Reviewer #1 (Public review):

Summary:

This computational modelling study addresses the important question of how neurons can learn non-linear functions using biologically realistic plasticity mechanisms. The study extends the previous related work on metaplasticity by Khodadadi et al. (2025), using the same detailed biophysical model and basic study design, while significantly simplifying the synaptic plasticity rule by removing non-linearities, reducing the number of free parameters, and limiting plasticity to only excitatory synapses. The rule itself is supervised by the presence or absence of a binary dopamine reward signal, and gated by separate calcium-sensitive thresholds for potentiation and depression. The author shows that, when paired with a strong form of dendritic non-linearity called a "plateau potential" and appropriate pre-existing dendritic clustering of features, this simpler learning mechanism can solve a non-linear classification task similar to the classic XOR logic operator, with equal or better performance than the previous publication. The primary claims of this publication are that metaplasticity is required for learning non-linear feature classification, and that simultaneous dynamics in two separate thresholds (for potentiation and depression) are critical in this process. By systematically studying the properties of a biophysically plausible supervised learning rule, this paper adds interesting insights into the mechanics of learning complex computations in single neurons.

Strengths:

The simplified form of the learning rule makes it easier to understand and study than previous metaplasticity rules, and makes the conclusions more generalizable, while preserving biological realism. Since similar biophysical mechanisms and dynamics exist in many different cell types across the whole brain, the proposed rule could easily be integrated into a wide range of computational models specializing in brain regions beyond the striatum (which is the focus of this study), making it of broad interest to computational neuroscientists. The general approach of systematically fixing or modifying each variable while observing the effects and interactions with other variables is sound and brings great clarity to understanding the dynamic properties and mechanics of the proposed learning rule.

Weaknesses:

General notes

(1) The credibility of the main claims is mainly limited by the very narrow range of model parameters that was explored, including several seemingly arbitrary choices that were not adequately justified or explored.

(2) The choice to use a morphologically detailed biophysical model, rather than a simpler multi-compartment model, adds a great deal of complexity that further increases uncertainty as to whether the conclusions can generalize beyond the specific choices of model and morphology studied in this paper.

(3) The requirement for pre-existing synaptic clustering, while not implausible, greatly limits the flexibility of this rule to solve non-linear problems more generally.

(4) In order to claim that two thresholds are truly necessary, the author would have to show that other well-known rules with a single threshold (e.g., BCM) cannot solve this problem. No such direct head-to-head comparisons are made, raising the question of whether the same task could be achieved without having two separate plasticity thresholds.

Specific notes

(1) Regarding the limited hyperparameter search:

(a) On page 5, the author introduces the upper LTP threshold Theta_LTP. It is not clear why this upper threshold is necessary when the weights are already bounded by w_max. Since w_max is just another hyperparameter, why not set it to a lower value if the goal is to avoid excessively strong synapses? The values of w_max and Theta_LTP appear to have been chosen arbitrarily, but this question could be resolved by doing a proper hyperparameter search over w_max in the absence of an upper Theta_LTP.

(b) The author does not explore the effect of having separate learning rates for theta_LTP and theta_LTD, which could also improve learning performance in the NFBP. A more comprehensive exploration of these parameters would make the inclusion of theta_max (and the specific value chosen) a lot less arbitrary.

(c) Figure 4 Supplements 3-4: The author shows results for a hyperparameter search of the learning rule parameters, which is important to see. However, the parameter search is very limited: only 3 parameter values were tried, and there is no explanation or rationale for choosing these specific parameters. In particular, the metaplasticity learning rates do not even span one order of magnitude. If the author wants to claim that the learning rule is insensitive to this parameter, it should be explored over a much broader range of values (e.g., something like the range [0.1-10]).

(2) Regarding the similarity to BCM, the author would ideally directly implement the BCM learning rule in their model, but at the least the author could have shown whether a slight variant of their rule presented here can be effective: for example having a single (plastic, not fixed) Ca-dependent threshold that applies to both LTP and LTD, with a single learning rate parameter.

(3) This paper is extremely similar (and essentially an extension) to the work of Khodadadi et al. (2025). Yet this paper is not mentioned at all in the introduction, and the relation between these papers is not made clear until the discussion, leaving me initially puzzled as to what problems this paper addresses that have not already been extensively solved. The introduction could be reworked to make this connection clearer while pointing out the main differences in approach (e.g., the important distinction between "boosting" nonlinearities and plateau potentials).

(4) The introduction is missing some citations of other recent work that has addressed single-neuron non-linear computation and learning, such as Gidon et al (2020); Jones & Kording (2021).

(5) Figure 1: The figure prominently features mGluR next to the CaV channel, but there is no mention of mGluR in the introduction. The introduction should be updated to include this.

(6) Could the author explain why there is a non-monotonic increase/decrease in the [Ca]_L in Figure 2B_4? Perhaps my confusion comes from not understanding what a single line represents. Does each line represent the [Ca] in a single spine (and if so, which spine), or is each line an average of all the spines in a given stim condition?

(7) Row 124 (page 4): L-type Ca microdomains (in which ions don't diffuse and therefore don't interact with Ca_NMDA) is a critical assumption of this model. The references for this appear only in the discussion, so when reading this paper, I found myself a bit confused about why the same ion is treated as two completely independent variables with separate dynamics. Highlighting the assumption (with citations) a bit more clearly in the results section when describing the rule would help with understanding.

(8) Row 149 (page 5): The current formulation of the update rule is not actually multiplicative. The fact that the update is weight-dependent alone does not make it a multiplicative rule, and judging by equation (1) it appears to simply be an additive rule with a weight regularization term that guarantees weight bounds. For example, a similar weight-dependent update is also a core component of BTSP (Milstein et al. 2021; Galloni et al. 2025), which is another well-known *additive* rule. An actual multiplicative rule implies that the update itself is applied via a multiplication, i.e. w_new = w_old * delta_w

For an example of a genuinely multiplicative rule, see: Cornford et al. 2024, "Brain-like learning with exponentiated gradients"). Multiplicative rules have very different properties to additive rules, since larger weights tend to grow quickly while small weights shrink towards 0.

(9) Equation 1 (page 5): Shouldn't the depression term be written as: (w_min - w)? This term would be negative if w is larger than w_min, leading to LTD. As it is written now, a large w and small w_min would just cause further potentiation instead of depression.

(10) In the introduction, the teaching signal is described in binary terms (DA peak, or DA pause), but in Equation 1, it actually appears to take on 3 different values. Could the author clarify what the difference is between a "DA pause" and the "no DA" condition? The way I read it, pause = absence of DA = no DA

(11) Figure 3: In these experimental simulations, DA feedback comes in 400ms after the stimulus. The author could motivate this choice a bit better and explain the significance of this delay. Clearly, the equations have a delta_t term, but as far as the learning algorithm is concerned, it seems like learning would be more effective at delta_t=0. Is the choice of 400ms mainly motivated by experimental observations? On a related note, is it meaningful that the 200ms delta_t before the next stimulus is shorter than the 400ms pause from the first stimulus? Wouldn't the DA that arrives shortly before a stimulus also have an effect on the learning rule?

(12) Figure 4C: How is it possible that the theta_LTP value goes higher than the upper threshold (dashed line)? Equation 3 implies that it should always be lower.

(13) Row 429 (page 11): The statement that "without metaplasticity the NFBP cannot be solved" is overly general and not supported by the evidence presented. There exist many papers in which people solve similar non-linear feature learning problems with Hebbian or other bio-plausible rules that don't have metaplasticity. A more accurate statement that can be made here is that the specific rule presented in this paper requires metaplasticity.

(14) The methods section does not make any mention of publicly available code or a GitHub repository. The author should add a link to the code and put some effort into improving the documentation so that others can more easily assess the code and reproduce the simulations.

friar tells romeo that the prince had shown him mercy instead of killing him he decided to banish him but romeo insist that this is worse then executing him because he would be separated from Juliet

benvolio is trying to get mercutio to leave because it is hot outside and their rivals the capulets are here mercutio jokes that benvolio is actually the one that likes to start fights and is easily angered

Specificity: narrowing down thesis. Precision: Hitting the nail on the head so to speak, with the thesis statement.

[Paper-level Aggregated] PMCID: PMC7099049

Evidence Type(s): Prognostic, Functional

Justification: Prognostic: The SNPs rs16879870, rs2641256, rs2761591, and rs854936 were significantly associated with HNSCC survival, indicating their potential role in predicting patient outcomes. Functional: The genotypes of rs16879870 and rs854936 were significantly associated with the expression of genes GJB7 and RTN4R, respectively, suggesting a functional impact on gene expression related to HNSCC.

Gene→Variant (gene-first): FGFR4(2264):AUC from 0 NA:rs16879870 SCIMP(388325):rs2641256 DCDC1(341019):rs2761591 NA:rs854936

Genes: FGFR4(2264) NA SCIMP(388325) DCDC1(341019)

Variants: AUC from 0 rs16879870 rs2641256 rs2761591 rs854936

[Paper-level Aggregated] PMCID: PMC6333965

Evidence Type(s): Oncogenic, Prognostic

Justification: Oncogenic: The text mentions that mutations in PIK3CA (E545K) and CDKN2A (R58X) are potentially targetable, indicating their role in cancer development. Additionally, TP53 mutations (R209Q/W, R243W/Q) are associated with cell cycle deregulation, further supporting their oncogenic potential. Prognostic: The text states that mutations in TP53, CDKN2A, and CCND1 are significantly associated with poorer overall survival, indicating their prognostic value in the context of cancer outcomes.

Gene→Variant (gene-first): PIK3CA(5290):E545K TP53(7157):R209Q/W TP53(7157):R243W/Q CDKN2A(1029):R58X

Genes: PIK3CA(5290) TP53(7157) CDKN2A(1029)

Variants: E545K R209Q/W R243W/Q R58X

eLife Assessment

This valuable study describes significant differences in prey capture behavior between PSD-95 knock-out and wild-type mice, despite prior work by the same authors showing only modest visual deficits in the former. The data convincingly demonstrated prey capture performance in PSD-95 knock-out mice to improve under monocular viewing conditions. However, this finding alone was inadequate to support the interpretation of results as revealing a deficit in binocular visual integration, especially given the lack of eye and head tracking data or consideration of alternative explanations for the observed behavior.

Reviewer #1 (Public review):

Summary:

PSD95 has long been studied in detail to understand molecular mechanisms of synaptic plasticity as related to specific cell types (excitatory), circuits (visual cortex) and circuit development and function (ocular dominance plasticity ). While much was known about the molecular and cellular details of its function, it remained unclear whether and how it might contribute to the development of specific aspects of visual perception. While overall vision is preserved in PSD95 KO (Knockout) mice, studying natural, visually-guided prey capture behavior revealed robust, yet specific, perturbations to binocular processing during the behavior.

Strengths:

A major strength of the paper is being able to quantify precise measures of the visual aspects versus the motor aspects of prey pursuit. Comparing changes in behavior due to monocular occlusion was particularly revealing that mice indeed employ binocular summation to extract visual cues useful for prey pursuit. This result further suggested that in cases with poor binocular vision, monocular input can improve perceptual and behavioral processes as it does in human subjects with comparable challenges.

The study not only provided a useful finding regarding the function of PSD95, but also outlined a useful general approach toward identifying and quantifying specific deficits in binocular summation. This is likely to broadly impact studies of visual system development, behavior, and neural circuit function. The careful attention to details, observations, and openness about subject variance will also be helpful to those studying specific visual pursuit and natural prey capture behavior in the mouse.

Weaknesses:

Lack of eye movement monitoring and detailed head movement analysis preclude total certainty for the interpretation of observed behaviors.

Author response:

We thank the reviewers for their thorough and constructive evaluation of our manuscript titled “PSD-95 drives binocular vision maturation critical for predation”. The reviewers raised several important conceptual and technical points. Here, we address and provide additional context on the major themes and outline our planned revisions.

We acknowledge that the current prey capture task cannot directly adjudicate between PSD-95 binocular vision impairments or sensorimotor processing deficits. However, we did not observe any major impairment supporting a sensorimotor processing deficit, in contrast to a major impairment in line with binocular vision impairment. Evidence from Huang et al. (2015) [1], Favaro et al. (2018) [2] and our data with the visual water task (VWT) — thus requiring identical sensorimotor but differential visual processing—clearly demonstrated intact visual acuity but impaired orientation discrimination in PSD-95 KO mice. Therefore, we believe that a binocular integration deficit is the most likely explanation of PSD-95 KO binocular impairments. In line with this, it is unlikely that aberrations in binocular eye movements account for the observations. We appreciate that alternative explanations remain possible and merit explicit discussion. Accordingly, we intend to expand the discussion of these alternatives.

Importantly, we will provide additional experimental data demonstrating that knock-down of PSD-95 in V1 but not in superior colliculus, significantly decreases orientation discrimination analyzed with the VWT, as we had shown for PSD-95 KO mice (while control knock-down does not have this effect). We believe that this new evidence better delineates the potential neuroanatomical locus of the PSD-95-associated deficits.

Furthermore, we will provide additional head movement analyses, as suggested by Reviewer 1. Specifically, we will investigate the head angle in relation to the cricket (azimuth) in time (±1 second) around prey contact under light and dark conditions.

We will also address the potential impact of PSD-95 KO learning deficits. We agree that there are more impairments in the PSD-95 KO brain, as has been published previously. But strikingly, the binocular impairment was dominating the sensory processing. This cannot be convincingly explained by learning deficits. In fact, we have observed improved learning of PSD-95 KO mice with some tasks (e.g. cocaine conditioned place preference) [3], but no significant differences in the VWT [1,2]. Learning differences were described for another PSD-95 mouse line, expressing the N-terminus with two PDZ domains [4]. To avoid potential learning dependent confounds, we have chosen salient stimuli, like water aversion, and prey capture to reduce impacts of potential learning defects.

We agree on the strength of the random dot stereograms to isolate stereoscopic computations. However, it requires special filters in front of either eye, which renders it unsuitable for the VWT. The lengthy training with less silent stimuli of water reward, could potentially add additional confounds of PSD-95 KO deficits. Thus, we think that this would be something for future experiments to allow for integration of different visual inputs. However, the combined improved performance of WT mice with binocular vision for prey capture (depth percept) and orientation discrimination (summation) is already supporting the importance of binocular vision in mice and the dominant defect in PSD-95 KO mice.

Finally, we will address the other points raised by the reviewers through clearer exposition and reorganization of the manuscript.

Once again, we would like to thank the reviewers for their thoughtful and constructive feedback, which we believe will substantially strengthen the manuscript.

(1) Huang, X., Stodieck, S. K., Goetze, B., Cui, L., Wong, M. H., Wenzel, C., Hosang, L., Dong, Y., Löwel, S., and Schlüter, O. M. (2015). Progressive maturation of silent synapses governs the duration of a critical period. Proc. Natl. Acad. Sci. 112, E3131–E3140. https://doi.org/10.1073/pnas.1506488112.

(2) Favaro, P.D., Huang, X., Hosang, L., Stodieck, S., Cui, L., Liu, Y., Engelhardt, K.-A., Schmitz, F., Dong, Y., Löwel, S., et al. (2018). An opposing function of paralogs in balancing developmental synapse maturation. PLOS Biol. 16, e2006838. https://doi.org/10.1371/journal.pbio.2006838.

(3) Shukla, A., Beroun, A., Panopoulou, M., Neumann, P.A., Grant, S.G., Olive, M.F., Dong, Y., and Schlüter, O.M. (2017). Calcium‐permeable AMPA receptors and silent synapses in cocaine‐conditioned place preference. EMBO J. 36, 458–474. https://doi.org/10.15252/embj.201695465.

(4) Migaud, M., Charlesworth, P., Dempster, M., Webster, L.C., Watabe, A.M., Makhinson, M., He, Y., Ramsay, M.F., Morris, R.G.M., Morrison, J.H., et al. (1998). Enhanced long-term potentiation and impaired learning in mice with mutant postsynaptic density-95 protein. Nature 396, 433–439. https://doi.org/10.1038/24790.

Reviewer #1 (Public review):

Review of the revised submission:

I thank the authors for their detailed consideration of my comments and for the additional data, analyses, and clarifications they have incorporated. The new behavioral experiments, quantification of targeted manipulations, and expanded methodological details strengthen the manuscript and address many of my initial concerns. While some questions remain for future work, the authors' careful responses and the additional evidence provided help resolve the main issues I raised, and I am generally satisfied with the revisions.

