not the same quantification been done for the proposed novel EndoA3 endocytic cargo ICAM-1? Furthermore, if endocytosis of ICAM-1 and ALCAM is diminished following EndoA3 knockdown, the expression level on the cell surface would presumably increase accordingly. This has been shown for ALCAM previously and should also be quantified for ICAM-1.

In Figure 4A the authors show micrographs from a live-cell Airyscan movie (Movie S6) of a CD8+ T cell incubated with HeLa cells stably expressing HLA-A*68012 and transiently expressing ICAM1-EGFP. From the movie, it seems that some ICAM-1 positive vesicles in one of the HeLa cells are moving towards the T cell. However, it does not appear like the T cell has formed a stable immunological synapse but rather perhaps a motile kinapse. Furthermore, to conclude that the ICAM-1 positive vesicles are transported toward the T cell in a polarized manner, vesicles from multiple cells should be tracked and their overall directionality should be analyzed. It would also strengthen the paper if the authors could show additional evidence for polarization of the cancer cells in response to T-cell interaction.

Finally, in Figures 4D-G, the authors show that the contact area between CD8+ T cells and LB33-MEL cells is increased in response to siRNA-mediated knockdown of EndoA3 and VPS26A. While this could be caused by reduced polarized delivery of ICAM-1 and ALCAM to the interface between the cells, it could also be caused by other factors such as increased cell surface expression of these proteins due to diminished endocytosis, and/or morphological changes in the cancer cells resulting from disrupted membrane traffic. More experimental evidence is needed to support the working model in Figure 4H.

Reviewer #1 (Public review):

Summary:

In this study, Ana Lapao et al. investigated the roles of Rab27 effector SYTL5 in cellular membrane trafficking pathways. The authors found that SYTL5 localizes to mitochondria in a Rab27A-dependent manner. They demonstrated that SYTL5-Rab27A positive vesicles containing mitochondrial material are formed under hypoxic conditions, thus they speculate that SYTL5 and Rab27A play roles in mitophagy. They also found that both SYTL5 and Rab27A are important for normal mitochondrial respiration. Cells lacking SYTL5 undergo a shift from mitochondrial oxygen consumption to glycolysis which is a common process known as the Warburg effect in cancer cells. Based on the cancer patient database, the author noticed that low SYTL5 expression is related to reduced survival for adrenocortical carcinoma patients, indicating SYTL5 could be a negative regulator of the Warburg effect and potentially tumorigenesis.

Strengths:

The authors take advantage of multiple techniques and novel methods to perform the experiments.

(1) Live-cell imaging revealed that stably inducible expression of SYTL5 co-localized with filamentous structures positive for mitochondria. This result was further confirmed by using correlative light and EM (CLEM) analysis and western blotting from purified mitochondrial fraction.

(2) In order to investigate whether SYTL5 and RAB27A are required for mitophagy in hypoxic conditions, two established mitophagy reporter U2OS cell lines were used to analyze the autophagic flux.

Weaknesses:

This study revealed a potential function of SYTL5 in mitophagy and mitochondrial metabolism. However, the mechanistic evidence that establishes the relationship between SYTL5/Rab27A and mitophagy is insufficient. The involvement of SYTL5 in ACC needs more investigation. Furthermore, images and results supporting the major conclusions need to be improved.

Reviewer #2 (Public review):

Summary:

The authors provide convincing evidence that Rab27 and STYL5 work together to regulate mitochondrial activity and homeostasis.

Strengths:

The development of models that allow the function to be dissected, and the rigorous approach and testing of mitochondrial activity

Weaknesses:

There may be unknown redundancies in both pathways in which Rab27 and SYTL5 are working which could confound the interpretation of the results.

Suggestions for revision:

Given that Rab27A and SYTL5 are members of protein families it would be important to exclude any possible functional redundancies coming from Rab27B expression or one of the other SYTL family members. For Rab27 this would be straightforward to test in the assays shown in Figure 4 and Supplementary Figure 5. For SYTL5 it might be sufficient to include some discussion about this possibility.

Suggestions for Discussion:

Both Rab27A and STYL5 localize to other membranes, including the endolysosomal compartments. How do the authors envisage the mechanism or cellular modifications that allow these proteins, either individually or in complex to function also to regulate mitochondrial function? It would be interesting to have some views.

Reviewer #3 (Public review):

Summary:

In the manuscript by Lapao et al., the authors uncover a role for the RAB27A effector protein SYTL5 in regulating mitochondrial function and turnover. The authors find that SYTL5 localizes to mitochondria in a RAB27A-dependent way and that loss of SYTL5 (or RAB27A) impairs lysosomal turnover of an inner mitochondrial membrane mitophagy reporter but not a matrix-based one. As the authors see no co-localization of GFP/mScarlet tagged versions of SYTL5 or RAB27A with LC3 or p62, they propose that lysosomal turnover is independent of the conventional autophagy machinery. Finally, the authors go on to show that loss of SYTL5 impacts mitochondrial respiration and ECAR and as such may influence the Warburg effect and tumorigenesis. Of relevance here, the authors go on to show that SYTL5 expression is reduced in adrenocortical carcinomas and this correlates with reduced survival rates.

Strengths:

There are clearly interesting and new findings here that will be relevant to those following mitochondrial function, the endocytic pathway, and cancer metabolism.

Weaknesses:

The data feel somewhat preliminary in that the conclusions rely on exogenously expressed proteins and reporters, which do not always align.

As the authors note there are no commercially available antibodies that recognize endogenous SYTL5, hence they have had to stably express GFP-tagged versions. However, it appears that the level of expression dictates co-localization from the examples the authors give (though it is hard to tell as there is a lack of any kind of quantitation for all the fluorescent figures). Therefore, the authors may wish to generate an antibody themselves or tag the endogenous protein using CRISPR.

In relation to quantitation, the authors found that SYTL5 localizes to multiple compartments or potentially a few compartments that are positive for multiple markers. Some quantitation here would be very useful as it might inform on function.

The authors find that upon hypoxia/hypoxia-like conditions that punctate structures of SYTL5 and RAB27A form that are positive for Mitotracker, and that a very specific mitophagy assay based on pSu9-Halo system is impaired by siRNA of SYTL5/RAB27A, but another, distinct mitophagy assay (Matrix EGFP-mCherry) shows no change. I think this work would strongly benefit from some measurements with endogenous mitochondrial proteins, both via immunofluorescence and western blot-based flux assays.

A really interesting aspect is the apparent independence of this mitophagy pathway on the conventional autophagy machinery. However, this is only based on a lack of co-localization between p62 or LC3 with LAMP1 and GFP/mScarlet tagged SYTL5/RAB27A. However, I would not expect them to greatly colocalize in lysosomes as both the p62 and LC3 will become rapidly degraded, while the eGFP and mScarlet tags are relatively resistant to lysosomal hydrolysis. -/+ a lysosome inhibitor might help here and ideally, the functional mitophagy assays should be repeated in autophagy KOs.

The link to tumorigenesis and cancer survival is very interesting but it is not clear if this is due to the mitochondrially-related aspects of SYTL5 and RAB27A. For example, increased ECAR is seen in the SYTL5 KO cells but not in the RAB27A KO cells (Fig.5D), implying that mitochondrial localization of SYTL5 is not required for the ECAR effect. More work to strengthen the link between the two sections in the paper would help with future directions and impact with respect to future cancer treatment avenues to explore.

Reviewer #1 (Public review):

Summary:

Amaral et al. presents a study investigating the mesoscale modelling and dynamics of bolalipids.

Strengths:

The figures in this paper are exceptional. Both those to outline and introduce the lipid types, but also the quality and resolution of the plots. The data held within also appears to be outstanding and of significant (hopefully) general interest.

Weaknesses:

In the introduction, I would like to have read more specifics on the biological role of bolalipids. Archaea are mentioned, but this kingdom is huge - there must be specific species that can be discussed where bolalipids are integral to archaeal life. The authors should go beyond 'extremophiles'. In short, they should unpack why the general audience should be interested in these lipids, within a subset of organisms that are often forgotten about.

Reviewer #2 (Public review):

Summary:

The authors aimed to understand the biophysical properties of archeal membranes made of bolalipids. Bacterial and eukaryotic membranes are made of lipids that self-assemble into bilayers. Archea, instead, use bolalipids, lipids that have two headgroups and can span the entire bilayer. The authors wanted to determine if the unique characteristics of archaea, which are often extremophiles, are in part due to the fact that their membranes contain bolalipids.

The authors develop a minimal computational model to compare the biophysics of bilayers made of lipids, bolalipids, and mixtures of the two. Their model enables them to determine essential parameters such as bilayer phase diagrams, mechanical moduli, and the bilayer behavior upon cargo inclusion and remodeling.

The author demonstrates that bolalipid bilayers behave as binary mixtures, containing bolalipids organized either in a straight conformation, spanning the entire bilayer, or in a u-shaped one, confined to a single leaflet. This dynamic mixture allows bolalipid bilayers to be very sturdy but also provides remodeling. However, remodeling is energetically more expensive than with standard lipids. The authors speculate that this might be why lipids were more abundant in the evolutionary process.

Strengths:

This is a wonderful paper, a very fine piece of scholarship. It is interesting from the point of view of biology, biophysics, and material science. The authors mastered the modeling and analysis of these complex systems. The evidence for their findings is really strong and complete. The paper is written superbly, the language is precise and the reading experience is very pleasant. The plots are very well-thought-out.

Weaknesses:

I would not talk about weaknesses, because this is really a nice paper. If I really had to find one, I would have liked to see some clear predictions of the model expressed in such a way that experimentalists could design validation experiments.

Reviewer #3 (Public review):

Summary:

The authors have studied the mechanics of bolalipid and archaeal mixed-lipid membranes via comprehensive molecular dynamics simulations. The Cooke-Deserno 3-bead-per-lipid model is extended to bolalipids with 6 beads. Phase diagrams, bending rigidity, mechanical stability of curved membranes, and cargo uptake are studied. Effects such as the formation of U-shaped bolalipids, pore formation in highly curved regions, and changes in membrane rigidity are studied and discussed. The main aim has been to show how the mixture of bolalipids and regular bilayer lipids in archaeal membrane models enhances the fluidity and stability of these membranes.

Strengths:

The authors have presented a wide range of simulation results for different membrane conditions and conformations. For the most part, the analyses and their results are presented clearly and concisely. Figures, supplementary information, and movies very well present what has been studied. The manuscript is well-written and is easy to follow.

Major issues:

The Cooke-Deserno model, while very powerful for biophysical analysis of membranes at the mesoscale, is very much void of chemical information. It is parameterized such that it is good in producing fluid membranes and predicting values for bending rigidity, compressibility, and even thermal expansion coefficient falling in the accepted range of values for bilayer membranes. But it still represents a generic membrane. Now, the authors have suggested a similar model for the archaeal bolalipids, which have chemically different lipids (the presence of cyclopentane rings for one), and there is no good justification for using the same pairwise interactions between their representative beads in the coarse-grained model. This does not necessarily diminish the worth of all the authors' analyses. What is at risk here is the confusion between "what we observe this model of bolalipid- or mixed-membranes do" and "how real bolalipid-containing archaeal membranes behave at these mechanical and thermal conditions.".

Another more specific, major issue has to do with using the Hamm-Kozlov model for fitting the power spectrum of thermal undulations. The 1/q^2 term can very well be attributed to membrane tension. While a barostat is indeed used, have the authors made absolutely sure that the deviation from 1/q^4 behavior does not correspond to lateral tension? I got more worried when I noticed in the SI that the simulations had been done with combined "fix langevin" and "fix nph" LAMMPS commands. This combination does not result in a proper isothermal-isobaric ensemble. The importance of tilt terms for bolalipids is indeed very interesting, but I believe more care is needed to establish that.

This issue is reinforced when considering Figure 3B. These results suggest that increasing the fraction of regular lipids increases the tilt modulus, with the maximum value achieved for a normal Cooke-Deserno bilayer void of bolalipids. But this is contradictory. For these bilayers, we don't need the tilt modulus in the first place.

Also, from the SI, I gathered that the authors have neglected the longest wavelength mode because it is not equilibrated. If this is indeed the case, it is a dangerous thing to do, because with a small membrane patch, this mode can very well change the general trend of the power spectrum. As a lot of other analyses in the manuscript rely on these measurements, I believe more elaboration is in order.

The authors have found that "there is a strong dependency of the bending rigidity on the membrane mean curvature of stiffer bolalipids." The effect is negative, with the membrane becoming less stiff at higher mean curvatures. Why is that? I would assume that with more flexible bolalipids, the possibility of reorganization into U-shaped chains should affect the bending rigidity more (as Figure 2E suggests). While for a stiff bolalipid, not much would change if you increase the mean curvature. This should be either a tilt effect, or have to do with asymmetry between the leaflets. But on the other hand, the tilt modulus is shown to decrease with increasing bolalipid rigidity. The authors get back to this issue only on page 10, when they consider U-shaped lipids in the inner and outer leaflets and write, "this suggested that an additional membrane-curving mechanism must be involved." But then again, in the Discussion, the authors write, "It is striking that membranes made from stiffer bolalipids showed a curvature-dependent bending modulus, which is a clear signature that bolalipid membranes exhibit plastic behavior during membrane reshaping," adding to the confusion.

This issue is repeated when the authors study nanoparticle uptake. They write: "to reconcile these seemingly conflicting observations we reason that the bending rigidity, similar to Figure 2F, is not constant but softens upon increasing membrane curvature, due to dynamic change in the ratio between bolalipids in straight and U-shaped conformation. Hence, bolalipid membranes show stroking plastic behavior as they soften during reshaping." But the softening effect that they refer to, as shown in Figure 4B, occurs for very stiff bolalipids, for which not much switching to U-shaped conformation should occur.

Another major issue is with what the authors refer to as the "effective temperature". While plotting phase diagrams for kT/eps value is absolutely valid, I'm not a fan of calling this effective temperature. It is a dimensionless quantity that scales linearly with temperature, but is not a temperature. It is usually called a "reduced temperature". Then the authors refer to their findings as studying the stability of archaeal membranes at high temperatures. I have to disagree because eps is not the only potential parameter in the simulations (there are at least space exclusion and angle-bending stiffnesses) so one cannot identify changing eps with changing the global simulation temperature. This only works when you have one potential parameter, like an LJ gas.

Minor issues:

As the authors have noted, the fact that the membrane curvature can change the ratio of U-shaped to straight bolalipids would render the curvature elasticity non-linear (though the term "plastic" should not be used, as this is still structurally reversible when the stress is removed. Technically, it is hypoelastic behavior, possibly with hysteresis.) With this in mind, when the authors use essentially linear elastic models for fluctuation analysis, they should make a comparison of maximum curvatures occurring in simulations with a range that causes significant changes in bolalipid conformational ratios.

The Introduction section of the manuscript is written with a biochemical approach, with very minor attention to the simulation works on this system. Some molecular dynamics works are only cited as existing previous work, without mentioning what has already been studied in archaeal membranes. While some information, like the binding of ESCRT proteins to archaeal membranes, though interesting, helps little to place the study within the discipline. The Introduction should be revised to show what has already been studied with simulations (as the authors mention in the Discussion) and how the presented research complements it.

The authors have been a bit loose with using the term "stability". I'd like to see the distinction in each case, as in "chemical/thermal/mechanical/conformational stability".

In the original Cooke-Deserno model, a so-called "poorman's angle-bending term" is used, which is essentially a bond-stretching term between the first and third particle. However, I notice the authors using the full harmonic angle-bending potential. This should be mentioned.

The analysis of energy of U-shaped lipids with the linear model E=c_0 + c_1 * k_bola is indeed very interesting. I am curious, can this also be corroborated with mean energy measurements? The minor issue is calling the source of the favorability of U-shaped lipids "entropic", while clearly an energetic contribution is found. The two conformations, for example, might differ in the interactions with the neighboring lipids.

The authors write in the Discussion, "In any case, our results indicate that membrane remodelling, such as membrane fission during membrane traffic, is much more difficult in bolalipid membranes [34]." Firstly, I'm not sure if studying the dependence of budding behavior on adhesion energy with nanoparticles is enough to make claims about membrane fission. Secondly, why is the 2015 paper by Markus Deserno cited here?

In the SI, where the measurement of the diffusion coefficient is discussed, the expression for D is missing the power 2 of displacement.

Where cargo uptake is discussed, the term "adsorption energy" is used. I think the more appropriate term would be "adhesion energy".

Typos:<br /> Page 1, paragraph 2: Adaption → Adaptation.<br /> Page 10, paragraph 1: Stroking → Striking.

Reviewer #1 (Public review):

G. Squiers et al. analyzed a previously reported CRISPR genetic screening dataset of engineered GLUT4 cell-surface presentation and identified the Commander complex subunit COMMD3 as being required for endosomal recycling of specific cargo proteins, such as transferrin receptor (TfR), to the cell surface. Through comparison of COMMD3-KO and other Commander subunit-KO cells, they demonstrated that the role of COMMD3 in mediating TfR recycling is independent of the Commander complex. Structural analysis and co-immunoprecipitation followed by mass spectrometry revealed that TfR recycling by COMMD3 relies on ARF1. COMMD3 interacts with ARF1 through its N-terminal domain (NTD) to stabilize ARF1. A mutation in the NTD of COMMD3, which disrupts the NTD-ARF1 interaction, failed to rescue cell surface TfR in COMMD3-KO cells. In conclusion, the authors assert that COMMD3 stabilizes ARF1 in a Commander complex-independent manner, which is essential for recycling specific cargo proteins from endosomes to the plasma membrane.

The conclusions of this paper are generally supported by data, but some validation experiments and control conditions should be included to strengthen the study.

(1) Commander-Independent Role of COMMD3:<br /> While the authors provided evidence to support the Commander-independent role of COMMD3-such as the absence of other Commander subunits in the CRISPR screen and not decreased COMMD3 levels in other subunit-KO cells-direct evidence is lacking. The mutation that specifically disrupts the COMMD3-ARF1 interaction could serve as a valuable tool to directly address this question.

(2) Role of ARF1 in Cargo Selection:<br /> The Commander-independent function of COMMD3 appears cargo-dependent and relies on ARF1's role in cargo selection. The authors should investigate whether KO/KD of ARF1 reduces cell surface levels of ITGA6 and TfR.

(3) Impact on TfR Stability:<br /> Figure 7D suggests that TfR protein levels are reduced in COMMD3-KO cells, potentially due to degradation caused by disrupted recycling. This raises the question of whether the observed reduction in cell surface TfR is due to impaired endosomal recycling or decreased total protein levels. The authors should quantify the ratio of cell surface protein to total protein for TfR, GLUT-SPR, and ITGA6 in COMMD3-KO cells.

Reviewer #2 (Public review):

Summary:

The Commander complex is a key player in endosomal recycling which recruits cargo proteins and facilitates the formation of tubulo-vesicular carriers. Squiers et al found COMMD3, a subunit of the Commander complex, could interact directly with ARF1 and regulate endosomal recycling.

Strengths:

Overall, this is a nice study that provides some interesting knowledge on the function of the Commander complex.

Weaknesses:

Several issues should be addressed.

(1) All existing data suggest that COMMD3 is a subunit of the Commander complex. Is there any evidence that COMMD3 can exist as a monomer?

(2) In Figure 9, the author emphasizes COMMD3-dependent cargo and Commander-dependent cargo. Can the authors speculate what distinguishes these two types of cargo? Do they contain sequence-specific motifs?

(3) What could be the possible mechanism underlying the observation that the knockout of COMMD3 results in larger early endosomes? How is the disruption of cargo retrieval related to the increase in endosome size?

Reviewer #3 (Public review):

Summary:

The manuscript by Squiers and colleagues uncovers a Commander-independent function for COMMD3 in endosomal recycling. The authors identified COMMD3 as a regulator of endosomal recycling for GLUT4-SPR through unbiased genetic screens. Subsequently, the authors performed COMMD3 knockout experiments to assess endosomal morphology and trafficking, demonstrating that COMMD3 regulates endosomal trafficking in a Commander-independent manner. Furthermore, the authors identified and confirmed that the N-terminal domain (NTD) of COMMD3 interacts with the GTPase Arf1. Using structure-guided mutations, they demonstrated that the COMMD3-Arf1 interaction is critical for the Commander-independent function of COMMD3.

Overall, the manuscript presents compelling evidence for a Commander-independent role of COMMD3, and I agree with the author's interpretations. The manuscript uses a combination of genetic screening, microscopy, and structural and biochemical approaches to examine and support the conclusions. This is an excellent and intriguing study and I have only a few comments and suggestions to improve the manuscript further.

Reviewer #1 (Public review):

In this study, Marocco and colleagues perform a deep characterization of the complex molecular mechanism guiding the recognition of a particular CELLmotif previously identified in hepatocytes in another publication. Having miR-155-3p with or without this CELLmotif as the initial focus, the authors identify 21 proteins differentially binding to these two miRNA versions. From there, they decided to focus on PCBP2. They elegantly demonstrate PCBP2 binding to the miR-155-3p WT version but not to the CELLmotif-mutated version. miR-155-3p contains a hEXOmotif identified in a different report, whose recognition is largely mediated by another RNA-binding protein called SYNCRIP. Interestingly, mutation of the hEXOmotif contained in miR-155-3p did not only blunt SYNCRIP binding but also PCBP2 binding despite the maintenance of the CELLmotif. This indicates that somehow SYNCRIP binding is a pre-requisite for PCBP2 binding. EMSA assay confirms that SYNCRIP is necessary for PCBP2 binding to miR-155-3p, while PCBP2 is not needed for SYNCRIP binding. The authors aim to extend these findings to other miRNAs containing both motifs. For that, they perform a small-RNA-Seq of EVs released from cells knockdown for PCBP2 versus control cells, identifying a subset of miRNAs whose expression either increases or decreases. The assumption is that those miRNAs containing PCBP2-binding CELLmotif should now be less retained in the cell and go more to extracellular vesicles, thus reflecting a higher EV expression. The specific subset of miRNAs having both the CELLmotif and hEXOmotif (9 miRNAs) whose expressions increase in EVs due to PCBP2 reduction is also affected by knocking-down SYNCRIP in the sense that reduction of SYNCRIP leads to lower EV sorting. Further experiments confirm that PCBP2 and SYNCRIP bind to these 9 miRNAs and that knocking down SYNCRIP impairs their EV sorting.

While the process studied in this work is novel and interesting, there are several aspects of this manuscript that should be improved:

(1) First of all, the nature of the CELLmotif and the hEXOmotif they are studying is extremely confusing. For the CELLmotif, the authors seem to focus on the Core CELLmotif AUU A/G in some experiments and the extended 7-nucleotide version in others. The fact that these CELLmotif and hEXOmotif are not shown anywhere in the figures (I mean with the full nucleotide variability described in the original publications) but only referred to in the text further complicates the identification of the motifs and the understanding of the experiments. Moreover, I am not convinced that the sequences they highlight in grey correspond to the original CELLmotif in all cases. For instance, in the miR-155-3p sequence, GCAUU is highlighted in grey. However, the original CELLmotif is basically 7-nucleotide long: C, A/U, G/A/C, U, U/A, C/G/A, A/U/C or CAGUUCA in its more abundant version. I can only see clearly the presence of the Core CELLmotif AUUA in miR-155-3p; however, the last A is not highlighted in grey. It is true that there is some nucleotide variability in each position in the originally reported CELLmotif by the authors in ref. 5 and the hEXOmotifs in ref. 7; however, not all nucleotides are equally likely to be found in each position. This fact seems to be not to be taken into account by the authors as they took basically any sequence with any length and almost sequence combination as valid CELLmotif. This means that I cannot identify the CELLmotif in many cases among the ones they highlight in grey. Instead, they should really focus on the most predominant CELLmotif sequence or, instead, take a reduced subset of "more abundant" CELLmotif versions from the ones that could fit in the originally described CELLmotif. Altogether, the authors need to explain much better what they have considered as the CELLmotif, what is the Core CELLmotif and what is hEXOmotif in each case and restrict to the most likely versions of the CELLmotif and hEXOmotif.

(2) Validation of EV isolation method: first, a large part of Supplementary Figure 2 is not readable. EV markers seem to be enriched in EV isolates; however, more EV and cell markers should be assayed to fulfill MISEV guidelines.

(3) A key variable is missing in Supplementary Figure 2, which is whether PCBP2 or SYNCRIP knockdowns impair EV secretion rates. A quantification of the nr vesicles released per cell upon knocking down each of these factors would be essential to rule out that any of the effects seen throughout the paper are not due to reduced or enhanced EV production rather than miRNA sorting/retention.

(4) The EMSA experiment is important to support their claims. Given the weak bands that are shown, the authors need to show all their replicates to convince the readers that it is reproducible.

(5) Although the bindings of SYNCRIP and PCBP2 to miR-155-3p and other miRNAs having both hEXOmotif and CELLmotif seem clear, the need for SYNCRIP binding to allow for PCBP2-mediated cellular retention is counterintuitive. What happens to those miRNAs that only contain a CELLmotif in terms of cellular retention and SYNCRIP dependence for cellular retention? In this regard, a representative miRNA (miR-31-3p) is analyzed in several experiments, showing that PCBP2 does not bind to it unless a hEXOmotif is introduced (Figure 3). However, this type of experiment should definitely be extended to other miRNAs containing only CELLmotif without hEXOmotif.

(6) Along the same line, I am missing another important experiment: the artificial incorporation of CELLmotif. For example, miR-365-2-5p lacks a CELLmotif but has a hEXOmotif. Does PCBP2 bind to this miRNA upon incorporation of CELLmotif? Does this lead now to enhanced cellular retention of this miRNA?

(7) What would be the net effect of knocking down both SYNCRIP and PCBP2 at the same time? Would this neutralize each other's effect or would the lack of one impose on the other? That could help in understanding the complex interplay between these two factors for mediating cellular retention and EV sorting.

(8) The authors have here a great opportunity to shed some light on an unclear aspect of miRNA EV sorting and cellular retention: whether the RBPs go together with the miRNA to the EVs or not. While the original paper describing hEXOmotif found SYNCRIP in EVs, another publication (Jeppesen et al, Cell 2019; PMID: 30951670) later found this RBP being very scarce in small EVs compared to cellular bodies or large EVs (Supplementary Tables 3 and 4 in that publication). Can the authors find SYNCRIP and PCBP2 in the EVs? Another important question would be the colocalization of these RBPs in the place where the miRNA selection is supposed to take place: in multivesicular bodies (MVB). Is there a colocalization of these RBPs with MVBs in the cell?

(9) In Figure 4C, the authors state in the text that CELLmotif and hEXOmotif are present in extra-seed region; however, for miR-181d-5p and miR-122-3p this is not true as their CELLmotifs fall within the seed sequence.

(10) The authors need to describe how they calculate the EV/cell ratio in gene expression in some experiments (for instance, Figures 1H, 4D, etc). Did they use any housekeeping gene for EV RNA content, the same RNA load, or some other alternative method to normalize EV vs cell RNA content?

(11) I would suggest that the authors speculate a bit in the discussion section on how the interaction between PCBP2 and SYNCRIP takes place. Do they contain any potential interacting domain? The binding of one to the miRNA would impose a topological interference on the binding of the other?

Reviewer #2 (Public review):

Summary:

The author of this manuscript aimed to uncover the mechanisms behind miRNA retention within cells. They identified PCBP2 as a crucial factor in this process, revealing a novel role for RNA-binding proteins. Additionally, the study discovered that SYNCRIP is essential for PCBP2's function, demonstrating the cooperative interaction between these two proteins. This research not only sheds light on the intricate dynamics of miRNA retention but also emphasizes the importance of protein interactions in regulating miRNA behavior within cells.

Strengths:

This paper makes important progress in understanding how miRNAs are kept inside cells. It identifies PCBP2 as a key player in this process, showing a new role for proteins that bind RNA. The study also finds that SYNCRIP is needed for PCBP2 to work, highlighting how these proteins work together. These discoveries not only improve our knowledge of miRNA behavior but also suggest new ways to develop treatments by controlling miRNA locations to influence cell communication in diseases. The use of liver cell models and thorough experiments ensures the results are reliable and show their potential for RNA-based therapies

Weaknesses:

Despite its strengths, the manuscript has several notable limitations. The study's exclusive focus on hepatocytes limits the applicability of the findings to other cell types and physiological contexts. While the interaction between PCBP2 and SYNCRIP is well-characterized, the manuscript lacks detailed insights into the structural basis of this interaction and the dynamic regulation of their binding. The generalization of the findings to a broader spectrum of miRNAs and RNA-binding proteins (RBPs) remains underexplored, leaving gaps in understanding the full scope of miRNA compartmentalization.