Review of original submission:

Summary

In this article, Kawanabe-Kobayashi et al., aim to examine the mechanisms by which stress can modulate pain in mice. They focus on the contribution of noradrenergic neurons (NA) of the locus coeruleus (LC). The authors use acute restraint stress as a stress paradigm and found that following one hour of restraint stress mice display mechanical hypersensitivity. They show that restraint stress causes the activation of LC NA neurons and the release of NA in the spinal cord dorsal horn (SDH). They then examine the spinal mechanisms by which LC→SDH NA produces mechanical hypersensitivity. The authors provide evidence that NA can act on alphaA1Rs expressed by a class of astrocytes defined by the expression of Hes (Hes+). Furthermore, they found that NA, presumably through astrocytic release of ATP following NA action on alphaA1Rs Hes+ astrocytes, can cause an adenosine-mediated inhibition of SDH inhibitory interneurons. They propose that this disinhibition mechanism could explain how restraint stress can cause the mechanical hypersensitivity they measured in their behavioral experiments.

Strengths:

(1) Significance. Stress profoundly influences pain perception; resolving the mechanisms by which stress alters nociception in rodents may explain the well-known phenomenon of stress-induced analgesia and/or facilitate the development of therapies to mitigate the negative consequences of chronic stress on chronic pain.

(2) Novelty. The authors' findings reveal a crucial contribution of Hes+ spinal astrocytes in the modulation of pain thresholds during stress.

(3) Techniques. This study combines multiple approaches to dissect circuit, cellular, and molecular mechanisms including optical recordings of neural and astrocytic Ca2+ activity in behaving mice, intersectional genetic strategies, cell ablation, optogenetics, chemogenetics, CRISPR-based gene knockdown, slice electrophysiology, and behavior.

Weaknesses:

(1) Mouse model of stress. Although chronic stress can increase sensitivity to somatosensory stimuli and contribute to hyperalgesia and anhedonia, particularly in the context of chronic pain states, acute stress is well known to produce analgesia in humans and rodents. The experimental design used by the authors consists of a single one-hour session of restraint stress followed by 30 min to one hour of habituation and measurement of cutaneous mechanical sensitivity with von Frey filaments. This acute stress behavioral paradigm corresponds to the conditions in which the clinical phenomenon of stress-induced analgesia is observed in humans, as well as in animal models. Surprisingly, however, the authors measured that this acute stressor produced hypersensitivity rather than antinociception. This discrepancy is significant and requires further investigation.

(2) Specifically, is the hypersensitivity to mechanical stimulation also observed in response to heat or cold on a hotplate or coldplate?

(3) Using other stress models, such as a forced swim, do the authors also observe acute stress-induced hypersensitivity instead of stress-induced antinociception?

(4) Measurement of stress hormones in blood would provide an objective measure of the stress of the animals.

(5) Results:

(a) Optical recordings of Ca2+ activity in behaving rodents are particularly useful to investigate the relationship between Ca2+ dynamics and the behaviors displayed by rodents.

(b) The authors report an increase in Ca2+ events in LC NA neurons during restraint stress: Did mice display specific behaviors at the time these Ca2+ events were observed such as movements to escape or orofacial behaviors including head movements or whisking?

(c) Additionally, are similar increases in Ca2+ events in LC NA neurons observed during other stressful behavioral paradigms versus non-stressful paradigms?

(d) Neuronal ablation to reveal the function of a cell population.

(e) The proportion of LC NA neurons and LC→SDH NA neurons expressing DTR-GFP and ablated should be quantified (Figures 1G and J) to validate the methods and permit interpretation of the behavioral data (Figures 1H and K). Importantly, the nocifensive responses and behavior of these mice in other pain assays in the absence of stress (e.g., hotplate) and a few standard assays (open field, rotarod, elevated plus maze) would help determine the consequences of cell ablation on processing of nociceptive information and general behavior.

(f) Confirmation of LC NA neuron function with other methods that alter neuronal excitability or neurotransmission instead of destroying the circuit investigated, such as chemogenetics or chemogenetics, would greatly strengthen the findings. Optogenetics is used in Figure 1M, N but excitation of LC→SDH NA neuron terminals is tested instead of inhibition (to mimic ablation), and in naïve mice instead of stressed mice.

(g) Alpha1Ars. The authors noted that "Adra1a mRNA is also expressed in INs in the SDH".

(h) The authors should comprehensively indicate what other cell types present in the spinal cord and neurons projecting to the spinal cord express alpha1Ars and what is the relative expression level of alpha1Ars in these different cell types.

(i) The conditional KO of alpha1Ars specifically in Hes5+ astrocytes and not in other cell types expressing alpha1Ars should be quantified and validated (Figure 2H).

(j) Depolarization of SDH inhibitory interneurons by NA (Figure 3). The authors' bath applied NA, which presumably activates all NA receptors present in the preparation.

k) The authors' model (Figure 4H) implies that NA released by LC→SDH NA neurons leads to the inhibition of SDH inhibitory interneurons by NA. In other experiments (Figure 1L, Figure 2A), the authors used optogenetics to promote the release of endogenous NA in SDH by LC→SDH NA neurons. This approach would investigate the function of NA endogenously released by LC NA neurons at presynaptic terminals in the SDH and at physiological concentrations and would test the model more convincingly compared to the bath application of NA.

(l) As for other experiments, the proportion of Hes+ astrocytes that express hM3Dq, and the absence of expression in other cells, should be quantified and validated to interpret behavioral data.

(m) Showing that the effect of CNO is dose-dependent would strengthen the authors' findings.

(n) The proportion of SG neurons for which CNO bath application resulted in a reduction in recorded sIPSCs is not clear.

(o) A1Rs. The specific expression of Cas9 and guide RNAs, and the specific KD of A1Rs, in inhibitory interneurons but not in other cell types expressing A1Rs should be quantified and validated.

(6) Methods:

It is unclear how fiber photometry is performed using "optic cannula" during restraint stress while mice are in a 50ml falcon tube (as shown in Figure 1A).

Author response:

The following is the authors’ response to the original reviews.

Public reviews:

Reviewer #1 (Public review):

Summary:

In this article, Kawanabe-Kobayashi et al., aim to examine the mechanisms by which stress can modulate pain in mice. They focus on the contribution of noradrenergic neurons (NA) of the locus coeruleus (LC). The authors use acute restraint stress as a stress paradigm and found that following one hour of restraint stress mice display mechanical hypersensitivity. They show that restraint stress causes the activation of LC NA neurons and the release of NA in the spinal cord dorsal horn (SDH). They then examine the spinal mechanisms by which LC→SDH NA produces mechanical hypersensitivity. The authors provide evidence that NA can act on alphaA1Rs expressed by a class of astrocytes defined by the expression of Hes (Hes+). Furthermore, they found that NA, presumably through astrocytic release of ATP following NA action on alphaA1Rs Hes+ astrocytes, can cause an adenosine-mediated inhibition of SDH inhibitory interneurons. They propose that this disinhibition mechanism could explain how restraint stress can cause the mechanical hypersensitivity they measured in their behavioral experiments.

Strengths:

(1) Significance. Stress profoundly influences pain perception; resolving the mechanisms by which stress alters nociception in rodents may explain the well-known phenomenon of stress-induced analgesia and/or facilitate the development of therapies to mitigate the negative consequences of chronic stress on chronic pain.

(2) Novelty. The authors' findings reveal a crucial contribution of Hes+ spinal astrocytes in the modulation of pain thresholds during stress.

(3) Techniques. This study combines multiple approaches to dissect circuit, cellular, and molecular mechanisms including optical recordings of neural and astrocytic Ca2+ activity in behaving mice, intersectional genetic strategies, cell ablation, optogenetics, chemogenetics, CRISPR-based gene knockdown, slice electrophysiology, and behavior.

Weaknesses:

(1) Mouse model of stress. Although chronic stress can increase sensitivity to somatosensory stimuli and contribute to hyperalgesia and anhedonia, particularly in the context of chronic pain states, acute stress is well known to produce analgesia in humans and rodents. The experimental design used by the authors consists of a single one-hour session of restraint stress followed by 30 min to one hour of habituation and measurement of cutaneous mechanical sensitivity with von Frey filaments. This acute stress behavioral paradigm corresponds to the conditions in which the clinical phenomenon of stress-induced analgesia is observed in humans, as well as in animal models. Surprisingly, however, the authors measured that this acute stressor produced hypersensitivity rather than antinociception. This discrepancy is significant and requires further investigation.

We thank the reviewer for evaluating our work and for highlighting both its strengths and weaknesses. As stated by the reviewer, numerous studies have reported acute stress-induced antinociception. However, as shown in a new additional table (Table S1) in which we have summarized previously published data using the acute restraint stress model employed in our present study, most studies reporting antinociceptive effects of acute restraint stress assessed behavioral responses to heat stimuli or formalin. This observation is consistent with the findings from our previous study (Uchiyama et al., Mol Brain, 2022 (PMID: 34980215)). The present study also confirms that acute restraint stress reduces behavioral responses to noxious heat (see also our response to Comment #2 below). In contrast to the robust and consistent antinociceptive effects observed with thermal stimuli, some studies evaluating behavioral responses to mechanical stimuli have reported stress-induced hypersensitivity (see Table S1), which aligns with our current findings. Taken together, these data support our original notion that the effects of acute stress on pain-related behaviors depend on several factors, including the nature, duration, and intensity of the stressor, as well as the sensory modality assessed in behavioral tests. We have incorporated this discussion and Table S1 into the revised manuscript (lines 344-353). Furthermore, we have slightly modified the text including the title, replacing "pain facilitation" with "mechanical pain hypersensitivity" to more accurately reflect our research focus and the conclusion of this study that LC^→SDH NAergic signaling to spinal astrocytes is required for stress-induced mechanical pain hypersensitivity. Finally, while mouse models of stress could provide valuable insights, the clinical relevance of stress-induced mechanical pain hypersensitivity remains to be elucidated and requires further investigation. We hope these clarifications address your concerns.

(2) Specifically, is the hypersensitivity to mechanical stimulation also observed in response to heat or cold on a hotplate or coldplate?

Thank you for your important comment. We have now conducted additional behavioral experiments to assess responses to heat using the hot-plate test. We found that mice subjected to restraint stress did not exhibit behavioral hypersensitivity to heat stimuli; instead, they displayed antinociceptive responses (Figure S2; lines 95-98). These results are consistent with our previous findings (Uchiyama et al., Mol Brain, 2022 (PMID: 34980215)) as well as numerous other reports (Table S1).

(3) Using other stress models, such as a forced swim, do the authors also observe acute stress-induced hypersensitivity instead of stress-induced antinociception?

As suggested by the reviewer, we conducted a forced swim test. We found that mice subjected to forced swimming, which has been reported to produce analgesic effects on thermal stimuli (Contet et al., Neuropsychopharmacology, 2006 (PMID: 16237385)), did not exhibit any changes in mechanical pain hypersensitivity (Figure S2; lines 98-99). Furthermore, a previous study demonstrated that mechanical pain sensitivity is enhanced by other stress models, such as exposure to an elevated open platform for 30 min (Kawabata et al., Neuroscience, 2023 (PMID: 37211084)). However, considering our data showing that changes in mechanosensory behavior induced by restraint stress depend on the duration of exposure (Figure S1), and that restraint stress also produced an antinociceptive effect on heat stimuli (Figure S2), stress-induced modulation of pain is a complex phenomenon influenced by multiple factors, including the stress model, intensity, and duration, as well as the sensory modality used for behavioral testing (lines 100-103).

(4) Measurement of stress hormones in blood would provide an objective measure of the stress of the animals.

A previous study has demonstrated that plasma corticosterone levels—a stress hormone—are elevated following a 1-hour exposure to restraint stress in mice (Kim et al., Sci Rep, 2018 (PMID: 30104581)), using a stress protocol similar to that employed in our current study. We have included this information with citing this paper (lines 104-105).

(5) Results:

(a) Optical recordings of Ca2+ activity in behaving rodents are particularly useful to investigate the relationship between Ca2+ dynamics and the behaviors displayed by rodents.

In the optical recordings of Ca²⁺ activity in LC neurons, we monitored mouse behavior during stress exposure. We have now included a video of this in the revised manuscript (video; lines 111-114).

(b) The authors report an increase in Ca2+ events in LC NA neurons during restraint stress: Did mice display specific behaviors at the time these Ca2+ events were observed such as movements to escape or orofacial behaviors including head movements or whisking?

By reanalyzing the temporal relationship between Ca²⁺ events and mouse behavior during stress exposure, we found that the Ca²⁺ transients and escape behaviors (struggling) occurred almost simultaneously (video). A similar temporal correlation is also observed in Ca²⁺ responses in the bed nucleus of the stria terminalis (Luchsinger et al., Nat Commun, 2021 (PMID: 34117229)). The video file has been included in the revised manuscript (video; lines 111-113, 552-553, 573-575).

Additionally, as described in the Methods section and shown in Figure S2 of the initial version (now Figure S3), non-specific signals or artifacts—such as those caused by head movements—were corrected (although such responses were minimal in our recordings).

(c) Additionally, are similar increases in Ca2+ events in LC NA neurons observed during other stressful behavioral paradigms versus non-stressful paradigms?

We appreciate the reviewer's valuable suggestion. Since the present, initial version of our manuscript focused on acute restraint stress, we did not measure Ca²⁺ events in LC-NA neurons in other stress models, but a recent study has shown an increase in Ca²⁺ responses in LC-NA neurons by social defeat stress (Seiriki et al., BioRxiv, https://www.biorxiv.org/content/10.1101/2025.03.07.641347v1).

(d) Neuronal ablation to reveal the function of a cell population.

This method has been widely used in numerous previous studies as an effective experimental approach to investigate the role of specific neuronal populations—including SDH-projecting LC-NA neurons (Ma et al., Brain Res, 2022 (PMID: 34929182); Kawanabe et al., Mol Brain, 2021 (PMID: 33971918))—in CNS function.

(e) The proportion of LC NA neurons and LC→SDH NA neurons expressing DTR-GFP and ablated should be quantified (Figures 1G and J) to validate the methods and permit interpretation of the behavioral data (Figures 1H and K). Importantly, the nocifensive responses and behavior of these mice in other pain assays in the absence of stress (e.g., hotplate) and a few standard assays (open field, rotarod, elevated plus maze) would help determine the consequences of cell ablation on processing of nociceptive information and general behavior.

As suggested, we conducted additional experiments to quantitatively analyze the number of LC^→SDH-NA neurons. We used WT mice injected with AAVretro-Cre into the SDH (L4 segment) and AAV-FLEx[DTR-EGFP] into the LC. In these mice, 4.4% of total LC-NA neurons [positive for tyrosine hydroxylase (TH)] expressed DTR-GFP, representing the LC^→SDH-NA neuronal population (Figure S4; lines 126-127). Furthermore, treatment with DTX successfully ablated the DTR-expressing LC^→SDH-NA neurons. Importantly, the neurons quantified in this analysis were specifically those projecting to the L4 segment of the SDH; therefore, the total number of SDH-projecting LC-NA neurons across all spinal segments is expected to be much higher.

We also performed the rotarod and paw-flick tests to assess motor function and thermal sensitivity following ablation of LC^→SDH-NA neurons. No significant differences were observed between the ablated and control groups (Figure S5; lines 131-134), indicating that ablation of these neurons does not produce non-specific behavioral deficits in motor function or other sensory modalities.

(f) Confirmation of LC NA neuron function with other methods that alter neuronal excitability or neurotransmission instead of destroying the circuit investigated, such as chemogenetics or chemogenetics, would greatly strengthen the findings. Optogenetics is used in Figure 1M, N but excitation of LCLC^→SDH NA neuron terminals is tested instead of inhibition (to mimic ablation), and in naïve mice instead of stressed mice.

We appreciate the reviewer’s comment. The optogenetic approach is useful for manipulating neuronal excitability; however, prolonged light illumination (> tens of seconds) can lead to undesirable tissue heating, ionic imbalance, and rebound spikes (Wiegert et al., Neuron, 2017 (PMID: 28772120)), making it difficult to apply in our experiments, in which mice are exposed to stress for 60 min. For this reason, we decided to employ the cell-ablation approach in stress experiments, as it is more suitable than optogenetic inhibition. In addition, as described in our response to weakness (1)-a) by Reviewer 3 (Public review), we have now demonstrated the specific expression of DTRs in NA neurons in the LC, but not in A5 or A7 (Figure S4; lines 127-128), confirming the specificity of LCLC^→SDH-NAergic pathway targeting in our study. Chemogenetics represent another promising approach to further strengthen our findings on the role of LCLC^→SDH-NA neurons, but this will be an important subject for future studies, as it will require extensive experiments to assess, for example, the effectiveness of chemogenetic inhibition of these neurons during 60 min of restraint stress, as well as optimization of key parameters (e.g., systemic DCZ doses).

(g) Alpha1Ars. The authors noted that "Adra1a mRNA is also expressed in INs in the SDH".

The expression of α_1ARs in inhibitory interneurons in the SDH is consistent with our previous findings (Uchiyama et al., Mol Brain, 2022 (PMID: 34980215)) as well as with scRNA-seq data (http://linnarssonlab.org/dorsalhorn/, Häring et al., Nat Neurosci, 2018 (PMID: 29686262)).

(h) The authors should comprehensively indicate what other cell types present in the spinal cord and neurons projecting to the spinal cord express alpha1Ars and what is the relative expression level of alpha1Ars in these different cell types.

According to the scRNA-seq data (https://seqseek.ninds.nih.gov/genes, Russ et al., Nat Commun, 2021 (PMID: 34588430); http://linnarssonlab.org/dorsalhorn/, Häring et al., Nat Neurosci, 2018 (PMID: 29686262)), we confirmed that α_1ARs are predominantly expressed in astrocytes and inhibitory interneurons in the spinal cord. Also, an α_1AR-expressing excitatory neuron population (Glut14) expresses Tacr1, GPR83, and Tac1 mRNAs, markers that are known to be enriched in projection neurons of the SDH. This raises the possibility that α_1A Rs may also be expressed in a subset of projection neurons, although further experiments are required to confirm this. In DRG neurons, α_1AR expression was detected to some extent, but its level seems to be much lower than in the spinal cord (http://linnarssonlab.org/drg/ Usoskin et al., Nat Neurosci, 2015 (PMID: 25420068)). Consistent with this, primary afferent glutamatergic synaptic transmission has been shown to be unaffected by α_1AR agonists (Kawasaki et al., Anesthesiology, 2003 (PMID: 12606912); Li and Eisenach, JPET, 2001 (PMID: 11714880)). This information has been incorporated into the Discussion section (lines 317-319).

(i) The conditional KO of alpha1Ars specifically in Hes5+ astrocytes and not in other cell types expressing alpha1Ars should be quantified and validated (Figure 2H).

We have previously shown a selective KO of α_1AR in Hes5⁺ astrocytes in the same mouse line (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)). This information has been included in the revised text (line 166-167).

(j) Depolarization of SDH inhibitory interneurons by NA (Figure 3). The authors' bath applied NA, which presumably activates all NA receptors present in the preparation.

We believe that the reviewer’s concern may pertain to the possibility that NA acts on non-Vgat⁺ neurons, thereby indirectly causing depolarization of Vgat⁺ neurons. As described in the Method section of the initial version, in our electrophysiological experiments, we added four antagonists for excitatory and inhibitory neurotransmitter receptors—CNQX (AMPA receptor), MK-801 (NMDA receptor), bicuculline (GABA_A receptor), and strychnine (glycine receptor)—to the artificial cerebrospinal fluid to block synaptic inputs from other neurons to the recorded Vgat⁺ neurons. Since this method is widely used for this purpose in many previous studies (Wu et al., J Neurosci, 2004 (PMID: 15140934); Liu et al., Nat Neurosci, 2010 (PMID: 20835251)), it is reasonable to conclude that NA directly acts on the recorded SDH Vgat⁺ interneurons to produce excitation (lines 193-196).

(k) The authors' model (Figure 4H) implies that NA released by LC→SDH NA neurons leads to the inhibition of SDH inhibitory interneurons by NA. In other experiments (Figure 1L, Figure 2A), the authors used optogenetics to promote the release of endogenous NA in SDH by LC→SDH NA neurons. This approach would investigate the function of NA endogenously released by LC NA neurons at presynaptic terminals in the SDH and at physiological concentrations and would test the model more convincingly compared to the bath application of NA.

We appreciate the reviewer’s valuable comment. As noted, optogenetic stimulation of LC^→SDH-NA neurons would indeed be useful to test this model. However, in our case, it is technically difficult to investigate the responses of Vgat⁺ inhibitory neurons and Hes5⁺ astrocytes to NA endogenously released from LC^→SDH-NA neurons. This would require the use of Vgat-Cre or Hes5-CreERT2 mice, but employing these lines precludes the use of NET-Cre mice, which are necessary for specific and efficient expression of ChrimsonR in LC^→SDH-NA neurons. Nevertheless, all of our experimental data consistently support the proposed model, and we believe that the reviewer will agree with this, without additional experiments that is difficult to conduct because of technical limitations (lines 382-388).

(l) As for other experiments, the proportion of Hes+ astrocytes that express hM3Dq, and the absence of expression in other cells, should be quantified and validated to interpret behavioral data.

We thank the reviewer for raising this point. In our experiments, we used an HA-tag (fused with hM3Dq) to confirm hM3Dq expression. However, it is difficult to precisely analyze individual astrocytes because, as shown in Figure 3J, the boundaries of many HA-tag⁺ astrocytes are indistinguishable. This seems to be due to the membrane localization of HA-tag, the complex morphology of astrocytes, and their tile-like distribution pattern (Baldwin et al., Trends Cell Biol, 2024 (PMID: 38180380)). Nevertheless, our previous study demonstrated that ~90% of astrocytes in the superficial laminae are Hes5⁺ (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)), and intra-SDH injection of AAV-hM3Dq labeled the majority of superficial astrocytes (Figure 3J). Thus, AAV-FLEx[hM3Dq] injection into Hes5-CreERT2 mice allows efficient expression of hM3Dq in Hes5⁺ astrocytes in the SDH. Importantly, our previous studies using Hes5-CreERT2 mice have confirmed that hM3Dq is not expressed in other cell types (neurons, oligodendrocytes, or microglia) (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652); Kagiyama et al., Mol Brain, 2025 (PMID: 40289116)). This information regarding the cell-type specificity has now been briefly described in the revised version (lines 218-219).

(m) Showing that the effect of CNO is dose-dependent would strengthen the authors' findings.

Thank you for your comment. We have now demonstrated a dose-dependent effect of CNO on Ca²⁺ responses in SDH astrocytes (please see our response to Major Point (4) from Reviewer #2 (Recommendations for the Authors) (Figure S7; lines 225-228). In addition, we also confirmed that the effect of CNO is not nonspecific, as CNO application did not alter sIPSCs in spinal cord slices prepared from mice lacking hM3Dq expression in astrocytes (Figure S7; lines 225-228).

(n) The proportion of SG neurons for which CNO bath application resulted in a reduction in recorded sIPSCs is not clear.

We have included individual data points in each bar graph to more clearly illustrate the effect of CNO on each neuron (Figure 3L, N).

(o) A1Rs. The specific expression of Cas9 and guide RNAs, and the specific KD of A1Rs, in inhibitory interneurons but not in other cell types expressing A1Rs should be quantified and validated.

In addition to the data demonstrating the specific expression of SaCas9 and sgAdora1 in Vgat⁺ inhibitory neurons shown in Figure 3G of the initial version, we have now conducted the same experiments with a different sample and confirmed this specificity: SaCas9 (detected via HA-tag) and sgAdora1 (detected via mCherry) were expressed in PAX2⁺ inhibitory neurons (Author response image 1). Furthermore, as shown in Figure 3H and I in the initial version, the functional reduction of A₁Rs in inhibitory neurons was validated by electrophysiological recordings. Together, these results support the successful deletion of A₁Rs in inhibitory neurons.

Author response image 1.

Expression of HA-tag and mCherry in inhibitory neurons (a different sample from Figure 3G) SaCas9 (yellow, detected by HA-tag) and mCherry (magenta) expression in the PAX2⁺ inhibitory neurons (cyan) at 3 weeks after intra-SDH injection of AAV-FLEx[SaCas9-HA] and AAV-FLEx[mCherry]-U6-sgAdora1 in Vgat-Cre mice. Arrowheads indicate genome-editing Vgat⁺ cells. Scale bar, 25 µm.

(6) Methods:

It is unclear how fiber photometry is performed using "optic cannula" during restraint stress while mice are in a 50ml falcon tube (as shown in Figure 1A).

We apologize for the omission of this detail in the Methods section. To monitor Ca²⁺ events in LC-NA neurons during restraint stress, we created a narrow slit on the top of the conical tube, allowing mice to undergo restraint stress while connected to the optic fiber (see video). This information has now been added to the Methods section (lines 552-553).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Scientific rigor:

It is unclear if the normal distribution of the data was determined before selecting statistical tests.

We apologize for omitting this description. For all statistical analyses in this study, we first assessed the normality of the data and then selected appropriate statistical tests accordingly. We have added this information to the revised manuscript (lines 711-712).

(2) Nomenclature:

(a) Mouse Genome Informatics (MGI) nomenclature should be used to describe mouse genotypes (i.e., gene name in italic, only first letter is capitalized, alleles in superscript).

(b) FLEx should be used instead of flex.

Thank you for the suggestion. We have corrected these terms (including FLEx) according to MGI nomenclature.

Reviewer #2 (Public review):

Summary:

This study investigates the role of spinal astrocytes in mediating stress-induced pain hypersensitivity, focusing on the LC (locus coeruleus)-to-SDH (spinal dorsal horn) circuit and its mechanisms. The authors aimed to delineate how LC activity contributes to spinal astrocytic activation under stress conditions, explore the role of noradrenaline (NA) signaling in this process, and identify the downstream astrocytic mechanisms that influence pain hypersensitivity.

The authors provide strong evidence that 1-hour restraint stress-induced pain hypersensitivity involves the LC-to-SDH circuit, where NA triggers astrocytic calcium activity via alpha1a adrenoceptors (alpha1aRs). Blockade of alpha1aRs on astrocytes - but not on Vgat-positive SDH neurons - reduced stress-induced pain hypersensitivity. These findings are rigorously supported by well-established behavioral models and advanced genetic techniques, uncovering the critical role of spinal astrocytes in modulating stress-induced pain.

However, the study's third aim - to establish a pathway from astrocyte alpha1aRs to adenosine-mediated inhibition of SDH-Vgat neurons - is less compelling. While pharmacological and behavioral evidence is intriguing, the ex vivo findings are indirect and lack a clear connection to the stress-induced pain model. Despite these limitations, the study advances our understanding of astrocyte-neuron interactions in stress-pain contexts and provides a strong foundation for future research into glial mechanisms in pain hypersensitivity.

Strengths:

The study is built on a robust experimental design using a validated 1-hour restraint stress model, providing a reliable framework to investigate stress-induced pain hypersensitivity. The authors utilized advanced genetic tools, including retrograde AAVs, optogenetics, chemogenetics, and subpopulation-specific knockouts, allowing precise manipulation and interrogation of the LC-SDH circuit and astrocytic roles in pain modulation. Clear evidence demonstrates that NA triggers astrocytic calcium activity via alpha1aRs, and blocking these receptors effectively reduces stress-induced pain hypersensitivity.

Weaknesses:

Despite its strengths, the study presents indirect evidence for the proposed NA-to-astrocyte(alpha1aRs)-to-adenosine-to-SDH-Vgat neurons pathway, as the link between astrocytic adenosine release and stress-induced pain remains unclear. The ex vivo experiments, including NA-induced depolarization of Vgat neurons and chemogenetic stimulation of astrocytes, are challenging to interpret in the stress context, with the high CNO concentration raising concerns about specificity. Additionally, the role of astrocyte-derived D-serine is tangential and lacks clarity regarding its effects on SDH Vgat neurons. The astrocyte calcium signal "dip" after LC optostimulation-induced elevation are presented without any interpretation.

We appreciate the reviewer's careful reading of our paper. According to the reviewer's comments, we have performed new additional experiments and added some discussion in the revised manuscript (please see the point-by-point responses below).

Reviewer #2 (Recommendations for the authors):

The astrocyte-mediated pathway of NA-to-astrocyte (alpha1aRs)-to-adenosine-to-SDH Vgat neurons (A1R) in the context of stress-induced pain hypersensitivity requires more direct evidence. While the data showing that the A1R agonist CPT inhibits stress-induced hypersensitivity and that stress combined with Aβ fiber stimulation increases pERK in the SDH are intriguing, these findings primarily support the involvement of A1R on Vgat neurons and are only behaviorally consistent with SDH-Vgat neuronal A1R knockdown. The role of astrocytes in this pathway in vivo remains indirect. The ex vivo chemogenetic Gq-DREADD stimulation of SDH astrocytes, which reduced sIPSCs in Vgat neurons in a CPT-dependent manner, needs revision with non-DREADD+CNO controls to validate specificity. Furthermore, the ex vivo bath application of NA causing depolarization in Vgat neurons, blocked by CPT, adds complexity to the data leaving me wondering how astrocytes are involved in such processes, and it does not directly connect to stress-induced pain hypersensitivity. These findings are potentially useful but require additional refinement to establish their relevance to the stress model.

We thank the reviewer for the insightful feedback. First, regarding the role of astrocytes in this pathway in vivo, we showed in the initial version that mechanical pain hypersensitivities induced by intrathecal NA injection and by acute restraint stress were attenuated by both pharmacological blockade and Vgat⁺ neuron-specific knockdown of A₁Rs (Figure 4A, B). Given that NA- and stress-induced pain hypersensitivity is mediated by α_1AR-dependent signaling in Hes5⁺ astrocytes (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652); this study), these findings provide in vivo evidence supporting the involvement of the NA → Hes5⁺ astrocyte (via α_1ARs) → adenosine → Vgat⁺ neuron (via A₁Rs) pathway. As noted in the reviewer’s major comment (2), in vivo monitoring of adenosine dynamics in the SDH during stress exposure would further substantiate the astrocyte-to-neuron signaling pathway. However, we did not detect clear signals, potentially due to several technical limitations (see our response below). Acknowledging this limitation, we have now added a new paragraph in the end of Discussion section to address this issue. Second, the specificity of the effect of CNO has now been validated by additional experiments (see our response to major point (4)). Third, the reviewer’s concern regarding the action of NA on Vgat⁺ neurons has also been addressed (see our response to major point (3) below).

Major points:

(1) The in vivo pharmacology using DCK to antagonize D-serine signaling from alpha1a-activated astrocytes is tangential, as there is limited evidence on how Vgat neurons (among many others) respond to D-serine. This aspect requires more focused exploration to substantiate its relevance.

We propose that the site of action of D-serine in our neural circuit model is the NMDA receptors (NMDARs) on excitatory neurons, a notion supported by our previous findings (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652); Kagiyama et al., Mol Brain, 2025 (PMID: 40289116)). However, we cannot exclude the possibility that D-serine also acts on NMDARs expressed by Vgat⁺ inhibitory neurons. Nevertheless, given that intrathecal injection of D-serine in naïve mice induces mechanical pain hypersensitivity (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)), it appears that the pronociceptive effect of D-serine in the SDH is primarily associated with enhanced pain processing and transmission, presumably via NMDARs on excitatory neurons. We have added this point to the Discussion section in the revised manuscript (lines 325-330).

(2) Additionally, employing GRAB-Ado sensors to monitor adenosine dynamics in SDH astrocytes during NA signaling would significantly strengthen conclusions about astrocyte-derived adenosine's role in the stress model.

We agree with the reviewer’s comment. Following this suggestion, we attempted to visualize NA-induced adenosine (and ATP) dynamics using GRAB-ATP and GRAB-Ado sensors (Wu et al., Neuron, 2022 (PMID: 34942116); Peng et al., Science, 2020 (PMID: 32883833)) in acutely isolated spinal cord slices from mice after intra-SDH injection of AAV-hSyn-GRABATP_1.0 and -GRABAdo_1.0. We confirmed expression of these sensors in the SDH (Author response image 2a) and observed increased signals after bath application of ATP (0.1 or 1 µM) or adenosine (1 µM) (Author response image 2b, c). However, we were unable to detect clear signals following NA stimulation (Author response image 2b, c). The reason for this lack of detectable changes remains unclear. If the release of adenosine from astrocytes is a highly localized phenomenon, it may be measurable using high-resolution microscopy capable of detecting adenosine levels at the synaptic level and more sensitive sensors. Further investigation will therefore be required (lines 340-341).

Author response image 2.

Ex vivo imaging of GRAB-ATP and GRAB-Ado sensors.(a) Representative images of GRAB_ATP1.0 (left, green) or GRAB_Ado1.0 (right, green) expression in the SDH at 3 weeks after SDH injection of AAV-hSyn-GRAB_Ado1.0 or AAV-hSyn-GRAB_Ado1.0 in Hes5-CreERT2 mice. Scale bar, 200 µm. (b) Left: Representative fluorescence images showing GRAB_ATP1.0 responses before and after perfusion with NA or ATP. Right: Representative traces showing responses to ATP (0.1 and 1 µM) or NA (10 µM). (c) Left: Representative fluorescence images showing GRABAdo1.0 responses before and after perfusion with NA or adenosine (Ado). Right: Representative traces showing responses to Ado (0.01, 0.1, and 1 µM), NA (10 µM), or no application (negative control).

(3) The interpretation of Figure 3D is challenging. The manuscript implies that 20 μM NA acts on Adra1a receptors on Vgat neurons to depolarize them, but this concentration should also activate Adra1a on astrocytes, leading to adenosine release and potential inhibition of depolarization. The observation of depolarization despite these opposing mechanisms requires explanation, as does the inhibition of depolarization by bath-applied A1R agonist. Of note, 20 μM NA is a high concentration for Adra1a activation, typically responsive at nanomolar levels. The discussion should reconcile this with prior studies indicating dose-dependent effects of NA on pain sensitivity (e.g., Reference 22).