Furthermore, the therapeutic implications of these findings, though promising, are not directly connected to specific disease models or clinical scenarios, reducing their immediate translational impact. The manuscript would also benefit from a deeper discussion of potential upstream regulators of PCBP2 and SYNCRIP and the influence of cellular or environmental factors on their activity. Additionally, it is important to note that SYNCRIP has already been recognized as a major regulator of miRNA loading in extracellular vesicles (EVs). However, the purity of EVs is a concern, as the author only performed crude extraction methods without further purification using an iodixanol density gradient. The study also lacks in vivo evidence of PCBP2's role in exosomal miRNA export.

Joint Public Review:

In this manuscript, Weiguang Kong et al. investigate the role of immunoglobulin M (IgM) in antiviral defense in the teleost largemouth bass (Micropterus salmoides). The study employs an IgM depletion model, viral infection experiments, and complementary in vitro assays to explore the role of IgM in systemic and mucosal immunity. The authors conclude that IgM is crucial for both systemic and mucosal antiviral defense, highlighting its role in viral neutralization through direct interactions with viral particles. The study's findings have theoretical implications for understanding immunoglobulin function across vertebrates and practical relevance for aquaculture immunology.

Strengths:

The manuscript applies multiple complementary approaches, including IgM depletion, viral infection models, and histological and gene expression analyses, to address an important immunological question. The study challenges established views that IgT is primarily responsible for mucosal immunity, presenting evidence for a dual role of IgM at both systemic and mucosal levels. If validated, the findings have evolutionary significance, suggesting the conserved role of IgM as an antiviral effector across jawed vertebrates for over 500 million years. The practical implications for vaccine strategies targeting mucosal immunity in fish are noteworthy, addressing a key challenge in aquaculture.

Weaknesses:

Several conceptual and technical issues undermine the strength of the evidence:

Monoclonal Antibody (MoAb) Validation: The study relies heavily on a monoclonal antibody to deplete IgM, but its specificity and functionality are not adequately validated. The epitope recognized by the antibody is not identified, and there is no evidence excluding cross-reactivity with other isotypes. Mass spectrometry, immunoprecipitation, or Western blot analysis using tissue lysates with varying immunoglobulin expression levels would strengthen the claim of IgM-specific depletion.

IgM Depletion Kinetics: The rapid depletion of IgM from serum and mucus (within one day) is unexpected and inconsistent with prior literature. Additional evidence, such as Western blot analyses comparing treated and control fish, is necessary to confirm this finding.

Novelty of Claims: The manuscript claims a novel role for IgM in viral neutralization, despite extensive prior literature demonstrating this role in fish. This overstatement detracts from the contribution of the study and requires a more accurate contextualization of the findings.

Support for IgM's Crucial Role: The mortality data following IgM depletion do not fully support the claim that IgM is indispensable for antiviral defense. The survival of IgM-depleted fish remains high (75%) compared to non-primed controls (~50%), suggesting that other immune components may compensate for IgM loss.

Presentation of IgM Depletion Model: The study describes the IgM depletion model as novel, although similar models have been previously published (e.g., Ding et al., 2023). This should be clarified to avoid overstating its novelty.

While the manuscript attempts to address an important question in teleost immunology, the current evidence is insufficient to fully support the authors' conclusions. Addressing the validation of the monoclonal antibody, re-evaluating depletion kinetics, and tempering claims of novelty would strengthen the study's impact. The findings, if rigorously validated, have important implications for understanding the evolution of vertebrate immunity and practical applications in fish health management.

This work is of interest to immunologists, evolutionary biologists, and aquaculture researchers. The methodological framework, once validated, could be valuable for studying immunoglobulin function in other non-model organisms and for developing targeted vaccine strategies. However, the current weaknesses limit its broader applicability and impact.

Reviewer #1 (Public review):

Summary:

PRMT1 overexpression is linked to poor survival in cancers, including acute megakaryocytic leukemia (AMKL). This manuscript describes the important role of PRMT1 in the metabolic reprograming in AMKL. In a PRMT1-driven AMKL model, only cells with high PRMT1 expression induced leukemia, which was effectively treated with the PRMT1 inhibitor MS023. PRMT1 increased glycolysis, leading to elevated glucose consumption, lactic acid accumulation, and lipid buildup while downregulating CPT1A, a key regulator of fatty acid oxidation. Treatment with 2-deoxy-glucose (2-DG) delayed leukemia progression and induced cell differentiation, while CPT1A overexpression rescued cell proliferation under glucose deprivation. Thus, PRMT1 enhances AMKL cell proliferation by promoting glycolysis and suppressing fatty acid oxidation.

Strengths:

This study highlights the clinical relevance of PRMT1 overexpression with AMKL, identifying it as a promising therapeutic target. A key novel finding is the discovery that only AMKL cells with high PRMT1 expression drive leukemogenesis, and this PRMT1-driven leukemia can be effectively treated with the PRMT1 inhibitor MS023. The work provides significant metabolic insights, showing that PRMT1 enhances glycolysis, suppresses fatty acid oxidation, downregulates CPT1A, and promotes lipid accumulation, which collectively drive leukemia cell proliferation. The successful use of the glucose analogue 2-deoxy-glucose (2-DG) to delay AMKL progression and induce cell differentiation underscores the therapeutic potential of targeting PRMT1-related metabolic pathways. Furthermore, the rescue experiment with ectopic Cpt1a expression strengthens the mechanistic link between PRMT1 and metabolic reprogramming. The study employs robust methodologies, including Seahorse analysis, metabolomics, FACS analysis, and in vivo transplantation models, providing comprehensive and well-supported findings. Overall, this work not only deepens our understanding of PRMT1's role in leukemia progression but also opens new avenues for targeting metabolic pathways in cancer therapy.

Weaknesses:

This study, while significant, has some limitations.

(1) The findings rely heavily on a single AMKL cell line, with no validation in patient-derived samples to confirm clinical relevance or even another type of leukemia line. Adding the discussion of PRMT1's role in other leukemia types will increase the impact of this work.

(2) The observed heterogeneity in Prmt1 expression is noted but not further investigated, leaving gaps in understanding its broader implications.

(3) Some figures and figure legends didn't include important details or had not matching information. For example,<br /> • Figure 2D, E, F, I (wrong label with D), p-value was not shown. Panel I figure legend is missing.<br /> • Figure 6E, F, p value was not shown.<br /> • Line 272-278, figures should be Figures 7 D-F.

(4) Some wording is not accurate, such as line 80 "the elevated level of PRMT1 maintains the leukemic stem cells", the study is using the cell line, not leukemia stem cells.

(5) In the disease model, histopathology of blood, spleen, and BM should be shown.

(6) Can MS023 treatment reverse the metabolic changes in PRMT1 overexpression AMKL cells?

(7) It would be helpful if a summary graph is provided at the end of the manuscript.

Reviewer #2 (Public review):

Summary:

The manuscript explores the role of PRMT1 in AMKL, highlighting its overexpression as a driver of metabolic reprogramming. PRMT1 overexpression enhances the glycolytic phenotype and extracellular acidification by increasing lactate production in AMKL cells. Treatment with the PRMT1 inhibitor MS023 significantly reduces AMKL cell viability and improves survival in tumor-bearing mice. Intriguingly, PRMT1 overexpression also increases mitochondrial number and mtDNA content. High PRMT1-expressing cells demonstrate the ability to utilize alternative energy sources dependent on mitochondrial energetics, in contrast to parental cells with lower PRMT1 levels.

Strengths:

This is a conceptually novel and important finding as PRMT1 has never been shown to enhance glycolysis in AMKL, and provides a novel point of therapeutic intervention for AMKL.

Weaknesses:

(1) The manuscript lacks detailed molecular mechanisms underlying PRMT1 overexpression, particularly its role in enhancing survival and metabolic reprogramming via upregulated glycolysis and diminished oxidative phosphorylation (OxPhos). The findings primarily report phenomena without exploring the reasons behind these changes.

(2) The article shows that PRMT1 overexpression leads to augmented glycolysis and low reliance on the OxPhos. However, the manuscript also shows that PMRT1 overexpression leads to increased mitochondrial number and mitochondrial DNA content and has an elevated NADPH/NAD+ ratio. Further, these overexpressing cells have the ability to better survive on alternative energy sources in the absence of glucose compared to low PMRT1-expressing parental cells. Surprisingly, the seashores assay in PRMT1 overexpressing cells showed no further enhancement in the ECAR after adding mitochondrial decoupler FCCP, indicating the truncated mitochondrial energetics. These results are contradicting and need a more detailed explanation in the discussion.

(3) How was disease penetrance established following the 6133/PRMT1 transplant before MS023 treatment?

(4) The 6133/PRMT1 cells show elevated glycolysis compared to parental 6133; why did the author choose the 6133 cells for treatment with the MS023 and ECAR assay (Fig.3 b)? The same is confusing with OCR after inhibitor treatment in 6133 cells; the figure legend and results section description are inconsistent.

(5) The discussion is too brief and incoherent and does not adequately address key findings. A comprehensive rewrite is necessary to improve coherence and depth.

(6) The materials and methods section lacks a description of statistical analysis, and significance is not indicated in several figures (e.g., Figures 1C, D, F; Figures 2D, E, F, I). Statistical significance must be consistently indicated. The methods section requires more detailed descriptions to enable replication of the study's findings.

(7) Figures are hazy and unclear. They should be replaced with high-resolution images, ensuring legible text and data.

(8) Correct the labeling in Figure 2I by removing the redundant "D."

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 a role in mediating information transfer across hippocampal-cortical networks for memory processes. SWRs are associated with the reactivation of representations of previous experiences, and associated reactivation in hippocampal and cortical regions has been proposed to have a role in memory formation, retrieval, planning, and memory-guided behavior. This study focuses on 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 the 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 throughout 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) While the study is able to extend previous findings that SWR-modulated PFC neurons have significantly lower spatial tuning that SWR-unmodulated neurons, the evidence presented does not support the main conclusion of the paper that only the unmodulated neurons are involved in spatial tuning and signaling upcoming choice, implying that SWR-modulated neurons are not involved in predicting upcoming choice, as stated in the abstract. This conclusion makes a categorical distinction between two neuronal populations, that SWR-modulated neurons are involved and SWR-unmodulated are not involved in predicting upcoming choice, which requires evidence that clearly shows this absolute distinction. However, in the analyses showing non-local population decoding in PFC for predicting upcoming choice, the results show that SWR-unmodulated neurons have higher firing rates than SWR-modulated neurons, which is not a categorical distinction. Higher firing rates do not imply that only SWR-unmodulated neurons are contributing to the non-local decoding. They may contribute more than SWR-modulated neurons, but there are no follow-up analyses to assess the contribution of the two sub-populations to non-local decoding.

(2) Further, the results show that during non-local representations of the hippocampus of the upcoming options, SWR-excited PFC neurons were more active during hippocampal representations of the upcoming choice, and SWR-inhibited PFC neurons were less active during hippocampal representations of the alternative choice. This clearly suggests that SWR-modulated neurons are involved in signaling upcoming choice, at least during hippocampal non-local representations, which contradicts the main conclusion of the paper.

(3) Similarly, one of the analyses shows that PFC nonlocal representations show no preference for hippocampal SWRs or hippocampal theta phase. However, the examples shown for non-local representations clearly show that these decodes occur prior to the start of the trajectory, or during running on the central zone or start arm. The time period of immobility prior to the start arm running will have a higher prevalence of SWRs and that during running will have a higher prevalence of theta oscillations and theta sequences, so non-local decoded representations have to sub-divided according to these known local-field potential phenomena for this analysis, which is not followed.

(4) The primary phenomenon that the manuscript relies on is the modulation of PFC neurons by hippocampal SWRs, so it is necessary to perform the PFC population decoding analyses during SWRs (or examine non-local decoding that occurs specifically during SWRs), as reported in previous studies of PFC reactivation during SWRs, to see if there is any distinction between modulated and unmodulated neurons in this reactivation. Even in the case of independent PFC reactivation as reported by one study, this PFC reactivation was still reported to occur during hippocampal SWRs, therefore decoding during SWRs has to be examined. Similarly, the phenomenon of theta cycle skipping is related to theta sequence representations, so decoding during PFC and hippocampal theta sequences has to be examined before coming to any conclusions.

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:

(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).

(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?

(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).<br /> 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.<br /> 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.<br /> Although some previous work is cited, the current narrative of the results section may lead the reader to think that these results are new, which I think is unfair. Previous evidence of the same phenomena should be cited all along the results and what is new and/or different from previous results should be clearly stated and discussed. Pure replications of previous works may actually just be supplementary figures. It is not fair that the titles of paragraphs and main figures correspond to notions that are well established in the literature (e.g., Figure 2, 2nd paragraph of results, etc.).<br /> d) My opinion is that, overall, the paper gives the impression of being somewhat rushed and lacking attention to detail. Many figure panels are difficult to understand due to incomplete legends and visualizations with tiny, indistinguishable details. Moreover, some previous works are not correctly cited. I tried to make a list of everything I spotted below.

Reviewer #1 (Public review):

This study presents evidence that a special group of place cells, those tuned to fast-gamma oscillations, play a key role in theta sequence development. How theta sequences are formed and developed during experience is an important question, because these sequences have been implicated in several cognitive functions of place cells, including memory-guided spatial navigation. The revised version of this paper has been significantly improved. Major concerns in the previous round of review on technical and conceptual aspects of the relationship between gamma oscillations and theta sequences are addressed. The main conclusion is supported by the data presented.

Reviewer #2 (Public review):

This manuscript addresses an important question which has not yet been solved in the field, what is the contribution of different gamma oscillatory inputs to the development of "theta sequences" in the hippocampal CA1 region. Theta sequences have received much attention due to their proposed roles in encoding short-term behavioral predictions, mediating synaptic plasticity, and guiding flexible decision making. Gamma oscillations in CA1 offer a readout of different inputs to this region and have been proposed to synchronize neuronal assemblies and modulate spike timing and temporal coding. However, the interactions between these two important phenomena have not been sufficiently investigated. The authors conducted place cell and local field potential (LFP) recordings in the CA1 region of rats running on a circular track. They then analyzed the phase locking of place cell spikes to slow and fast gamma rhythms, the evolution of theta sequences during behavior and the interaction between these two phenomena. They found that place cell with the strongest modulation by fast gamma oscillations were the most important contributors to the early development of theta sequences and that they also displayed a faster form of phase precession within slow gamma cycles nested with theta. The results reported are interesting and support the main conclusions of the authors. However, the manuscript needs significant improvement in several aspects regarding data analysis, description of both experimental and analytical methods and alternative interpretations, as I detail below.

• The experimental paradigm and recordings should be explained at the beginning of the Results section. Right now, there is no description whatsoever which makes it harder to understand the design of the study.<br /> • An important issue that needs to be addressed is the very small fraction of CA1 cells phased-locked to slow gamma rhythms (3.7%). This fraction is much lower than in many previous studies, that typically report it in the range of 20-50 %. However, this discrepancy is not discussed by the authors. This needs to be explained and additional analysis considered. One analysis that I would suggest, although there are also other valid approaches, is to, instead of just analyze the phase locking in two discrete frequency bands, to compute the phase locking will all LFP frequencies from 25-100 Hz. This will offer a more comprehensive and unbiased view of the gamma modulation of place cell firing. Alternative metrics to mean vector length that are less sensitive to firing rates, such as pairwise phase consistency index (Vinck et a., Neuroimage, 2010), could be implemented. This may reveal whether the low fraction of phase locked cells could be due to a low number of spikes entering the analysis.<br /> • From the methods, it is not clear to me whether the reference LFP channel was consistently selected to be a different one that where the spikes analyzed were taken. This is the better practice to reduce the contribution of spike leakage that could substantially inflate the coupling with faster gamma frequencies. These analyses need to be described in more detail.<br /> • The initial framework of the authors of classifying cells into fast gamma and not fast gamma modulated implies a bimodality that may be artificial. The authors should discuss the nuances and limitations of this framework. For example, several previous work has shown that the same place cell can couple to different gamma oscillations (e.g., Lastoczni et al., Neuron, 2016; Fernandez-Ruiz et al., Neuron, 2017; Sharif et al., Neuron,2021).<br /> • It would be useful to provide a more through characterization of the physiological properties of FG and NFG cells, as this distinction is the basis of the paper. Only very little characterization of some place cell properties is provided in Figure 5. Important characteristics that should be very feasible to compare include average firing rate, burstiness, estimated location within the layer (i.e., deep vs superficial sublayers) and along the transverse axis (i.e., proximal vs distal), theta oscillation frequency, phase precession metrics (given their fundamental relationship with theta sequences), etc.<br /> • It is not clear to me how the analysis in Figure 6 was performed. In Fig. 6B I would think that the grey line should connect with the bottom white dot in the third panel, which would the interpretation of the results.

Comments on revisions:

The authors have conducted new analysis to address the issues I and the other reviewers raised in our original revision. As a result, the revised manuscript has been substantially improved.

Reviewer #1 (Public review):

Summary:

The manuscript by Hall et al reports a genome-wide map of supercoiling in yeast using psoralen as a probe that intercalates more effectively into underwound DNA and can then be fixed in place by UV-cross-linking. Sites of cross-linking are revealed by exonuclease digestion and sequencing. Cross-linking is compared with samples that are first fixed with formaldehyde, permeabilized, digested with Dpn II to release unrestrained torsion, and then crosslinked. The authors promote this "zero-torsion" approach as an improvement that corrects for nucleosomes (or binding by other macromolecules) that mask psoralen binding. The investigators then examine patterns of psoralen binding (and hence supercoiling) that are associated with promoter strength, promoter type (sequence-specific transcription factor dependent, insulator associated, or general TFs only) and gene length.

Strengths:

This is an interesting paper that reports an approach that reveals some new information about the relationship between torsional stress and gene activity in the yeast genome. The method is logical and interesting and provides evidence that spread of torsional stress through the genome is regulated.

Weaknesses:

The analysis is not entirely novel, and I believe that more valuable information can be culled from these datasets than is reported here.

Reviewer #2 (Public review):

Summary:

This study describes a novel method for mapping torsional stress in the genome of Saccharomyces cerevisiae using trimethylpsoralen (TMP). It introduces a procedure to establish a zero-torsion baseline while preserving the chromatin state by treating cells with formaldehyde before releasing torsion with restriction enzyme digestion.

This approach allows foer more accurate differentiation between torsional stress effects and accessibility effects in the psoralen signal. The results confirm that psoralen crosslinking is strongly affected by accessibility of the DNA and to a much more limited extent by the torsional stress of the DNA. Subtracting the baseline signal (no torsion) from the total signal allows detecting torsional stress, although TMP accessibility is still affecting the read out. The authors confirm the validity of the method by studying torsional stress in dependence of transcription levels, gene length and relative gene orientation. They propose that torsional stress may play a role in recruiting topoisomerases and regulating 3D genome architecture via cohesin. They also suggest that transcription factor binding might insulate negative supercoiling originated form transcription of neighboring divergent genes.

Strengths:

This paper offers a potentially interesting tool for future work.

Weaknesses:

The signal-to-background ratio, which represents the torsional fraction, appears to be quite limited relative to the overall signal (roughly 20x less, according to the scales in figs 2a and 2b, raising concerns about the robustness of the conclusions. It is clear from these figures, for instance, that a non-negligible fraction of the remaining signal is still dependent on DNA accessibility, revealing the nucleosomes footprints in spite of the fact that subtracting the zero-torsion signal should theoretically hinder the accessibility component. Because of this, some of the conclusions might be flawed, in that what is attributed to torsional stress might in reality be due, partially or fully, to accessibility issues.

Specific points:

Lines 226-227: "rotation may be more restricted with a lengthening in the RNA transcript, which is known to be associated with large machinery, such as spliceosomes". This argument is not appropriate to correlate torsional stress with gene length. Spliced genes are rare and generally short in yeast, generally in ribosomal proteins genes.

Lines 256-257 In discussing that torsional stress must hinder Pol II progression, the authors write: "Pol II has a minimal presence in the intergenic region between divergent genes and is enriched in the intergenic region between convergent genes, consistent with a previous finding that after termination, Pol II tends to remain on the DNA downstream of the terminator". The connection between Pol II distribution and torsional stress is unclear. Pol ii is depleted at promoters and is enriched at at 3'-end of convergent genes most likely because this ChIP signal is the sum of signals from the two convergent genes. The fact that positive torsional stress is observed in these region does not mean that polymerases accumulate because the torsional stress hinder Pol II progression. To claim elongation defects the authors should repeat the same analysis with stranded data (e.g. NET-seq or CRAC) and assess if polymerases transcribing these regions accumulate more when facing convergent genes compared to tandem genes. The claim that after termination the Pol II tends to remain on the DNA appears to be meaningless - the authors probably mean after RNA processing.

Lines 275-277: "These data provide evidence that the (+) supercoiling generated by transcription may facilitate genome folding in coordination with other participating proteins". This is an overstatement. It is known that cohesins accumulate between convergent genes. The fact that there is torsional stress in the same position does not imply that supercoiling participates in genome folding. These could be independent events, or even, supercoiling might depend on cohesins

Lines 289-290 "torsion generated from one gene can impact the expression of its neighboring gene, consistent with previous findings that the expression of these genes is coupled" the existence of negative torsional stress in a common intergenic region for two genes does not imply that torsion is causally associated to gene expression coupling

Lines 291-292: "Another large class of S. cerevisiae promoters (termed "TFO") are regulated by insulator ssTFs, such as Reb1 and Abf1, which decouple interactions between neighbouring genes" In these cases and others that depend on an activator binding the authors detect a region of accessibility interrupted by a valley, which they interpret as a topological insulator. However, the valley might be generated because of decreased TMP accessibility due to of TF binding.

Reviewer #3 (Public review):

Summary:

The authors describe a new method for measuring DNA torsion in cells using the photoactivatable intrastrand cross-linker trimethyl psoralen (TMP). However, their method differs from previous TMP-based torsion mapping methods by comparing formaldehyde cross-linked and torsionally trapped chromatin to torsion-relieved (zero-torsion) chromatin in parallel. Comparison between the two datasets reveals a very slight difference, but enough to provide extremely high resolution genome-wide maps of torsion in the yeast genome. This direct comparison of the two maps confirms that blockage of TMP binding by nucleosomes and some DNA-binding proteins from TMP intercalation is a major complication of previous methods, and analysis of the data provides a glimpse of chromatin-based processes from within the DNA gyre.

Strengths:

In addition to providing direct evidence for the twin-supercoiled domain model and for torsional effects at transcription start (TSS) and end (TES) sites, the authors' analyses reveal some novel features of yeast higher-order structure. These include the cohesin-dependent anchoring of DNA loops at sites of positive supercoiling and the insulation of torsion between closely spaced divergent genes by general transcription factors, which implies that these factors resist free rotation. The fact that method should be generalizable to complex eukaryotic cells with large genomes, and the implications for understanding how torsion impacts transcription and gene regulation will be of substantial interest to a broad community.

Weaknesses:

No serious weaknesses.

Reviewer #1 (Public review):

Summary:

This paper concerns mechanisms of foraging behavior in C. elegans. Upon removal from food, C. elegans first executes a stereotypical local search behavior in which it explores a small area by executing many random, undirected reversals and turns called "reorientations." If the worm fails to find food, it transitions to a global search in which it explores larger areas by suppressing reorientations and executing long forward runs (Hills et al., 2004). At the population level, the reorientation rate declines gradually. Nevertheless, about 50% of individual worms appear to exhibit an abrupt transition between local and global search, which is evident as a discrete transition from high to low reorientation rate (Lopez-Cruz et al., 2019). This observation has given rise to the hypothesis that local and global search correspond to separate internal states with the possibility of sudden transitions between them (Calhoun et al., 2014). The main conclusion of the paper is that it is not necessary to posit distinct internal states to account for discrete transitions from high to low reorientation rates. On the contrary, discrete transitions can occur simply because of the stochastic nature of the reorientation behavior itself.

Strengths:

The strength of the paper is the demonstration that a more parsimonious model explains abrupt transitions in the reorientation rate.

Weaknesses:

(1) Use of the Gillespie algorithm is not well justified. A conventional model with a fixed dt and an exponentially decaying reorientation rate would be adequate and far easier to explain. It would also be sufficiently accurate - given the appropriate choice of dt - to support the main claims of the paper, which are merely qualitative. In some respects, the whole point of the paper - that discrete transitions are an epiphenomenon of stochastic behavior - can be made with the authors' version of the model having a constant reorientation rate (Figure 2f).

(2) In the manuscript, the Gillespie algorithm is very poorly explained, even for readers who already understand the algorithm; for those who do not it will be essentially impossible to comprehend. To take just a few examples: in Equation (1), omega is defined as reorientations instead of cumulative reorientations; it is unclear how (4) follows from (2) and (3); notation in (5), line 133, and (7) is idiosyncratic. Figure 1a does not help, partly because the notation is unexplained. For example, what do the arrows mean, what does "*" mean?

(3) In the model, the reorientation rate dΩ⁄dt declines to zero but the empirical rate clearly does not. This is a major flaw. It would have been easy to fix by adding a constant to the exponentially declining rate in (1). Perhaps fixing this obvious problem would mitigate the discrepancies between the data and the model in Figure 2d.

(4) Evidence that the model fits the data (Figure 2d) is unconvincing. I would like to have seen the proportion of runs in which the model generated one as opposed to multiple or no transitions in reorientation rate; in the real data, the proportion is 50% (Lopez). It is claimed that the "model demonstrated a continuum of switching to non-switching behavior" as seen in the experimental data but no evidence is provided.

(5) The explanation for the poor fit between the model and data (lines 166-174) is unclear. Why would externally triggered collisions cause a shift in the transition distribution?

(6) The discussion of Levy walks and the accompanying figure are off-topic and should be deleted.

Reviewer #2 (Public review):

Summary:

In this study, the authors build a statistical model that stochastically samples from a time-interval distribution of reorientation rates. The form of the distribution is extracted from a large array of behavioral data, and is then used to describe not only the dynamics of individual worms (including the inter-individual variability in behavior), but also the aggregate population behavior. The authors note that the model does not require assumptions about behavioral state transitions, or evidence accumulation, as has been done previously, but rather that the stochastic nature of behavior is "simply the product of stochastic sampling from an exponential function".

Strengths:

This model provides a strong juxtaposition to other foraging models in the worm. Rather than evoking a behavioral transition function (that might arise from a change in internal state or the activity of a cell type in the network), or evidence accumulation (which again maps onto a cell type, or the activity of a network) - this model explains behavior via the stochastic sampling of a function of an exponential decay. The underlying model and the dynamics being simulated, as well as the process of stochastic sampling, are well described and the model fits the exponential function (Equation 1) to data on a large array of worms exhibiting diverse behaviors (1600+ worms from Lopez-Cruz et al). The work of this study is able to explain or describe the inter-individual diversity of worm behavior across a large population. The model is also able to capture two aspects of the reorientations, including the dynamics (to switch or not to switch) and the kinetics (slow vs fast reorientations). The authors also work to compare their model to a few others including the Levy walk (whose construction arises from a Markov process) to a simple exponential distribution, all of which have been used to study foraging and search behaviors.

Weaknesses:

This manuscript has two weaknesses that dampen the enthusiasm for the results. First, in all of the examples the authors cite where a Gillespie algorithm is used to sample from a distribution, be it the kinetics associated with chemical dynamics, or a Lotka-Volterra Competition Model, there are underlying processes that govern the evolution of the dynamics, and thus the sampling from distributions. In one of their references, for instance, the stochasticity arises from the birth and death rates, thereby influencing the genetic drift in the model. In these examples, the process governing the dynamics (and thus generating the distributions from which one samples) is distinct from the behavior being studied. In this manuscript, the distribution being sampled is the exponential decay function of the reorientation rate (lines 100-102). This appears to be tautological - a decay function fitted to the reorientation data is then sampled to generate the distributions of the reorientation data. That the model performs well and matches the data is commendable, but it is unclear how that could not be the case if the underlying function generating the distribution was fit to the data.

The second weakness is somewhat related to the first, in that absent an underlying mechanism or framework, one is left wondering what insight the model provides. Stochastic sampling a function generated by fitting the data to produce stochastic behavior is where one ends up in this framework, and the authors indeed point this out: "simple stochastic models should be sufficient to explain observably stochastic behaviors." (Line 233-234). But if that is the case, what do we learn about how the foraging is happening? The authors suggest that the decay parameter M can be considered a memory timescale; which offers some suggestion, but then go on to say that the "physical basis of M can come from multiple sources". Here is where one is left for want: The mechanisms suggested, including loss of sensory stimuli, alternations in motor integration, ionotropic glutamate signaling, dopamine, and neuropeptides are all suggested: these are basically all of the possible biological sources that can govern behavior, and one is left not knowing what insight the model provides. The array of biological processes listed is so variable in dynamics and meaning, that their explanation of what governs M is at best unsatisfying. Molecular dynamics models that generate distributions can point to certain properties of the model, such as the binding kinetics (on and off rates, etc.) as explanations for the mechanisms generating the distributions, and therefore point to how a change in the biology affects the stochasticity of the process. It is unclear how this model provides such a connection, especially taken in aggregate with the previous weakness.

Providing a roadmap of how to think about the processes generating M, the meaning of those processes in search, and potential frameworks that are more constrained and with more precise biological underpinning (beyond the array of possibilities described) would go a long way to assuaging the weaknesses.

Reviewer #3 (Public review):

Summary:

This intriguing paper addresses a special case of a fundamental statistical question: how to distinguish between stochastic point processes that derive from a single "state" (or single process) and more than one state/process. In the language of the paper, a "state" (perhaps more intuitively called a strategy/process) refers to a set of rules that determine the temporal statistics of the system. The rules give rise to probability distributions (here, the probability for turning events). The difficulty arises when the sampling time is finite, and hence, the empirical data is finite, and affected by the sampling of the underlying distribution(s). The specific problem being tackled is the foraging behavior of C. elegans nematodes, removed from food. Such foraging has been studied for decades, and described by a transition over time from 'local'/'area-restricted' search'(roughly in the initial 10-30 minutes of the experiments, in which animals execute frequent turns) to 'dispersion', or 'global search' (characterized by a low frequency of turns). The authors propose an alternative to this two-state description - a potentially more parsimonious single 'state' with time-changing parameters, which they claim can account for the full-time course of these observations.

Figure 1a shows the mean rate of turning events as a function of time (averaged across the population). Here, we see a rapid transient, followed by a gradual 4-5 fold decay in the rate, and then levels off. This picture seems consistent with the two-state description. However, the authors demonstrate that individual animals exhibit different "transition" statistics (Figure 1e) and wish to explain this. They do so by fitting this mean with a single function (Equations 1-3).

Strengths:

As a qualitative exercise, the paper might have some merit. It demonstrates that apparently discrete states can sometimes be artifacts of sampling from smoothly time-changing dynamics. However, as a generic point, this is not novel, and so without the grounding in C. elegans data, is less interesting.

Weaknesses:

(1) The authors claim that only about half the animals tested exhibit discontinuity in turning rates. Can they automatically separate the empirical and model population into these two subpopulations (with the same method), and compare the results?

(2) The equations consider an exponentially decaying rate of turning events. If so, Figure 2b should be shown on a semi-logarithmic scale.

(3) The variables in Equations 1-3 and the methods for simulating them are not well defined, making the method difficult to follow. Assuming my reading is correct, Omega should be defined as the cumulative number of turning events over time (Omega(t)), not as a "turn" or "reorientation", which has no derivative. The relevant entity in Figure 1a is apparently , i.e. the mean number of events across a population which can be modelled by an expectation value. The time derivative would then give the expected rate of turning events as a function of time.

(4) Equations 1-3 are cryptic. The authors need to spell out up front that they are using a pair of coupled stochastic processes, sampling a hidden state M (to model the dynamic turning rate) and the actual turn events, Omega(t), separately, as described in Figure 2a. In this case, the model no longer appears more parsimonious than the original 2-state model. What then is its benefit or explanatory power (especially since the process involving M is not observable experimentally)?

(5) Further, as currently stated in the paper, Equations 1-3 are only for the mean rate of events. However, the expectation value is not a complete description of a stochastic system. Instead, the authors need to formulate the equations for the probability of events, from which they can extract any moment (they write something in Figure 2a, but the notation there is unclear, and this needs to be incorporated here).

(6) Equations 1-3 have three constants (alpha and gamma which were fit to the data, and M0 which was presumably set to 1000). How does the choice of M0 affect the results?

(7) M decays to near 0 over 40 minutes, abolishing omega turns by the end of the simulations. Are omega turns entirely abolished in worms after 30-40 minutes off food? How do the authors reconcile this decay with the leveling of the turning rate in Figure 1a?

(8) The fit given in Figure 2b does not look convincing. No statistical test was used to compare the two functions (empirical and fit). No error bars were given (to either). These should be added. In the discussion, the authors explain the discrepancy away as experimental limitations. This is not unreasonable, but on the flip side, makes the argument inconclusive. If the authors could model and simulate these limitations, and show that they account for the discrepancies with the data, the model would be much more compelling. To do this, I would imagine that the authors would need to take the output of their model (lists of turning times) and convert them into simulated trajectories over time. These trajectories could be used to detect boundary events (for a given size of arena), collisions between individuals, etc. in their simulations and to see their effects on the turn statistics.

(9) The other figures similarly lack any statistical tests and by eye, they do not look convincing. The exception is the 6 anecdotal examples in Figure 2e. Those anecdotal examples match remarkably closely, almost suspiciously so. I'm not sure I understood this though - the caption refers to "different" models of M decay (and at least one of the 6 examples clearly shows a much shallower exponential). If different M models are allowed for each animal, this is no longer parsimonious. Are the results in Figure 2d for a single M model? Can Figure 2e explain the data with a single (stochastic) M model?

(10) The left axes of Figure 2e should be reverted to cumulative counts (without the normalization).

(11) The authors give an alternative model of a Levy flight, but do not give the obvious alternative models:<br /> a) the 1-state model in which P(t) = alpha exp (-gamma t) dt (i.e. a single stochastic process, without a hidden M, collapsing equations 1-3 into a single equation).<br /> b) the originally proposed 2-state model (with 3 parameters, a high turn rate, a low turn rate, and the local-to-global search transition time, which can be taken from the data, or sampled from the empirical probability distributions). Why not? The former seems necessary to justify the more complicated 2-process model, and the latter seems necessary since it's the model they are trying to replace. Including these two controls would allow them to compare the number of free parameters as well as the model results. I am also surprised by the Levy model since Levy is a family of models. How were the parameters of the Levy walk chosen?

(12) One point that is entirely missing in the discussion is the individuality of worms. It is by now well known that individual animals have individual behaviors. Some are slow/fast, and similarly, their turn rates vary. This makes this problem even harder. Combined with the tiny number of events concerned (typically 20-40 per experiment), it seems daunting to determine the underlying model from behavioral statistics alone.

(13) That said, it's well-known which neurons underpin the suppression of turning events (starting already with Gray et al 2005, which, strangely, was not cited here). Some discussion of the neuronal predictions for each of the two (or more) models would be appropriate.

(14) An additional point is the reliance entirely on simulations. A rigorous formulation (of the probability distribution rather than just the mean) should be analytically tractable (at least for the first moment, and possibly higher moments). If higher moments are not obtainable analytically, then the equations should be numerically integrable. It seems strange not to do this.

In summary, while sample simulations do nicely match the examples in the data (of discontinuous vs continuous turning rates), this is not sufficient to demonstrate that the transition from ARS to dispersion in C. elegans is, in fact, likely to be a single 'state', or this (eq 1-3) single state. Of course, the model can be made more complicated to better match the data, but the approach of the authors, seeking an elegant and parsimonious model, is in principle valid, i.e. avoiding a many-parameter model-fitting exercise.

As a qualitative exercise, the paper might have some merit. It demonstrates that apparently discrete states can sometimes be artifacts of sampling from smoothly time-changing dynamics. However, as a generic point, this is not novel, and so without the grounding in C. elegans data, is less interesting.

Reviewer #2 (Public review):

The authors identified new target elements for prostaglandin E2 (PGE2) through which insulin release can be regulated in pancreatic beta cells under physiological conditions. In vitro extracellular exposure to PGE2 could directly and dose-dependently inhibit the potassium channel Kv2.2. In vitro pharmacology revealed that this inhibition occurs through the EP2/4 receptors, which activate protein kinase A (PKA). By screening specific sites of the Kv2.2 channel, the target phosphorylation site (S448) for PKA regulation was found. The physiological relevance of the described signaling cascade was investigated and confirmed in vivo, using a Kv2.2 knockdown mouse model.

The strength of this manuscript is the novelty of the (EP2/4-PKA-Kv2.2 channel) molecular pathway described and the comprehensive methodological toolkit the authors have relied upon.

The introduction is detailed and contains all the information necessary to place the claims in context. Although the dataset is comprehensive and a logical lead is consistently built, there is one important point to consider: to clarify that the described signaling pathway is characteristic of normal physiological conditions and thus differs from pathological changes. It would be useful to carry out basic experiments in a diabetes model (regardless of in mouse or rat even).

Comments on revisions:

The authors addressed my comments sufficiently. I have no additional questions to clarify.

Reviewer #1 (Public review):

O'Neill et al. have developed a software analysis application, miniML, that enables the quantification of electrophysiological events. They utilize a supervised deep learned-based method to optimize the software. miniML is able to quantify and standardize the analyses of miniature events, using both voltage and current clamp electrophysiology, as well as optically driven events using iGluSnFR3, in a variety of preparations, including in the cerebellum, calyx of held, golgi cell, human iPSC cultures, zebrafish, and Drosophila. The software appears to be flexible, in that users are able to hone and adapt the software to new preparations and events. Importantly, miniML is an open source software free for researchers to use and enables users to adapt new features using Python.

Overall this new software has the potential to become widely used in the field and an asset to researchers. Importantly, a new graphical user interface has been generated that enables more user control and a more user-friendly experience. Further, the authors demonstrate how miniML performs relative to other platforms that have been developed, and highlight areas where miniML works optimally. With these revisions, miniML should now be of considerable benefit and utility to a variety of researchers.

Reviewer #2 (Public review):

Summary:

This paper presents miniML as a supervised method for detection of spontaneous synaptic events. Recordings of such events are typically of low SNR, where state-of-the-art methods are prone to high false favourable rates. Unlike current methods, training miniML requires neither prior knowledge of the kinetics of events nor the tuning of parameters/thresholds.

The proposed method comprises four convolutional networks, followed by a bi-directional LSTM and a final fully connected layer, which outputs a decision event/no event per time window. A sliding window is used when applying miniML to a temporal signal, followed by an additional estimation of events' time stamps. miniML outperforms current methods for simulated events superimposed on real data (with no events) and presents compelling results for real data across experimental paradigms and species.

Strengths:

The authors present a pipeline for benchmarking based on simulated events superimposed on real data (with no events). Compared to five other state-of-the-art methods, miniML leads to the highest detection rates and is most robust to specific choices of threshold values for fast or slow kinetics. A major strength of miniML is the ability to use it for different datasets. For this purpose, the CNN part of the model is held fixed and the subsequent networks are trained to adapt to the new data. This Transfer Learning (TL) strategy reduces computation time significantly and more importantly, it allows for using a substantially smaller data set (compared to training a full model) which is crucial as training is supervised (i.e. uses labeled examples).

Weaknesses:<br /> The authors do not indicate how the specific configuration of miniML was set, i.e. number of CNNs, units, LSTM, etc. Please provide further information regarding these design choices, whether they were based on similar models or if chosen based on performance.

The data for the benchmark system was augmented with equal amounts of segments with/without events. Data augmentation was undoubtedly crucial for successful training.<br /> (1) Does a balanced dataset reflect the natural occurrence of events in real data? Could the authors provide more information regarding this matter?<br /> (2) Please provide a more detailed description of this process as it would serve users aiming to use this method for other sub-fields.

The benchmarking pipeline is indeed valuable and the results are compelling. However, the authors do not provide comparative results for miniML for real data (figures 4-8). TL does not apply to the other methods. In my opinion, presenting the performance of other methods, trained using the smaller dataset would be convincing of the modularity and applicability of the proposed approach.

Impact:

Accurate detection of synaptic events is crucial for the study of neural function. miniML has a great potential to become a valuable tool for this purpose as it yields highly accurate detection rates, it is robust, and is relatively easily adaptable to different experimental setups.

Comments on revisions:

The revised manuscript presents a compelling framework. The performance of mini ML is thouroughly explored and compared to several benchmarks. The training process along with other technical issues are now described in a satisfactory level of detail.<br /> I think the authors did a great job. They answered all claims and concerns raised by me and the other reviewers.

Reviewer #1 (Public review):

Summary:

This is a new and important system that can efficiently train mice to perform a variety of cognitive tasks in a flexible manner. It is innovative and opens the door to important experiments in the neurobiology of learning and memory.

Strengths:

Strengths include: high n's, a robust system, task flexibility, comparison of manual-like training vs constant training, circadian analysis, comparison of varying cue types, long-term measurement, and machine teaching.

Weaknesses:

I find no major problems with this report.

Minor weaknesses:

(1) Line 219: Water consumption per day remained the same, but number of trails triggered was more as training continued. First, is this related to manual-type training? Also, I'm trying to understand this result quantitatively, since it seems counter-intuitive: I would assume that with more trials, more water would be consumed since accuracy should go up over training (so more water per average trial). Am I understanding this right? Can the authors give more detail or understanding to how more trials can be triggered but no more water is consumed despite training?

(2) Figure 2J: The X-axis should have some label: at least "training type". Ideally, a legend with colors can be included, although I see the colors elsewhere in the figure. If a legend cannot be added, then the color scheme should be explained in the caption.

(3) Figure 2K: What is the purple line? I encourage a legend here. The same legend could apply to 2J.

(4) Supplementary Figure S2 D: I do not think the phrase "relying on" is correct. Instead, I think "predicted by" or "correlating with" might be better.

Reviewer #2 (Public review):

Summary:

The manuscript by Yu et al. describes a novel approach for collecting complex and different cognitive phenotypes in individually housed mice in their home cage. The authors report a simple yet elegant design that they developed for assessing a variety of complex and novel behavioral paradigms autonomously in mice.

Strengths:

The data are strong, the arguments are convincing, and I think the manuscript will be highly cited given the complexity of behavioral phenotypes one can collect using this relatively inexpensive ($100/box) and high throughput procedure (without the need for human interaction). Additionally, the authors include a machine learning algorithm to correct for erroneous strategies that mice develop which is incredibly elegant and important for this approach as mice will develop odd strategies when given complete freedom.

Weaknesses:

(1) A limitation of this approach is that it requires mice to be individually housed for days to months. This should be discussed in depth.

(2) A major issue with continuous self-paced tasks such as the autonomous d2AFC used by the authors is that the inter-trial intervals can vary significantly. Mice may do a few trials, lose interest, and disengage from the task for several hours. This is problematic for data analysis that relies on trial duration to be similar between trials (e.g., reinforcement learning algorithms). It would be useful to see the task engagement of the mice across a 24-hour cycle (e.g., trials started, trials finished across a 24-hour period) and approaches for overcoming this issue of varying inter-trial intervals.

(3) Movies - it would be beneficial for the authors to add commentary to the video (hit, miss trials). It was interesting watching the mice but not clear whether they were doing the task correctly or not.

(4) The strength of this paper (from my perspective) is the potential utility it has for other investigators trying to get mice to do behavioral tasks. However, not enough information was provided about the construction of the boxes, interface, and code for running the boxes. If the authors are not willing to provide this information through eLife, GitHub, or their own website then my evaluation of the impact and significance of this paper would go down significantly.

Minor concerns:

Learning rate is confusing for Figure 3 results as it actually refers to trials to reach the criterion, and not the actual rate of learning (e.g., slope).

Reviewer #3 (Public review):

Summary:

In this set of experiments, the authors describe a novel research tool for studying complex cognitive tasks in mice, the HABITS automated training apparatus, and a novel "machine teaching" approach they use to accelerate training by algorithmically providing trials to animals that provide the most information about the current rule state for a given task.

Strengths:

There is much to be celebrated in an inexpensively constructed, replicable training environment that can be used with mice, which have rapidly become the model species of choice for understanding the roles of distinct circuits and genetic factors in cognition. Lingering challenges in developing and testing cognitive tasks in mice remain, however, and these are often chalked up to cognitive limitations in the species. The authors' findings, however, suggest that instead, we may need to work creatively to meet mice where they live. In some cases, it may be that mice may require durations of training far longer than laboratories are able to invest with manual training (up to over 100k trials, over months of daily testing) but the tasks are achievable. The "machine teaching" approach further suggests that this duration could be substantially reduced by algorithmically optimizing each trial presented during training to maximize learning.

Weaknesses:

Cognitive training and testing in rodent models fill a number of roles. Sometimes, investigators are interested in within-subjects questions - querying a specific circuit, genetically defined neuron population, or molecule/drug candidate, by interrogating or manipulating its function in a highly trained animal. In this scenario, a cohort of highly trained animals that have been trained via a method that aims to make their behavior as similar as possible is a strength.

However, often investigators are interested in between-subjects questions - querying a source of individual differences that can have long-term and/or developmental impacts, such as sex differences or gene variants. This is likely to often be the case in mouse models especially, because of their genetic tractability. In scenarios where investigators have examined cognitive processes between subjects in mice who vary across these sources of individual difference, the process of learning a task has been repeatedly shown to be different. The authors do not appear to have considered individual differences except perhaps as an obstacle to be overcome.

The authors have perhaps shown that their main focus is highly-controlled within-subjects questions, as their dataset is almost exclusively made up of several hundred young adult male mice, with the exception of 6 females in a supplemental figure. It is notable that these female mice do appear to learn the two-alternative forced-choice task somewhat more rapidly than the males in their cohort.

Considering the implications for mice modeling relevant genetic variants, it is unclear to what extent the training protocols and especially the algorithmic machine teaching approach would be able to inform investigators about the differences between their groups during training. For investigators examining genetic models, it is unclear whether this extensive training experience would mitigate the ability to observe cognitive differences, or select the animals best able to overcome them - eliminating the animals of interest. Likewise, the algorithmic approach aims to mitigate features of training such as side biases, but it is worth noting that the strategic uses of side biases in mice, as in primates, can benefit learning, rather than side biases solely being a problem. However, the investigators may be able to highlight variables selected by the algorithm that are associated with individual strategies in performing their tasks, and this would be a significant contribution.

A final, intriguing finding in this manuscript is that animal self-paced training led to much slower learning than "manual" training, by having the experimenter introduce the animal to the apparatus for a few hours each day. Manual training resulted in significantly faster learning, in almost half the number of trials on average, and with significantly fewer omitted trials. This finding does not necessarily argue that manual training is universally a better choice because it leads to more limited water consumption. However, it suggests that there is a distinct contribution of experimenter interactions and/or switching contexts in cognitive training, for example by activating an "occasion setting" process to accelerate learning for a distinct period of time. Limiting experimenter interactions with mice may be a labor-saving intervention, but may not necessarily improve performance. This could be an interesting topic of future investigation, of relevance to understanding how animals of all species learn.

Reviewer #1 (Public review):

Summary:

The authors aim to predict ecological suitability for the transmission of highly pathogenic avian influenza (HPAI) using ecological niche models. This class of models identify correlations between the locations of species or disease detections and the environment. These correlations are then used to predict habitat suitability (in this work, ecological suitability for disease transmission) in locations where surveillance of the species or disease has not been conducted. The authors fit separate models for HPAI detections in wild birds and farmed birds, for two strains of HPAI (H5N1 and H5Nx) and for two time periods, pre- and post-2020. The authors also validate models fitted to disease occurrence data from pre-2020 using post-2020 occurrence data.

Strengths:

The authors follow the established methods of Dhingra et al., 2016 to provide an updated spatial assessment of HPAI transmission suitability for two time periods, pre- and post-2020. They explore further methods of model cross-validation and consider the diversity of the bird species that HPAI has been detected in.

Weaknesses:

The precise ecological niche that the authors are modelling here is ambiguous: if we treat the transmission of HPAI in the wild bird population and in poultry populations as separate transmission cycles, linked by spillover events, then these transmission cycles are likely to have fundamentally different ecological niches. While an "index case" in farmed poultry is relevant to the wildlife transmission cycle, further within-farm and farm-to-farm transmission is likely to be contingent on anthropogenic factors, rather than the environment. Similarly, we would expect "index cases" in outbreaks of HPAI in mammals to be relevant to transmission risk in wild birds - this data is not included in this manuscript. Such "index cases" in farmed poultry occur under separate ecological conditions to subsequent transmission in farmed poultry, so should be separated if possible. Some careful editing of the language used in the manuscript may elucidate some of my questions related to model conceptualisation.

The authors' handling of sampling bias in disease detection data in poultry is possibly inappropriate: one would expect the true spatial distribution of disease surveillance in poultry to be more closely correlated with poultry farming density, in contrast to human population density. This shortcoming in the modelling workflow possibly dilutes a key finding of the Results, that the transmission risk of HPAI in poultry is greatest in areas where poultry farming density is high.

Reviewer #2 (Public review):

Summary:

This study aimed to determine which spatial factors (conceived broadly as environmental, agronomic and socio-economic) explain greater avian influenza case numbers reported since 2020 (2020--2022) by comparing similar models built with data from the period 2015--2020. The authors have chosen an environmental niche modelling approach, where detected infections are modelled as a function of spatial covariates extracted at the location of each case. These covariates are available over the entire world so that the predictions can be projected back to space in the form of a continuous map.

Strengths:

The authors use boosted regression trees as the main analytical tool, which always feature among the best-performing models for environmental niche models (also known as habitat suitability models). They run replicate sets of the analysis for each of their model targets (wild/domestic x pathogen variant), which can help produce stable predictions. The authors take steps to ameliorate some forms of expected bias in the detection of cases, such as geographic variation in surveillance efforts, and in general more detections near areas of higher human population density.

Weaknesses:

The study is not altogether coherent with respect to time. Data sets for the response (N5H1 or N5Hx case data in domestic or wild birds ) are divided into two periods; 2015--2020, and 2020--2022. Each set is modelled using a common suite of covariates that are not time-varying. That suggests that causation is inferred by virtue of cases being in different geographic areas in those two time periods. Furthermore, important predictors such as chicken density appear to be informed (in the areas of high risk) from census data from before 2010. The possibility for increased surveillance effort *through time* is overlooked, as is the possibility that previously high-burden locations may implement practice changes to reduce vulnerability.

Reviewer #1 (Public review):

Wang et al. investigated how sexual failure influences sweet taste perception in male Drosophila. The study revealed that courtship failure leads to decreased sweet sensitivity and feeding behavior via dopaminergic signaling. Specifically, the authors identified a group of dopaminergic neurons projecting to the subesophageal zone that interacts with sweet-sensing Gr5a+ neurons. These dopaminergic neurons positively regulate the sweet sensitivity of Gr5a+ neurons via DopR1 and Dop2R receptors. Sexual failure diminishes the activity of these dopaminergic neurons, leading to reduced sweet-taste sensitivity and sugar-feeding behavior in male flies. These findings highlight the role of dopaminergic neurons in integrating reproductive experiences to modulate appetitive sensory responses.

Previous studies have explored the dopaminergic-to-Gr5a+ neuronal pathways in regulating sugar feeding under hunger conditions. Starvation has been shown to increase dopamine release from a subset of TH-GAL4 labeled neurons, known as TH-VUM, in the subesophageal zone. This enhanced dopamine release activates dopamine receptors in Gr5a+ neurons, heightening their sensitivity to sugar and promoting sucrose acceptance in flies. Since the function of the dopaminergic-to-Gr5a+ circuit motif has been well established, the primary contribution of Wang et al. is to show that mating failure in male flies can also engage this circuit to modulate sugar-feeding behavior. This contribution is valuable because it highlights the role of dopaminergic neurons in integrating diverse internal state signals to inform behavioral decisions.

An intriguing discrepancy between Wang et al. and earlier studies lies in the involvement of dopamine receptors in Gr5a+ neurons. Prior research has shown that Dop2R and DopEcR, but not DopR1, mediate starvation-induced enhancement of sugar sensitivity in Gr5a+ neurons. In contrast, Wang et al. found that DopR1 and Dop2R, but not DopEcR, are involved in the sexual failure-induced decrease in sugar sensitivity in these neurons. I wish the authors had further explored or discussed this discrepancy, as it is unclear how dopamine release selectively engages different receptors to modulate neuronal sensitivity in a context-dependent manner.

The data presented by Wang et al. are solid and effectively support their conclusions. However, certain aspects of their experimental design, data analysis, and interpretation warrant further review, as outlined below.

(1) The authors did not explicitly indicate the feeding status of the flies, but it appears they were not starved. However, the naive and satisfied flies in this study displayed high feeding and PER baselines, similar to those observed in starved flies in other studies. This raises the concern that sexually failed flies may have consumed additional food during the 4.5-hour conditioning period, potentially lowering their baseline hunger levels and subsequently reducing PER responses. This alternative explanation is worth considering, as an earlier study demonstrated that sexually deprived males consumed more alcohol, and both alcohol and food are known rewards for flies. To address this concern, the authors could remove food during the conditioning phase to rule out its influence on the results.

(2) Figure 1B reveals that approximately half of the males in the Failed group did not consume sucrose, yet Figure 1-S1A suggests that the total volume consumed remained unchanged. Were the flies that did not consume sucrose omitted from the dataset presented in Figure 1-S1A? If so, does this imply that only half of the male flies experience sexual failure, or that sexual failure affects only half of males while the others remain unaffected? The authors should clarify this point.

(3) The evidence linking TH-GAL4 labeled dopaminergic neurons to reduced sugar sensitivity in Gr5a+ neurons in sexually failed males could be further strengthened. Ideally, the authors would have activated TH-GAL4 neurons and observed whether this restored GCaMP responses in Gr5a+ neurons in sexually failed males. Instead, the authors performed a less direct experiment, shown in Figures 3-S1C and D. The manuscript does not describe the condition of the flies used in this experiment, but it appears that they were not sexually conditioned. I have two concerns with this experiment. First, no statistical analysis was provided to support the enhancement of sucrose responses following activation of TH-GAL4 neurons. Second, without performing this experiment in sexually failed males, the authors lack direct evidence to confirm that the dampened response of Gr5a+ neurons to sucrose results from decreased activity in TH-GAL4 neurons.

(4) The statistical methods used in this study are poorly described, making it unclear which method was used for each experiment. I suggest that the authors include a clear description of the statistical methods used for each experiment in the figure legends. Furthermore, as I have pointed out, there is a lack of statistical comparisons in Figures 3-S1C and D, a similar problem exists for Figures 6E and F.

(5) The experiments in Figure 5 lack specificity. The target neurons in this study are Gr5a+ neurons, which are directly involved in sugar sensing. However, the authors used the less specific Dop1R1- and Dop2R-GAL4 lines for their manipulations. Using Gr5a-GAL4 to specifically target Gr5a+ neurons would provide greater precision and ensure that the observed effects are directly attributable to the modulation of Gr5a+ neurons, rather than being influenced by potential off-target effects from other neuronal populations expressing these dopamine receptors.

(6) I found the results presented in Fig. 6F puzzling. The knockdown of Dop2R in Gr5a+ neurons would be expected to decrease sucrose responses in naive and satisfied flies, given the role of Dop2R in enhancing sweet sensitivity. However, the figure shows an apparent increase in responses across all three groups, which contradicts this expectation. The authors may want to provide an explanation for this unexpected result.

(7) In several instances in the manuscript, the authors described the effects of silencing dopamine signaling pathways or knocking down dopamine receptors in Gr5a neurons with phrases such as 'no longer exhibited reduced sweet sensitivity' (e.g., L269 and L288), 'prevent the reduction of sweet sensitivity' (e.g., L292), or 'this suppression was reversed' (e.g. L299). I found these descriptions misleading, as they suggest that sweet sensitivity in naive and satisfied groups remains normal while the reduction in failed flies is specifically prevented or reversed. However, this is not the case. The data indicate that these manipulations result in an overall decrease in sweet sensitivity across all groups, such that a further reduction in failed flies is not observed. I recommend revising these descriptions to accurately reflect the observed phenotypes and avoid any confusion regarding the effects of these manipulations.

Reviewer #2 (Public review):

Summary:

The authors exposed naïve male flies to different groups of females, either mated or virgin. Male flies can successfully copulate with virgin females; however, they are rejected by mated females. This rejection reduces sugar preference and sensitivity in males. Investigating the underlying neural circuits, the authors show that dopamine signaling onto GR5a sensory neurons is required for reduced sugar preference. GR5a sensory neurons respond less to sugar exposure when they lack dopamine receptors.

Strengths:

The findings add another strong phenotype to the existing dataset about brain-wide neuromodulatory effects of mating. The authors use several state-of-the-art methods, such as activity-dependent GRASP to decipher the underlying neural circuitry. They further perform rigorous behavioral tests and provide convincing evidence for the local labellar circuit.

Weaknesses:

The authors focus on the circuit connection between dopamine and gustatory sensory neurons in the male SEZ. Therefore, it is still unknown how mating modulates dopamine signaling and what possible implications on other behaviors might result from a reduced sugar preference.

Reviewer #3 (Public review):

Summary

In this work, the authors asked how mating experience impacts reward perception and processing. For this, they employ fruit flies as a model, with a combination of behavioral, immunostaining, and live calcium imaging approaches.

Their study allowed them to demonstrate that courtship failure decreases the fraction of flies motivated to eat sweet compounds, revealing a link between reproductive stress and reward-related behaviors. This effect is mediated by a small group of dopaminergic neurons projecting to the SEZ. After courtship failure, these dopaminergic neurons exhibit reduced activity, leading to decreased Gr5a+ neuron activity via Dop1R1 and Dop2R signaling, and leading to reduced sweet sensitivity. The authors therefore showed how mating failure influences broader behavioral outputs through suppression of the dopamine-mediated reward system and underscores the interactions between reproductive and reward pathways.