Like the reviewer, we also considered that bath-applied NA could activate α_1ARs expressed on Hes5⁺ astrocytes. To clarify this point, we have performed additional patch-clamp recordings and found that knockdown of A₁Rs in Vgat⁺ neurons tended to increase the proportion of Vgat⁺ neurons with NA-induced depolarizing responses (Figure S8). Therefore, it is conceivable that NA-induced excitation of Vgat⁺ neurons may involve both a direct effect of NA activating α_1ARs in Vgat⁺ neurons and an indirect inhibitory signaling from NA-stimulated Hes5⁺ astrocytes via adenosine (lines 298-300).

The concentration of NA used in our ex vivo experiments is higher than that typically used in vitro with αR-_1Aexpressing cell lines or primary culture cells, but is comparable to concentrations used in other studies employing spinal cord slices (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652); Baba et al., Anesthesiology, 2000 (PMID: 10691236); Lefton et al., Science, 2025 (PMID: 40373122)). In slice experiments, drugs must diffuse through the tissue to reach target cells, resulting in a concentration gradient. Therefore, higher drug concentrations are generally necessary in slice experiments, in contrast to cultured cell experiments, where drugs are directly applied to target cells. Importantly, we have previously shown that the pharmacological effects of 20 μM NA on Vgat⁺ neurons and Hes5⁺ astrocytes are abolished by loss of α_1ARs in these cells (Uchiyama et al., Mol Brain, 2022 (PMID: 34980215); Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)), confirming the specificity of these NA actions.

Regarding the dose-dependent effect of NA on pain sensitivity, NA-induced pain hypersensitivity is abolished in Hes5⁺ astrocyte-specific α_1AR-KO mice (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)), indicating that this behavior is mediated by α_1ARs expressed on Hes5⁺ astrocytes. In contrast, the suppression of pain sensitivity by high doses of NA was unaffected in the KO mice (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)), suggesting that other adrenergic receptors may contribute to this phenomenon. Clarifying the responsible receptors will require future investigation.

(4) In Figure 3K-M, the CNO concentration used (100 μM) is unusually high compared to standard doses (1 to a few μM), raising concerns about potential off-target effects. Including non-hM3Dq controls and using lower CNO concentrations are essential to validate the specificity of the observed effects. Similarly, the study should clarify whether astrocyte hM3Dq stimulation alone (without NA) would induce hyperpolarization in Vgat neurons and how this interacts with NA-induced depolarization.

We acknowledge that the concentration of CNO used in our experiments is relatively high compared to that used in other reports. However, in our experiments, application of CNO at 1, 10, and 100 μM induced Ca²⁺ increases in GCaMP6-expressing astrocytes in spinal cord slices in a concentration-dependent manner (Figure S7). Among these, 100 μM CNO most effectively replicated the NA-induced Ca²⁺ signals in astrocytes. Based on these findings, we selected this concentration for use in both the current and previous studies (Kohro et al., Nat Neurosci., 2020 (PMID: 33020652)). Importantly, to rule out non-specific effects, we conducted control experiments using spinal cord slices from mice that did not express hM3Dq in astrocytes and confirmed that CNO had no effect on Ca²⁺ responses in astrocytes and sIPSCs in substantial gelatinosa (SG) neurons (Figure S7; lines 223-228). Thus, although the CNO concentration used is relatively high, the observed effects of CNO are not non-specific but result from the chemogenetic activation of hM3Dq-expressing astrocytes.

In this study, we used Hes5-CreERT2 and Vgat-Cre mice to manipulate gene expression in Hes5⁺ astrocytes and Vgat⁺ neurons, respectively. In order to fully address the reviewer’s comment, the use of both Cre lines is necessary. However, simultaneous and independent genetic manipulation in each cell type using Cre activity alone is not feasible with the current genetic tools. We have mentioned this as a technical limitation in the Discussion section (lines 382-388).

(5) The role of D-serine released by hM3Dq-stimulated astrocytes in (separately) modulating sub-types of neurons including excitatory neurons and Vgat positives needs more detailed discussion. If no effect of D-serine on Vgat neurons is observed, this should be explicitly stated, and the discussion should address why this might be the case.

As mentioned in our response to Major Point (1) above, we have added a discussion of this point in the revised manuscript (lines 325-330).

(6) Finally, the observed "dip" in astrocyte calcium signals below baseline following the large peaks with LC optostimulation should be discussed further, as understanding this phenomenon could provide valuable insights into astrocytic signaling dynamics in the context of single acute or repetitive chronic stress.

Thank you for your comment. We found that this phenomenon was not affected by pretreatment with the α_1AR-specific antagonist silodosin (Author response image 3), which effectively suppressed Ca²⁺ elevations evoked by stimulation of LC-NA neurons (Figure 2F). This implies that the phenomenon is independent of α_1AR signaling. Elucidating the detailed underlying mechanism remains an important direction for future investigation.

Author response image 3.

The observed "dip" in astrocyte Ca²⁺ signals was not affected by pretreatment with the α_1AR-specific antagonist silodosin. Representative traces of astrocytic GCaMP6m signals in response to optogenetic stimulation of LC-NAe^→SDHrgic axons/terminals in a spinal cord slice. Each trace shows the GCaMP6m signal before and after optogenetic stimulation (625 nm, 1 mW, 10 Hz, 5 ms pulse duration, 10 s). Slices were pretreated with silodosin (40 nM) for 5 min prior to stimulation.

Reviewer #3 (Public review):

Summary:

This is an exciting and timely study addressing the role of descending noradrenergic systems in nocifensive responses. While it is well-established that spinally released noradrenaline (aka norepinephrine) generally acts as an inhibitory factor in spinal sensory processing, this system is highly complex. Descending projections from the A6 (locus coeruleus, LC) and the A5 regions typically modulate spinal sensory processing and reduce pain behaviours, but certain subpopulations of LC neurons have been shown to mediate pronociceptive effects, such as those projecting to the prefrontal cortex (Hirshberg et al., PMID: 29027903).

The study proposes that descending cerulean noradrenergic neurons potentiate touch sensation via alpha-1 adrenoceptors on Hes5+ spinal astrocytes, contributing to mechanical hyperalgesia. This finding is consistent with prior work from the same group (dd et al., PMID:). However, caution is needed when generalising about LC projections, as the locus coeruleus is functionally diverse, with differences in targets, neurotransmitter co-release, and behavioural effects. Specifying the subpopulations of LC neurons involved would significantly enhance the impact and interpretability of the findings.

Strengths:

The study employs state-of-the-art molecular, genetic, and neurophysiological methods, including precise CRISPR and optogenetic targeting, to investigate the role of Hes5+ astrocytes. This approach is elegant and highlights the often-overlooked contribution of astrocytes in spinal sensory gating. The data convincingly support the role of Hes5+ astrocytes as regulators of touch sensation, coordinated by brain-derived noradrenaline in the spinal dorsal horn, opening new avenues for research into pain and touch modulation.

Furthermore, the data support a model in which superficial dorsal horn (SDH) Hes5+ astrocytes act as non-neuronal gating cells for brain-derived noradrenergic (NA) signalling through their interaction with substantia gelatinosa inhibitory interneurons. Locally released adenosine from NA-stimulated Hes5+ astrocytes, following acute restraint stress, may suppress the function of SDH-Vgat+ inhibitory interneurons, resulting in mechanical pain hypersensitivity. However, the spatially restricted neuron-astrocyte communication underlying this mechanism requires further investigation in future studies.

Weaknesses

(1) Specificity of the LC Pathway targeting

The main concern lies with how definitively the LC pathway was targeted. Were other descending noradrenergic nuclei, such as A5 or A7, also labelled in the experiments? The authors must convincingly demonstrate that the observed effects are mediated exclusively by LC noradrenergic terminals to substantiate their claims (i.e. "we identified a circuit, the descending LC→SDH-NA neurons").

(a) For instance, the direct vector injection into the LC likely results in unspecific effects due to the extreme heterogeneity of this nucleus and retrograde labelling of the A5 and A7 nuclei from the LC (i.e., Li et al., PMID: 26903420).

We appreciate the reviewer's valuable comments. To address this point, we performed additional experiments and demonstrated that intra-SDH injection of AAVretro-Cre followed by intra-LC injection of AAV2/9-EF1α-FLEx[DTR-EGFP] specifically results in DTR expression in NA neurons of the LC, but not of the A5 or A7 regions (Figure S4; lines 127-128). These results confirm the specificity of targeting the LC^→SDH-NAergic pathway in our study.

(b) It is difficult to believe that the intersectional approach described in the study successfully targeted LC→SDH-NA neurons using AAVrg vectors. Previous studies (e.g., PMID: 34344259 or PMID: 36625030) demonstrated that similar strategies were ineffective for spinal-LC projections. The authors should provide detailed quantification of the efficiency of retrograde labelling and specificity of transgene expression in LC neurons projecting to the SDH.

Thank you for your comment. As we described in our response to the weakness (5)-e) of Reviewer #1 (Public review), our additional analysis showed that, under our experimental conditions, expression of genes (for example DTR) was observed in 4.4% of NA (TH⁺) neurons in the LC (Figure S4; lines 126-127).

The reasons for this difference between the previous studies and our current study is unclear; however, it is likely attributed to methodological differences, including the type of viral vectors employed, species differences (mouse (PMID: 34344259, our study) vs. rat (PMID: 36625030)), the amount of AAV injected into the SDH (300 nL at three sites (PMID: 34344259), and 300 nL at a single site (our study)) and LC (500 nL at a single site (PMID: 34344259), and 300 nL at a single site (our study)), as well as the depth of AAV injection in the SDH (200–300 µm from the dorsal surface of the spinal cord (PMID: 34344259), and 120–150 µm in depth from the surface of the dorsal root entry zone (our study)).

(c) Furthermore, it is striking that the authors observed a comparably strong phenotypical change in Figure 1K despite fewer neurons being labelled, compared to Figure 1H and 1N with substantially more neurons being targeted. Interestingly, the effect in Figure 1K appears more pronounced but shorter-lasting than in the comparable experiment shown in Figure 1H. This discrepancy requires further explanation.

Although only a representative section of the LC was shown in the initial version, LC^→SDH-NA neurons are distributed rostrocaudally throughout the LC, as previously reported (Llorca-Torralba et al., Brain, 2022 (PMID: 34373893)). Our additional experiments analyzing multiple sections of the anterior and posterior regions of the LC have now revealed that approximately sixty LC^→SDH-NA neurons express DTR, and these neurons are eliminated following DTX treatment (Figure S4; lines 126-128) (it should be noted that these neurons specifically project to the L4 segment of the SDH, and the total number of LC^→SDH-NA neurons is likely much higher). Considering the specificity of LC^→SDH-NAergic pathway targeting demonstrated in our study (as described above), together with the fact that primary afferent sensory fibers from the plantar skin of the hindpaw predominantly project to the L4 segment of the SDH, these data suggest that the observed behavioral changes are attributable to the loss of these neurons and that ablation of even a relatively small number of NA neurons in the LC can have a significant impact on behavior. We have added this hypothesis in the Discussion section (lines 373-382).

Regarding the data in Figures 1H and 1K, as the reviewer pointed out, a statistically significant difference was observed at 90 min in mice with ablation of LC-NA neurons, but not in those with LC^→SDH-NA neuron ablation. This is likely due to a slightly higher threshold in the control group at this time point (Figure 1K), and it remains unclear whether there is a mechanistic difference between the two groups at this specific time point.

(d) A valuable addition would be staining for noradrenergic terminals in the spinal cord for the intersectional approach (Figure 1J), as done in Figures 1F/G. LC projections terminate preferentially in the SDH, whereas A5 projections terminate in the deep dorsal horn (DDH). Staining could clarify whether circuits beyond the LC are being ablated.

As suggested, we performed DTR immunostaining in the SDH; however, we did not detect any DTR immunofluorescence there. A similar result was also observed in the spinal terminals of DTR-expressing primary afferent fibers (our unpublished data). The reason for this is unclear, but to the best of our knowledge, no studies have clearly shown DTR expression at presynaptic terminals, which may be because the action of DTX on the neuronal cell body is necessary for cell ablation. Nevertheless, as described in our response to the weakness (5)-f) by Reviewer 1 (Public review), we have now confirmed the specific expression of DTR in the LC, but not in the A5 and A7 regions (Figure S4; lines 127-128).

(e) Furthermore, different LC neurons often mediate opposite physiological outcomes depending on their projection targets-for example, dorsal LC neurons projecting to the prefrontal cortex PFCx are pronociceptive, while ventral LC neurons projecting to the SC are antinociceptive (PMIDs: 29027903, 34344259, 36625030). Given this functional diversity, direct injection into the LC is likely to result in nonspecific effects.

To avoid behavioral outcomes resulting from a mixture of facilitatory and inhibitory effects caused by activating the entire population of LC-NA neurons, we employed a specific manipulation targeting LC^→SDH-NA neurons using AAV vectors. The specificity of this manipulation was confirmed in our previous study (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)) and in the current study (Figure S4). Using this approach, we previously demonstrated that LC neurons can exert pronociceptive effects via astrocytes in the SDH (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)). This pronociceptive role is further supported by the current study, which uses a more selective manipulation of LC^→SDH-NA neurons through a NET-Cre mouse line. In addition, intrathecal administration of relatively low doses of NA in naïve mice clearly induces mechanical pain hypersensitivity. Nevertheless, we have also acknowledged that several recent studies have reported an inhibitory role of LC^→SDH-NA neurons in spinal nociceptive signaling. The reason for these differing behavioral outcomes remains unclear, but several methodological differences may underlie the discrepancy. First, the degree of LC^→SDH-NA neuronal activity may play a role. Although direct comparisons between studies reporting pro- and anti-nociceptive effects are difficult, our previous studies demonstrated that intrathecal administration of high doses of NA in naïve mice does not induce mechanical pain hypersensitivity (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)). Second, the sensory modality used in behavioral testing may be a contributing factor as the pronociceptive effect of NA appears to be selectively observed in responses to mechanical, but not thermal, stimuli (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)). This sensory modality-selective effect is also evident in mice subjected to acute restraint stress (Table S1). Therefore, the role of LC^→SDH-NA neurons in modulating nociceptive signaling in the SDH is more complex than previously appreciated, and their contribution to pain regulation should be reconsidered in light of factors such as NA levels, sensory modality, and experimental context. In revising the manuscript, we have included some points described above in the Discussion (lines 282-291).

Conclusion on Specificity: The authors are strongly encouraged to address these limitations directly, as they significantly affect the validity of the conclusions regarding the LC pathway. Providing more robust evidence, acknowledging experimental limitations, and incorporating complementary analyses would greatly strengthen the manuscript.

We appreciate the reviewer’s comments. We fully acknowledge the limitations raised and agree that addressing them directly is important for the rigor of our conclusions on the LC pathway. To this end, we have performed additional experiments (e.g., Figure A and S4), which are now included in the revised manuscript. Furthermore, we have also newly added a new paragraph for experimental limitations in the end of Discussion section (lines 373-408). We believe these new data substantially strengthen the validity of our findings and have clarified these points in the Discussion section.

(2) Discrepancies in Data

(a) Figures 1B and 1E: The behavioural effect of stress on PWT (Figure 1E) persists for 120 minutes, whereas Ca2+ imaging changes (Figure 1B) are only observed in the first 20 minutes, with signal attenuation starting at 30 minutes. This discrepancy requires clarification, as it impacts the proposed mechanism.

Thank you for your important comment. As pointed out by the reviewer, there is a difference between the duration of behavioral responses and Ca²⁺ events, although the exact time point at which the PWT begins to decline remains undetermined (as behavioral testing cannot be conducted during stress exposure). A similar temporal difference was also observed following intraplantar injection of capsaicin (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)); while LC^→SDH-NA neuron-mediated astrocytic Ca²⁺ responses in SDH astrocytes last for 5–10 min after injection, behavioral hypersensitivity peaks around 60 min post-injection and gradually returns to baseline over the subsequent 60–120 min. These findings raise the possibility that astrocyte-mediated pain hypersensitivity in the SDH may involve a sustained alteration in spinal neural function, such as central sensitization. We have added this hypothesis to the Discussion section of the revised manuscript (lines 399-408), as it represents an important direction for future investigation.

(b) Figure 4E: The effect is barely visible, and the tissue resembles "Swiss cheese," suggesting poor staining quality. This is insufficient for such an important conclusion. Improved staining and/or complementary staining (e.g., cFOS) are needed. Additionally, no clear difference is observed between Stress+Ab stim. and Stress+Ab stim.+CPT, raising doubts about the robustness of the data.