Concern

My main concern regarding this study lies in the way the authors chose to present their results. If I understood correctly, they provided evidence that mating failure induces a decrease in the fraction of flies exhibiting PER. However, they also showed that food consumption was not affected (Fig. 1, supplement), suggesting that individuals who did eat consumed more. This raises questions about the analysis and interpretation of the results. Should we consider the group as a whole, with a reduced sensitivity to sweetness, or should we focus on individuals, with each one eating more? I am also concerned about how this could influence the results obtained using live imaging approaches, as the flies being imaged might or might not have been motivated to eat during the feeding assays. I would like the authors to clarify their choice of analysis and discuss this critical point, as the interpretation of the results could potentially be the opposite of what is presented in the manuscript.

Reviewer #1 (Public review):

The authors set out to develop a contextual fear learning (CFC) paradigm in head-fixed mice that would produce freezing as the conditioned response. Typically, lick suppression is the conditioned response in such designs, but this (1) introduces a potential confounding influence of reward learning on neural assessments of aversion learning and (2) does not easily allow comparison of head-fixed studies with extensive previous work in freely moving animals, which use freezing as the primary conditioned response.

The first part of this study is a report on the development and outcomes of 3 variations of the CFC paradigm in a virtual reality environment. The fundamental design is strong, with head-fixed mice required to run down a linear virtual track to obtain a water reward. Once trained, the water reward is no longer necessary and mice will navigate virtual reality environments. There are rigorous performance criteria to ensure that mice that make it to the experimental stage show very low levels of inactivity prior to fear conditioning. These criteria do result in only 40% of the mice making it to the experimental stage, but high rates of activity in the VR environment are crucial for detecting learning-related freezing. It is possible that further adjustments to the procedure could improve attrition rates.

Paradigm versions 1 and 2 vary the familiarity of the control context while paradigm versions 2 and 3 vary the inter-shock interval. Paradigm version 1 is the most promising, showing the greatest increase in conditioned freezing (~40%) and good discrimination between contexts (delta ~15-20%). Paradigm version 2 showed no clear evidence of learning - average freezing at recall day 1 was not different than pre-shock freezing. First-lap freezing showed a difference, but this single-lap effect is not useful for many of the neural circuit questions for which this paradigm is meant to facilitate. Also, the claim that mice extinguished first-lap freezing after 1 day is weak. Extinction is determined here by the loss of context discrimination, but this was not strong to begin with. First-lap freezing does not appear to be different between Recall Day 1 and 2, but this analysis was not done. Paradigm version 3 has some promise, but the magnitude of the context discrimination is modest (~10% difference in freezing). Thus, further optimization of the VR CFC will be needed to achieve robust learning and extinction. This could include factors not thoroughly tested in this study, including context pre-exposure timing and duration and shock intensity and frequency.

The second part of the study is a validation of the head-fixed CFC VR protocol through the demonstration that fear conditioning leads to the remapping of dorsal CA1 place fields, similar to that observed in freely moving subjects. The results support this aim and largely replicate previous findings in freely moving subjects. One difference from previous work of note is that VR CFC led to the remapping of the control environment, not just the conditioning context. The authors present several possible explanations for this lack of specificity to the shock context, further underscoring the need for further refinement of the CFC protocol before it can be widely applied. While this experiment examined place cell remapping after fear conditioning, it did not attempt to link neural activity to the learned association or freezing behavior.

In summary, this is an important study that sets the initial parameters and neuronal validation needed to establish a head-fixed CFC paradigm that produces freezing behaviors. In the discussion, the authors note the limitations of this study, suggest the next steps in refinement, and point to several future directions using this protocol to significantly advance our understanding of the neural circuits of threat-related learning and behavior.

Reviewer #2 (Public review):

Summary:

In this manuscript, Krishnan et al devised three paradigms to perform contextual fear conditioning in head-fixed mice. Each of the paradigms relied on head-fixed mice running on a treadmill through virtual reality arenas. The authors tested the validity of three versions of the paradigms by using various parameters. As described below, I think there are several issues with the way the paradigms are designed and how the data are interpreted. Moreover, as Paradigm 3 was published previously in a study by the same group, it is unclear to me what this manuscript offers beyond the validations of parameters used for the previous publication. Below, I list my concerns point-by-point, which I believe need to be addressed to strengthen the manuscript.

Major comments

(1) In the analysis using the LME model (Tables 1 and 2), I am left wondering why the mice had increased freezing across recall days as well as increased generalization (increased freezing to the familiar context, where shock was never delivered). Would the authors expect freezing to decrease across recall days, since repeated exposure to the shock context should drive some extinction? This is complicated by the analysis showing that freeing was increased only on retrieval day 1 when analyzing data from the first lap only. Since reward (e.g., motivation to run) is removed during the conditioning and retrieval tests, I wonder if what the authors are observing is related to decreased motivation to perform the task (mice will just sit, immobile, not necessarily freezing per se). I think that these aspects need to be teased out.

(2) Related to point 1, the authors actually point out that these changes could be due to the loss of the water reward. So, in line 304, is it appropriate to call this freezing? I think it will be very important for the authors to exactly define and delineate what they consider as freezing in this task, versus mice just simply sitting around, immobile, and taking a break from performing the task when they realize there is no reward at the end.

(3) In the second paradigm, mice are exposed to both novel and (at the time before conditioning) neutral environments just before fear conditioning. There is a big chance that the mice are 'linking' the memories (Cai et al 2016) of the two contexts such that there is no difference in freezing in the shock context compared to the neutral context, which is what the authors observe (Lines 333-335). The experiment should be repeated such that exposure to the contexts does not occur on the conditioning day.

(4) On lines 360-361, the authors conclude that extinction happens rapidly, within the first lap of the VR trial. To my understanding, that would mean that extinction would happen within the first 5-10 seconds of the test (according to Figure S1E). That seems far too fast for extinction to occur, as this never occurs in freely behaving mice this quickly.

(5) Throughout the different paradigms, the authors are using different shock intensities. This can lead to differences in fear memory encoding as well as in levels of fear memory generalization. I don't think that comparisons can be made across the different paradigms as too many variables (including shock intensity - 0.5/0.6mA can be very different from 1.0 mA) are different. How can the authors pinpoint which works best? Indeed, they find Paradigm 3 'works' better than Paradigm 2 because mice discriminate better between the neutral and shock contexts. This can definitely be driven by decreased generalization from using a 0.6mA shock in Paradigm 3 compared to 1.0 mA shock in Paradigm 2.

(6) There are some differences in the calcium imaging dataset compared to other studies, and the authors should perform additional testing to determine why. This will be integral to validating their head-fixed paradigm(s) and showing they are useful for modeling circuit dynamics/behaviors observed in freely behaving mice. Moreover, the sample size (number of mice) seems low.

(7) It appears that the authors have already published a paper using Paradigm 3 (Ratigan et al 2023). If they already found a paradigm that is published and works, it is unclear to me what the current manuscript offers beyond that initial manuscript.

(8) As written, the manuscript is really difficult to follow with the averages and standard error reported throughout the text. This reporting in the text occurred heterogeneously throughout the text, as sometimes it was reported and other times it was not. Cleaning this reporting up throughout the paper would greatly improve the flow of the text and qualitative description of the results.

Reviewer #3 (Public review):

Summary:

Krishnan et al. present a novel contextual fear conditioning (CFC) paradigm using a virtual reality (VR) apparatus to evaluate whether conditioned context-induced freezing can be elicited in head-fixed mice. By combining this approach with two-photon imaging, the authors aim to provide high-resolution insights into the neural mechanisms underlying learning, memory, and fear. Their experiments demonstrate that head-fixed mice can discriminate between threat and non-threat contexts, exhibit fear-related behavior in VR, and show context-dependent variability during extinction. Supplemental analyses further explore alternative behaviors and the influence of experimental parameters, while hippocampal neuron remapping is tracked throughout the experiments, showcasing the paradigm's potential for studying memory formation and extinction processes.

Strengths:

Methodological Innovation: The integration of a VR-based CFC paradigm with real-time two-photon imaging offers a powerful, high-resolution tool for investigating the neural circuits underlying fear, learning, and memory.

Versatility and Utility: The paradigm provides a controlled and reproducible environment for studying contextual fear learning, addressing challenges associated with freely moving paradigms.

Potential for Broader Applications: By demonstrating hippocampal neuron remapping during fear learning and extinction, the study highlights the paradigm's utility for exploring memory dynamics, providing a strong foundation for future studies in behavioral neuroscience.

Comprehensive Data Presentation: The inclusion of supplemental figures and behavioral analyses (e.g., licking behaviors and variability in extinction) strengthens the manuscript by addressing additional dimensions of the experimental outcomes.

Weaknesses:

Characterization of Freezing Behavior: The evidence supporting freezing behavior as the primary defensive response in VR is unclear. Supplementary videos suggest the observed behaviors may include avoidance-like actions (e.g., backing away or stopping locomotion) rather than true freezing. Additional physiological measurements, such as EMG or heart rate, are necessary to substantiate the claim that freezing is elicited in the paradigm.

Analysis of Extinction: Extinction dynamics are only analyzed through between-group comparisons within each Recall day, without addressing within-group changes in behavior across days. Statistical comparisons within groups would provide a more robust demonstration of extinction processes.

Low Sample Sizes: Paradigm 1 includes conditions with very low sample sizes (N=1-3), limiting the reliability of statistical comparisons regarding the effects of shock number and intensity. Increasing sample sizes or excluding data from mice that do not match the conditions used in Paradigms 2 and 3 would improve the rigor of the analysis.

Potential Confound of Water Reward: The authors critique the use of reward in conjunction with fear conditioning in prior studies but do not fully address the potential confound introduced by using water reward during the training phase in their own paradigm.

Reviewer #1 (Public review):

Summary:

In this remarkable study, the authors use some of their recently-developed oxytocin receptor knockout voles (Oxtr1-/- KOs) to re-examine how oxytocin might influence partner preference. They show that shorter cohabitation times lead to decreased huddling time and partner preference in the KO voles, but with longer periods preference is still established, i.e., the KO animals have a slower rate of forming preference or are less sensitive to whatever cues or experiences lead to the formation of the pair bond as measured by this assay. This helps relate the authors' recent study to the rest of the literature on oxytocin and partner preference in prairie voles. To better understand what might lead to slower partner preference, they quantified changes to the durations and frequency of huddling. In separate assays, they also found that Oxtr1-/- KOs interacted more with stranger males than wild-type females. In a partner choice assay, they found that wild-type males prefer wild-type females more than Oxtr1-/- KO females. They then performed bulk RNA-Seq profiling of nucleus accumbens of both wild-type and Oxtr1-/- KO males and females, either housed with animals of the same sex or paired with a wild-type of the opposite sex. 13 differentially expressed genes were identified, mostly due to downregulation in wild-type females. These genes were also identified in a module lost in the Oxtr1-/- voles by correlated expression profiling. They also compared results of transcriptional profiling in female and male wild-type vs Oxtr1-/- voles (independently of bonding state) and found hundreds of differentially expressed genes in nucleus accumbens, mostly in females and often with some relation to neural development and/or autism. Some of the reduction in the transcript was confirmed with in-situs, as well as compared to changes in transcription in the lateral septum and paraventricular nucleus (PVN) of the hypothalamus. Finally, they find fewer oxytocin+ and AVP+ neurons in the anterior PVN.

Strengths:

This is an important study helping to reveal the effects of oxytocin receptor knockout on behavior and gene expression. The experiments are thorough and reveal a surprising number of genetic and anatomical differences, with some sexual dimorphism as well, and the authors have more carefully examined the behavioral changes after shorter and longer periods of partner preference formation.

Weaknesses:

It is surprising that given all the genetic changes identified by the authors, the behavioral phenotypes are fairly mild. The extent of gene changes also might be under-reported given the variability in the behavior and relatively low number of animals profiled.

Reviewer #2 (Public review):

Summary:

This manuscript uses a recently published oxytocin receptor null prairie vole line to examine the effects of this mutation on pair bonding behavior and PVN gene expression. Results reveal that Oxtr sex specifically influences early courtship behavior and partner preference formation as well as suppressing promiscuity toward novel potential mates. PVN gene expression varies between Oxtr null and WT prairie voles.

Strengths:

Behavioral analyses extend beyond the typical reporting of frequency and duration. The gene expression models and analyses are well-done and convincing. The experimental designs and approaches are strong.

Weaknesses:

More details and background literature explaining the role of the Oxt system in pair bonding behaviors is necessary, particularly for the Introduction. The authors overstate several times that Oxtr expression is not necessary for partner preference formation, based on their previous findings. However, it does appear, particularly, in the short cohabitation that it is necessary. Thus, the nuanced answer may be that Oxt may accelerate partner preference formation. Improving the presentation of the statistics and figures will make the manuscript more reader-friendly.

Reviewer #1 (Public review):

Summary:

The authors attempted to identify whether a new deep-learning model could be applied to both resting and task state fMRI data to predict cognition and dopaminergic signaling. They found that resting state and moving watching conditions best predict episodic memory, but only movie watching predicts both episodic and working memory. A negative 'brain gap' (where the model trained on brain connectivity predicts worse performance than what is actually observed) was associated with less physical activity, poorer cardiovascular function, and lower D1R availability.

Strengths:

The paper should be of broad interest to the journal's readership, with implications for cognitive neuroscience, psychiatry, and psychology fields. The paper is very well-written and clear. The authors use two independent datasets to validate their findings, including two of the largest databases of dopamine receptor availability to link brain functional connectivity/activity with neurochemical signaling.

Weaknesses:

The deep learning findings represent a relatively small extension/enhancement of knowledge in a very crowded field.

It's unclear from these results how much utility the brain gaps provide above and beyond observed performance. It would be helpful to take a median split of the dataset on observed performance and plot aside the current Figure 3 results to see how the cardiovascular and physical activity measures differ based on actual performance. Could the authors perform additional analyses describing how much additional variance is explained in these measures by including brain gaps?

Some of the imaging findings require deeper analysis. For Figure 1f - Which default mode regions have high salience? DMN is a huge network with subregions having differing functions.

Along the same lines, were the striatal D1R findings regionally specific at all? It would be informative to test whether the three nuclei (Accumbens, Caudate, Putamen) and/or voxelwise models would show something above and beyond what is achieved from averaging D1R across the striatum. What about cortical D1R, which is highly abundant, strongly associated with cognitive (especially WM) performance, and has much unique variance beyond striatal D1R? https://www.science.org/doi/full/10.1126/sciadv.1501672. The PET findings are one of the unique strengths of this paper and are underexplored. It's also unclear if the measure of brain entropy should simply be averaged across all regions.

It is not clear from the text that the authors met the preconditions for mediation analysis (that is, demonstrating significant correlations between D1R and entropy, in addition to the correlation with brain gap. The authors should report this as well.

Was age controlled for in the mediation analysis? I would not consider this result valid unless that is the case.

The discussion section is long, but the authors would do better to replace some less helpful sections (e.g., the paragraph on methodological tweaks to parcellations and model alignment) with a couple of other important points, including:

(1) Discuss the 'sweet-spot' of movie watching for behavior prediction in the context of studies showing that task states 'quench' neural variability: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007983. This may not be mutually exclusive of the discussion on dopamine and signal-to-noise ratio, but it would be helpful for the authors to discuss their potential overlap vs. unique contributions to the observed findings.

(2) The argument that dopamine signaling increases signal-to-noise ratio is based on some preclinical data as well as correlational data using fMRI with pharmacological challenges. It is less clear how PET-derived estimates of D1R and D2R availability equate to 'dopamine signaling' as it is thought of in this context. Presumably, based on these data, higher D1R or D2R availability would be related to greater levels of tonic dopaminergic signaling. However, in the case of the COBRA dataset with D2R estimates, those are based on raclopride -- which competes with endogenous dopamine for the D2 receptor. Therefore, someone with higher levels of endogenous dopamine signaling should theoretically have lower raclopride binding and lower D2R estimates. I'm not arguing that the authors' logic is flawed or that D1R and D2R are not good measures of dopamine signaling, but I'd ask the authors to dig into the literature and describe more direct potential links for how greater receptor availability might be associated with greater dopamine signaling (and hence lower entropy). Adding this to the discussion would be very valuable for PET research.

Reviewer #2 (Public review):

Summary:

The authors developed a deep learning model based on a DenseNet CNN architecture to predict two cognitive functions: working memory and episodic memory, from functional connectivity matrices. These matrices were recorded under three conditions: during rest, a working memory task, and a movie, and were treated as images for the CNN algorithm. They tested their model's performance across different conditions and a separate dataset with a different age distribution (using the same MRI scanner, scanning configurations, and cognitive tests). They also calculated the "brain cognition gap" based on the model trained on resting functional connectivity to predict working memory. Extending from the commonly used index "brain age," the brain cognition gap was defined as the difference between the working memory score predicted by their model (predicted working memory) and the working memory score based on the working memory test itself (observed working memory). This brain cognition gap was found to be associated with physical activity, education, and cardiovascular risk. The authors also conducted additional mediation tests to examine whether regional functional variability mediated the relationship between PET-derived measures of dopamine and the brain cognition gap.

Strengths:

The major strength of this manuscript is the extensive effort the authors have put into creating a new 'biomarker' that links deep learning with fMRI, PET, physical activity, education, and cardiovascular risk across two studies. This effort is impressive.

Weaknesses:

There are several weaknesses in the current methods and results, making many of the claims unconvincing. These weaknesses include:

(1) The lack of baseline models to benchmark the predictive performance of their DenseNet models.

(2) The inappropriate calculation of the brain cognition gap due to the lack of control for regression-toward-the-mean and the influence of the working memory itself (a common practice in brain age studies).

(3) The lack of benchmarking of the brain cognition gap against the 'corrected' brain age gap and the direct prediction of physical activity, education, and cardiovascular risk.

(4) Minimal justification for their PET mediation analysis.

Regarding the impact of the work on the field and the utility of the methods and data to the community, I see its potential. However, addressing all the weaknesses listed above is crucial and likely to change the conclusions of the results.

It is important to note that many statements in the manuscript are overstated, making the contribution of the manuscript seem exaggerated.

For instance, the abstract claims "there is a lack of objective biomarkers to accurately predict cognitive function," and the discussion states, "across various studies, the correlation between predicted and actual fluid intelligence typically hovers around 0.25 (98-100)." However, a meta-analysis by Vieira and colleagues (2022 https://doi.org/10.1016/j.intell.2022.101654) found over 37 studies up to 2020 predicting cognitive abilities from fMRI with machine learning, with 24 studies published in 2019-20 alone. Since 2020, with the rise of machine learning and AI, even more studies have likely been published on this topic, all claiming to show objective biomarkers to accurately predict cognitive function. Vieira and colleagues also found an average performance of these objective biomarkers in predicting general cognition at r = .42, similar to what was found in this manuscript. Based on this alone, it is unclear how novel or superior their method is without a proper systematic benchmark.

Similarly, the authors claim superior performance of deep learning and mischaracterize machine learning algorithms: "In particular, deep neural networks (DNN) methods have been successfully applied to behavioral and disease prediction (24-26), and have been found to outperform other machine learning approaches (27-29)," and "Deep learning approaches overcome the limitation of predictive techniques that solely rely on linear associations between connectivity and behavioral phenotypes (17)." However, the superiority of deep learning is debatable. Studies show comparable performance between machine learning (such as kernel regression) and deep learning (such as fully-connected neural networks, BrainNetCNN, Graph CNN (GCNN), and temporal CNN), e.g., He and colleagues (2019) and Vieira and colleagues (2024) https://doi.org/10.1016/j.neuroimage.2019.116276 and Vieira and colleagues' https://doi.org/10.1101/2024.03.07.583858.

Moreover, many non-deep learning predictive techniques are non-linear, e.g., XGBoost, CatBoost, random forest, kernel ridge, and support vector regression with non-linear kernels (such as RBF and polynomial). Thus, stating that machine learning can only model linear relationships is incorrect. Moreover, for the small amount of data the authors had, some might argue that a linear algorithm might be more appropriate to balance the bias-variance trade-off in prediction. Again, without a proper systematic benchmark, it is unclear how well their DenseNet algorithm performs compared to other algorithms.

Regarding the Brain Age literature, the authors also misinterpreted recent findings: "However, a recent study suggests that brain age predictions contribute minimally compared to chronological age for explaining cognitive decline (65), implying that cognitive predictions are more reliable." In this study, Tetereva and colleagues (2024) (https://doi.org/10.7554/eLife.87297.4) showed that non-deep-learning machine learning can make good predictions from MRI on both chronological age (with r up to .88) and fluid cognition (with r up to .627). Using the combination of functional connectivity matrices across rest and tasks to predict fluid cognition, they found performance at r = .565, comparable to what was found in the current manuscript with deep learning. Nonetheless, while brain age predicted chronological age well (and brain cognition predicted fluid cognition well), it was problematic to predict fluid cognition from brain age. They showed that, because brain age, by design, shared so much common variance with chronological age, brain age and chronological age captured the same variance of fluid cognition. When chronological age was controlled for in the prediction of fluid cognition, brain age no longer had high predictive ability. In the case of the current manuscript, the brain cognition gap is not appropriately controlled for cognition (to be more precise, a working memory score). I expect the performance in predicting physical activity, education, and cardiovascular risk will drop dramatically once cognition is controlled for. There are at least two ways to control cognition according to Tetereva and colleagues' study (see more in the recommendations).

The authors mentioned, "The third aim of the current study is to uncover the contribution of dopamine (DA) integrity to brain-cognition gaps." However, I fail to see how mediation analysis would test this. The authors also mentioned, "Insufficient DA modulation can affect neurocognitive functions detrimentally (69, 74, 76-78)." They should test if DA levels are related to working memory scores in their study, and if so, whether the relationship is mediated by the "corrected" brain-cognition gaps. Note see more on the recommendation for the calculation of the "corrected" brain-cognition gaps.

Reviewer #3 (Public review):

Summary:

This paper by Esmaeili and co-authors presents a connectome prediction study to predict episodic memory and relate prediction errors to other phonotypic variables.

Strengths:

(1) A primary and external validation dataset.

(2) Novel use of prediction errors (i.e., brain-cognitive gap).

(3) A wide range of data was investigated.

Weaknesses:

(1) Lack of comparisons to other methods for prediction.

(2) Several different points are being investigated that don't allow any particular one to shine through.

(3) Some choices of analysis are not well-motivated.

(4) How do the n-back connectomes perform for prediction if the authors do not regress task activations from the n-back task?

(5) I am a little concerned about overfitting with the convolutional neural net. For example, the drop-off in prediction performance in the external sample is stark. How does the deep learning approach used here compare to something simpler, like a connectome-based predictive model or ridge regression?

(6) It may be nice to try the other models in the validation dataset. This would also provide a sense of the overfitting that may be going on with overfitting.

(7) While predictive models increase the power over association studies, they still require large samples to prevent overfitting. Do the authors have a sense of the power their main and external validation sample sizes provide?

(8) I am not sure that the Mann-Whitney is the correct test for comparing the distributions of prediction performances. The distributions are dependent on each other as they are each predicting the same outcomes. Using the typical degrees of freedom formula would overestimate the degrees of freedom.

(9) The brain cognition gap is interesting. It is very similar conceptually to the brain age gap. When associating the brain age gap with other phenotypes, typically age is regressed from the brain age gap and the other phenotype. In other words, age is typically associated with a brain age gap as individuals at the tail ages often show the largest gaps. Is the brain cognition gap correlated with episodic memory and do the group differences hold if episodic memory is controlled for?

(10) I have the same question for the dopamine results. Particularly, in the correlations that are divided by brain cognition gap sign. I could see these types of patterns arise due to a correlation with a third variable.

Reviewer #1 (Public review):

Summary:

This is a very well-written paper presenting interesting findings related to the recovery following the end-Permian event in continental settings, from N China. The finding is timely as the topic is actively discussed in the scientific community. The data provides additional insights into the faunal, and partly, floral global recovery following the EPE, adding to the global picture.

Strengths:

The conclusions are supported by an impressive amount of sedimentological and paleontological data (mainly trace fossils) and illustrations.

Weaknesses:

The occurrence of MISS (Microbially Induced Sedimentary Structures) could be discussed more in detail as these provide interesting information directly linked to the delayed recovery of the biota.

Reviewer #2 (Public review):

Summary:

A rapid recovery of the ecosystems during the late Early Triassic, in the aftermath of the end-Permian mass extinction, is discussed based on different types of fossils.

Strengths:

The combined study of invertebrate trace fossils, tetrapod bones, and plant remains together with their stratigraphic distribution in different sections provides a convincing case to support a rapid recovery as the authors hypothesize.

Weaknesses:

The study is based on three regions with Triassic successions from the North China block. While a first-hand study of other localities of similar age would be ideal, this is of course a difficult task. Instead, the authors provide comparisons with other worldwide regions to build their case and support the initial hypothesis.

Reviewer #3 (Public review):

Summary:

This manuscript by Guo and colleagues features the documentation and interpretation of three successions of continental to marginal marine deposits spanning the P/T transition and their respective ichnofaunas. Based on these new data inferences concerning end-Permian mass extinction and Triassic recovery in the tropical realm are discussed.

Strengths:

The manuscript is well-written and organized and includes a large amount of new lithological and ichnological data that illuminate ecosystem evolution in a time of large-scale transition. The lithological documentations, facies interpretations, and ichnotaxonomic assignments look okay (with a few exceptions).

Weaknesses:

Some interpretations in Table 1 could be questioned: For facies association FA2 the interpretation as „terrestrial facies with periodical flooding" should be put into the right column and, given the fossil content, other interpretations, such as "marine facies" or "lagoonal environment" with some plant debris and (terrestrial) animal remains washed in, could also be possible. For FA3 the statement "bioturbation is absent" is in conflict with the next statement "strata are moderately reworked". For FA5 the observation of a "monospecific ichnoassemblage" contradicts the listing of several ichnotaxa.

Concerning the structure of the manuscript, certain hypotheses related to the end-Permian mass extinction and the process of the P/T extinction and recovery, namely the existence of a long-persisting "tropic dead zone" are introduced as a foregone conclusion to which the new data seemingly shall be fit as corroborating evidence. Some of the data - e.g. the presence of a supposedly Smithian-age ichnofauna are interpreted as a fast recovery shortening the duration of the "tropic dead zone" episode - but these interpretations could also be interpreted as contradicting the idea of a "dead zone" sensu stricto in favour of a "normal" post-extinction environment with low diversity and occurrence of typical disaster taxa. Due to their large error bars the early Triassic radiometric ages did not put much of a constraint on the age determination of the earliest post-extinction ichnofaunas discussed here.

Considering the somewhat equivocal evidence and controversial ideas about the P/T transition, the introduction could be improved by describing how the idea of a "tropic dead zone" arose against the background of earlier ideas, alternative views, and conflicting data. In the discussion section, alternative interpretations of the extensive data presented here - e.g. proximal-distal shifts in lithofacies with respect to the sediment source, sea level changes, preservation bias, the local occurrence of hostile environments instead of a regional scale, etc. should be discussed, also to avoid the impression that the author's conclusion was driven by confirmation bias.

Contrary to the authors' claim, Figures S7 and S8 suggest that burrow size does not vary much within the studied sections. Size decreases and increases in the Shichuanhe and Liulin sections do not contemporaneously, are usually within the error-bar range, and might be driven by ichnotaxa composition, i.e. the presence or absence of larger ichnotaxa, rather than by size changes in the same ichnotaxon (and producer group). Here the measurement data would be needed as well to check the basis of the authors' interpretations.

Some arthropod tracks assigned here to Kouphichnium might not represent limulid traces but other (non-marine) arthropod taxa in accordance with their occurrence in terrestrial facies/non-marine units of the succession. More generally, the ichnotaxonomy of arthropod trackways is not yet well reserved - beyond Kouphichnium and Diplichnites various similar-looking types may occur that can have a variety of distinct insect, crustacean, millipede, etc. producers (including larval stages).

Reviewer #1 (Public review):

Summary:

The authors investigate the relationship between 3D chromatin architecture and innate immune gene regulation in monocytes from patients with alcohol-associated hepatitis (AH). Using Hi-C technology, they attempt to identify structural changes in the genome that correlate with altered gene expression. Their central claim is that genome restructuring contributes to the hyper-inflammatory phenotype associated with AH.

Strengths:

(1) The manuscript employs Hi-C technology, which, in principle, is a powerful approach for studying genome organization.

(2) The focus on disease-relevant genes, particularly innate immune loci, provides a contextually important angle for understanding AH.

Weaknesses:

(1) Sample Size: The study relies on an exceptionally small cohort (4 AH patients and 4 healthy controls), rendering the results statistically underpowered and highly susceptible to variability.

(2) Hi-C Resolution unpaired to RNA seq: The data are presented at a resolution of 100kb, which is insufficient to uncover meaningful chromatin interactions at the level of individual genes. This data is unpaired.