As suggested, we performed c-FOS immunostaining and obtained clearer results (Figure 4E,F; lines 243-252). We also quantitatively analyzed the number of c-FOS⁺ cells in the superficial laminae, and the results are consistent with those obtained from the pERK experiments.

(c) Discrepancy with Existing Evidence: The claim regarding the pronociceptive effect of LC→SDH-NAergic signalling on mechanical hypersensitivity contrasts with findings by Kucharczyk et al. (PMID: 35245374), who reported no facilitation of spinal convergent (wide-dynamic range) neuron responses to tactile mechanical stimuli, but potent inhibition to noxious mechanical von Frey stimulation. This discrepancy suggests alternative mechanisms may be at play and raises the question of why noxious stimuli were not tested.

In our experiments, ChrimsonR expression was observed in the superficial and deeper laminae of the spinal cord (Figure S6). Due to the technical limitations of the optical fibers used for optogenetics, the light stimulation could only reach the superficial laminae; therefore, it may not have affected the activity of neurons (including WDR neurons) located in the deeper laminae. Furthermore, the study by Kucharczyk et al. (Brain, 2022 (PMID: 35245374)) employed a stimulation protocol that differed from ours, applying continuous stimulation over several minutes. Given that the levels of NA released from LC^→SDH-NAergic terminals in the SDH increase with the duration of terminal stimulation (as shown in Figure 2B), longer stimulation may result in higher levels of NA in the SDH. Considering also our data indicating that the pro- and anti-nociceptive effects of NA are dose dependent (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)), these differences may be related to LC^→SDH-NA neuron activity, NA levels in the SDH, and the differential responses of SDH neurons in the superficial versus deeper laminae (lines 388-395).

(3) Sole reliance on Von Frey testing

The exclusive use of von Frey as a behavioural readout for mechanical sensitisation is a significant limitation. This assay is highly variable, and without additional supporting measures, the conclusions lack robustness. Incorporating other behavioural measures, such as the adhesive tape removal test to evaluate tactile discomfort, the needle floor walk corridor to assess sensitivity to uneven or noxious surfaces, or the kinetic weight-bearing test to measure changes in limb loading during movement, could provide complementary insights. Physiological tests, such as the Randall-Selitto test for noxious pressure thresholds or CatWalk gait analysis to evaluate changes in weight distribution and gait dynamics, would further strengthen the findings and allow for a more comprehensive assessment of mechanical sensitisation.

Thank you for your suggestion. Based on our previous findings that Hes5⁺ astrocytes in the SDH selectively modulate mechanosensory signaling (Kohro et al., Nat Neurosci, 2020 (PMID: 33020652)), the present study focused on behavioral responses to mechanical stimuli using von Frey filaments. As we have not previously conducted most of the behavioral tests suggested by the reviewers, and as we currently lack the necessary equipments for these tests (e.g., Randall–Selitto test, CatWalk gait analysis, and weight-bearing test), we were unable to include them in this study. However, it will be of great interest in future research to investigate whether activation of the LC^→SDH-NA neuron-to-SDH Hes5⁺ astrocyte signaling pathway similarly sensitizes behavioral responses to other types of mechanical stimuli and also to investigate the sensory modality-selective pro- and antinociceptive role of LC^→SDH-NAergic signaling in the SDH (lines 396-399).

Overall Conclusion

This study addresses an important and complex topic with innovative methods and compelling data. However, the conclusions rely on several assumptions that require more robust evidence. Specificity of the LC pathway, experimental discrepancies, and methodological limitations (e.g., sole reliance on von Frey) must be addressed to substantiate the claims. With these issues resolved, this work could significantly advance our understanding of astrocytic and noradrenergic contributions to pain modulation.

We have made every effort to address the reviewer’s concerns through additional experiments and analyses. Based on the new control data presented, we believe that our explanation is reasonable and acceptable. Although additional data cannot be provided on some points due to methodological constraints and limitations of the techniques currently available in our laboratory, we respectfully submit that the evidence presented sufficiently supports our conclusions.

Reviewer #3 (Recommendations for the authors):

A lot of beautiful and challenging-to-collect data is presented. Sincere congratulations to all the authors on this achievement!

Notwithstanding, please carefully reconsider the conclusions regarding the LC pathway, as additional evidence is required to ensure their specificity and robustness.

We thank the reviewer for the kind comments and for raising an important point regarding the LC pathway. The reviewer’s feedback prompted us to conduct additional investigations to further strengthen the validity of our conclusions. We have incorporated these new data and analyses into the revised manuscript, and we believe that these revisions substantially enhance the robustness and reliability of our findings.

I would make this bolder - on first read I'm thinking that I would maybe even make this the header because it's very striking and head-nod-y

Reviewer #1 (Public review):

Summary:

This study by Howe and colleagues investigates the role of the posterolateral cortical amygdala (plCoA) in mediating innate responses to odors, specifically attraction and aversion. By combining optogenetic stimulation, single-cell RNA sequencing, and spatial analysis, the authors identify a topographically organized circuit within plCoA that governs these behaviors. They show that specific glutamatergic neurons in the anterior and posterior regions of plCoA are responsible for driving attraction and avoidance, respectively, and that these neurons project to distinct downstream regions, including the medial amygdala and nucleus accumbens, to control these responses.

Strengths:

The major strength of the study is the thoroughness of the experimental approach, which combines advanced techniques in neural manipulation and mapping with high-resolution molecular profiling. The identification of a topographically organized circuit in plCoA and the connection between molecularly defined populations and distinct behaviors is a notable contribution to understanding the neural basis of innate motivational responses. Additionally, the use of fucntional manipulations adds depth to the findings, offering valuable insights into the functionality of specific neuronal populations.

Weaknesses:

Previously described weaknesses in the study's methods and interpretation were fully addressed during revision. Locomotor behavior of the mice during head-fixed imaging experiments was added and analysis of the correlation of locomotion with neural activity was also added.

This work provides significant insights into the neural circuits underlying innate behaviors and opens new avenues for further research. The findings are particularly relevant for understanding the neural basis of motivational behaviors in response to sensory stimuli, and the methods used could be valuable for researchers studying similar circuits in other brain regions. If the authors address the methodological issues raised, this work could have a substantial impact on the field, contributing to both basic neuroscience and translational research on the neural control of behavior.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study by Howe and colleagues investigates the role of the posterolateral cortical amygdala (plCoA) in mediating innate responses to odors, specifically attraction and aversion. By combining optogenetic stimulation, single-cell RNA sequencing, and spatial analysis, the authors identify a topographically organized circuit within plCoA that governs these behaviors. They show that specific glutamatergic neurons in the anterior and posterior regions of plCoA are responsible for driving attraction and avoidance, respectively, and that these neurons project to distinct downstream regions, including the medial amygdala and nucleus accumbens, to control these responses.

Strengths:

The major strength of the study is the thoroughness of the experimental approach, which combines advanced techniques in neural manipulation and mapping with high-resolution molecular profiling. The identification of a topographically organized circuit in plCoA and the connection between molecularly defined populations and distinct behaviors is a notable contribution to understanding the neural basis of innate motivational responses. Additionally, the use of functional manipulations adds depth to the findings, offering valuable insights into the functionality of specific neuronal populations.

Weaknesses:

There are some weaknesses in the study's methods and interpretation. The lack of clarity regarding the behavior of the mice during head-fixed imaging experiments raises the possibility that restricted behavior could explain the absence of valence encoding at the population level.

We agree with idea that head-fixation may alter the state of the animal and the neural encoding of odor. To address this, we have provided further analysis of walking behavior during the imaging sessions, which is provided in Figure S2. Overall, we could not identify any clear patterns in locomotor behavior that are odor-specific. Moreover, when neural activity was sorted depending on the behavioral state (walking, pausing or fleeing) we didn’t observe any apparent patterns in odor-evoked neural activity. This is now discussed in the Results and Limitations sections of the manuscript.

Furthermore, while the authors employ chemogenetic inhibition of specific pathways, the rationale for this choice over optogenetic inhibition is not fully addressed, and this could potentially affect the interpretation of the results.

The rationale was logistical. First, inhibition of over a timescale of minutes is problematic with heat generation during prolonged optical stimulation. Second, our behavioral apparatus has a narrow height between the ceiling and floor, making tethering difficult. This is now explained the results section. The trade-off of using chemogenetics is that we are silencing neurons and not specific projections. However, because we find that NAc- and MeA- projecting neurons have little shared collateralization, we believe the conclusion of divergent pathways still stands. This is now discussed in the Limitations section.

Additionally, the choice of the mplCoA for manipulation, rather than the more directly implicated anterior and posterior subregions, is not well-explained, which could undermine the conclusions drawn about the topographic organization of plCoA.

We targeted the middle region of plCoA because it contains a mixture of cell types found in both the anterior and posterior plCoA, allowing us to test the hypothesis that cell types, not intra plCoA location, elicit different responses. Had we targeted the anterior or posterior regions, we would expect to simply recapitulate the result from activation of random cells in each region. As a result, we think stimulation in the middle plCoA is a better test for the contribution of cell types. We have now clarified this in the text.

Despite these concerns, the work provides significant insights into the neural circuits underlying innate behaviors and opens new avenues for further research. The findings are particularly relevant for understanding the neural basis of motivational behaviors in response to sensory stimuli, and the methods used could be valuable for researchers studying similar circuits in other brain regions. If the authors address the methodological issues raised, this work could have a substantial impact on the field, contributing to both basic neuroscience and translational research on the neural control of behavior.

Reviewer #2 (Public review):

Summary:

The manuscript by the Root laboratory and colleagues describes how the posterolateral cortical amygdala (plCoA) generates valenced behaviors. Using a suite of methods, the authors demonstrate that valence encoding is mediated by several factors, including spatial localization of neurons within the plCoA, glutamatergic markers, and projection. The manuscript shows convincingly that multiple features (spatial, genetic, and projection) contribute to overall population encoding of valence. Overall, the authors conduct many challenging experiments, each of which contains the relevant controls, and the results are interpreted within the framework of their experiments.

Strengths:

- For a first submission the manuscript is well constructed, containing lots of data sets and clearly presented, in spite of the abundance of experimental results.

- The authors should be commended for their rigorous anatomical characterizations and posthoc analysis. In the field of circuit neuroscience, this is rarely done so carefully, and when it is, often new insights are gleaned as is the case in the current manuscript.

- The combination of molecular markers, behavioral readouts and projection mapping together substantially strengthen the results.

- The focus on this relatively understudied brain region in the context is valence is well appreciated, exciting and novel.

Weaknesses:

- Interpretation of calcium imaging data is very limited and requires additional analysis and behavioral responses specific to odors should be considered. If there are neural responses behavioral epochs and responses to those neuronal responses should be displayed and analyzed.

We have now considered this, see response above.

- The effect of odor habituation is not considered.

We considered this, but we did not find any apparent differences in valence encoding as measured by the proportion of neurons with significant valence scores across trials (see Figure 1J).

- Optogenetic data in the two subregions relies on very careful viral spread and fiber placement. The current anatomy results provided should be clear about the spread of virus in A-P, and D-V axis, providing coordinates for this, to ensure readers the specificity of each sub-zone is real.

We were careful to exclude animals for improper targeting. The spread of virus is detailed in Figures S3, S8 & S9.

- The choice of behavioral assays across the two regions doesn't seem balanced and would benefit from more congruency.

The choice of the 4-quadrant assay was used because this study builds off of our prior experiments that demonstrate a role for the plCoA in innate behavior. It is noteworthy that the responses to odor seen in this assay are generally in agreement with other olfactory behavioral assays, so one wouldn’t predict a different result. Moreover, the approach and avoidance responses measured in this assay are precisely the behaviors we wish to understand. We did examine other non-olfactory behavioral readouts (Figures S3, S8), and didn’t observe any effect of manipulation of these pathways.

- Rationale for some of the choices of photo-stimulation experiment parameters isn't well defined.

The parameters for photo-stimulation were based on those used in our past work (Root et al., 2014). We used a gradient of frequency from 1-10 Hz based on the idea that odor likely exists in a gradient and this was meant to mimic a potential gradient, though we don’t know if it exists. The range in stimulation frequencies appears to align with the actual rate of firing of plCoA neurons (Iurilli et al., 2017).

Reviewer #3 (Public review):

Summary:

Combining electrophysiological recording, circuit tracing, single cell RNAseq, and optogenetic and chemogenetic manipulation, Howe and colleagues have identified a graded division between anterior and posterior plCoA and determined the molecular characteristics that distinguish the neurons in this part of the amygdala. They demonstrate that the expression of slc17a6 is mostly restricted to the anterior plCoA whereas slc17a7 is more broadly expressed. Through both anterograde and retrograde tracing experiments, they demonstrate that the anterior plCoA neurons preferentially projected to the MEA whereas those in the posterior plCoA preferentially innervated the nucleus accumbens. Interestingly, optogenetic activation of the aplCoA drives avoidance in a spatial preference assay whereas activating the pplCoA leads to preference. The data support a model that spatially segregated and molecularly defined populations of neurons and their projection targets carry valence specific information for the odors. The discoveries represent a conceptual advance in understanding plCoA function and innate valence coding in the olfactory system.

Strengths:

The strongest evidence supporting the model comes from single cell RNASeq, genetically facilitated anterograde and retrograde circuit tracing, and optogenetic stimulation. The evidence clear demonstrates two molecularly defined cell populations with differential projection targets. Stimulating the two populations produced opposite behavioral responses.

Weaknesses:

There are a couple of inconsistencies that may be addressed by additional experiments and careful interpretation of the data.

Stimulating aplCoA or slc17a6 neurons results in spatial avoidance, and stimulating pplCoA or slc17a7 neurons drives approach behaviors. On the other hand, the authors and others in the field also show that there is no apparent spatial bias in odor-driven responses associated with odor valence. This discrepancy may be addressed better. A possibility is that odor-evoked responses are recorded from populations outside of those defined by slc17a6/a7. This may be addressed by marking activated cells and identifying their molecular markers. A second possibility is that optogenetic stimulation activates a broad set of neurons that and does not recapitulate the sparseness of odor responses. It is not known whether sparsely activation by optogenetic stimulation can still drive approach of avoidance behaviors.

We agree that marking specific genetic or projection defined neurons could help to clarify if there are some neurons have more selective valence responses. However, we are not able to perform these experiments at the moment. We have included new data demonstrating that sparser optogenetic activation evokes behaviors similar in magnitude as the broader activation (see Figure S4).

The authors show that inhibiting slc17a7 neurons blocks approaching behaviors toward 2-PE. Consistent with this result, inhibiting NAc projection neurons also inhibits approach responses. However, inhibiting aplCOA or slc17a6 neurons does not reduce aversive response to TMT, but blocking MEA projection neurons does. The latter two pieces of evidence are not consistent with each other. One possibility is that the MEA projecting neurons may not be expressing slc17a6. It is not clear that the retrogradely labeling experiments what percentage of MEA- and NACprojecting neurons express slc17a6 and slc17a7. It is possible that neurons expressing neither VGluT1 nor VGluT2 could drive aversive or appetitive responses. This possibility may also explain that silencing slc17a6 neurons does not block avoidance.

We have now performed RNAscope staining on retrograde tracing to better define this relationship. Although the VGluT1 and VGluT2 neurons have biased projections to the MeA and NAc, respectively, there is some nuance detailed in Figure S10. Generally, MeA projecting neurons are predominately VGluT2+, whereas NAc projecting have about 20% that express both. Some (less than 35%) retrogradely labeled neurons were not detected as VGluT1 or VGluT2 positive, suggesting that other populations could also contribute. We agree that the discrepancy between MeA-projection and VGluT2 silencing is likely due to incomplete targeting of the MeA-projecting population with the VGluT2-cre line. This is included in the Discussion section.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Main:

(1) For the head-fixed imaging experiments, what is the behavior of the mice during odor exposure? Could the weak reliability of individual neurons be due to a lack of approach or avoidance behavior? Could restricted behavior also explain the lack of valence encoding at the population level?

We agree that this is a limitation of head-fixed recordings. In the revised manuscript we did attempt to characterize their behavioral response, and look for correlations in odor representation. Although we did find different patterns of odor-evoked walking behavior, these patterns were not reliable or specific to particular odors (Figure S2). For example, one might expect aversive odors to pause walking or elicit a fast fleeing-like response, but we did not observe any apparent differences for locomotion between odors as all odors evoked a mixture of responses (Figure S2A-D, text lines 208-232). We then examined responses to odor depending on the behavioral state (walking, pausing or fleeing) and didn’t observe any apparent patterns in odor responses (Figure S2E,F). Lastly, we acknowledge in the text that the lack of valence encoding may be an artifact of head-fixation (see lines 849-857).

(2) For the optogenetic manipulations of Vglut1 and Vglut2 neurons, why was the injection and fiber targeted to the medial portion of the plCoA, if the hypothesis was that these glutamatergic neuron populations in different regions (anterior or posterior) are responsible for approach and avoidance? 