(3) Functional Validation: The manuscript lacks experiments to directly link changes in chromatin architecture with gene expression or monocyte function, leaving the claims speculative.

(4) Data Integration: The lack of Hi-C with ATAC and RNA-seq data handicaps the analysis and really makes it superficial. In short, it does not convincingly demonstrate a functional relationship.

(5) Confounding Factors: The manuscript neglects critical confounding variables such as comorbidities, medications, and lifestyle factors, which could influence chromatin structure and gene expression independently of AH.

Appraisal of the Aims and Results:

The manuscript sets out to establish a connection between chromatin architecture and AH pathology. However, the study fails to achieve its stated aims due to inadequate methods and insufficient data. The conclusions drawn from the Hi-C analyses alone are poorly supported, and the lack of functional validation undermines the credibility of the proposed mechanisms. Overall, the results do not provide compelling evidence to substantiate the authors' claims.

Impact on the Field and Utility to the Community:

The work, in its current form, is unlikely to have a meaningful impact on the field. The limited scope, methodological shortcomings, and lack of robust data significantly diminish its potential utility. Without addressing these critical gaps, the study does not offer new insights into the role of genome architecture in AH or provide useful methodologies or datasets for the community.

Additional Context:

The manuscript would benefit from a more comprehensive analysis of potential mechanisms underlying the observed changes, including the interplay between chromatin architecture and epigenetic modifications. Furthermore, longitudinal studies or therapeutic interventions could provide insights into the dynamic aspects of genome restructuring in AH. These considerations are entirely absent from the current study.

Conclusion:

The manuscript does not achieve its stated goals and does not present sufficient evidence to support its conclusions. The limitations in sample size, resolution, and experimental rigor severely hinder its contribution to the field. Addressing these fundamental flaws will be essential for the work to be considered a meaningful addition to the literature.

Reviewer #2 (Public review):

Summary:

Dr. Adam Kim and collaborators study the changes in chromatin structure in monocytes obtained from alcohol-associated hepatitis (AH) when compared to healthy controls (HC). Through the usage of high throughput chromatin conformation capture technology (Hi-C), they collected data on contact frequencies between both contiguous and distal DNA windows (100 kB each); mainly within the same chromosome. From the analyses of those data in the two cohorts under analysis, authors describe frequent pairs of regions subject to significant changes in contact frequency across cohorts. Their accumulation onto specific regions of the genome -referred to as hotspots- motivated authors to narrow down their analyses to these disease-associated regions, in many of which, authors claim, a number of key innate immune genes can be found. Ultimately, the authors try to draw a link between the changes observed in chromatin architecture in some of these hotspots and the differential co-expression of the genes lying within those regions, as ascertained in previous single-cell transcriptomic analyses.

Strengths:

The main strength of this paper lies in the generation of Hi-C data from patients, a valuable asset that, as the authors emphasize, offers critical insights into the role of chromatin architecture dysregulation in the pathogenesis of alcohol-associated hepatitis (AH). If confirmed, the reported findings have the potential to highlight an important, yet overlooked, aspect of cellular dysregulation-chromatin conformation changes - not only in AH but potentially in other immune-related conditions with a component of pathological inflammation.

Weaknesses:

In what I regard as the two most important weaknesses of the work, I feel that they are more methodological than conceptual. The first of these issues concerns the perhaps insufficient level of description provided on the definition of some key types of genomic regions, such as topologically associated domains, DNA hotspots, or even DNA loci showing significant changes in contact frequency between AH and HC. In spite of the importance of these concepts in the paper, no operational, explicit description of how are they defined, from a statistical point of view, is provided in the current version of the manuscript.

Without these definitions, some of the claims that authors make in their work become hard to sustain. Some examples are the claim that randomizing samples does not lead to significant differences between cohorts; the claim that most of the changes in contact frequency happen locally; or the claim that most changes do not alter the structure of TADs, but appear either within, or between TADs. In my viewpoint, specific descriptions and implementation of proper tests to check these hypotheses and back up the mentioned specific claims, along with the inclusion of explicit results on these matters, would contribute very significantly to strengthening the overall message of the paper.

The second notable weakness of the study pertains to the characterization of the changes observed around immune genes in relation to genome-wide expectations. Although the authors suggest that certain hotspots contain a high number of immune-related genes, no enrichment analysis is provided to verify whether these regions indeed harbor a higher concentration of such genes compared to other genomic areas. It would be important for readers to be promptly informed if no such enrichment is observed, for in that case, the presence of some immune genes within these hotspots would carry more limited implications.

Additionally, the criteria used to define a hotspot are not clearly outlined, making it difficult to assess whether the changes in contact frequencies around the immune genes highlighted in figures 5-8 are truly more pronounced than what would be expected genome-wide.

Reviewer #3 (Public review):

In this manuscript, the authors use HiC to study the 3D genome of CD14+ CD16+ monocytes from the blood of healthy and those from patients with Alcohol-associated Hepatitis.

Overall, the authors perform a cursory analysis of the HiC data and conclude that there are a large number of changes in 3D genome architecture between healthy and AH patient monocytes. They highlight some specific examples that are linked to changes in gene expression. The analysis is of such a preliminary nature that I would usually expect to see the data from all figures in just one or two figures.

In addition, I have a number of concerns regarding the experimental design and the depth of the analyses performed that I think must be addressed.

(1) There is a myriad of literature that describes the existence of cell type-specific 3D genome architecture. In this manuscript, there is an assumption by the authors that the CD14+ CD16+ monocytes represent the same population from both healthy and diseased patients. Therefore, the authors conclude that the differences they see in the HiC data are due to disease-related changes in the equivalent cell types. However, I am concerned that the AH patient monocytes may have differentiated due to their environment so that they are in fact akin to a different cell type and the 3D genome changes they describe reflect this. This is supported by published articles for example: Dhanda et al., Intermediate Monocytes in Acute Alcoholic Hepatitis Are Functionally Activated and Induce IL-17 Expression in CD4+ T Cells. J Immunol (2019) 203 (12): 3190-3198, in which they show an increased frequency of CD14+ CD16+ intermediate monocytes in AH patients that are functionally distinct.

I suggest that if the authors would like to study the specific effects of AH on 3D genome architecture then they should carefully FACsort the equivalent monocyte populations from the healthy and AH patients.

(2) The analysis of the HiC data is quite preliminary. In the 3D genome field, it is usual to report the different scales of genome architecture, for example, compartments, topologically associated domains (TADs), and loops. I think that reporting this information and how it changes in AH patients in the appropriate cell types would be of great interest to the field.

Reviewer #1 (Public review):

Summary:

Winkler et al. present brain activity patterns related to complex motor behaviour by combining whole-head magnetoencephalography (MEG) with subthalamic local field potential (LFP) recordings from people with Parkinson's disease. The motor task involved repetitive circular movements with stops or reversals associated with either predictable or unpredictable cues. Beta and gamma frequency oscillations are described, and the authors found complex interactions between recording sites and task conditions. For example, they observed stronger modulation of connectivity in unpredictable conditions. Moreover, STN power varied across patients during reversals, which differed from stopping movements. The authors conclude that cortex-STN beta modulation is sensitive to movement context, with potential relevance for movement redirection.

Strengths:

This study employs a unique methodology, leveraging the rare opportunity to simultaneously record both invasive and non-invasive brain activity to explore oscillatory networks.

Weaknesses:

It is difficult to interpret the role of the STN in context of reversals, because no consistent activity pattern emerged.

Comments on revisions: The authors have adequately addressed my comments.

Reviewer #2 (Public review):

Summary:

This study examines the role of beta oscillations in motor control, particularly during rapid changes in movement direction among patients with Parkinson's disease. The researchers utilized magnetoencephalography (MEG) and local field potential (LFP) recordings from the subthalamic nucleus to investigate variations in beta band activity within the cortex and STN during the initiation, cessation, and reversal of movements, as well as the impact of external cue predictability on these dynamics. The primary finding indicates that beta oscillations more effectively signify the start and end of motor sequences than transitions within those sequences. The article is well-written, clear, and concise.

Strengths:

The use of a continuous motion paradigm with rapid reversals extends the understanding of beta oscillations in motor control beyond simple tasks. It offers a comprehensive perspective on subthalamo-cortical interactions by combining MEG and LFP.

Comments on revisions: I am satisfied with the revisions. I do not have further comments on the revised manuscript.

Reviewer #3 (Public review):

Summary:

The study highlights how the initiation, reversal, and cessation of movements are linked to changes in beta synchronization within the basal ganglia-cortex loops. It was observed that different movement phases, such as starting, stopping briefly, and stopping completely, affect beta oscillations in the motor system.

It was found that unpredictable cues lead to stronger changes in STN-cortex beta coherence. Additionally, specific patterns of beta and gamma oscillations related to different movement actions and contexts were observed. Stopping movements was associated with a lack of the expected beta rebound during brief pauses within a movement sequence.

Overall, the results underline the complex and context-dependent nature of motor control and emphasize the role of beta oscillations in managing movement according to changing external cues.

Strengths:

The paper is very well written, clear and appears methodologically sound.

Although the use of continuous movement (turning) with reversals is more naturalistic than many previous button push paradigms.

Weaknesses:

The generalizability of the findings are somewhat curtailed by the fact that this was performed peri-operatively during the period of the microlesion effect. Given the availability of sensing enabled DBS devices now and HD-EEG, does MEG offer a significant enough gain in spatial localizability to offset the fact that it has to be done shortly postoperatively with externalized leads, with attendant stun effect? Specifically, for paradigms that are not asking very spatially localized questions as a primary hypothesis?

Further investigation of the gamma signal seems warranted, even though it has a slightly lower proportional change in amplitude in beta. Given that the changes in gamma here are relatively wide band, this could represent a marker of neural firing that could be interestingly contrasted against the rhythm account presented.

Comments on revisions: I congratulate the authors on their paper and their revisions and I have no further comments. I look forward to seeing the continuous analyses in the future. Good luck!

Reviewer #1 (Public review):

Summary:

This study is an important follow-up to their prior work - Wong et al. (2019), starting with clear questions and hypotheses, followed by a series of thoughtful and organized experiments. The method and results are convincing. Experiment 1 demonstrated the sensory preconditioned fear with few (8) or many (32) sound-light pairings. Experiments 2A and 2B showed the role of PRh NMDA receptors during conditioning for online integration, revealing that this contribution is present only after few sound-light pairings, not after many sound-light pairings. Experiments 3A and 3B showed the contribution of PRh-BLA communication to online integration, again only after few but not after many. Contrary to Experiments 3A and 3B, Experiments 4A and 4B showed the contribution of PRh-BLA communication to integration at test only after many but not few sound-light pairings.

Strengths:

Throughout the manuscript, the methods and results are clearly organized and described, and the use of statistics is solid, all contributing to the overall clarity of the research. The discussion section was also well written, effectively comparing the current research with the prior work and offering insightful interpretations and potential future directions for this line of research.

All my previous concerns have been well addressed in this revised version. I do not have further concerns about the current version of this manuscript.

Reviewer #2 (Public review):

This manuscript builds on the authors' earlier work, most recently Wong et al. 2019, in which they showed the importance of the perirhinal cortex (PRh) during the first-order conditioning stage of sensory preconditioning. Sensory preconditioning requires learning between two neutral stimuli (S2-S1) and subsequent development of a conditioned response to one of the neutral stimuli after pairing of the other stimulus with a motivationally relevant unconditioned stimulus (S1-US). One highly debated question regarding the mechanisms of learning of sensory preconditioning has been whether conditioned responses evoked by the indirectly trained stimulus (S2) occur through a mediated representation at the time of the first-order US training, or whether the conditioned responses develop through a chained evoked representation (S2--> S1 --> US) at the time of test. The authors' prior findings provided strong evidence for PRh being involved in mediated learning during the first-order training. They showed that protein synthesis was required during the first-order S1-US learning to support the conditioned response to the indirectly trained stimulus (S2) at test.

One question remaining following the previous paper was whether certain conditions may promote a chaining mechanism over mediated learning, as there is some evidence for chained representations at the time of test. In this paper, the authors directly address this important question and find unambiguous results that the extent of training during the preconditioning stage impacts the involvement of PRh during the first-order conditioning or stage 2. They show that putative blockade of synaptic changes in PRh, using an NMDA antagonist, disrupts responding to the preconditioned cue at test during shorter duration preconditioning training (8 trials), but not during extended training (32 trials). They also show that this is the case for communication between the PRh and BLA during the same stage of training using a contralateral inactivation approach. This confirms their previous findings in 2019 of connectivity between these regions for the short duration training, while they observe here for the first time that this is not the case for extended training. Finally, they show that with extended training, communication between BLA and the PRh is required at the final test of the preconditioned stimulus, but not for the short duration training.

Strengths:

The results are clear and extremely consistent across experiments within this paper as well as with earlier work. The experiments here are thorough, well-conceived, and address an important and highly debated question in the field regarding the neural and psychological mechanisms underlying sensory preconditioning. This work is highly impactful for the field as the debate over mediated versus chaining mechanisms has been an important topic for more than 70 years.

Comments on revisions:

Thank you for addressing all of my concerns in considerable detail. I have no more suggestions for the authors. This is a fantastic paper both in the experimental design and the execution as well as in the high quality of writing.

Reviewer #3 (Public review):

The authors tested whether: 1. The number of stimulus-stimulus pairings alters whether preconditioned fear depends on online integration during formation of the stimulus-outcome memory or during the probe test/mobilization phase, when the original stimulus, which was never paired with aversive events, elicits fear via chaining of stimulus-stimulus and stimulus-outcome memories. They found that sensory preconditioning was successful with either 8 or 32 stimulus-stimulus pairings. Perirhinal cortex NMDA receptor blockade during stimulus-outcome learning impaired preconditioning following 8 but not 32 pairings during preconditioning. Therefore, perirhinal cortex NMDA activity is required for online integration or mediated learning. Perirhinal-basolateral amygdala had nearly identical effects with the same interpretation: these areas communicate during stimulus-outcome learning, and this online communication is required for later expressing preconditioned fear. Disconnection prior to the probe test, when chaining might occur, had different effects: it impaired the expression of preconditioned fear in rats that received 32, but not 8, pairings during preconditioning. The study has several strengths and provides a thoughtful discussion of future experiments. The study is highly impactful and significant; the authors were successful in describing the behavioral and neurobiological mechanisms of mediated learning versus chaining in sensory preconditioning, which is often debated in the learning field. Therefore this study will have a significant impact on the behavioral neurobiology and learning fields.

Strengths:

Careful, rigorous experimental design and statistics

The discussion leaves open questions that are very much worth exploring. For example - why did perirhinal-amygdala disconnection prior to the probe have no effect in the 8-pairing group, when bilateral perirhinal inactivation did (in Wong et al, 2019)? The authors propose that perirhinal cortex outputs bypass the amygdala during the probe test, which is an excellent hypothesis to test.

The experiments are very explicitly hypothesis-driven, and the authors provide evidence of how and why mediated learning and chaining occur during sensory-sensory learning.

Joint Public Review:

Summary:

The behavioral switch between foraging and mating is important for resource allocation in insects. This study characterizes the role of sulfakinin and the sulfakinin receptor 1 in changes in olfactory responses associated with foraging versus mating behavior in the oriental fruit fly (Bactrocera dorsalis), a significant agricultural pest. This pathway regulates food consumption and mating receptivity in other species; here the authors use genetic disruption of sulfakinin and sulfakinin receptor 1 to provide strong evidence that changes in sulfakinin signaling modulate antennal responses to food versus pheromonal cues and alter the expression of ORs that detect relevant stimuli.

Strengths:

The authors utilize multiple complementary approaches including CRISPR/Cas9 mutagenesis, behavioral characterization, electroantennograms, RNA sequencing and heterologous expression to convincingly demonstrate the involvement of the sulfakinin pathway in the switch between foraging and mating behaviors. The use of both sulfakinin peptide and receptor mutants is a strength of the study and implicates specific signaling actors.

Weaknesses:

The authors demonstrate that SKR is expressed in olfactory neurons, however there are additional potential sites of action that may contribute to these results.

Reviewer #1 (Public review):

The study investigates light chains (LCs) using three distinct approaches, with a focus on identifying a conformational fingerprint to differentiate amyloidogenic light chains from multiple myeloma light chains. The study's major contribution is the identification of a low-populated "H state," which the authors propose as a unique marker for AL-LCs. While this finding is promising, the review highlights several strengths and weaknesses. Strengths include the valuable contribution of identifying the H state and the use of multiple approaches, which provide a comprehensive understanding of LC structural dynamics. Weaknesses include a lack of physical insights explaining the changes.

Reviewer #2 (Public review):

Summary:

This well-written manuscript addresses an important but recalcitrant problem - molecular mechanism of protein misfolding in Ig light chain (LC) amyloidosis (AL), a major life-threatening form of systemic human amyloidosis. The authors use expertly recorded and analyzed small-angle X-ray scattering (SAXS) data as a restraint for molecular dynamics simulations (called M&M). Six patient-based LC proteins are explored, including four AL and two non-AL. The authors report a partially populated "H-state" determined computationally, wherein the two domains in an LC molecule acquire a straight rather than bent conformation, with an extended interdomain linker; this H-state distinguishes AL from non-AL LCs. H-D exchange mass spectrometry is used to support this conclusion. This is a novel and interesting finding with potentially important translational implications.

Strengths:

Expertly recorded and analyzed SAXS data combined with clever M&M simulations lead to a novel and interesting conclusion, which is supported by limited H-D exchange data.<br /> Stabilization of the CL-CL interface is a good idea that may help protect a subset of AL LCs from misfolding in amyloid.

Computational M&M evidence is convincing and is supported by SAXS data, which are used as restraints for simulations. Although Kratky plots reported in the main MS Fig. 1 show significant differences between the data and the structural model for only one AL protein, AL-55, H-state is also inferred for other AL proteins.

Apparent limitations:

HDX MS results show that residues 35-50 from VL-VL and VL-CL dimerization interface are less protected in AL vs. non-AL proteins, which is consistent with the H-state. However, the small number of proteins yielding useful HDX data (three AL and one non-AL) suggests that this conclusion should be treated with caution. It is unclear whether the conformational heterogeneity depicted in M&M simulations is consistent with HDX results, and whether prior HDX studies of AL and MM LCs are consistent with the conclusions that a particular domain-domain interface is weakened in AL vs. non-AL LCs. The butterfly plots in Fig. 5 could benefit from the X-axis labeling with the peptide fragments.

Reviewer #3 (Public review):

Summary:

This study identifies confirmational fingerprints of amylodogenic light chains, that set them apart from the non-amylodogenic ones.

Strengths:

The research employs a comprehensive combination of structural and dynamic analysis techniques, providing evidence that conformational dynamics at VL-CL interface and structural expansion are distinguished features of amylodogenic LCs.

Weaknesses:

The sample size is limited, which may affect the generalizability of the findings. Additionally, the study could benefit from deeper analysis of specific mutations driving this unique conformation to further strengthen therapeutic relevance.

Furthermore. p-value (statistical significance) of Rg difference should be computer. Finally, significance of mutations (SHM?) at the interface, such as A40G should be compared with previous observations. (Garofalo et al., 2021)

Reviewer #1 (Public review):

Summary:

This manuscript by Ishii et al utilize a classical, but extremely understudied, female self-paced assay to directly address aspects of female sxual motivation independent from the male's behavior. This allowed for clear separation of appetitive and consummatory events, of which whole brain unbiased activity was mapped. Mating completion in females was then focused to the medial preoptic nucleus where the authors performed a rigorous set of single-cell GCaMP recordings in populations marked by Vglut2 and Vgat, finding the latter display stronger and prolonged activity after the onset of mating completion. Finally, they demonstrate function to these Fos-TRAPPED completion cells demonstrating their capacity to suppress female sexual behavior.

Strengths:

This manuscript sought to explicitly explore female mating drive as dictated by the female, a very rare angle for those studying mating behavior which almost always is controlled by the male's behavior. To achieve this, the authors went back to old literature and modified a classical paradigm in which measurable approach and avoidance of male conspecifics can be measured in female mice using a self-paced mating assay. Strengths include a detailed quantification of female behaviors demonstrating a robust attenuated sexual motivation in females after mating completion. To determine the neural basis behind this, a brain wide analysis of cells responding to mating completion in the female brain was conducted which revealed numerous anatomical regions displaying increased Fos activity, including the MPOA, of which the authors concentrated the remaining of their study. Employing microendoscopic imaging, the authors discovered that this mating completion signal was strongly represented in the MPOA. The single cell data analyses are of very high quality as is the number of individual cells resolved. While they identified both excitatory and inhibitory cell types that were activated by mating completion, they found the latter exhibited stronger and more persistent activity. Segmentation into individual mating behaviors reinforced the importance of GABAergic completion cells, which display prolonged activity late after the onset of mating completion. This information provides a potential mechanism for how female mice suppress further mating activity following completion. The authors then definitively demonstrate this function by TRAP'ping completion cells with chemogenetic actuators and show that CNO-induced activation of these cells specifically and strongly suppresses female sexul behavior. All experiments were extremely well-designed and performed carefully and expertly with the necessary controls solidifying the conclusions.

Weaknesses:

While there are no glaring weaknesses in this study, it should be noted that a great deal of literature has pinpointed the MPOA (and specifically inhibitory cells in this area) as being critical to sexual behavior, including female mating. However, no study to my knowledge has explored self-paced female mating with such fine control over manipulating and monitoring cellular activity in this region. In addition, this study may act to inspire others to further explore the additional brain regions found to show upregulation of neural activity (Fos) during mating completion in the female using the data sets generated here.

Comments on revisions: The data has been provided in a public database.

Reviewer #2 (Public review):

Summary:

In this set of studies, authors identify cFos activation in neurons in female mice that mated with males, and after experiencing male sexual behavior that is either restricted to appetitive behavior or including ejaculation. The medial preoptic nucleus was identified as an area with high cFos induction following ejaculation. Characterization of neurochemical phenotypes of cfos-expressing neurons showed a heterogenous distribution of activated neurons in the MPOA, including both inhibitory and excitatory cell types. Next, in vivo calcium imaging was used to show activation of Vgat and Vglut neurons in female mice MPOA after displaying sniffing of the male, experiencing male appetitive, or male consummatory sexual behavior, demonstrating significantly higher activation and of a greater subpopulation of Vgat neurons than Vglut neurons. Moreover, greatest activation of Vgat neurons was detected following experiencing ejaculation, and ejaculation activated different subpopulations of MPOA cells than consummatory or appetitive sexual behaviors experienced by the female. Finally, pharmaco-genetic activation of the subpopulation of MPOA neurons that were previously activated following ejaculation resulted in a significant reduction of approach behavior by the female mice towards the male, interpreted as suppression of female sexual motivation. In conclusion, a subpopulation of inhibitory cells in the MPOA is activated in female mice after experiencing ejaculation, in turn contributing to suppression of sexual approach behavior.

Strengths:

The current set of studies replicates previous findings that ejaculation causes longer latencies to initiate interactions with a male after receiving an ejaculation in a paced mating paradigm, which is widely validated and extensively used to investigate sexual behavior in female rodents. Studies also confirm that ejaculation increases cFos expression in the MPOA, while extending prior findings with a careful analysis of the neurochemical phenotype of activated neurons. A major strength of the studies is the use of cell-specific in vivo imaging and pharmaco-genetic activation to reveal a functional role of specific neuronal ensemble within the MPOA for post ejaculatory female sexual behavior.

Weaknesses:

The authors include an elegant manipulation of ejaculation-activated neurons in the MPOA using DREADD. However, this study was limited to show that activation of previously activated cells was sufficient to reduce approach behavior in a paced mating paradigm and receiving intromissions in a home cage mating paradigm. An inhibition approach using DREADD would have been a great complement to this study as it would have examined if activation of the cells was required. Moreover, additional tests for sexual motivation would have greatly strengthened the overall conclusions.

Reviewer #3 (Public review):

Summary:

Ishii et al used molecular genetics, behavioral analyses, in vivo neural activity imaging, and neural activity manipulations in mice to study the functional role of a subset of medial preoptic area (MPOA) neurons in the regulation of female sexual drive. They first employed a self-paced mating assay during which a female could control the amount of interaction time with a male to assess female sexual drive after completion of mating. The authors observed that after mating completion (i.e., male ejaculation) females spend significantly less time interacting with males, indicating that their sexual drive is reduced. Next, the authors performed a brain-wide analysis of neurons activated following male ejaculation and identified the MPOA as a strong candidate region. One caveat is that the activity labeling was not exclusive to neurons activated following male ejaculation but included all neurons activated before, during, and after the mating encounter. However, in this revised version of the manuscript, the authors have included a key control group that labels all neurons activated up to but not including male ejaculation. Comparison of the number of activated neurons in these two groups revealed a significant additional set of neurons in the female MPOA following ejaculation. Importantly, the authors also provided in vivo calcium imaging data showing that a subset of MPOA neurons responds significantly and specifically to male ejaculation and not other behaviors during the social encounter. The authors performed these studies in both excitatory and inhibitory populations of the MPOA. Their analysis identified a subpopulation of inhibitory neurons that exhibit sustained increased activity for 90 sec following male ejaculation. Finally, the authors used chemogenetics to activate MPOA neurons during home cage mating, condition place preference, pup retrieval, and the self-paced mating assay. They found that activation of female MPOA neurons that were previously activated following male ejaculation significantly reduces mating behaviors and time spent interacting with a male during the self-paced mating assay. Whereas, activation of female MPOA neurons that were previously activated during consummatory behaviors but not male ejaculation does not alter mating behaviors and time spent interacting with a male. Therefore, MPOA neurons activated following ejaculation are sufficient to suppress female sexual motivation.

The authors' experimental execution is rigorous and well performed. Their data identify inhibitory neurons in the female MPOA as a neural locus that is activated following male ejaculation and whose prolonged activity plays a key role in the regulation of female sexual motivation. The addition of some key control groups to this revised version of the manuscript greatly strengthens the interpretation of the authors' findings.

Strengths:

(1) The use of the self-paced mating assay in combination with neural imaging and manipulation to assess female sexual drive is innovative. The authors correctly assert that relatively little is known about how male ejaculation affects sexual motivation in females as compared to males. Therefore, the data collected from these studies is important and valuable.

(2) The authors provide convincing histological data and analyses to verify and validate their brain-wide activity labeling, neural imaging, and chemogenetic studies.

(3) The single cell in vivo calcium imaging data are well performed and analyzed. They provide key insights into the activity profiles of both excitatory and inhibitory neurons in the female MPOA during mating encounters. The authors identification of an inhibitory subpopulation of female MPOA neurons that is selectively activated following completion of mating is fundamental for future experiments which could potentially find a molecular marker for this population and specifically manipulate these neurons to understand their role in female sexual motivation in greater detail.

(4) The authors provide convincing evidence that activation of female MPOA neurons activated following male ejaculation is sufficient to suppress female sexual motivation. Importantly, the authors addition of the consummatory-hM3Dq group demonstrates that activation of female MPOA neurons activated during mating behaviors prior to male ejaculation is not sufficient to suppress female sexual motivation.

Weaknesses:

In this revised version of the manuscript, the authors have added important controls as well as additional clarifying text that adequately address the weaknesses that were present in the original version of the manuscript.

Reviewer #3 (Public review):

Human and simian immunodeficiency viruses (HIV and SIV, respectively) evolved numerous mechanisms to compromise effective immune responses but the underlying mechanisms remain incompletely understood. Here, Yamamoto and Matano examined the humoral immune response in a large number of rhesus macaques infected with the difficult-to-neutralize SIVmac239 strain and identified a subgroup of animals showing significant neutralizing Ab responses. Sequence analyses revealed that in most of these animals (7/9) but only a minority in the control group (2/19) SIVmac variants containing a CD8+ T-cell escape mutation of G63E/R in the viral Nef gene emerged. Functional analyses revealed that this change attenuates the ability of Nef to stimulate PI3K/Akt/mTORC2 signalling. The authors propose that this improved induction of SIVmac239 nAb is reciprocal to antibody dysregulation caused by a previously identified human PI3K gain-of-function mutation associated with impaired anti-viral B-cell responses. Altogether, the results suggest that PI3K signalling plays a role in B-cell maturation and generation of effective nAb responses. Preliminary data indicate that Nef might be transferred from infected T cells to B cells by direct contact. However, the exact mechanism and the relevance for vaccine development requires further studies

The strengths of the study are that the authors analyzed a large number of SIVmac-infected macaques to unravel the biological significance of the known effect of the interaction of Nef with PI3K/Akt/mTORC2 signaling. This is interesting and may provide a novel means to improve humoral immune responses to HIV. In the revised version the authors made an effort to address previous concerns. Especially, they provide data supporting that Nef might be transferred to B cells by direct cell-cell contact. In addition, they provide some evidence that G63R that also emerged in most animals does not share the disruptive effect of G63G although experimental examination and discussion why G63R might emerge remains poor. A weakness that remains is that some effects of the G63E mutation are modest and effects were not compared to SIVmac constructs lacking Nef entirely. The evidence for a role of Nef G63E mutation on PI3K and the association with improved nAb responses is convincing and it is appreciated that the authors provide additional evidence for a potential impact of "soluble" Nef on neighboring B cells. The presentation of the experimental set-up and the results has been improved but is in part still challenging to comprehend. It seems that direct cell-cell contact is required and membranes are exchanged. Since Nef is associated with cellular membranes this might lead to some transfer of Nef to B cells. However, the immunological and functional consequences of this largely remain to be determined. Alternatively, Nef-mediated manipulation of helper CD4 T cells might also impact B cell function and effective humoral immune responses. Additional editing of the manuscript has been performed to make the results accessible to a broad readership.

Reviewer #1 (Public review):

Summary:

Wang et al. generate XAP5 and XAP5L knockout mice and find that they are male infertile due to spermatogonial/meiotic arrest and reduced sperm motility, respectively. CUT & Tag data were added in this revision in order to support that XAP5 and XAP5L are antagonistic transcription factors of cilliogenesis.

Strengths:

Knockout mouse models provided strong evidence to indicate that XAP5 and XAP5L are critical for spermatogenesis. RNA-seq and CUT & Tag are valuable sources to further explore their molecular mechanisms.

Weaknesses:

The authors claim that XAP5 and XAP5L transcriptionally regulate sperm flagella development; however, expression, physiological role, and molecular evidence do not well support this concept. This reviewer still thinks the physiological roles of XAP5 and XAP5l are different. (i) XAP5 is expressed at spermatogonia within testes while XAP5l is localized at round/elongating spermatids (their expressions are different). (ii) Spermatogenesis was arrested at spermatogonia/early spermatocyte stage in Xap5-KO mice while sperm abnormalities were observed in Xap5l-KO mice (their roles are different). This reviewer still can't get the authors' viewpoint that XAP5 and XAP5l are 'antagonistic relationship' to regulate sperm flagella development. RNA-seq and CUT & Tag data are valuable source; however, this reviewer suggests exploring how XAP5 regulates spermatogonia differentiation and how XAP5l regulates sperm flagella motility.

Reviewer #2 (Public review):

In this study, Wang et al., report the significance of XAP5L and XAP5 in spermatogenesis which are involved in transcriptional regulation of the ciliary gene in testes. In a previous study, the authors demonstrated that XAP5 is a transcription factor required for flagellar assembly in Chlamydomonas. Continuing from their previous study, the authors examined conserved role of the XAP5 and XAP5L, which are the orthologue pair in mammals.

XAP5 and XAP5L express ubiquitously and testis specifically, respectively, and their absence in testes causes male infertility with defective spermatogenesis. Interestingly, XAP5 deficiency arrest germ cell development at pachytene stage, whereas XAP5L absence causes impaired flagellar formation. RNA-seq analyses demonstrated that XAP5 deficiency suppresses ciliary gene expression including Foxj1 and Rfx family genes in early testis. By contrast, XAP5L deficiency abnormally remains Foxj1 and Rfx genes in mature sperm. From the results, the authors conclude that XAP5 and XAP5L are the antagonistic transcription factor to function at the upstream of Foxj1 and Rfx family genes.

The current version of the manuscript well represents this reviewer's initial concerns and supports author's claim. Key transcription factors for ciliogenesis, Foxj1 and Rfx2, are direct downstream targets for XAP5 and XAP5L and their common motifs well explain their antagonistic function in sperm flagellar development. All the results well demonstrate that ancient transcription factors, XAP5 and XAP5L, are upstream transcription factors to modulate flagellar development in male mammalian germ line.

Reviewer #1 (Public review):

Summary:

In this manuscript, Corso-Diaz et al, focus on the NRL transcription factor (TF), which is critical for retinal rod photoreceptor development and function. The authors profile NRL's protein interactome, revealing several RNA-binding proteins (RBPs) among its components. Notably, many of these RBPs are associated with R-loop biology, including DHX9 helicase, which is the primary focus of this study. R-loops are three-stranded nucleic acid structures that frequently form during transcription. The authors demonstrate that R-loop levels increase during photoreceptor maturation and establish an interaction between NRL TF and DHX9 helicase. The association between NRL and RBPs like DHX9 suggests a cooperative regulation of gene expression in a cell-type-specific manner, an intriguing discovery relevant to photoreceptor health. Since DHX9 is a key regulator of R-loop homeostasis, the study proposes a potential mechanism where a cell-type-specific TF controls the expression of certain genes by modulating R-loop homeostasis. The authors also identify another R-loop resolvase, DDX5 as having weaker interaction with NRL, perhaps due to indirect mechanism.

This is a very interesting study providing genome-wide mapping of R-loops in mammalian retina, which shows an enrichment of R-loops over intergenic regions as well as genes encoding neuronal function factors. The R-loop-enriched genes are longer than genes without R-loops, which supports previous findings from studies in neuronal cells. This is a very relevant study highlighting the possible mechanism of gene expression regulation via interactions between TFs, RNA binding proteins, and R-loops. In that regard, it would be very interesting to uncover the biological relevance of such gene regulation. The authors provide adequate evidence of interaction between R-loops and NRL TF via in vitro IP assay and genomic co-localization, however, this interaction can be mediated via multiple R-loop or RNA-binding proteins. Thus, follow-up studies would be appropriate to characterize this interaction in more detail.

Reviewer #2 (Public review):

Summary:

The Authors utilize biochemical approaches to determine and validate NRL protein-protein interactions to further understand the mechanisms by which the NRL transcription factor controls rod photoreceptor gene regulatory networks. Observations that NRL displays numerous protein-protein interactions with RNA-binding proteins, many of which are involved in R-loop biology, led the authors to investigate the role of RNA and R-loops in mediating protein-protein interactions and profile the co-localization of R-loops with NRL genomic occupancy.

Strengths:

Overall, the manuscript is well-written, providing succinct explanations of the observed results and potential implications. Additionally, the Authors use multiple orthogonal techniques and tissue samples to reproduce and validate that NRL interacts with DHX9 and DDX5. Experiments also utilize specific assays to understand the influence of RNA and R-loops on protein-protein interactions. The Authors also use state-of-the-art techniques to profile R-loop localization within the retina and integrate multiple previously established datasets to correlate R-loop presence with transcription factor binding and chromatin marks an attempt to understand the significance of R-loops in the retina.

Weaknesses:

In general, the Authors provide interpretations of the data that fit a narrative about NRL and the perceived significance of interactions with RNA binding proteins. Large-scale screens for NRL protein interactions were conducted but all of the data is not reported. For example, NRL IP-Mass Spec was performed, but the authors only provide interaction/detection data for identified interactions with known RNA binding proteins. We cannot assess the enrichment of interactions or specificity of interactions with RNA binding proteins based on the reported results. Additionally, the lack of experiments testing the functional significance of Nrl interactions with R-loops within the developing retina fails to provide novel biological insights into the regulation of gene regulatory networks. While this provides additional avenues for research in the future, it is unclear that NRL interaction with R-loops have physiological relevance for photoreceptor health or function.

Reviewer #1 (Public review):

The work by Kim et al. shows that a parameter generator for biophysical HH-like models can be trained through a GAN-based approach, to reproduce experimentally measured voltage responses and IV curves.<br /> A particularly interesting aspects of this generator is that, once it has been learned, it can be applied to new recordings to generate appropriate parameter sets at a low computational cost, a feature missing from more commonplace evolutionary approaches.

I appreciate the changes the authors have made to the manuscript. The authors have clarified their inverse gradient method. They also provide a better validation and a rich set of ablations. However, I still have major concerns that should be addressed.

Major concerns:

(1) The bad equilibria of the model still remain a concern, as well as other features like the transient overshoots that do not match with the data. I think they could achieve more accuracy here by assigning more weight to such specific features, through adding these as separate objectives for the generator explicitly. The traces contain a five-second current steps, and one second before and one second after the training step. This means that in the RMSE, the current step amplitude will dominate as a feature, as this is simply the state for which the data trace contains most time-points. Note that this is further exacerbated by using the IV curve as an auxiliary objective. I believe a better exploration of specific response features, incorporated as independently weighted loss terms for the generator, could improve the fit. E.g. an auxiliary term could be the equilibrium before and after the current step, another term could penalise response traces that do not converge back to their initial equilibrium, etc.

(2) The explanation of what the authors mean with 'inverse gradient operation' is clear now. However, this term is mathematically imprecise, as the inverse gradient does not exist because the gradient operator is not injective. The method is simply forward integration under the assumption that the derivate of the voltage is known at the grid time-points, and should be described as such.

(3) I appreciate that the authors' method provides parameters of models at a minimal computational cost compared to running an evolutionary optimization for every new recording. I also believe that with some tweaking of the objective, the method could improve in accuracy. However, I share reviewer 2's concerns that the evolutionary baseline methods are not sufficiently explored, as these methods have been used to successfully fit considerably more complex response patterns. One way out of the dilemma is to show that the EP-GAN estimated parameters provide an initial guess that considerably narrows the search space for the evolutionary algorithm. In this context, the authors should also discuss the recent gradient based methods such as Deistler et al. (https://doi.org/10.1101/2024.08.21.608979) or Jones et al (https://doi.org/10.48550/arXiv.2407.04025).

Reviewer #2 (Public review):

Summary:

Generating biophysically detailed computational models that capture the characteristic physiological properties of biological neurons for diverse cell types is an important and difficult problem in computational neuroscience. One major challenge lies in determining the large number of parameters of such models, which are notoriously difficult to fit to experimental data. Thereby, the computational and energy costs can be significant. The study 'ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses' by Kim et al. describes a computationally efficient approach for predicting model parameters of Hodgkin-Huxley neuron models using Generative Adversarial Networks (GANs) trained on simulation data. The method is applied to generate models for 9 non-spiking neurons in C. elegans based on electrophysiological recordings. While the generated models capture the responses of these neurons to some degree, they generally show significant deviations from the empirically observed responses in important features. Although EP-GAN shows clear benefits under limited compute, the results do not yet demonstrate the quality needed to match other state-of-the-art methods. Future work examining extended training, larger datasets, or hybrid approaches would help clarify whether EP-GAN can generate models of high quality. If so, this would indeed be a major step forward; if not, the computationally more expensive methods will remain essential.

Strengths:

The authors work on an important and difficult problem. A noteworthy strength of their approach is that once trained, the GANs can generate models from new empirical data with very little computational effort. The generated models reproduce the response to current injections reasonably well.

Weaknesses:

Major 1: Models do not faithfully capture empirical responses. While the models generated with EP-GAN reproduce the average voltage during current injections reasonably well, the dynamics of the response are generally not well captured. For example, for the neuron labeled RIM (Figure 2), the most depolarized voltage traces show an initial 'overshoot' of depolarization, i.e. they depolarize strongly within the first few hundred milliseconds but then fall back to a less depolarized membrane potential. In contrast, the empirical recording shows no such overshoot. Similarly, for the neuron labeled AFD, all empirically recorded traces slowly ramp up over time. In contrast, the simulated traces are mostly flat. Furthermore, all empirical traces return to the pre-stimulus membrane potential, but many of the simulated voltage traces remain significantly depolarized, far outside of the ranges of empirically observed membrane potentials. The authors trained an additional GAN (EP-GAN Extended) to improve the fit to the resting membrane potential. Interestingly, for one neuron (AWB), this improved the response during stimulation, which now reproduced the slowly raising membrane potentials observed empirically, however, the neuron still does not reliably return to its resting membrane potential. For the other two neurons, the authors report a decrease in accuracy in comparison to EP-GAN. While such deviations may appear small in the Root mean Square Error (RMSE), they likely indicate a large mismatch between the model and the electrophysiological properties of the biological neuron. The authors added a second metric during the revision - percentages of predicted membrane potential trajectories within empirical range. I appreciate this additional analysis. As the empirical ranges across neurons are far larger than the magnitude of dynamical properties of the response ('slow ramps', etc.), this metric doesn't seem to be well suited to quantify to which degree these dynamical properties are captured by the models.

Major 2: Comparison with other approaches is potentially misleading. Throughout the manuscript, the authors claim that their approach outperforms the other approaches tested. But compare the responses of the models in the present manuscript (neurons RIM, AFD, AIY) to the ones provided for the same neurons in Naudin et al. 2022 (https://doi.org/10.1371/journal. pone.0268380). Naudin et al. present models that seem to match empirical data far more accurately than any model presented in the current study. Naudin et al. achieved this using DEMO, an algorithm that in the present manuscript is consistently shown to be among the worst of all algorithms tested. I therefore strongly disagree with the authors claim that a "Comparison of EP-GAN with existing estimation methods shows EP-GAN advantage in the accuracy of estimated parameters". This may be true in the context of the benchmark performed in the study (i.e., a condition of very limited compute resources - 18 generations with a population size of 600, compare that to 2000 generations recommended in Naudin et al.), but while EP-GAN wins under these specific conditions (and yes, here the authors convincingly show that their EP-GAN produces by far the best results!), other approaches seem to win with respect to the quality of the models they can ultimately generate.

Major 3: As long as the quality of the models generated by the EP-GAN cannot be significantly improved, I am doubtful that it indeed can contribute to the 'ElectroPhysiome', as it seems likely that dynamics that are currently poorly captured, like slow ramps, or the ability of the neuron to return to its resting membrane potential, will critically affect network computations. If the authors want to motivate their study based on this very ambitious goal, they should illustrate that single neuron model generation with their approach is robust enough to warrant well-constrained network dynamics. Based on the currently presented results, I find the framing of the manuscript far too bold.

Major 4: The conclusion of the ablation study 'In addition the architecture of EP-GAN permits inference of parameters even when partial membrane potential and steady-state currents profile are given as inputs' does not seem to be justified given the voltage traces shown in Figure 3. For example, for RIM, the resting membrane potential stays around 0 mV, but all empirical traces are around -40mV. For AFD, all simulated traces have a negative slope during the depolarizing stimuli, but a positive slope in all empirically observed traces. For AIY, the shape of hyperpolarized traces is off. While it may be that by their metric neurons in the 25% category are classified as 'preserving baseline accuracy', this doesn't seem justified given the voltage traces presented in the manuscript. It appears the metric is not strict enough.

Reviewer #1 (Public review):

Koesters and colleagues investigated the role of the small GTPase Rab3A in homeostatic scaling of miniature synaptic transmission in primary mouse cortical cultures using electrophysiology and immunohistochemistry. The major finding is that TTX incubation for 48 hours does not induce an increase in the amplitude of excitatory synaptic miniature events in neuronal cortical cultures derived from Rab3A KO and Rab3A Earlybird mutant mice. NASPM application had comparable effects on mEPSC amplitude in control and after TTX, implying that Ca2+-permeable glutamate receptors are unlikely modulated during synaptic scaling. Immunohistochemical analysis revealed no significant changes in GluA2 puncta size, intensity, and integral after TTX treatment in control and Rab3A KO cultures. Finally, they provide evidence that loss of Rab3A in neurons, but not astrocytes, blocks homeostatic scaling. Based on these data, the authors propose a model in which neuronal Rab3A is required for homeostatic scaling of synaptic transmission, potentially through GluA2-independent mechanisms.

The major finding - impaired homeostatic up-scaling after TTX treatment in Rab3A KO and Rab3 earlybird mutant neurons - is supported by data of high quality. However, the paper falls short of providing any evidence or direction regarding potential mechanisms. The data on GluA2 modulation after TTX incubation are likely statistically underpowered, and do not allow drawing solid conclusions, such as GluA2-independent mechanisms of up-scaling.

The study should be of interest to the field because it implicates a presynaptic molecule in homeostatic scaling, which is generally thought to involve postsynaptic neurotransmitter receptor modulation. However, it remains unclear how Rab3A participates in homeostatic plasticity.

Major (remaining) point:

(1) Direct quantitative comparison between electrophysiology and GluA2 imaging data is complicated by many factors, such as different signal-to-noise ratios. Hence, comparing the variability of the increase in mini amplitude vs. GluA2 fluorescence area is not valid. Thus, I recommend removing the sentence "We found that the increase in postsynaptic AMPAR levels was more variable than that of mEPSC amplitudes, suggesting other factors may contribute to the homeostatic increase in synaptic strength." from the abstract.<br /> Similarly, the data do not directly support the conclusion of GluA2-independent mechanisms of homeostatic scaling. Statements like "We conclude that these data support the idea that there is another contributor to the TTX- induced increase in quantal size." should be thus revised or removed.

Reviewer #2 (Public review):

I thank the authors for their efforts in the revision. In general, I believe the main conclusion that Rab3A is required for TTX-induced homeostatic synaptic plasticity is well-supported by the data presented, and this is an important addition to the repertoire of molecular players involved in homeostatic compensations. I also acknowledge that the authors are more cautious in making conclusions based on the current evidence, and the structure and logic have been much improved.

The only major concern I have still falls on the interpretation of the mismatch between GluA2 cluster size and mEPSC amplitude. The authors argue that they are only trying to say that changes in the cluster size are more variable than those in the mEPSC amplitude, and they provide multiple explanations for this mismatch. It seems incongruous to state that the simplest explanation is a presynaptic factor when you have all these alternative factors that very likely have contributed to the results. Further, the authors speculate in the discussion that Rab3A does not regulate postsynaptic GluA2 but instead regulates a presynaptic contributor. Do the authors mean that, in their model, the mEPSC amplitude increases can be attributed to two factors- postsynaptic GluA2 regulation and a presynaptic contribution (which is regulated by Rab3A)? If so, and Rab3A does not affect GluA2 whatsoever, shouldn't we see GluA2 increase even in the absence of Rab3A? The data in Table 1 seems to indicate otherwise.

I also question the way the data are presented in Figure 5. The authors first compare 3 cultures and then 5 cultures altogether, if these experiments are all aimed to answer the same research question, then they should be pooled together. Interestingly, the additional two cultures both show increases in GluA2 clusters, which makes the decrease in culture #3 even more perplexing, for which the authors comment in line 261 that this is due to other factors. Shouldn't this be an indicator that something unusual has happened in this culture? Data in this figure is sufficient to support that GluA2 increases are variable across cultures, which hardly adds anything new to the paper or to the field. The authors further cite a study with comparable sample sizes, which shows a similar mismatch based on p values (Xu and Pozzo-Miller 2007), yet the effect sizes in this study actually match quite well (both ~160%). P values cannot be used to show whether two effects match, but effect sizes can. Therefore, the statement in lines 411-413 "... consistently leads to an increase in mEPSC amplitudes, and sometimes leads to an increase in synaptic GluA2 receptor cluster size" is not very convincing, and can hardly be used to support "the idea that there are additional sources contributing to the homeostatic increase in quantal size".

I would suggest simply showing mEPSC and immunostaining data from all cultures in this experiment as additional evidence for homeostatic synaptic plasticity in WT cultures, and leave out the argument for "mismatch". The presynaptic location of Rab3A is sufficient to speculate a presynaptic regulation of this form of homeostatic compensation.

Minor concerns:

(1) Line 214, I see the authors cite literature to argue that GluA2 can form homomers and can conduct currents. While GluA2 subunits edited at the Q/R site (they are in nature) can form homomers with very low efficiency in exogenous systems such as HEK293 cells (as done in the cited studies), it's unlikely for this to happen in neurons (they can hardly traffick to synapses if possible at all).

(2) Lines 221-222, the authors may have misinterpreted the results in Turrigiano 1998. This study does not show that the increase in receptors is most dramatic in the apical dendrite, in fact, this is the only region they have tested. The results in Figures 3b-c show that the effect size is independent of the distance from soma.

(3) Lines 309-310 (and other places mentioning TNFa), the addition of TNFa to this experiment seems out of place. The authors have not performed any experiment to validate the presence/absence of TNFa in their system (citing only 1 study from another lab is insufficient). Although it's convincing that glia Rab3A is not required for homeostatic plasticity here, the data does not suggest Rab3A's role (or the lack of) for TNFa in this process.

Reviewer #3 (Public review):

This manuscript presents a number of interesting findings that have the potential to increase our understanding of the mechanism underlying homeostatic synaptic plasticity (HSP). The data broadly support that Rab3A plays a role in HSP, although the site and mechanism of action remain uncertain.

The authors clearly demonstrate that Rab3A plays a role in HSP at excitatory synapses, with substantially less plasticity occurring in the Rab3A KO neurons. There is also no apparent HSP in the Earlybird Rab3A mutation, although baseline synaptic strength is already elevated. In this context, it is unclear if the plasticity is absent, already induced by this mutation, or just occluded by a ceiling effect due to the synapses already being strengthened. Occlusion may also occur in the mixed cultures when Rab3A is missing from neurons but not astrocytes. The authors do appropriately discuss these options. The authors have solid data showing that Rab3A is unlikely to be active in astrocytes, Finally, they attempt to study the linkage between changes in synaptic strength and AMPA receptor trafficking during HSP, and conclude that trafficking may not be solely responsible for the changes in synaptic strength during HSP.

Strengths:

This work adds another player into the mechanisms underlying an important form of synaptic plasticity. The plasticity is likely only reduced, suggesting Rab3A is only partially required and perhaps multiple mechanisms contribute. The authors speculate about some possible novel mechanisms, including whether Rab3A is active pre-synaptically to regulate quantal amplitude.

As Rab3A is primarily known as a pre-synaptic molecule, this possibility is intriguing. However, it is based on the partial dissociation of AMPAR trafficking and synaptic response and lacks strong support. On average, they saw a similar magnitude of change in mEPSC amplitude and GluA2 cluster area and integral, but the GluA2 data was not significant due to higher variability. It is difficult to determine if this is due to biology or methodology - the imaging method involves assessing puncta pairs (GluA2/VGlut1) clearly associated with a MAP2 labeled dendrite. This is a small subset of synapses, with usually less than 20 synapses per neuron analyzed, which would be expected to be more variable than mEPSC recordings averaged across several hundred events. However, when they reduce the mEPSC number of events to similar numbers as the imaging, the mESPC amplitudes are still less variable than the imaging data. The reason for this remains unclear. The pool of sampled synapses is still different between the methods and recent data has shown that synapses have variable responses during HSP. Further, there could be variability in the subunit composition of newly inserted AMPARs, and only assessing GluA2 could mask this (see below). It is intriguing that pre-synaptic changes might contribute to HSP, especially given the likely localization of Rab3A. But it remains difficult to distinguish if the apparent difference in imaging and electrophysiology is a methodological issue rather than a biological one. Stronger data, especially positive data on changes in release, will be necessary to conclude that pre-synaptic factors are required for HSP, beyond the established changes in post-synaptic receptor trafficking.

Other questions arise from the NASPM experiments, used to justify looking at GluA2 (and not GluA1) in the immunostaining. First, there is a strong frequency effect that is unclear in origin. One would expect NASPM to merely block some fraction of the post-synaptic current, and not affect pre-synaptic release or block whole synapses. But the change in frequency seems to argue (as the authors do) that some synapses only have CP-AMPARs, while the rest of the synapses have few or none. Another possibility is that there are pre-synaptic NASPM-sensitive receptors that influence release probability. Further, the amplitude data show a strong trend towards smaller amplitude following NASPM treatment (Fig 3B). The p value for both control and TTX neurons was 0.08 - it is very difficult to argue that there is no effect. The decrease on average is larger in the TTX neurons, and some cells show a strong effect. It is possible there is some heterogeneity between neurons on whether GluA1/A2 heteromers or GluA1 homomers are added during HSP. This would impact the conclusions about the GluA2 imaging as compared to the mEPSC amplitude data.

To understand the role of Rab3A in HSP will require addressing two main issues:

(1) Is Rab3A acting pre-synaptically, post-synaptically or both? The authors provide good evidence that Rab3A is acting within neurons and not astrocytes. But where it is acting (pre or post) would aid substantially in understanding its role. The general view in the field has been that HSP is regulated post-synaptically via regulation of AMPAR trafficking, and considerable evidence supports this view. More concrete support for the authors' suggestion of a pre-synaptic site of control would be helpful.

(2) Rab3A is also found at inhibitory synapses. It would be very informative to know if HSP at inhibitory synapses is similarly affected. This is particularly relevant as at inhibitory synapses, one expects a removal of GABARs or a decrease in GABA release (ie the opposite of whatever is happening at excitatory synapses). If both processes are regulated by Rab3A, this might suggest a role for this protein more upstream in the signaling; an effect only at excitatory synapses would argue for a more specific role just at those synapses.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors investigate the role of BEND2, a novel regulator of meiosis, in both male and female fertility. Huang et al have created a mouse model where the full-length BEND2 transcript is depleted but the truncated BEND2 version remains. This mouse model is fertile, and the authors used it to study the role of BEND2 on both male and female meiosis. Overall, the full-length BEND2 appears dispensable for male meiosis. The more interesting phenotype was observed in females. Females exhibit a lower ovarian reserve suggesting that full-length BEND2 is involved in the establishment of the primordial follicle pool.

Strengths:

The authors generated a mouse model that enabled them to study the role of BEND2 in meiosis. The role of BEND2 in female fertility is novel and enhances our knowledge of genes involved in the establishment of the primordial follicle pool.

Weaknesses:

The manuscript extensively explores the role of BEND2 in male meiosis; however, a more interesting result was obtained from the study of female mice.

Reviewer #2 (Public review):

In their manuscript entitled "BEND2 is a crucial player in oogenesis and reproductive aging", the authors present their findings that full-length BEND2 is important for repair of meiotic double strand break repair in spermatocytes, regulation of LINE-1 elements in spermatocytes, and proper oocyte meiosis and folliculogenesis in females. The manuscript utilizes an elegant system to specifically ablate the full-length form of BEND2 which has been historically difficult to study due to its location on the X chromosome and male sterility of global knockout animals.

The authors have been extremely responsive to reviewer critiques and have presented strong data and appropriate conclusions, making it an excellent addition to the field.

Reviewer #3 (Public review):

Huang et al. investigated the phenotype of Bend2 mutant mice which expressed truncated isoform. Bend2 deletion in male showed fertility and this enabled them to analyze the BEND2 function in females. They showed that Bend2 deletion in females showed decreasing follicle number which may lead to loss of ovarian reserve.

Strengths:

They found the truncated isoform of Bend2 and the depletion of this isoform showed decreasing follicle number at birth.

Weaknesses:

The authors showed novel factors that impact ovarian reserve. Although the number of follicles and conception rate are reduced in mutant mice, the in vitro fertilization rate is normal and follicles remain at 40 weeks of age. It is difficult to know how critical this is when applied to the human case.

Reviewer #1 (Public review):

Summary:

A central function of glial cells is the ensheathment of axons. Wrapping of larger-diameter axons involves myelin-forming glial classes (such as oligodendrocytes), whereas smaller axons are covered by non-myelin-forming glial processes (such as olfactory ensheathing glia). While we have some insights into the underlying molecular mechanisms orchestrating myelination, our understanding of the signaling pathways at work in non-myelinating glia remains limited. As non-myelinating glial ensheathment of axons is highly conserved in both vertebrates and invertebrates, the nervous system of Drosophila melanogaster, and in particular the larval peripheral nerves, have emerged as a powerful model to elucidate the regulation of axon ensheathment by a class of glia called wrapping glia. Using this model, this study seeks to specifically address the question, as to which molecular mechanisms contribute to the regulation of the extent of glial ensheathment focusing on the interaction of wrapping glia with axons.

Strengths and Weaknesses:

For this purpose, the study combines state-of-the-art genetic approaches with high-resolution imaging, including classic electron microscopy. The genetic methods involve RNAi-mediated knockdown, acute Crispr-Cas9 knock-outs, and genetic epistasis approaches to manipulate gene function with the help of cell-type specific drivers. The successful use of acute Crispr-Cas9 mediated knockout tools (which required the generation of new genetic reagents for this study) will be of general interest to the Drosophila community.

The authors set out to identify new molecular determinants mediating the extent of axon wrapping in the peripheral nerves of third-instar wandering Drosophila larvae. They could show that over-expressing a constitutive-active version of the Fibroblast growth factor receptor Heartless (Htl) causes an increase in wrapping glial branching, leading to the formation of swellings in nerves close to the cell body (named bulges). To identify new determinants involved in axon wrapping acting downstream of Htl, the authors next conducted an impressive large-scale genetic interaction screen (which has become rare, but remains a very powerful approach), and identified Uninflatable (Uif) in this way. Uif is a large single-pass transmembrane protein that contains a whole series of extracellular domains, including Epidermal growth factor-like domains. Linking this protein to glial branch formation is novel, as it has so far been mostly studied in the context of tracheal maturation and growth. Intriguingly, a knock-down or knock-out of uif reduces branch complexity and also suppresses htl over-expression defects. Importantly, uif over-expression causes the formation of excessive membrane stacks. Together these observations are in in line with the notion that htl may act upstream of uif.