We targeted the middle region of plCoA because it contains a mixture of cell types found in both the anterior and posterior plCoA, allowing us to test the hypothesis that cell types, not intraplCoA location, elicit different responses. Had we targeted the anterior or posterior regions, we would expect to simply recapitulate the result from activation of random cells in each region. As a result, we think stimulation in the middle plCoA is a better test for the contribution of cell types. We have clarified this in the text (Lines 417-419).

Could this explain the lack of necessity with the DREADD experiments? 

For the loss of function experiments, a larger volume of virus was injected to cover a larger area and we did confirm targeting of the appropriate areas. Though, it is always possible that the lack of necessity is due to incomplete silencing.

Further, why was an optogenetic inhibition approach not utilized? 

Although optogenetic inhibition could have plausibly been used instead, we chose chemogenetic inhibition for two reasons: First, for minutes-long periods of inhibition, optical illumination poses the risk of introducing heat related effects (Owen et al., 2019). In fact, we first tried optical inhibition but controls were exhibited unusually large variance. Second, it is more feasible in our assay as it has a narrow height between the floor and lid that complicates tethering to an optic fiber. Past experiments overcame this with a motorized fiber retraction system (Root et al., 2014), but this is highly variable with user-dependent effects, so we found chemogenetics to be a more practical strategy. We have added a sentence to explain the rationale (see lines 561-563).

(3) The specific subregion of the nucleus accumbens that was targeted should be named, as distinct parts of the nucleus accumbens can have very different functions. 

We attempted to define specific subregions of the nucleus accumbens and found that plCoA projection is not specific to the shell or core, anterior or posterior, rather it broadly innervates the entire structure. We have added a note about this in manuscript (see lines 470-471). Given that we did not find notable subregion-specific outputs within the NAc, targeting was directed to the middle region of NAc, with coordinates stated in the methods. 

(4) Why was an intersectional DREADD approach used to inhibit the projection pathways, as opposed to optogenetic inhibition? The DREADD approach could potentially affect all projection targets, and the authors might want to address how this could influence the interpretation of the results.

This is partly addressed above in point 2. As for interpretation, we acknowledge that the intersectional approach silences the neurons projecting to a given target and not the specific projection and we have been careful with the wording. Although this may complicate the conclusion, we did map the collaterals for NAc and MeA projecting neurons and find that neurons do not appreciably project to both targets and have minimal projections to other targets. We have now taken care to state that we silence the neurons projecting to a structure, not silencing the projection, and we acknowledge this caveat. However, since the MeA- and NAcprojecting neurons appear to be distinct from each other (largely not collateralizing to each other), the conclusion that these divergent pathways are required still stands. We have added discussion of this in the Limitations section (see lines 859-863).

Minor:

(1) Line 402 needs a reference.

We have added the missing reference (now line 441).

(2) The Supplemental Figure labeling in the main text should be checked carefully.

Thank you for pointing this out. We have fixed the prior errors.

(3) Panel letter D is missing from Figure 2.

This has been fixed.

Reviewer #2 (Recommendations for the authors):

Major Concerns, additional experiments:

- In the calcium imaging experiments mice were presented with the same odor many times. Overall responses to odor presentations were quite variable and appear to habituate dramatically (Figure S1F). The general conclusion from these experiments are a lack of consistent valence-specific responses of individual neurons, but I wonder if this conclusion is slightly premature. A few potential explanatory factors that may need additional attention are: -First, despite recording video of the mouse's face during experiments, no behavioral response to any odor is described. Is it possible these odors when presented in head-fixed conditions do not have the same valence?

Yes, we agree that this is a possibility. We have added a discussion in the Limitations section (see lines 849-857). We have also added additional behavioral analysis discussed below.

On trials with neural responses are there behavioral responses that could be quantified? 

We have now added data in which we attempt to characterize their behavioral response, to look for correlations in odor representation (see lines 208-228). Although we did observe different patterns of odor-evoked walking behavior, these patterns were not reliable or specific to particular odors (Figure S2). One might expect aversive odors to pause walking or elicit a fast fleeing-like response, but we did not observe any apparent differences for locomotion between odors (Figure S2A-D). Next, we examined responses to odor depending on the behavioral state (walking, pausing or fleeing) and didn’t observe any meaningful differences in odor responses (Figure S2E,F). Lastly, we acknowledge that the odor representation may be different in freely moving animals that exhibit dynamic responses to odor (see lines 859-857).

- Habituation seems to play a prominent role in the neural signals, is there a larger contribution of valence if you look only at the first delivery (or some subset of the 20 presentations) of an odor type for a given trial? 

Indeed, we considered this, but we did not find any apparent differences in valence encoding as measured by the proportion of neurons with significant valence scores across trials (see Figure 1J).

- Is it reasonable to exclude valence encoding as a possibility when largely neurons were unresponsive to the positive valence odors (2PE and peanut) chosen when looking at the average cluster response (Figure 1F)? 

It is true that we see fewer neurons responding to the appetitive odors (Figure 1H) and smaller average responses within the cluster, but some neurons do respond robustly. If these were valence responses, we would predict that neural responses should be similarly selective, but we do not observe any such selectivity. The sparseness of responses to appetitive odors does cause the average cluster analysis (Figure 1F) to show muted responses to these odors, consistent with the decreased responsivity to appetitive odors. Moreover, single neuron response analysis reveals that a given neuron is not more likely to respond to appetitive or aversive odors with any selectivity greater than chance. For these reasons, we think it is reasonable to conclude an absence of valence responses, which is consistent with the conclusion from another report (Iurilli et al., 2017).

- While the preference and aversion assay with 4 corners is an interesting set-up and provides a lot of data for this particular manuscript. It would be helpful to test additional behaviors to determine whether these circuits are more conserved. As it stands the current manuscript relies on very broad claims using a single behavioral readout. Some attempts to use head-fixed approaches with more defined odor delivery timelines and/or additional valenced behavioral readouts is warranted.

We appreciate the suggestion, but are not able to perform these experiments at the moment. The choice of the 4-quadrant assay was used because it built off of our prior experiments that demonstrate a role for the plCoA in innate behavior. It is noteworthy that the responses to odor seen in this assay are generally in agreement with other olfactory behavioral assays, so one wouldn’t predict a different result. The approach and avoidance responses measured in this assay are precisely the behaviors we wish to understand. Moreover, we did examine other nonolfactory behavioral readouts (Figures S3, S8), and didn’t observe any effect of manipulation of these pathways. Lastly, we have tried to define parameters for head-fixed behavior that would permit correlation of neural responses with behavior, including longer stimulations and closed loop locomotion control of odor concentration, but were unsuccessful at establishing parameters that generated reliable behavioral responses. We acknowledge that one limitation of the study is the limited behavioral tests with two odors and whether the circuits are more broadly necessary for other odors. 

Minor comments:

• Please define PID in the Results when it is first introduced.

Done (see line 154)

• Line 412 Figure S5C-N should be Figure S6C-N.

Fixed. Now Figure S8C-N due to additional figures (see line 451).

• Throughout the Discussion it would be helpful if the authors referred to specific Figure panels that support their statements (e.g. lines 654-656 "[...] which is supported by other findings presented here showing that both VGluT2+ and VGluT1+ neurons project to MeA, while the projection to NAc is almost entirely composed of VGluT1+ neurons".

Thank you for the suggestion. We have added figure references in the discussion.

• Line 778 "producing" should be "produce".

Corrected (see line 840)

• The figures are very busy, especially all the manipulations. The authors are commended for including each data point, but they might consider a more subtle design (translucent lines only for each animal, and one mean dot for the SEM), just to reduce the overall clutter of an already overwhelming figure set. But this is ultimately left to the authors to resolve and style to their liking. 

Thank you for the suggestion. We have tried some different styles but like the original best.

Reviewer #3 (Recommendations for the authors):

If within reach, I suggest that the author determine the percentage of retrogradely labeled neurons to NAc or MEA that expresses GluT1 and GluT2. 

We have done this for the middle region plCoA that has the greatest mixture of cell types (See Figure S10, lines 504-517). We find that the MeA projecting neurons are mostly VGluT2+ with a minority that express both VGluT1 and VGlut2. NAc-projecting neurons are primarily VGluT1+ with about 20% expressing VGlut2 as well.

It would also be nice to sparse label of aplCoA and pplCoA using ChR2 to see if sparse activation drives approach or avoidance. 

We agree that it would be useful to vary the sparseness of the ChR2 expression, to see if produces similar results. We examined this using sparsely labeled odor ensembles, as previously done (Root et al., 2014). Briefly, we used the Arc-CreER mouse to label TMT responsive neurons with a cre-dependent ChR2 AAV vector targeted to the anterior or posterior regions, while previously we had broadly targeted the entirety of plCoA. We had established that this labeling method captures about half of the active cells detected by Arc expression, which is on the order of hundreds of neurons rather than thousands by broad cre-independent expression. Remarkably, we get effects similar in magnitude that are not significantly different from that with broader activation of the anterior or posterior domains (see new Figure S4, lines 267-288). It still remains possible that there is a threshold number of neurons that are necessary to elicit behavior, but that is beyond the scope of the current study. However, these data indicate that the effect of activating anterior and posterior domains is not an artifact of broad stimulation.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC-2025-03280

Corresponding author(s): Stephan Gruber

1. General Statements [optional]

First, we would like to thank the editor at Review Commons for the efficient handling of our manuscript. We also apologize for our delayed response.

We are grateful to all three reviewers for their careful evaluation of our work and for their constructive feedback, which will provide a valuable basis for improving the figures and the text, as described below. We expect to be able to complete the revision following the plan described below quickly.

We note that the reviewer reports (Rev. #1 and Rev. #3) made us realize that the manuscript text was misleading on the following point. Although we used the purified ATP hydrolysis–deficient Smc protein for sybody isolation, this does not restrict the selection to a specific conformation. As described in detail in Vazquez-Nunez et al. (Figure 5), this mutant displays the ATP-engaged conformation only in a smaller fraction of complexes (~25% in the presence of ATP and DNA), consistent with prior in vivo observations reported by Diebold-Durand et al. (Figure 5). Rather than limiting the selection to a particular configuration, our aim was to reduce the prevalence of the predominant rod state in order to broaden the range of conformations represented during sybody selection. Consistent with this interpretation, only a small number of isolated sybodies show strong conformation-specific binding in the presence or absence of ATP/DNA, as observed by ELISA (now included in the manuscript). We will revise the manuscript text accordingly to clarify this point.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

• *

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Gosselin et al., develop a method to target protein activity using synthetic single-domain nanobodies (sybodies). They screen a library of sybodies using ribosome/ phage display generated against bacillus Smc-ScpAB complex. Specifically, they use an ATP hydrolysis deficient mutant of SMC so as to identify sybodies that will potentially disrupt Smc-ScpAB activity. They next screen their library in vivo, using growth defects in rich media as a read-out for Smc activity perturbation. They identify 14 sybodies that mirror smc deletion phenotype including defective growth in fast-growth conditions, as well as chromosome segregation defects. The authors use a clever approach by making chimeras between bacillus and S. pnuemoniae Smc to narrow-down to specific regions within the bacillus Smc coiled-coil that are likely targets of the sybodies. Using ATPase assays, they find that the sybodies either impede DNA-stimulated ATP hydrolysis or hyperactivate ATP hydrolysis (even in the absence of DNA). The authors propose that the sybodies may likely be locking Smc-ScpAB in the "closed" or "open" state via interaction with the specific coiled-coil region on Smc. I have a few comments that the authors should consider:

Major comments: 1. Lack of direct in vitro binding measurements: The authors do not provide measurements of sybody affinities, binding/ unbinding kinetics, stoichiometries with respect to Smc-ScpAB. Additionally, do the sybodies preferentially interact with Smc in ATP/ DNA-bound state? And, do the sybodies affect the interaction of ScpAB with SMC? It is understandable that such measurements for 14 sybodies is challenging, and not essential for this study. Nonetheless, it is informative to have biochemical characterization of sybody interaction with the Smc-ScpAB complex for at least 1-2 candidate sybodies described here.

We agree with the reviewer that adding such data would be reassuring and that obtaining solid data using purified components is not easy even for a smaller selection of sybodies. We have data that show direct binding of Smc to sybodies by various methods including ELISA, pull-downs and by biophysical methods (GCI). Initially, we omitted these data from the manuscript as we are convinced that the mapping data obtained with chimeric SMC proteins is more definitive and relevant. During the revision we will incorporate the ELISA data showing direct binding and also indicating a lack of preference for a specific state of Smc.

Many modes of sybody binding to Smc are plausible The authors provide an elaborate discussion of sybodies locking the Smc-ScpAB complex in open/ closed states. However, in the absence of structural support, the mechanistic inferences may need to be tempered. For example, is it also not possible for the sybodies to bind the inner interface of the coiled-coil, resulting in steric hinderance to coiled-coil interactions. It is also possible that sybody interaction disrupts ScpAB interaction (as data ruling this possibility out has not been provided). Thus, other potential mechanisms would be worth considering/ discussing. In this direction, did AlphaFold reveal any potential insights into putative binding locations?

We have attempted to map the binding by structure prediction, however, so far, even the latest versions of AlphaFold are not able to clearly delineate the binding interface. Indeed, many ways of binding are possible, including disruption of ScpAB interaction. However, since the main binding site is located on the SMC coiled coils, the later scenario would likely be an indirect consequence of altered coiled coil configuration, consistent with our current interpretation.

1. Sybody expression in vivo Have the authors estimated sybody expression in vivo? Are they all expressed to similar levels?

We have tagged selected sybodies with gfp and performed live cell imaging. This showed that they are all roughly equally expressed and that they localize as foci in the cell presumably by binding to Smc complexes loaded onto the chromosome at ParB/parS sites. We will include this data in the revised version of the manuscript.

1. Sybodies should phenocopy ATP hydrolysis mutant of Smc The sybodies were screened against an ATP hydrolysis deficient mutant of Smc, with the rationale that these sybodies would interfere this step of the Smc duty cycle. Does the expression of the sybodies in vivo phenocopy the ATP hydrolysis deficient mutant of Smc? Could the authors consider any phenotypic read-outs that can indicate whether the sybody action results in an smc-null effect or specifically an ATP hydrolysis deficient effect?

As eluded to above, we think that our selection gave rise to sybodies that bind various, possibly multiple Smc conformations. Consistent with this idea, the phenotypes are similar to null mutant rather than the ATP-hydrolysis defective EQ mutant, which display even more severe growth phenotypes. We will add the following notes to the text:

“These conditions favour ATP-engaged particles alongside the typically predominant ATP-disengaged rod-shaped state (add Vazquez Nunez et al., 2021).”

“ELISA data confirm that nearly all clones bind Smc-ScpAB; however, their binding shows little or no dependence on the presence of ATP or DNA.”

Minor comments: 1. It was surprising that no sybodies were found that could target both bacillus and spneu Smc. For example, sybodies targeting the head regions of Smc that might work in a more universal manner. Could the authors comment on the coverage of the sybodies across the protein structure?

It is rather common that sybodies (like antibodies and nanobodies) exhibit strong affinity differences between highly conserved proteins (> 90 % identity). The underlying reasons for such strong discrimination are i) location of less conserved residues primarily at the target protein surface and ii) the large interaction interface between sybody and target which offers multiple vulnerabilities for disturbance, in particular through bulky side chains resulting in steric clashes. Another frequently observed phenomenon is sybody binding to a dominant epitope, which also often applies to nanobodies and antibodies. A great example for this are the dominant epitopes on SARS-CoV-2 RBDs.

Growth curves (Fig. S3) show a large jump in recovery in growth under sybody induction conditions. Could the authors address this observation here and in the text?

We suppose that this recovery represents suppressor mutants and/or (more likely) improved growth in the absence of functional Smc during nutrient limitation (see Gruber et al., 2013 and Wang et al., 2013). We will add this statement to the text.

L41- Sentence correction: Loop can be removed. Ah, yes, sorry for this confusing error. Thank you. 4. L525 - bsuSmc 'E' :extra E can be removed. To do. Thank you. 5. References need to be properly formatted. To do. Thank you. 6. The authors should add in figure legend for Fig 1i) details on representation of the purple region, and explain the grey strokes for orientation of the loop. To do. 7. How many cells were analysed in the cell biological assays? Legends should include these information. To Be Included.

Reviewer #1 (Significance (Required)):

Overall, this is an impressive study that uses an elegant strategy to find inhibitors of protein activity in vivo. The manuscript is clearly written and the experiments are logical and well-designed. The findings from the study will be significant to the broad field of genome biology, synthetic biology and also SMC biology. Specifically, the coiled coil domain of SMC proteins have been proposed to be of high functional value. The authors have elegantly identified key coiled-coil regions that may be important for function, and parallelly exhibited potential of the use of synthetic sybody/designed binders for inhibition of protein activity.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Review: "Single Domain Antibody Inhibitors Target the Coiled Coil Arms of the Bacillus subtilis SMC complex" by Ophélie Gosselin et al, Review Commons RC-2025-03280 Structural Maintenance of Chromosome proteins (SMCs), a family of proteins found in almost all organisms, are organizers of DNA. They accomplish this by a process known as loop extrusion, wherein double-stranded DNA is actively reeled in and extruded into loops. Although SMCs are known to have several DNA binding regions, the exact mechanism by which they facilitate loop extrusion is not understood but is believed to entail large conformational changes. There are currently several models for loop extrusion, including one wherein the coiled coil (CC) arms open, but there is a lack of insightful experimentation and analysis to confirm any of these models. The work presented aims to provide much-needed new tools to investigate these questions: conformation-selective sybodies (synthetic nanobodies) that are likely to alter the CC opening and closing reactions. The authors produced, isolated, and expressed sybodies that specifically bound to Bacillus subtilis Smc-ScpAB. Using chimeric Smc constructs, where the coiled coils were partly replaced with the corresponding sequences from Streptococcus pneumoniae, the authors revealed that the isolated sybodies all targeted the same 4N CC element of the Smc arms. This region is likely disrupted by the sybodies either by stopping the arms from opening (correctly) or forcing them to stay open (enough). Disrupting these functional elements is suggested to cause the Smc-dependent chromosome organization lethal phenotype, implying that arm opening and closing is a key regulatory feature of bacterial Smc-ScpAB. In summary, the authors present a new method for trapping bacterial Smc's in certain conformations using synthetic antibodies. Using these antibodies, they have pinpointed the (previously suggested) 4N region of the coiled coils as an essential site for the opening and closing of the Smc coiled coil arms and that hindering these reactions blocks Smc-driven chromosomal organization. The work has important implications for how we might elucidate the mechanism of DNA loop extrusion by SMC complexes. Some specific comments: Line 75: "likely stabilizing otherwise rare intermediates of the conformational cycle." - sorry, why is that being concluded? Why not stabilizing longer-lived oncformations? We will clarify this statement!

Line 89: Sorry, possibly our lack of understanding: why first ribosome and then phage display?

Ribosome display offers to screen around 10^12 sybodies per selection round (technically unrestricted library size), while for phage display, the library size is restricted to around 10^9 sybodies due to the fact that production of a phage library requires transformation of the phagemid plasmid into E. coli, thereby introducing a diversity bottleneck. This is why the sybody platform starts off with ribosome display. It switches to phage display from round 2 onwards because the output of the initial round of ribosome display is around 10^6 sybodies, which can be easily transferred into the phage display format. Phage display is used to minimize selection biases. For more information, please consult the original sybody paper (PMID: 29792401).

Line 100: Why was only lethality selected? Less severe phenotypes not clear enough?

Yes, colony size is more difficult to score robustly, as the sizes of individual transformant colonies can vary quite widely. The number of isolated sybodies was at the limit of further analysis.

Line 106: Could it be tested somehow if convex and concave library sybodies fold in Bs?

We did not focus on the non-functional sybody candidates and only sybodies of the loop library turned out to cause functional consequences at the cellular level. Notably, we will include gfp-imaging showing that non-lethal sybodies are expressed to similar levels that toxic sybodies. Given the identical scaffold of concave and loop sybodies (they only differ in their CDR3 length), we expect that the concave sybodies fold in the cytoplasm of B. subtilis. For the convex sybodies exhibiting a different scaffold, this will be tested.

Line 125: Could Pxyl be repressed by glucose?

To our knowledge and experience, repression by glucose (catabolite repression) does not work well in this context in B. subtilis.

Line 131: The SMC replacement strain is a cool experiment and removes a lot of doubts!

Thank you! (we agree 😊)

Line 141: The mapping is good and looks reliable, but looks and feels like a tour de force? Of course, some cryo-EM would have been lovely (lines 228-229 understood, it has been tried!).

Yes, we have made several attempts at structural biology. Unfortunately, Smc-ScpAB is not well suited for cryo-EM in our hands and crystallography with Smc fragments and sybodies did not yield well-diffracting crystals.

Line 179: Mmmh. Do we not assume DNA binding on top of the dimerised heads to open the CC (clamp)?

We will clarify the text here.

Line 187: Having sybodies that presumably keep the CC together (closing) and some that do not allow them to come together correctly (opening) is really cool and probably important going forward.

Thank you!

Figure 1 Ai is not very colour-blind friendly.

We are sorry for this oversight. We will try to make the color scheme more inclusive. Thank you for the notification.

Optional: did the authors see any spontaneous mutations emerge that bypass the lethal phenotype of sybody expression?

No, we did not observe spontaneous mutations suppressing the phenotype, possibly due to the limited number of cell generations observed. We tried to avoid suppressors by limiting growth, but this may indeed be a good future approach for further fine map the binding sites and to obtain insights into the mechanism of inhibition.

Optional: we think it would be nice to try some biochemical experiment with BMOE/cysteine-crosslinked B. subtilis Smc in the mid-region (4N or next to it) of the Smc coiled coils to try to further strengthen the story. Some of the authors are experts in this technique and strains might already exist?

We have indeed tried to study the impact of sybody binding on Smc conformation by cysteine cross-linking. However, we were not convinced by the results and thus prefer not to draw any conclusions from them. We will add a corresponding note to the text.

Reviewer #2 (Significance (Required)):

The authors present a new method for trapping bacterial Smc's in certain conformations using synthetic antibodies. Using these antibodies, they have pinpointed the (previously suggested) 4N region of the coiled coils as an essential site for the opening and closing of the Smc coiled coil arms and that hindering these reactions blocks Smc-driven chromosomal organization. The work has important implications for how we might elucidate the mechanism of DNA loop extrusion by SMC complexes. Thank you!

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Gosselin et al. use the sybody technology to study effects of in vivo inhibition oft he Bacillus subtilis SMC complex. Smc proteins are central DNA binding elements of several complexes that are vital for chromosome dynamics in almost all organisms. Sybodies are selected from three different libraries of the single domain antibodies, using the „transition state" mutant Smc. They identify 14 such mutant sybodies that are lethal when expressed in vivo, because they prevent proper function of Smc. The authors present evidence suggesting that all obtained sybodies bind to a coiled-coil region close to the Smc „neck", and thereby interfere with the Smc activity cycle, as evidenced by defective ATPase activity when Smc is bound to DNA. The study is well done and presented and shows that the strategy is very potent in finding a means to quickly turn off a protein's function in vivo, much quicker than depleting the protein.

The authors also draw conclusions on the molecular mode of action of the SMC complex. The provide a number of suggestive experiments, but in my view mostly indirect evidence for such mechanism.

My main criticism ist hat the authors have used a single - and catalytically trapped form of SMC. They speculate why they only obtain sybodies from one library, and then only idenfity sybodies that bind to a rather small part oft he large Smc protein. While the approach is definitely valuable, it is biassed towards sybodies that bind to Smc in a quite special way, it seems. Using wild type Smc would be interesting, to make more robust statements about the action of sybodies potantially binding to different parts of Smc.

As explained above, we are quite confident the Smc ATPase mutation did not bias the selection in an obvious way. The surprising bias towards coiled coil binding sites has likely other explanations, as they likely form a preferred epitope recognized by sybodies.

Line 105: Alternatively, the other libraries did not produce good binders or these sybodies were 106 not stably expressed in B. subtilis. This could be tested using Western blotting - I am assuming sybody antibodies are commercially avalable. However, this test is not important for the overall study, it would just clarify a minor point.

While there are antibody fragments available to augment the size of sybodies (PMID: 40108246), these recognize 3D-epitopes and are thus not suited for Western blotting. We did not follow up on the negative results much, but would like to point out again that there are several biases that likely emerge for the same reason (bias to library, bias to coiled coil binding site). If correct, then likely few other sybodies are effectively lethal in B. subtilis, with the exception of the ones isolated and characterized. We have added this notion to the manuscript. We have also tested the expression of non-lethal sybodies by gfp-tagging and imaging. These results will be included in the revision.

Fig. 2B: is is odd to count Spo0J foci per cells, as it is clear from the images that several origins must be present within the fluorescent foci. I am fine with the „counting" method, as the images show there is a clear segregation defect when sybodies are expressed, I believe the authors should state, though, that this is not a replication block, but failure to segregate origins.

We agree that this is an important point and will add a corresponding comment to the text.

Testing binding sites of sybodies tot he SMC complex is done in an indirect manner, by using chimeric Smc constructs. I am surprised why the authors have not used in vitro crosslinking: the authors can purify Smc, and mass spectrometry analyses would identify sites where sybodies are crosslinked to Smc. Again, I am fine with the indirect method, but the authors make quite concrete statements on binding based on non-inhibition of chimeric Smc; I can see alternative explanations why a chimera may not be targeted.

We have made several attempts of testing direct binding with mixed outcomes and decided to not include those results in the light of the stronger and more relevant in vivo mapping. However, we will add ELISA results and briefly discuss grating coupled interferometry (GCI) data and pull-downs.

Smc-disrupting sybodies affect the ATPase activity in one of two ways. Again, rather indirect experiments. This leads to the point Revealing Smc arm dynamics through synthetic binders in the discussion. The authors are quite careful in stating that their experiments are suggestive for a certain mode of action of Smc, which is warranted.

In line 245, they state More broadly, the study demonstrates how synthetic binders can trap, stabilize, or block transient conformations of active chromatin-associated machines, providing a powerful means to probe their mechanisms in living cells. This is off course a possible scenario for the use of sybodies, but the study does not really trap Smc in a transient conformation, at least this is not clearly shown.

We agree and will carefully rephrase this statement. Thank you.

Overall, it is an interesting study, with a well-presented novel technology, and a limited gain of knowledge on SMC proteins. We respectfully disagree with the last point, since our unique results highlight the importance of the Smc coiled coils, which are otherwise largely neglected in the SMC literature, likely (at least in part) due the mild effect of single point mutations on coiled coil dynamics.

Reviewer #3 (Significance (Required)):

The work describes the gaining and use of single-binder antibodies (sybodies) to interfere with the function of proteins in bacteria. Using this technology for the SMC complex, the authors demonstrate that they can obtain a significant of binders that target a defined region is SMC and thereby interfere with the ATPase cycle.

The study does not present a strong gain of knowledge of the mode of action of the SMC complex.

As pointed out above, we respectfully disagree with this assertion.

• *

3. Description of the revisions that have already been incorporated in the transferred manuscript

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

• *

4. Description of analyses that authors prefer not to carry out

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

As pointed out above, there are a few minor points that we prefer not to experimentally address. In particular, we do not consider it as necessary to determine the expression levels of sybodies which were non-inhibitory. We also wish to note that we attempted to obtain structural additional biochemical data and to that end performed cryo-EM, crystallography and cysteine cross-linking experiments. Unfortunately, we did not obtain sybody complex structures and the cross-linking data were unfortunately not conclusive. We also wish to note that the first author has finished her PhD and left the lab, which limits our capacity to add additional experiments. However, as the reviewers also pointed out, the main conclusions are well supported by the data already.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Gosselin et al., develop a method to target protein activity using synthetic single-domain nanobodies (sybodies). They screen a library of sybodies using ribosome/ phage display generated against bacillus Smc-ScpAB complex. Specifically, they use an ATP hydrolysis deficient mutant of SMC so as to identify sybodies that will potentially disrupt Smc-ScpAB activity. They next screen their library in vivo, using growth defects in rich media as a read-out for Smc activity perturbation. They identify 14 sybodies that mirror smc deletion phenotype including defective growth in fast-growth conditions, as well as chromosome segregation defects. The authors use a clever approach by making chimeras between bacillus and S. pnuemoniae Smc to narrow-down to specific regions within the bacillus Smc coiled-coil that are likely targets of the sybodies. Using ATPase assays, they find that the sybodies either impede DNA-stimulated ATP hydrolysis or hyperactivate ATP hydrolysis (even in the absence of DNA). The authors propose that the sybodies may likely be locking Smc-ScpAB in the "closed" or "open" state via interaction with the specific coiled-coil region on Smc. I have a few comments that the authors should consider:

Major comments:

1. Lack of direct in vitro binding measurements: The authors do not provide measurements of sybody affinities, binding/ unbinding kinetics, stoichiometries with respect to Smc-ScpAB. Additionally, do the sybodies preferentially interact with Smc in ATP/ DNA-bound state? And, do the sybodies affect the interaction of ScpAB with SMC? It is understandable that such measurements for 14 sybodies is challenging, and not essential for this study. Nonetheless, it is informative to have biochemical characterization of sybody interaction with the Smc-ScpAB complex for at least 1-2 candidate sybodies described here.
2. Many modes of sybody binding to Smc are plausible The authors provide an elaborate discussion of sybodies locking the Smc-ScpAB complex in open/ closed states. However, in the absence of structural support, the mechanistic inferences may need to be tempered. For example, is it also not possible for the sybodies to bind the inner interface of the coiled-coil, resulting in steric hinderance to coiled-coil interactions. It is also possible that sybody interaction disrupts ScpAB interaction (as data ruling this possibility out has not been provided). Thus, other potential mechanisms would be worth considering/ discussing. In this direction, did AlphaFold reveal any potential insights into putative binding locations?
3. Sybody expression in vivo Have the authors estimated sybody expression in vivo? Are they all expressed to similar levels?
4. Sybodies should phenocopy ATP hydrolysis mutant of Smc The sybodies were screened against an ATP hydrolysis deficient mutant of Smc, with the rationale that these sybodies would interfere this step of the Smc duty cycle. Does the expression of the sybodies in vivo phenocopy the ATP hydrolysis deficient mutant of Smc? Could the authors consider any phenotypic read-outs that can indicate whether the sybody action results in an smc-null effect or specifically an ATP hydrolysis deficient effect?

Minor comments:

1. It was surprising that no sybodies were found that could target both bacillus and spneu Smc. For example, sybodies targeting the head regions of Smc that might work in a more universal manner. Could the authors comment on the coverage of the sybodies across the protein structure?
2. Growth curves (Fig. S3) show a large jump in recovery in growth under sybody induction conditions. Could the authors address this observation here and in the text?
3. L41- Sentence correction: Loop can be removed.
4. L525 - bsuSmc 'E' :extra E can be removed.
5. References need to be properly formatted.
6. The authors should add in figure legend for Fig 1i) details on representation of the purple region, and explain the grey strokes for orientation of the loop.
7. How many cells were analysed in the cell biological assays? Legends should include these information.

Significance

Overall, this is an impressive study that uses an elegant strategy to find inhibitors of protein activity in vivo. The manuscript is clearly written and the experiments are logical and well-designed. The findings from the study will be significant to the broad field of genome biology, synthetic biology and also SMC biology. Specifically, the coiled coil domain of SMC proteins have been proposed to be of high functional value. The authors have elegantly identified key coiled-coil regions that may be important for function, and parallelly exhibited potential of the use of synthetic sybody/designed binders for inhibition of protein activity.

Chief Terry Paul is inspried by Choctow approach which saw welcoming of gambling, big industry, and such -> brings this to Cape Breton and brings Mebertou out of poverty between 80s and now -> communal wealth - > reinvested into community and social programing, even dividends.

He was the architect of the clearwater deal.

Encomium means a speech or writing that praises someone or something highly.

The discussion of Shays’s Rebellion reveals how economic struggles and weak national power under the Articles of Confederation created serious unrest among farmers. While some leaders viewed the rebellion as a dangerous threat to order, others believed it represented the same revolutionary spirit that founded the country.

Reviewer #1 (Public review):

Summary:

This manuscript by Lin et al. presents a timely, technically strong study that builds patient-specific midbrain-like organoids (MLOs) from hiPSCs carrying clinically relevant GBA1 mutations (L444P/P415R and L444P/RecNcil). The authors comprehensively characterize nGD phenotypes (GCase deficiency, GluCer/GluSph accumulation, altered transcriptome, impaired dopaminergic differentiation), perform CRISPR correction to produce an isogenic line, and test three therapeutic modalities (SapC-DOPS-fGCase nanoparticles, AAV9-GBA1, and SRT with GZ452). The model and multi-arm therapeutic evaluation are important advances with clear translational value.

My overall recommendation is that the work undergo a major revision to address the experimental and interpretive gaps listed below.

Strengths:

(1) Human, patient-specific midbrain model: Use of clinically relevant compound heterozygous GBA1 alleles (L444P/P415R and L444P/RecNcil) makes the model highly relevant to human nGD and captures patient genetic context that mouse models often miss.

(2) Robust multi-level phenotyping: Biochemical (GCase activity), lipidomic (GluCer/GluSph by UHPLC-MS/MS), molecular (bulk RNA-seq), and histological (TH/FOXA2, LAMP1, LC3) characterization are thorough and complementary.