Further epistasis experiments using this model implicated also the Notch signaling pathway as a crucial regulator of glial wrapping: reduction in Notch signaling reduces wrapping, whereas over-activation of the pathway increases axonal wrapping (but does not cause the formation of bulges). Importantly, defects caused by the over-expression of uif can be suppressed by activated Notch signaling. Knock-down experiments in neurons suggest further that neither Delta nor Serrate act as neuronal ligands to activate Notch signaling in wrapping glia, whereas knock-down of Contactin, a GPI anchored Immunoglobulin domain-containing protein led to reduced axon wrapping by glia, and thus could act as an activating ligand in this context.

Based on these results the authors put forward a model proposing that Uif normally suppresses Notch signaling, and that activation of Notch by Contactin leads to suppression of Htl, to trigger the ensheathment of axons. While these are intriguing propositions, future experiments would need to conclusively address whether and how Uif could "stabilize" a specific membrane domain capable of interacting with specific axons. Moreover, to obtain evidence for Uif suppression by Notch to inhibit "precocious" axon wrapping and for a "gradual increase" of Notch signaling that silences uif and htl, (1) reporters for N and Htl signaling in larvae, (2) monitoring of different stages at a time point when branch extension begins, and (3) a reagent enabling to visualize Uif expression could be important next tools/approaches. Considering the qualitatively different phenotypes of reduced branching, compared to excessive membrane stacks close to cell bodies, it would perhaps be worthwhile to explore more deeply how membrane formation in wrapping glia is orchestrated at the subcellular level by Uif.

Finally, in light of the importance of correct ensheathment of axons by glia for neuronal function, this study will be of general interest to the glial biology community.

Reviewer #2 (Public review):

The FGF receptor Heartless has previously been implicated in Drosophila peripheral glial growth and axonal wrapping. Here, the authors perform a large-scale screen of over 2600 RNAi lines to find factors that control the downstream signaling in this process. They identify a transmembrane protein Uninflatable to be necessary for the formation of plasma membrane domains. They further find that a Uif regulatory target, Notch, is necessary for glial wrapping. Interestingly, additional evidence suggests Notch itself regulates uif and htl, suggesting a feedback system. Together, they propose that Uif functions as a "switch" to regulate the balance between glial growl and wrapping of axons.

Little is known about how glial cell properties are coordinated with axons, and the identification of Uif is a promising link to shed light on this orchestration. The manuscript is well-written, and the experiments are generally well-controlled. The EM studies in particular are of outstanding quality and really help to mechanistically dissect the consequences of Uif and Notch signaling in the regulation of glial processes. Together, this valuable study provides convincing evidence of a new player coordinating the interactions controlling the glial wrapping of axons.

Reviewer #2 (Public review):

Summary:

Previously, the authors developed a zebrafish model for cerebral cavernous malformations (CCMs) via CRISPR/Cas9-based mosaic inactivation of the ccm2 gene. This model yields CCM-like lesions in the caudal venous plexus of 2 days post-fertilization embryos and classical CNS cavernomas in 8-week fish that depend, like the mouse model, on the upregulation of the KLF2 transcription factor. Remarkably, the morpholino-based knockdown of the gene encoding the Beta1 adrenergic receptor or B1AR (adrb1; a hemodynamic regulator) in fish and treatment with the anti-adrenergic S enantiomer of propranolol in both fish and mice reduce the frequency and size of CMM lesions.

In the present study, the authors aim to test the model that adrb1 is required for CCM lesion development using adrb1 mutant fish (rather than morpholino-mediated knockdown and pharmacological treatments with the anti-adrenergic S enantiomer of propranolol or a racemic mix of metoprolol (a selective B1AR antagonist).

Strengths:

The goal of the work is important, and the findings are potentially highly relevant to cardiovascular medicine.

Comments on latest version:

This reviewer is largely satisfied and congratulates the authors on their updated work. However, the comments regarding the caveats of morpholino use and lack of validation that the morphants phenocopy the mutants using the readouts that they employ still stand (for instance, the tnnt2a MO has been extensively validated for phenocopying lack of cardiac contractility, not for the phenotypes under study). Finally, while using the cytosolic red line to mask a nuclear green readout is suboptimal (not for FRET reasons), this is now a minor issue given that all comparisons are made using this method and the increase in sample size.

Reviewer #1 (Public review):

Summary:

This impressive study presents a comprehensive scRNAseq atlas of the cranial region during neural induction, patterning, and morphogenesis. The authors collected a robust scRNAseq dataset covering six distinct developmental stages. The analysis focused on the neural tissue, resulting in a highly detailed temporal map of neural plate development. The findings demonstrate how different cell fates are organized in specific spatial patterns along the anterior-posterior and medial-lateral axes within the developing neural tissue. Additionally, the research utilized high-density single-cell RNA sequencing (scRNAseq) to reveal intricate spatial and temporal patterns independent of traditional spatial techniques.

The investigation utilized diffusion component analysis to spatially order cells based on their positioning along the anterior-posterior axis, corresponding to the forebrain, midbrain, hindbrain, and medial-lateral axis. By cross-referencing with MGI expression data, the identification of cell types was validated, affirming the expression patterns of numerous known genes and implicating others as differentially expressed along these axes. These findings significantly advance our understanding of the spatially regulated genes in neural tissues during early developmental stages. The emphasis on transcription factors, cell surface, and secreted proteins provides valuable insights into the intricate gene regulatory networks underpinning neural tissue patterning. Analysis of a second scRNAseq dataset where Shh signaling was inhibited by culturing embryos in SAG identified known and previously unknown transcripts regulated by Shh, including the Wnt pathway.

The data includes the neural plate and captures all major cell types in the head, including the mesoderm, endoderm, non-neural ectoderm, neural crest, notochord, and blood. With further analyses, this high-quality data promises to significantly advance our understanding of how these tissues develop in conjunction with the neural tissue, paving the way for future breakthroughs in developmental biology and genomics.

Strengths:

The data is well presented in the figures and thoroughly described in the text. The quality of the scRNAseq data and bioinformatic analysis is exceptional.

Weaknesses:

None

Reviewer #2 (Public review):

Summary:

Brooks et al. generate a compelling gene expression atlas of the early embryonic cranial neural plate. They generate single-cell transcriptome data from early cranial neural plate cells at 6 consecutive stages between E7.5 to E9. Utilizing computational analysis they infer temporal gene expression dynamics and spatial gene expression patterns along the anterior-posterior and mediolateral axis of the neural plate. Subsequent comparison with known gene expression patterns revealed a good agreement with their inferred patterns, thus validating their approach. They then focus on Sonic Hedgehog (Shh) signalling, a key morphogen signal, whose activities partition the neural plate into distinct gene expression domains along the mediolateral axis. Single-cell transcriptome analysis of embryos in which the Shh pathway was pharmacologically activated throughout the neural plate revealed characteristic changes in gene expression along the mediolateral axis and the induction of distinct Shh regulated gene expression programs in the developing fore-, mid- and hindbrain.

Strengths:

This manuscript provides a comprehensive transcriptomic characterisation of the developing cranial neural plate, a part of the embryo that to my knowledge has not been extensively analysed by single-cell transcriptomic approaches. The single-cell sequencing data appears to be of high quality and will be a great resource for the wider scientific community. Moreover, the computational analysis is well executed and the validation of the sequencing data using published gene expression patterns is convincing. In my opinion the authors completely achieved their aim of generating a reliable sequencing atlas of the early cranial neural plate. Conceptually, the findings that gene expression patterns differ along the rostrocaudal, mediolateral and temporal axes of the neural plate and that Shh signalling induces distinct target genes along the anterior-posterior axis of the nervous system are not completely unexpected. However, the comprehensive characterization of the spatiotemporal gene expression patterns and how they change upon ectopic activation of the Shh pathway will definitely contribute to a better understanding of neural plate patterning. Taken together, this is a well-executed study that describes a relevant scientific resource that will likely be of great use for the wider scientific community .

Weaknesses:

No weaknesses were identified.

Reviewer #3 (Public review):

Summary:

The authors performed a detailed single-cell analysis of the early embryonic cranial neural plate with unprecedented temporal resolution between embryonic days 7.5 and 8.75. They employed diffusion analysis to identify genes that correspond to different temporal and spatial locations within the embryo. Finally, they also examined the global response of cranial tissue to a Smoothened agonist.

Strengths:

Overall, this is an impressive resource, well-validated against sets of genes with known temporal and spatial patterns of expression. It will be of great value to investigators examining early stages of neural plate patterning, neural progenitor diversity, and the roles of signaling molecules and gene regulatory networks controlling regionalization and diversification of the neural plate.

Weaknesses:

The manuscript should be considered a resource. Experimental manipulation is limited to analysis of neural plate cells that were cultured in vitro for 12 hours with SAG. They have identified a significant set of previously unreported genes that are differentially expressed in the cranial neural plate. Some additional analyses might help to highlight novel hypotheses arising from this remarkable resource.

Comments on revisions: I am satisfied with the responses of the authors and do not have any further concerns.

Reviewer #2 (Public review):

Summary:

This paper derives the first three functional gradients in the left and right hippocampus across two datasets. These gradient maps are then compared to dopamine receptor maps obtained with PET, associated with age, and linked to memory. Results reveal links between dopamine maps and gradient 2, age with gradients 1 and 2, and memory performance.

Strengths:

This paper investigates how hippocampal gradients relate to aging, memory, and dopamine receptors, which are interesting and important questions. A strength of the paper is that some of the findings were replicated in a separate sample.

Assessment after revision:

The authors addressed concerns about unclear multiple comparison correction in the revision. The replication sample was primarily used to replicate the topographic organization of functional hippocampal-neocortical connectivity within the hippocampus across the adult lifespan, which was the central goal of this paper. Not all other analyses replicated, which the authors nicely clarified in the revised manuscript. Overall, this work is a thorough and valuable contribution to the literature.

Reviewer #3 (Public review):

Summary:

In this study, the authors analyzed the complex functional organization of the hippocampus using two separate adult lifespan datasets. They investigated how individual variations in the detailed connectivity patterns within the hippocampus relate to behavioral and molecular traits. The findings confirm three overlapping hippocampal gradients and reveal that each is linked to established functional patterns in the cortex, the arrangement of dopamine receptors within the hippocampus, and differences in memory abilities among individuals. By employing multivariate data analysis techniques, they identified older adults who display a hippocampal gradient pattern resembling that of younger individuals and exhibit better memory performance compared to their age-matched peers. This underscores the behavioral importance of maintaining a specific functional organization within the hippocampus as people age.

Strengths:

The evidence supporting the conclusions is compelling, based on a unique dataset, a rich set of carefully unpacked results, and a rigorous data analysis that is clearly explained and motivated. Possible confounds are carefully considered and ruled out.

Assessment after revision:

The authors improved the transparency of the statistical analyses by stating explicitly what tests and corrections were performed and clearly justifying the elected statistical approaches. They now also acknowledge and discuss the potential limitations of the presented PET analyses. Overall this is a rigorous and important contribution to the literature that will likely be of broad interest to basic and clinical neuroscience.

Reviewer #1 (Public review):

Based on the reviewers' comments, the authors conducted additional analyses to enhance their study. By utilizing publicly available datasets (Guo et al., 2017) capable of distinguishing the sex of embryos, they examined DNA methylation in male embryos and identified minor de novo DNA methylation events initiating at the 8-cell stage, predominantly on the X chromosome. However, this finding introduces confusion, as the authors had previously suggested that such minor de novo DNA methylation regulates imprinted X chromosome inactivation, a process specific to female embryos.

The key unresolved issue is whether minor de novo DNA methylation in female embryos occurs exclusively on the "inactive" X chromosome or on both the active and inactive X chromosomes. The authors did not provide direct evidence supporting de novo DNA methylation specifically at the inactive X chromosome. Furthermore, it remains unclear whether this methylation influences embryonic development independent of sex or is specific to female embryos undergoing imprinted X chromosome inactivation. While the authors present data on decreased live birth rates in Figure 2F, they did not address whether there is a sex bias among the live pups, such as male-biased survival. Clarifying this point would strengthen their conclusions.<br /> In summary, the critical issue with the revised manuscript is that it does not adequately resolve whether minor de novo DNA methylation regulates embryonic development irrespective of sex or specifically impacts female embryos where imprinted X chromosome inactivation occurs. This distinction is essential for understanding the broader implications of their findings.

Reviewer #2 (Public review):

Summary:

Yue et al. set out to determine if the low but measurable level of DNMT3B expression that is observed prior to the major wave of de novo DNA methylation has a function (ie before the epiblast stage) . Re-analyzing existing DNA methylation data from Smith et al. (2012) they find a very modest DNA methylation gain over a subset of promoters, on the order of 1%, occurring between the 8-cell and blastocyst stages, and refer to this as "minor de novo DNA methylation". They attempt to assess the relevance/functionality of this minor DNA methylation gain, and intriguingly report reduced H3K27me3 in Dnmt3b knockdown (KD) trophoblast cells that normally undergo imprinted X-chromosome inactivation (iXCI) before the blastocyst stage. In addition, they assess proliferation, differentiation, metabolic function, implantation rate and live birth rate of Dnmt3b KD blastocysts, and assign specific phenotypes to the loss of DNA methylation at this early stage..

Strengths:

Working with early embryos is technically demanding and as such the relevance of disrupting epigenetic factors specifically at this stage in development is less well studied. The detailed analyses of published data as well as DNMT3B depletion experiments presented in this manuscript provides food for thought for the epigenetics community.

Weaknesses:

- Throughout the manuscript, please represent DNA methylation changes as delta DNA methylation instead of fold change. In many figures, it is not clear what the unit of DNA methylation presented actually is. Readers should be made aware that the changes in DNA methylation observed are very modest and the threshold applied to the delta in DNA methylation is just 1% "( Δ DNA methylation > 0.01)").<br /> - The minimum coverage threshold and threshold applied For DNA methylation should be presented in each relevant figure. Currently for example, the latter is only mentioned not in the methods section but rather once in "Figure 2, figure supplement 1"<br /> - Indirect effects of disrupting DNMT3B at the earlier stages in development, when de novo DNAme levels are very low in the promoter regions of interest, should be considered. For example, de novo DNA methylation in repetitive regions/pericentric heterochromatin at this stage (not studied here) could be much higher than 1%. Disruption of such methylation, could result in a "sink effect", with loss of H3K27me3 at promoter regions (including on the inactive X-chromosome), due to aberrant repositioning of Polycomb complexes/PRC2 to such ectopic sites from which they are normally excluded, rather than a direct positive effect of the very low DNA methylation gain observed on Polycomb recruitment.<br /> The impact of depletion of DNMT3B on the major wave of de novo DNA methylation that takes place at the peri-implantion stage of embryonic development may also play a role in some of the later phenotypes observed. In other words when the failure of de novo methylation is more profound as levels of DNA methylation are much higher at these later stages as a consequence of DNMT3B activity.

Joint Public Review:

Summary:

This interesting study applies the PSMC model to a set of new genome sequences for migratory and nonmigratory thrushes and seeks to describe differences in the population size history among these groups. The authors create a set of summary statistics describing the PSMC traces - mean and standard deviation of Ne, plus a set of metrics describing the shape of the oldest Ne peak - and use these to compare across migratory and resident species (taking single samples sequenced here as representative of the species). The analyses are framed as supporting or refuting aspects of a biogeographic model describing colonization dynamics from tropical to temperate North and South America.

Strengths:

* This is a creative use of PSMC to test explicit a priori hypotheses about season migration and Ne. The PSMC analyses seem well done and the authors acknowledge much of the complexity of interpretation in the discussion.

* We appreciate the test-of-hypothesis design of the study and the explicit formulation of three main expectations to test. The data analysis has been done with appropriate available tools.

Key weaknesses from the original round of review:

* Short of developing some novel theory deep in the PSMC model, I think readers would need to see simulations showing that the analyses employed in this paper are capable of supporting or refuting their biogeographic hypothesis before viewing them as strongly supporting a specific biogeographic model. Tools like msprime and stdpopsim can be used to simulate genome-scale data with fairly complex biogeographic models. Running simulations of a thrush-like population under different biogeographic scenarios and then using PSMC to differentiate those patterns would be a more convincing argument for the biogeographic aspects of this paper. The other benefit of this approach would be to nail down a specific quantitative version of the taxon cycles model referenced in the abstract, and it would allow the authors to better study and explain the motivation behind the specific summary statistics they develop for PSMC posthoc analysis.

* The authors hypothesized that the wider realized breeding and ecological range characterising migrants versus resident lineages could be a major drive for increased effective population size and population expansion in migrants versus residents. I understand that this pattern (wider range in migrants) is a common characteristic across bird lineages and that it is viewed as a result of adapting to migration. A problem that I see in their dataset is that the breeding grounds range of the two groups are located in very different geographic areas (mainly South versus North America). The authors could have expanded their dataset to include species whose breeding grounds are from the two areas, regardless of their migratory behaviour, as a comparison to disentangle whether ecological differences of these two areas can affect the population sizes or growth rates.

* As I understand from previous literature, the time-scale to population growth and estimates of effective population sizes considered in the present paper for the resident versus migratory clades seem to widely predate the times to speciation for the same lineages, which were reported in previous work of the same authors (Everson et al 2019) and others (Termignoni-Garcia et al 2022). This piece of information makes the calculation of species-specific population size changes difficult to interpret in the light of lineages' comparison. It is unclear what the authors consider to be lineage-specific in these estimates, as the clades were likely undergoing substantial admixture during the time predating full isolation.

* Regarding the methodological difficulties in interpreting the impact of population structure on the estimates of effective population sizes with the PSMC approach, I would think that performing simulations to compare different scenarios of different degrees of structured populations would have helped substantially understand some of the outcomes.

* The authors use an average generation time for all taxa, but the citations imply generation time is known for at least some of them. Are there differences in generation time associated with migration? I am not a bird biologist, but quick googling suggests maybe this is the case? (https://doi.org/10.1111/1365-2656.13983). I think it important the authors address this, as differences in generation time I believe should affect estimates of Ne and growth.

[Editors' note: the original reviews in full are here: https://elifesciences.org/reviewed-preprints/90848/reviews. The reviewers were not available to comment on the latest version of the submission.]

Reviewer #1 (Public review):

Summary:

Even though this is not the first report that the mutation in the DNAH12 gene causes asthenoteratozoospermia, the current study explores the sperm phenotype in-depth. The authors show experimentally that the said mutation disrupts the proper axonemal arrangement and recruitment of DNALI1 and DNAH1 - proteins of inner dynein arms. Based on these results, the authors propose a functional model of DNAH12 in proper axonemal development. Lastly, the authors demonstrate that the male infertility caused by the studies mutation can be rescued by ICSI treatment at least in the mouse. This study furthers our understanding of male infertility caused by a mutation of axonemal protein DNAH12, and how this type of infertility can be overcome using assisted reproductive therapy.

Strengths:

This is an in-depth functional study, employing multiple, complementary methodologies to support the proposed working model.

Weaknesses:

The structure and interaction model between DNAH12, DNALI1, and DNAH1 relies on in silico methodologies, and further studies are required to validate these predictions.

Reviewer #2 (Public review):

Summary:

The authors first conducted whole exome sequencing for infertile male patients and families where they co-segregated the biallelic mutations in the Dynein Axonemal Heavy Chain 12 (DNAH12) gene. Sperm from patients with biallelic DNAH12 mutations exhibited a wide range of morphological abnormalities in both tails and heads, reminiscing a prevalent cause of male infertility, asthenoteratozoospermia. To deepen the mechanistic understanding of DNAH12 in axonemal assembly, the authors generated two distinct DNAH12 knockout mouse lines via CRISPR/Cas9, both of which showed more severe phenotypes than observed in patients. Ultrastructural observations and biochemical studies revealed the requirement of DNAH12 in recruiting other axonemal proteins and that the lack of DNAH12 leads to the aberrant stretching in the manchette structure as early as stage XI-XII. At last, the authors proposed intracytoplasmic sperm injection as a potential measure to rescue patients with DNAH12 mutations, where the knockout sperm culminated in the blastocyst formation with a comparable ratio to that in WT.

Strengths:

The authors convincingly showed the importance of DNAH12 in assembling cilia and flagella in both human and mouse sperm. This study is not a mere enumeration of the phenotypes, but a strong substantiation of DNAH12's essentiality in spermiogenesis, especially in axonemal assembly.

The analyses conducted include basic sperm characterizations (concentration, motility), detailed morphological observations in both testes and sperm (electron microscopy, immunostaining, histology), and biochemical studies (co-immunoprecipitation, mass-spec, computational prediction). Molecular characterizations employing knockout animals and recombinant proteins beautifully proved the interactions with other axonemal proteins.

Many proteins participate in properly organizing flagella, but the exact understanding of the coordination is still far from conclusive. The present study gives the starting point to untangle the direct relationships and order of manifestation of those players underpinning spermatogenesis. Furthermore, comparing flagella and trachea provides a unique perspective that attracts evolutional perspectives.

Weaknesses:

Seemingly minor, but the discrepancies found in patients and genetically modified animals were not fully explained. For example, both knockout mice vastly reduced the count of sperm in the epididymis and the motility, while phenotypes in patients were rather milder. Addressing the differences in the roles that the orthologs play in spermatogenesis would deepen the comprehensive understanding of axonemal assembly.

Comments on revisions:

The reviewer is satisfied with the authors' response.

Reviewer #1 (Public review):

Summary:

The paper demonstrated through a comprehensive multi-omics study of the oviduct that the transcriptomic and proteomic landscape of the oviduct at 4 different preimplantation periods was dynamic during natural fertilization, pseudopregnancy, and superovulation using three independent cell/tissue isolation and analytical techniques. This work is very important for understanding oviductal biology and physiology. In addition, the authors have made all the results available in a web search format, which will maximize the public's access and foster and accelerate research in the field.

Strengths:

(1) The manuscript addresses an important and interesting question in the field of reproduction: how does the oviduct at different regions adapt to the sperm and embryos for facilitating fertilization and preimplantation embryo development and transport?<br /> (2) Authors used cutting-edge techniques: Integrated multi-modal datasets followed with in vivo confirmation and machine learning prediction.<br /> (3) RNA-seq, scRNA-seq and proteomic results are immediately available to the scientific community in a web search format<br /> (4) Substantiated results indicate the source of inflammatory responses was the secretory cell population in the IU region when compared to other cell types; sperm modulate inflammatory responses in the oviduct; the oviduct displays immuno-dynamism.

In addition, the revised version has addressed weaknesses adequately.<br /> (1) The revised version provided a clear explanation and the rationale for using the superovulation model.<br /> (2) The revised version generated a graphic abstract/summary of their major findings.

Reviewer #2 (Public review):

The manuscript investigates oviductal responses to the presence of gametes and embryos using a multi-omics and machine learning-based approach. By applying RNA sequencing (RNA-seq), single-cell RNA sequencing (sc-RNA-seq), and proteomics, the authors identified distinct molecular signatures in different regions of the oviduct, proximal versus distal. The study revealed that sperm presence triggers an inflammatory response in the proximal oviduct, while embryo presence activates metabolic genes that provide nutrients to the developing embryos. Overall, this study offers valuable insights and will likely be of great interest to reproductive biologists and researchers in oviduct biology.

Reviewer #1 (Public review):

Summary:

In this study, the authors showed that enalapril was able to reduce cellular senescence and improve health status in aged mice. The authors further showed that phosphorylated Smad1/5/9 was significantly elevated and blocking this pathway attenuated the protection of cells from senescence. When middle-aged mice were treated with enalapril, the physiological performance in several tissues, including memory capacity, renal function, and muscle strength, exhibited significant improvement.

Strengths:

The strength of the study lies in the identification of the pSMAD1/5/9 pathway as the underlying mechanism mediating the anti-senescence effects of enalapril with comprehensive evaluation both in vitro and in vivo.

Weaknesses:

The major weakness of the study is the in vivo data. Despite the evidence shown in the in vitro study, there is no data to show that blocking the pSmad1/5/9 pathway is able to attenuate the anti-aging effects of enalapril in the mice. In addition, the aging phenotypes mitigation by enalapril is not evidenced by the extension of lifespan. If it is necessary to show that NAC is able to attenuate enalapril effects in the aging mice. In addition, it would be beneficial to test if enalapril is able to achieve similar rescue in a premature aging mouse model.

Reviewer #2 (Public review):

This manuscript presents an interesting study of enalapril for its potential impact on senescence through the activation of Smad1/5/9 signaling with a focus on antioxidative gene expression. Repurposing enalapril in this context provides a fresh perspective on its effects beyond blood pressure regulation. The authors make a strong case for the importance of Smad1/5/9 in this process, and the inclusion of both in vitro and in vivo models adds value to the findings. Below, I have a few comments and suggestions which may help improve the manuscript.

A major finding in the study is that phosphorylated Smad1/5/9 mediates the effects of enalapril. However, the manuscript focused on the Smad pathway relatively abruptly, and the rationale behind targeting this specific pathway is not fully explained. What makes Smad1/5/9 particularly relevant to the context of this study?

Furthermore, their finding that activation of Smad1/5/9 leads to a reduction of senescence appears somewhat contradictory to the established literature on Smad1/5/9 in senescence. For instance, studies have shown that BMP4-induced senescence involves the activation of Smad1/5/8 (Smad1/5/9), leading to the upregulation of senescence markers like p16 and p21 (JBC, 2009, 284, 12153). Similarly, phosphorylated Smad1/5/8 has been shown to promote and maintain senescence in Ras-activated cells (PLOS Genetics, 2011, 7, e1002359). Could the authors provide more detailed mechanistic insights into why enalapril seems to reverse the typical pro-senescent role of Smad1/5/9 in their study?

While the authors showed that enalapril increases pSmad1/5/9 phosphorylation, what are the expression levels of other key and related factors like Smad4, pSmad2, pSmad3, BMP2, and BMP4 in both senescent and non-senescent cells? These data will help clarify the broader signaling effects.

They used BMP receptor inhibitor LDN193189 to pharmacologically inhibit BMP signaling, but it would be more convincing to also include genetic validation (e.g., knockdown or knockout of BMP2 or BMP4). This will help confirm that the observed effects are truly due to BMP-Smad signaling and not off-target effects of the pharmacological inhibitor LDN.

I don't see the results on the changes in senescence markers p16 and p21 in the mouse models treated with enalapril. Similarly, the effects of enalapril treatment on some key SASP factors, such as TNF-α, MCP-1, IL-1β, and IL-1α, are missing, particularly in serum and tissues. These are important data to evaluate the effect of enalapril on senescence.

Given that enalapril is primarily known as an antihypertensive, it would be helpful to include data on how it affects blood pressure in the aged mouse models, such as systolic and diastolic blood pressure. This will clarify whether the observed effects are independent of or influenced by changes in blood pressure.

Reviewer #1 (Public review):

The manuscript by Lucie Oriol et al. revisits the understanding of interneurons in the ventral tegmental area (VTA). The study challenges the traditional notion that VTA interneurons exclusively form local synapses within the VTA. Key findings of the study indicate that VTA GABA and glutamate projection neurons also make local synapses within the VTA. This evidence suggests that functions previously attributed to VTA interneurons could be mediated by these projection neurons.

The study tested four genetic markers-Parvalbumin (PV), Somatostatin (SST), Mu-opioid receptor (MOR), and Neurotensin (NTS)-to determine if they selectively label VTA interneurons. The findings indicate that these markers label VTA projection neurons rather than selectively identifying interneurons. Using a combination of anatomical tracing and brain slice physiological recordings, the study demonstrates that VTA projection neurons make functional inhibitory or excitatory synapses locally within the VTA. These data challenge the conventional view that VTA GABA neurons are purely interneurons and suggests that inhibitory projection neurons can serve functions previously attributed to VTA interneurons. Thus, some functions traditionally ascribed to interneurons may be carried out by projection neurons with local synapses. This has significant implications for understanding the neural circuits underlying reward, motivation, and addiction.

Reviewer #2 (Public review):

Summary:

In this manuscript, authors use a combination of transgenic animals, intersectional viruses, retrograde tracing, and ex-vivo slice electrophysiology to show that VTA projections neurons synapse locally. First, the authors injected a cre-dependent channelrhodopsin into the VTA of PV, SST, MOR, and NTS-Cre mice. Importantly, PV, SST, MOR, and NTS are molecular markers previously used to describe VTA interneurons. Imaging of known VTA target regions identified that these neurons are not localized to the VTA and instead project to the PFC, NAc, VP, and LHb. Next, the authors used an intersectional viral strategy to label projections neurons with both GFP (membrane localized) and Syn:Ruby (release sites). These experiments identified that VTA projection neurons also make intra-VTA synapses. Finally, the authors use a combination of optogenetics and ex-vivo slice electrophysiology to show that neurons projecting from the VTA to the NAc/VP/PFC also synapse locally. Overall, the conclusions are well supported by the data.

Strengths:

Previous literature has described Pvalb, Sst, Oprm1, and Nts as selective markers of VTA interneurons. Here, the authors make use of cre driver lines to show that neurons defined by these genes are not classically-defined interneurons and project to known VTA target regions. Additionally, the authors convincingly use intersectional viral approaches and slice electrophysiology to show that projection neurons synapse onto neighboring cells within the VTA

Reviewer #3 (Public review):

Summary:

This study from Oriol et al. first uses transgenic animals to examine projection targets of specific subtypes of VTA GABA neurons (expressing PV, SST, MOR, or NTS). They follow this with a set of optogenetic experiments showing that VTA projection neurons (regardless of genetic subtype) make local functional connections within the VTA itself. Both of these findings are important advances in the field. Notably, both GABAergic and glutamatergic neurons in the VTA likely exhibit these combined long/short-range projections.

Strengths:

The main strength of this study is the series of optogenetic/electrophysiological experiments that provide detailed circuit connectivity of VTA neurons. The long-range projections to the VP (but not other targets) are also verified to have functional excitatory and inhibitory components. Overall, the experiments are well executed and the results are very relevant in light of the rapidly growing knowledge about the complexity and heterogeneity of VTA circuitry.

Another strength of this study is the well-written and thoughtful discussion regarding the current findings in the context of the long-standing question of whether the VTA does or does not have true interneurons.

Comments on revisions:

The authors have addressed all of my questions admirably, and the final result is considerably improved and remains a valuable contribution to the field.

Reviewer #1 (Public review):

Summary:

The authors used weighted ensemble enhanced sampling molecular dynamics (MD) to test the hypothesis that a double mutant of Abl favors the DFG-in state relative to the WT and therefore causes the drug resistance to imatinib.

Strengths:

The authors employed the state-of-the-art weighted ensemble MD simulations with three novel progress coordinates to explore the conformational changes the DFG motif of Abl kinase. The hypothesis regarding the double mutant's drug resistance is novel.

Weaknesses:

The study contains many uncertain aspects. A major revision is needed to strengthen the support for the conclusions.

(1) Specifically, the authors need to define the DFG conformation using criteria accepted in the field, for example, see https://klifs.net/index.php.

(2) Convergence needs to be demonstrated for estimating the population difference between different conformational states.

(3) The DFG flip needs to be sampled several times to establish free energy difference.

(4) The free energy plots do not appear to show an intermediate state as claimed.

(5) The trajectory length of 7 ns in both Figure 2 and Figure 4 needs to be verified, as it is extremely short for a DFG flip that has a high free energy barrier.

(6) The free energy scale (100 kT) appears to be one order of magnitude too large.

(7) Setting the DFG-Asp to the protonated state is not justified, because in the DFG-in state, the DFG-Asp is clearly deprotonated.

(8) Finally, the authors should discuss their work in the context of the enormous progress made in theoretical studies and mechanistic understanding of the conformational landscape of protein kinases in the last two decades, particularly with regard to the DFG flip.

Reviewer #2 (Public review):

Summary:

This is a well-written manuscript on the mechanism of the DFG flip in kinases. This conformational change is important for the toggling of kinases between active (DFG-in) and inactive (DFG-out) states. The relative probabilities of these two states are also an important determinant of the affinity of inhibitors for a kinase. However, it is an extremely slow/rare conformational change, making it difficult to capture in simulations. The authors show that weighted ensemble simulations can capture the DFG flip and then delve into the mechanism of this conformational change and the effects of mutations.

Strengths:

The DFG flip is very hard to capture in simulations. Showing that this can be done with relatively little simulation by using enhanced sampling is a valuable contribution. The manuscript gives a nice description of the background for non-experts.

Weaknesses:

I was disappointed by the anecdotal approach to presenting the results. Molecular processes are stochastic and the authors have expertise in describing such processes. However, they chose to put most statistical analysis in the SI. The main text instead describes the order of events in single "representative" trajectories. The main text makes it sound like these were most selected as they were continuous trajectories from the weighted ensemble simulations. I would much rather hear a description of the highest probability pathway(s) with some quantification of how probable they are. That would give the reader a clear sense of how representative the events described are.

I appreciated the discussion of the strengths/weaknesses of weighted ensemble simulations. Am I correct that this method doesn't do anything to explicitly enhance sampling along orthogonal degrees of freedom? Maybe a point worth mentioning if so.

I don't understand Figure 3C. Could the authors instead show structures corresponding to each of the states in 3B, and maybe also a representative structure for pathways 1 and 2?

Why introduce S1 and DFG-inter? And why suppose that DFG-inter is what corresponds to the excited state seen by NMR?

It would be nice to have error bars on the populations reported in Figure 3.

I'm confused by the attempt to relate the relative probabilities of states to the 32 kca/mol barrier previously reported between the states. The barrier height should be related to the probability of a transition. The DFG-out state could be equiprobable with the DFG-in state and still have a 32 kcal/mol barrier separating them.

How do the relative probabilities of the DFG-in/out states compare to experiments, like NMR?

Do the staggered and concerted DFG flip pathways mentioned correspond to pathways 1 and 2 in Figure 3B, or is that a concept from previous literature?

Joint Public Review:

Summary:

This study used a simulation approach with a large-scale compilation of published meta-analytic data sets to address the generalizability of meta-analyses. The authors used prediction interval/distribution as a central tool to evaluate whether future meta-analysis is likely to generate a non-zero effect.

Strengths:

Although the concept of prediction intervals is commonly taught in statistics courses, its application in meta-analysis remains relatively rare. The authors' creative use of this concept, combined with the decomposition of heterogeneity, provides a new perspective for meta-analysts to evaluate the generalizability of their findings. As such, I consider this to be a timely and practically valuable development.

Weaknesses:

First, in their re-analysis of the compiled meta-analytic data to assess generalizability, the authors used a hierarchical model with only the intercept as a fixed effect. In practice, many meta-analyses include moderators in their models. Ignoring these moderators could result in attributing heterogeneity to unexplained variation at the study or paper level, depending on whether the moderators vary across studies or papers. As a consequence, the prediction interval may be inaccurately wide or narrow, leading to an erroneous assessment of the generalizability of results derived from large meta-analytic data sets. A more accurate approach would be to include the same moderators as in the original meta-analyses and generate prediction intervals that reflect the effects of these moderators.

Second, the authors used a t-distribution to generate the prediction intervals and distributions for the hierarchical meta-analysis model. While the t-distribution is exact for prediction intervals in linear models, it is not strictly appropriate for models with random effects. This discrepancy arises because the variances of random effects must be estimated from the data, and using a t-distribution for prediction intervals does not account for the uncertainty in estimating these variance components. Unless the data is perfectly balanced (i.e., all random effects are nested and sample sizes within each level of the random factor are equal), it is well established that t-distribution (or equivalently, F-distribution) based hypothesis testing and confidence/prediction intervals are typically anti-conservative. As recommended in the linear mixed models literature, bootstrapping methods or some form of degrees-of-freedom correction would be more appropriate for generating prediction intervals in this context.

Finally, the authors define generalizability as the likelihood that a future study will yield a significantly non-zero effect. While this is certainly useful information, it is not necessarily the primary concern for many meta-analyses or individual studies. In fact, many studies aim to understand the mean response or effect within a specific context, rather than focusing on whether a future study will produce a significant result. For many research questions, the concern is not whether a future study will generate a significant finding, but whether the true mean response is different from zero. In this regard, the authors may have overstated the importance of knowing the outcome of a single future study, and framing this as the sole goal of research seems somewhat misguided.

Reviewer #1 (Public review):

Summary:

The manuscript by Senn and colleagues presents a comprehensive study on the developing synthetic gene circuits targeting mutant RAS-expressing cells. This study aims to exploit these RAS-targeting circuits as cancer cell classifiers, enabling the selective expression of an output protein in correlation with RAS activity. The system is based on the bacterial two-component system NarX/NarL. A RAS-binding domain, the RBDCRD domain of the RAS effector protein CRAF, is fused to the histidine kinase domain, which carries an inactivating amino acid exchange either in its ATP-binding site (N509A) or in its phosphorylation site (H399Q). Dimerization or nanocluster formation of RAS-GTP reconstitutes an active histidine kinase sensor dimer that phosphorylates the response regulator NarL. The phosphorylated DNA-binding protein NarL, fused to the transcription activator domain VP48, binds its responsive element and induces the expression of the output protein. In comparison to mutated RAS, the effect of the RAS activator SOS-1 and the RAS inhibitor NF1 on the sensing ability as well as the tunability of the RAS sensor were examined. A RAS targeting circuit with an AND gate was designed by expressing the RAS sensor proteins under the control of defined MAPK response elements, resulting in a large increase in the dynamic range between mutant and wild-type RAS. Finally, the RAS targeting circuits were evaluated in detail in a set of twelve cancer cell lines expressing endogenous levels of mutant or wild-type RAS or oncogenes affecting RAS signaling upstream or downstream.

Strengths:

This proof-of-concept study convincingly demonstrates the potential of synthetic gene circuits to target oncogenic RAS in tumor cell lines and to function, at least in part, as an RAS mutant cell classifier.

Weaknesses:

The use of an appropriate "therapeutic gene" might revert the oncogenic properties of RAS mutant cell lines. However, a therapeutic strategy based on this four-plasmid-based system might be difficult to implement in RAS-driven solid cancers.

Reviewer #2 (Public review):

The manuscript describes an interesting approach towards designing genetic circuits to sense different RAS mutants in the context of cancer therapeutics. The authors created sensors for mutant RAS and incorporated feed-forward control that leverages endogenous RAS/MAPK signaling pathways in order to dramatically increase the circuits' dynamic range. The modularity of the system is explored through the individual screening of several RAS binding domains, transmembrane domains, and MAPK response elements, and the author further extensively screened different combinations of circuit components. This is an impressive synthetic biology demonstration that took it all the way to cancer cell lines. However, given the sole demonstrated output in the form of fluorescent proteins, the authors' claims related to therapeutic implications require additional empirical evidence or, otherwise, expository revision.

Major comments:

"These therapies are limited to cancers with KRASG12C mutations" is technically accurate. However, in this fast-moving field, there are examples such as MRTX1133 which holds the promise to target the very G12D mutation that is the focus of this paper. There are broader efforts too. It would help the readers better appreciate the background if the authors could update the intro to reflect the most recent landscape of RAS-targeting drugs.

Only KRASG12D was used as a model in the design and optimization work of the genetic circuits. Other mutations should be quite experimentally feasible and comparisons of the circuits' performances across different KRAS mutations would allow for stronger claims on the circuits' generalizability. Particularly, the cancer cell line used for circuit validation harbored a KRASG13D mutation. While the data presented do indeed support the circuit's "generalizability," the model systems would not have been consistent in the current set of data presented.

In Figure 2a, the text claims that "inactivation of endogenous RAS with NF1 resulted in a lower YFP/RBDCRD-NarX expression," but Figure 2a does not show a statistically significant reduction in expression of SYFP (measured by "membrane-to-total signal ratio [RU]).

The therapeutic index of the authors' systems would be better characterized by a functional payload, other than florescent proteins, that for example induce cell death, immune responses, etc.

Regarding data presented in "Mechanism of action" (Figure 2), the observations are interesting and consistent across different fluorescent reporters. However, with regard to interpretations of the underlying molecular mechanisms, it is not clear whether the different output levels in 2b, 2c, and 2d are due to the pathway as described by the authors or simply from varied expression levels of RBDCRD-NarX itself (2a) that is nonlinearly amplified by the rest of the circuit. From a practical standpoint, this caveat is not critical with respect to the signal-to-noise ratios in later parts of the paper. From a mechanistic interpretation standpoint, claims made forth in this section are not clearly substantiated. Some additional controls would be nice. For example, if the authors express NarXs that constitutively dimerize on the membrane, what would the RasG12D-responsiveness look like? Does RasG12D alter the input-output curve of NarL-RE? How would Figure 4f compare to a NaxR constitutively dimerized control that only relies on transcriptional amplification of the Ras-dependent promoters? It's also possible that these Ras could affect protein production at the post-transcriptional or even post-translational levels, which were not adequately considered.

The text claims that "in contrast to what we saw in HEK293 overexpressing RAS (Figure 5d), the "AND-gate" RAS-targeting circuits do not generate higher output than the EF1a-driven, binding-triggered RAS sensor in HCT-116. Instead, the improved dynamic range results from decreased leakiness in HCT- 116k.o." Comparing the experiment from Figure 5d, which looks at activation in KRASG12D and KRASWT, to the experiments in Figure 6b-d, which looks at activation in HCT-116WT and HCT-116KO is misleading. In Fig 5d., cells are transfected with KRASG12D and KRASWT to emulate high levels of mutant RAS and high levels of wild-type RAS. In Figures 6b-d, HCT-116WT has endogenous levels of mutant RAS, while the KCT-116KO is a knock-out cell line, and does not have mutant or WT RAS. Therefore, the improved dynamic range or "decreased leakiness in HCT-116KO" in comparison to Figure 5d. is more comparable to the NF1 condition from Figure 2, which deactivates endogenous RAS. While this may not be feasible, the most accurate comparison would have been an HCT-116KO line with KRASWT stably integrated.

We couldn't locate the citation or discussion of Figure 4d in the text. Conversely, based on the text description, Figure 6g would contain exciting results. But we couldn't find Figure 6g anywhere ... unless it was a typo and the authors meant Figure 6f, in which case the cool results in Figure S8 could use more elaboration in the main text.

Reviewer #3 (Public review):

Summary:

Mutations that result in consistent RAS activation constitute a major driver of cancer. Therefore, RAS is a favorable target for cancer therapy. However, since normal RAS activity is essential for the function of normal cells, a mechanism that differentiates aberrant RAS activity from normal one is required to avoid severe adverse effects. To this end, the authors designed and optimized a synthetic gene circuit that is induced by active RAS-GTP. The circuit components, such as RAS-GTP sensors, dimerization domains, and linkers. To enhance the circuit selectivity and dynamic range, the authors designed a synthetic promoter comprised of MAPK-responsive elements to regulate the expression of the RAS sensors, thus generating a feed-forward loop regulating the circuit components. Circuit outputs with respect to circuit design modification were characterized in standard model cell lines using basal RAS activity, active RAS mutants, and RAS inactivation.

This approach is interesting. The design is novel and could be implemented for other RAS-mediated applications. The data support the claims, and while this circuit may require further optimization for clinical application, it is an interesting proof of concept for targeting aberrant RAS activity.

Strengths:

Novel circuit design, through optimization and characterization of the circuit components, solid data.

Weaknesses:

This manuscript could significantly benefit from testing the circuit performance in more realistic cell lines, such as patient-derived cells driven by RAS mutations, as well as in corresponding non-cancer cell lines with normal RAS activity. Furthermore, testing with therapeutic output proteins in vitro, and especially in vivo, would significantly strengthen the findings and claims.

Reviewer #1 (Public review):

The manuscript presents a novel nonlinear mathematical model that addresses a critical gap in our understanding of how cell shape transitions in response to ECM stiffness. The focus on the interplay between actomyosin contractility and ECM stiffening is highly relevant, especially in the context of cancer invasion and tissue morphogenesis. The originality of the proposed trizonal model is commendable, as it offers a comprehensive framework that could significantly advance the field.

More specifically, the paper makes a significant contribution by providing a model that can predict multimodal cell shapes based on motility levels, which is a substantial improvement over current constitutive models. The potential to calibrate the model against experimental cell shape data is a strong point, as it ensures that the model's predictions are grounded in empirical evidence. The methodology appears to be rigorous and should provide reliable results when applied. This advancement could lead to a better understanding of the complex dynamics involved in cell-matrix interactions, particularly at intermediate ranges of collagen density. The potential applications of this research are vast and span across various medical and biological fields. The ability to predict cancer-induced tissue impairment, cachexia, and muscle injury, as well as to assess therapeutic methods, is particularly noteworthy. The mention of specific treatments like Blebbistatin and HAPLN1 treatments further adds depth to the discussion and highlights the practical relevance of the model.

I'm curious if the authors could further elaborate on the use of this model to examine cellular unjamming transition or the cell shape changes during cancer invasion in various scenarios. Some discussions on that aspect will be helpful. It will also be useful to provide some perspectives on how this model could be integrated with others in a multi-scale modeling framework for understanding cell shape transitions during collective cell migration in various physiologically relevant scenarios.

I recommend some minor revisions but overall, this is a very nice paper.

Reviewer #2 (Public review):

In this work the authors develop a mathematical model that incorporates three contributions to cellular force generation in 3D matrices: (1) actively generated contractile forces via myosin motors and consumption of ATP; (2) the energy stored in the extracellular matrix as it is deformed by the contractile cell; and (3), the energy associated with the interactions at the interface between the matrix and the cell, e.g. at focal adhesions. The authors make predictions about the dependence of cell shape on these three contributions.

The authors succeed in making a number of predictions of how cell shapes will depend on these contributions to force generation. However, these predictions seem to be largely buried in the supplemental material and come in a form that will be accessible to a certain type of physicist and modeler but will likely not be accessible to many experimentalists who may want to test the predictions of the model. The authors show a comparison between their expected cell shape distributions and those predicted by the model, under multiple regimes: cells in two different concentrations of collagen (Figure 4c), cells with inhibited myosin and therefore reduced contractility (Figure 4d), cells with impaired interactions with the ECM (Figure 4e), and for cells with both contractility and ECM interactions impaired. They find a strong agreement between the experiments and their predictions. However, it should be noted that there are multiple "tuning parameters" in their model, so the ability to match experiment and theory may not be ultimately so surprising.

While the authors do achieve their aim of building this modeling and testing it in comparison to experimental data, the text is frequently unclear and doesn't seem to have the right information at the right place and time to allow the reader to most clearly understand the motivation, the approach, or the results. A number of elements of this manuscript were confusing to this reviewer, and I discuss these below in the hopes that raising these points here can bring more clarity in future revisions, and/or that readers will be able to provide additional insight or attention to these questions.

There are certain elements of the writing that obscure, rather than clarify, the model and the results. For example, the authors frequently refer to "matrix stiffening" and "strain stiffening", which are typically used in the literature to describe the phenomenon whereby an applied force changes the mechanical properties of the substrate; here, for example in regard to the discussion of Figure 4C, these terms instead seem to be simply referring to the experimental intervention of exposing different cells to different concentrations of the collagen matrix. While there may be some element of classically understood strain stiffening, incorporated into the model as the function f(λ_i), this doesn't seem to match the experimental validation - which, as described above, is not about strain stiffening but instead simply uses softer vs. stiffer gels. Therefore, it is unclear what exactly is meant throughout the manuscript by strain stiffening - does it mean "difference in stiffness between two conditions" or does it mean "change in substrate stiffness upon application of force"?

Furthermore, while the introductory text emphasizes collective migration, the model itself focuses on the interactions between single cells and their environments. The emphasis on collective migration and cell shape in the introduction invokes previous literature focusing on collective phase transitions, but that is misleading. This paper is all about individual cell mechanics, not about collective migration or unjamming.

The experimental validation seems to have a significant flaw. The mechanics and interactions of the cellular extensions seem to be completely ignored. We see, in Figure 4, that cell bodies are outlined to determine cell shape, but that the extremely long extensions are simply ignored. We know from previous studies that these extensions are generating quite a bit of traction and are contractile, and yet they've been excluded from the analysis. This doesn't make physical sense or fit with previous literature, and would seem to indicate that the regimes predicted by the model are missing an essential component of force generation and cell-matrix interaction.

Reviewer #1 (Public review):

Summary:

This manuscript by Kremer et al. characterizes the tissue-specific responses to changes in TFAM levels and mtDNA copy number in prematurely aging mice (polg mutator model). The authors find that overexpression of TFAM can have beneficial or detrimental effects depending on the tissue type. For instance, increased TFAM levels increase mtDNA copy number in the spleen and improve spleen homeostasis but do not elevate mtDNA copy number in the liver and impair mtDNA expression. Similarly, the consequences of reduced TFAM expression are tissue-specific. Reduced TFAM levels improve brown adipocyte tissue function while other tissues are unaffected. The authors conclude that these tissue-specific responses to altered TFAM levels demonstrate that there are tissue-specific endogenous compensatory mechanisms in response to the continuous mutagenesis produced in the prematurely aging mice model, including upregulation of TFAM expression, elevated mtDNA copy number, and altered mtDNA gene expression. Thus, the impact of genetically manipulating global TFAM expression is limited and there must be other determinants of mtDNA copy number under pathological conditions beyond TFAM.

Strengths:

Overall, this is an interesting study. It does a good job of demonstrating that given the multi-functional role of TFAM, the outcome of manipulating its activity is complex.

Weaknesses:

No major weaknesses were noted. We have minor suggestions for improving the clarity of the manuscript that are detailed in the "recommendations for the authors" section.

Reviewer #2 (Public review):

Summary:

This study by Kremer et al. investigates the impact of modulation of expression of TFAM, a key protein involved in mitochondrial DNA (mtDNA) packaging and expression, in mtDNA mutator mice, which carry random mtDNA mutations. While previous research suggested that increasing TFAM could counteract the pathological effects of mtDNA mutations, this study reveals that the effects of TFAM modulation are tissue-specific. These findings highlight the complexity of mtDNA copy number regulation and gene expression, emphasizing that TFAM alone is not the sole determinant of mtDNA levels in contexts where oxidative phosphorylation is impaired. Other factors likely play a significant role, underscoring the need for nuanced approaches when targeting TFAM for therapeutic interventions.

Strengths:

The data presented in the manuscript is of high quality and supports major conclusions.

Weaknesses:

The statistical methods used are not clearly described, and some marked non-significant results appear visually significant, which raises concerns about data analysis.

Data presentation requires improvement.

Reviewer #1 (Public review):

Summary:

In this manuscript submitted by Qi et al., the authors study the RNA methylation mechanism by the METTL3-METTL14 complex. This complex catalyzes the major epitranscriptome methylation mark of nuclear RNA, including mRNA and lncRNAs. They catalyze the transfer of methyl group from SAM to convert the N6 of adenosine in RNA to m6A. Mutations in this complex have been associated with several diseases, such as type 2 diabetes and several types of cancer. The primary focus of this study was to understand the post-catalytic state of the METTL3-14 bound to a structural mimic of a reaction product known as N6-methyladenosine monophosphate (m6A) using X-ray crystallography. The authors show that the m6A occupies a novel pocket at the interface of the METTL3-14 complex and identified that residues interacting with m6A are mutated in several cancers. Furthermore, the authors demonstrate that the mutations lead to a significant loss in catalytic activity, alter RNA binding, and hinder the proper positioning of the substrate adenine in the active site. Lastly, the authors perform supervised molecular dynamics simulations to understand the effect of the mutations on the interaction network with m6A. The evidence for this study is good, with the combination of X-ray, functional assays, and molecular dynamics justifying their overall conclusions. This structure is significant as it provides new insights into the structural determinants of known cancer-associated mutations of this important class of enzymes. However, some issues need to be addressed.

Strengths:

(1) The X-ray structure is well determined, and the density map has the quality to observe all the interactions of the METTL3-14 complex with m6A.

(2) The structure reveals a novel 'cryptic pocket' in the complex that is 16 Å away from the SAM binding site. It is a functional m6A-sensor, illustrating a mechanism where the complex switches its functionality from an m6A writer to a reader.

(3) The structure illustrates that the residues forming cryptic pockets are found in multiple Cancer-associated mutations and are well conserved across several organisms.

(4) The functional assays (methyl transferase, RNA binding, kinetic, and SPR assays) provide a complete picture of the effect of the mutations on the activity of the METTL3-14 complex.

(5) Molecular dynamics simulations were done to understand the impact of the mutations on the pocket structure and its dynamics and support the X-ray structure findings.

Weaknesses:

(1) Although the X-ray structure is well determined, the statistics are a bit troubling, particularly the Ramachandran, Sidechain and RSRZ outliers. It is well above the average for structures at that resolution. Maybe the use of alternative software such as ISOLDE may be adequate to improve those parameters.

(2) The authors should expand their discussion as to why the affinity for the product is higher than the substrate and the implications on the mechanism.

(3) The SPR profiles of the association kinetics look to have several minor association-dissociation events occurring. Multiple binding sites? Authors should provide an explanation for such behavior. Also, what is the structural explanation of the difference in binding modes between the wt vs. mutant (one vs. two-state binding modes)?

(4) In materials and methods, it shows the data in Figure 2a was fitted to a Michaelis-Menten equation, however, the Y axis shows Normalized methylation and not initial rates. The authors should elaborate on their approach. In addition, more than three initial velocity rate points per protein are needed to fit a Michaelis-Menten curve confidently. Additionally, where can the Michaelis-Menten parameters be found?

Reviewer #2 (Public review):

Summary:

Qi et al. determined the X-ray crystallographic structure of the methyltransferase core of the obligate heterodimeric complex METTL3-METTL14 in complex with methyladenosine monophosphate (m6A), a product mimic for the methylation of adenosine, to a resolution of 2.5 Å. Their structure appears to reveal a cryptic binding pocket for m6A that had not previously been identified. Using full-length protein produced in insect cells, Qi et al. determined the methyltransferase activity of wildtype METTL3-METTL14 and compared it to that of mutant forms of the protein that have been implicated in cancer. In addition to methyltransferase activity, the authors used both fluorescence polarization assays and surface plasmon resonance to investigate the affinities and kinetics of RNA binding to wildtype and mutant forms of the full-length complex. The results indicate that mutations in the methyltransferase core of two separate arginine residues alter the dynamics of RNA binding and enzyme specificity of METTL3-METTL14. The authors go on to use a combination of supervised molecular dynamics simulations and comparisons to recently published structures to propose a "swivelling" mechanism for the transfer of the methylated substrate from the catalytic site of the complex to the novel cryptic pocket.

Strengths:

I appreciated the inclusion of supplementary data showing the purity and monodispersity of the protein used for crystallization as well as the omit map and other electron density maps to support the placement of the product mimic in the cryptic site. The authors use a combination of complementary biophysical techniques to test the effects of mutations that were identified in the literature as being clinically important and to develop a hypothesis for the large-scale translocation required for the enzymatic product to move from the catalytic site to the cryptic pocket. The use of molecular dynamics simulations to attempt to indirectly visualize how this translocation might occur in vivo was well done.

Weaknesses:

Even taking into account the 2.5 Å resolution of the structure, the model is not refined to the point that it could be. Some waters seem to be built into blobs of density that aren't particularly convincing, and other seemingly obvious waters aren't built at all. The structure validation report supports this and shows that overall, and in the context of 2.5 Å resolution, this is not a great model. A good many parts of the structural analysis don't seem consistent with what I see when I look at the model and density in terms of proposed interactions in the cryptic pocket. Much of the language used in the manuscript is too strong when the model is quite speculative.

Reviewer #1 (Public review):

Summary:

The manuscript by Li and colleagues describes the impact of deficiency on the DKGα and ζ on Treg cells and follicular responses. The experimental approach is based on the characterization of double KO mice that show the emergence of autoimmune manifestations that include the production of autoantibodies. Additionally, there is an increase in Tfh cells, but also Tfr cells in these mice deficient in both DKGα and ζ. Although the observations are interesting, the interpretation of the observations is difficult in the absence of data related to single mutations. While a supplementary figure shows that the autoimmune manifestations are more severe in the DKGα and ζ deficient mice, prior observations show that a single DKGα deficiency has an impact on Treg homeostasis. As such, the contribution of the two chains to the overall phenotype is hard to establish.

Strengths:

Well-conducted experiments with informative mouse models with defined genetic defects.

Weaknesses:

The major weakness is the lack of clarity concerning what can be attributed to simultaneous DKGα and ζ deficiency versus deficiency on DKGα or ζ alone.

Some interpretations are also not conclusively supported by data.

Reviewer #2 (Public review):

Summary:

In this manuscript, Li et al investigate the combined role of diacylglycerol (DAG) kinases (DGK) a and z in Foxp3+ Treg cells function that prevent autoimmunity. The authors generated DGK a and z Treg-specific double knockout mice (DKO) by crossing Dgkalpha-/- mice to DgKzf and Foxp3YFPCre/+ mice. The resulting "DKO" mice thus lack DGK a in all cells and DGK z in Foxp3+Treg cells. The authors show that the DKO mice spontaneously develop autoimmunity, characterized by multiorgan inflammatory infiltration and elevated anti-double-strand DNA (dsDNA), -single-strand DNA (ssDNA), and -nuclear autoantibodies. The authors attribute the DKO mice phenotype to Foxp3+Treg dysfunction, including accelerated conversion into "exTreg" cells with pathogenic activity. Interestingly, the combined deficiency of DGK a and z seems to release Treg cell dependence on CD28-mediated costimulatory signals, which the authors show by crossing their DKO mice to CD28-/- mice (TKO mice), which also develop autoimmunity.

Strengths:

The phenotypes of the mutant mice described in the manuscript are striking, and the authors provide a comprehensive analysis of the functional processes altered by the lack of DGKs.

Weaknesses:

One aspect that could be better explored is the direct role of "ex-Tregs" in causing pathogenesis in the models utilized.

However, overall, this is an important report that makes a significant addition to the understanding of DAG kinases in Treg cell biology.