(3) Use of isogenic CRISPR correction: Generating an isogenic line (WT/P415R) and demonstrating partial rescue strengthens causal inference that the GBA1 mutation drives many observed phenotypes.

(4) Parallel therapeutic testing in the same human platform: Comparing enzyme delivery (SapC-DOPS-fGCase), gene therapy (AAV9-GBA1), and substrate reduction (GZ452) within the same MLO system is an elegant demonstration of the platform's utility for preclinical evaluation.

(5) Good methodological transparency: Detailed protocols for MLO generation, editing, lipidomics, and assays allow reproducibility

Weaknesses:

(1) Limited genetic and biological replication

(a) Single primary disease line for core mechanistic claims. Most mechanistic data derive from GD2-1260 (L444P/P415R); GD2-10-257 (L444P/RecNcil) appears mainly in therapeutic experiments. Relying primarily on one patient line risks conflating patient-specific variation with general nGD mechanisms.

(b) Unclear biological replicate strategy. It is not always explicit how many independent differentiations and organoid batches were used (biological replicates vs. technical fields of view).

(c) A significant disadvantage of employing brain organoids is the heterogeneity during induction and potential low reproducibility. In this study, it is unclear how many independent differentiation batches were evaluated and, for each test (for example, immunofluorescent stain and bulk RNA-seq), how many organoids from each group were used. Please add a statement accordingly and show replicates to verify consistency in the supplementary data.

(d) Isogenic correction is partial. The corrected line is WT/P415R (single-allele correction); residual P415R complicates the interpretation of "full" rescue and leaves open whether the remaining pathology is due to incomplete correction or clonal/epigenetic effects.

(e) The authors tested week 3, 4, 8, 15, and 28 old organoids in different settings. However, systematic markers of maturation should be analyzed, and different maturation stages should be compared, for example, comparing week 8 organoids to week 28 organoids, with immunofluorescent marker staining and bulk RNAseq.

(f) The manuscript frequently refers to Wnt signaling dysregulation as a major finding. However, experimental validation is limited to transcriptomic data. Functional tests, such as the use of Wnt agonist/inhibitor, are needed to support this claim (see below).

(g) Suggested fixes/experiments

Add at least one more independent disease hiPSC line (or show expanded analysis from GD2-10-257) for key mechanistic endpoints (lipid accumulation, transcriptomics, DA markers)

Generate and analyze a fully corrected isogenic WT/WT clone (or a P415R-only line) if feasible; at minimum, acknowledge this limitation more explicitly and soften claims.

Report and increase independent differentiations (N = biological replicates) and present per-differentiation summary statistics.

(2) Mechanistic validation is insufficient

(a) RNA-seq pathways (Wnt, mTOR, lysosome) are not functionally probed. The manuscript shows pathway enrichment and some protein markers (p-4E-BP1) but lacks perturbation/rescue experiments to link these pathways causally to the DA phenotype.

(b) Autophagy analysis lacks flux assays. LC3-II and LAMP1 are informative, but without flux assays (e.g., bafilomycin A1 or chloroquine), one cannot distinguish increased autophagosome formation from decreased clearance.

(c) Dopaminergic dysfunction is superficially assessed. Dopamine in the medium and TH protein are shown, but no neuronal electrophysiology, synaptic marker co-localization, or viability measures are provided to demonstrate functional recovery after therapy.

(d) Suggested fixes/experiments

Perform targeted functional assays:

(i) Wnt reporter assays (TOP/FOP flash) and/or treat organoids with Wnt agonists/antagonists to test whether Wnt modulation rescues DA differentiation.

(ii)Test mTOR pathway causality using mTOR inhibitors (e.g., rapamycin) or 4E-BP1 perturbation and assay effects on DA markers and autophagy.

Include autophagy flux assessment (LC3 turnover with bafilomycin), and measure cathepsin activity where relevant.

Add at least one functional neuronal readout: calcium imaging, MEA recordings, or synaptic marker quantification (e.g., SYN1, PSD95) together with TH colocalization.

(3) Therapeutic evaluation needs greater depth and standardization

(a) Short windows and limited durability data. SapC-DOPS and AAV9 experiments range from 48 hours to 3 weeks; longer follow-up is needed to assess durability and whether biochemical rescue translates into restored neuronal function.

(b) Dose-response and biodistribution are under-characterized. AAV injection sites/volumes are described, but transduction efficiency, vg copies per organoid, cell-type tropism quantification, and SapC-DOPS penetration/distribution are not rigorously quantified.

(c) Specificity controls are missing. For SapC-DOPS, inclusion of a non-functional enzyme control (or heat-inactivated fGCase) would rule out non-specific nanoparticle effects. For AAV, assessment of off-target expression and potential cytotoxicity is needed.

(d) Comparative efficacy lacking. It remains unclear which modality is most effective in the long term and in which cellular compartments.

(e) Suggested fixes/experiments

Extend follow-up (e.g., 6+ weeks) after AAV/SapC dosing and evaluate DA markers, electrophysiology, and lipid levels over time.

Quantify AAV transduction by qPCR for vector genomes and by cell-type quantification of GFP+ cells (neurons vs astrocytes vs progenitors).

Include SapC-DOPS control nanoparticles loaded with an inert protein and/or fluorescent cargo quantitation to show distribution and uptake kinetics.

Provide head-to-head comparative graphs (activity, lipid clearance, DA restoration, and durability) with statistical tests.

(4) Model limitations not fully accounted for in interpretation

(a) Absence of microglia and vasculature limits recapitulation of neuroinflammatory responses and drug penetration, both of which are important in nGD. These absences could explain incomplete phenotypic rescues and must be emphasized when drawing conclusions about therapeutic translation.

(b) Developmental vs degenerative phenotype conflation. Many phenotypes appear during differentiation (patterning defects). The manuscript sometimes interprets these as degenerative mechanisms; the distinction must be clarified.

(c) Suggested fixes

Tone down the language throughout (Abstract/Results/Discussion) to avoid overstatement that MLOs fully recapitulate nGD neuropathology.

Add plans or pilot data (if available) for microglia incorporation or vascularization to indicate how future work will address these gaps.

(5) Statistical and presentation issues

(a) Missing or unclear sample sizes (n). For organoid-level assays, report the number of organoids and the number of independent differentiations.

(b) Statistical assumptions not justified. Tests assume normality; where sample sizes are small, consider non-parametric tests and report exact p-values.

(c) Quantification scope. Many image quantifications appear to be from selected fields of view, which are then averaged across organoids and differentiations.

(d) RNA-seq QC and deposition. Provide mapping rates, batch correction details, and ensure the GEO accession is active. Include these in Methods/Supplement.

(e) Suggested fixes

Add a table summarizing biological replicates, technical replicates, and statistical tests used for each figure panel.

Recompute statistics where appropriate (non-parametric if N is small) and report effect sizes and confidence intervals.

(6) Minor comments and clarifications

(a) The authors should validate midbrain identity further with additional regional markers (EN1, OTX2) and show absence/low expression of forebrain markers (FOXG1) across replicates.

(b) Extracellular dopamine ELISA should be complemented with intracellular dopamine or TH+ neuron counts normalized per organoid or per total neurons.

(c) For CRISPR editing: the authors should report off-target analysis (GUIDE-seq or targeted sequencing of predicted off-targets) or at least in-silico off-target score and sequencing coverage of the edited locus.

(d) It should be clarified as to whether lipidomics normalization is to total protein per organoid or per cell, and include representative LC-MS chromatograms or method QC.

(e) Figure legends should be improved in order to state the number of organoids, the number of differentiations, and the exact statistical tests used (including multiple-comparison corrections).

(f) In the title, the authors state "reveal disease mechanisms", but the studies mainly exhibit functional changes. They should consider toning down the statement.

(7) Recommendations

This reviewer recommends a major revision. The manuscript presents substantial novelty and strong potential impact but requires additional experimental validation and clearer, more conservative interpretation. Key items to address are:

(a) Strengthening genetic and biological replication (additional lines or replicate differentiations).

(b) Adding functional mechanistic validation for major pathways (Wnt/mTOR/autophagy) and providing autophagy flux data.

(c) Including at least one neuronal functional readout (calcium imaging/MEA/patch) to demonstrate functional rescue.

(d) Deepening therapeutic characterization (dose, biodistribution, durability) and including specificity controls.

(e) Improving statistical reporting and explicitly stating biological replicate structure.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript by Lin et al. presents a timely, technically strong study that builds patientspecific midbrain-like organoids (MLOs) from hiPSCs carrying clinically relevant GBA1 mutations (L444P/P415R and L444P/RecNcil). The authors comprehensively characterize nGD phenotypes (GCase deficiency, GluCer/GluSph accumulation, altered transcriptome, impaired dopaminergic differentiation), perform CRISPR correction to produce an isogenic line, and test three therapeutic modalities (SapC-DOPS-fGCase nanoparticles, AAV9GBA1, and SRT with GZ452). The model and multi-arm therapeutic evaluation are important advances with clear translational value.

My overall recommendation is that the work undergo a major revision to address the experimental and interpretive gaps listed below.

Strengths:

(1) Human, patient-specific midbrain model: Use of clinically relevant compound heterozygous GBA1 alleles (L444P/P415R and L444P/RecNcil) makes the model highly relevant to human nGD and captures patient genetic context that mouse models often miss.

(2) Robust multi-level phenotyping: Biochemical (GCase activity), lipidomic (GluCer/GluSph by UHPLC-MS/MS), molecular (bulk RNA-seq), and histological (TH/FOXA2, LAMP1, LC3) characterization are thorough and complementary.

(3) Use of isogenic CRISPR correction: Generating an isogenic line (WT/P415R) and demonstrating partial rescue strengthens causal inference that the GBA1 mutation drives many observed phenotypes.

(4) Parallel therapeutic testing in the same human platform: Comparing enzyme delivery (SapC-DOPS-fGCase), gene therapy (AAV9-GBA1), and substrate reduction (GZ452) within the same MLO system is an elegant demonstration of the platform's utility for preclinical evaluation.

(5) Good methodological transparency: Detailed protocols for MLO generation, editing, lipidomics, and assays allow reproducibility

Weaknesses:

(1) Limited genetic and biological replication

(a) Single primary disease line for core mechanistic claims. Most mechanistic data derive from GD2-1260 (L444P/P415R); GD2-10-257 (L444P/RecNcil) appears mainly in therapeutic experiments. Relying primarily on one patient line risks conflating patient-specific variation with general nGD mechanisms.

We thank the reviewer for highlighting the importance of genetic and biological replication. An additional patient-derived iPSC line was included in the manuscript, therefore, our study includes two independent nGD patient-derived iPSC lines, GD2-1260 (GBA1^L444P/P415R) and GD2-10-257 (GBA1^{L444P/RecNcil}), both of which carry the severe mutations associated with nGD. These two lines represent distinct genetic backgrounds and were used to demonstrate the consistency of key disease phenotypes (reduced GCase activity, elevated substrate, impaired dopaminergic neuron differentiation, etc.) across different patient’s MLOs. Major experiments (e.g., GCase activity assays, substrate, immunoblotting for DA marker TH, and therapeutic testing with SapC-DOPS-fGCase, AAV9-GBA1) were performed using both patient lines, with results showing consistent phenotypes and therapeutic responses (see Figs. 2-6, and Supplementary Figs. 4-5). To ensure clarity and transparency, a new Supplementary Table 2 summarizes the characterization of both the GD2-1260 and GD2-10-257 lines.

(b) Unclear biological replicate strategy. It is not always explicit how many independent differentiations and organoid batches were used (biological replicates vs. technical fields of view).

Biological replication was ensured in our study by conducting experiments in at least 3 independent differentiations per line, and technical replicates (multiple organoids/fields per batch) were averaged accordingly. We have clarified biological replicates and differentiation in the figure legends. 

(c) A significant disadvantage of employing brain organoids is the heterogeneity during induction and potential low reproducibility. In this study, it is unclear how many independent differentiation batches were evaluated and, for each test (for example, immunofluorescent stain and bulk RNA-seq), how many organoids from each group were used. Please add a statement accordingly and show replicates to verify consistency in the supplementary data.

In the revision, we have clarified biological replicates and differentiation in the figure legend in Fig.1E; Fig.2B,2G; Fig.3F, 3G; Fig.4B-C,E,H-J, M-N; Fig.6D; and Fig.7A-C, I.

(d) Isogenic correction is partial. The corrected line is WT/P415R (single-allele correction); residual P415R complicates the interpretation of "full" rescue and leaves open whether the remaining pathology is due to incomplete correction or clonal/epigenetic effects.

We attempted to generate an isogenic iPSC line by correcting both GBA1 mutations (L444P and P415R). However, this was not feasible because GBA1 overlaps with a highly homologous pseudogene (PGBA), which makes precise editing technically challenging. Consequently, only the L444P mutation was successfully corrected, and the resulting isogenic line retains the P415R mutation in a heterozygous state. Because Gaucher disease is an autosomal recessive disorder, individuals carrying a single GBA1 mutation (heterozygous carriers) do not develop clinical symptoms. Therefore, the partially corrected isogenic line, which retains only the P415R allele, represents a clinically relevant carrier model. Consistent with this, our results show that GCase activity was restored to approximately 50% of wild-type levels (Fig.4B-C), supporting the expected heterozygous state. These findings also make it unlikely that the remaining differences observed are due to clonal variation or epigenetic effects.

(e) The authors tested week 3, 4, 8, 15, and 28 old organoids in different settings. However, systematic markers of maturation should be analyzed, and different maturation stages should be compared, for example, comparing week 8 organoids to week 28 organoids, with immunofluorescent marker staining and bulk RNAseq.

We agree that a systematic analysis of maturation stages is essential for validating the MLO model. Our data integrated a longitudinal comparison across multiple developmental windows (Weeks 3 to 28) to characterize the transition from progenitors to mature/functional states for nGD phenotyping and evaluation of therapeutic modalities: 1) DA differentiation (Wks 3 and 8 in Fig. 3): qPCR analysis demonstrated the progression of DA-specific programs. We observed a steady increase in the mature DA neuron marker TH and ASCL1. This was accompanied by a gradual decrease in early floor plate/progenitor markers FOXA2 and PLZF, indicating a successful differentiation path from progenitors to differentiated/mature DA neurons. 2) Glycosphingolipid substrates accumulation (Wks 15 and 28 in Fig 2): To assess late-stage nGD phenotyping, we compared GluCer and GluSph at Week 15 and Week 28. This comparison highlights the progressive accumulation of substrates in nGD MLOs, reflecting the metabolic consequences of the disease at different mature stage. 3) Organoid growth dynamics (Wks 4, 8, and 15 in new Fig. 4): The new Fig. 4 tracks physical maturation through organoid size and growth rates across three key time points, providing a macro-scale verification of consistent development between WT and nGD groups. By comparing these early (Wk 3-8) and late (Wk 15-28) stages, we confirmed that our MLOs transition from a proliferative state to a post-mitotic, specialized neuronal state, satisfied the requirement for comparing distinct maturation stages.

(f) The manuscript frequently refers to Wnt signaling dysregulation as a major finding. However, experimental validation is limited to transcriptomic data. Functional tests, such as the use of Wnt agonist/inhibitor, are needed to support this claim (see below).

We agree that the suggested experiments could provide additional mechanistic insights into this study and will consider them in future work.

(g) Suggested fixes / experiments

Add at least one more independent disease hiPSC line (or show expanded analysis from GD2-10-257) for key mechanistic endpoints (lipid accumulation, transcriptomics, DA markers).

Additional line iPSC GD2-10-257 derived MLO was included in the manuscript. This was addressed above [see response to Weaknesses (1)-a]. 

Generate and analyze a fully corrected isogenic WT/WT clone (or a P415R-only line) if feasible; at minimum, acknowledge this limitation more explicitly and soften claims.

We attempted to generate an isogenic iPSC line by correcting both GBA1 mutations (L444P and P415R). However, this was unsuccessful because the GBA1 gene overlaps with a pseudogene (PGBA) located 16 kb downstream of GBA1, which shares 96-98% sequence similarity with GBA1 (Ref#1, #2), which complicates precise editing. GBA1 is shorter (~5.7 kb) than PGBA (~7.6 kb). The primary exonic difference between GBA1 and PGBA is a 55-bp deletion in exon 9 of the pseudogene. As a result, the isogenic line we obtained carries only the P415R mutation, and L444P was corrected to the normal sequence. We have included this limitation in the Methods as “This gene editing strategy is expected to also target the GBA1 pseudogene due to the identical target sequence, which limits the gene correction on certain mutations (e.g., P415R)”. 

References:

(1) Horowitz M., Wilder S., Horowitz Z., Reiner O., Gelbart T., Beutler E. The human glucocerebrosidase gene and pseudogene: structure and evolution. Genomics (1989). 4, 87–96. doi:10.1016/0888-7543(89)90319-4

(2) Woo EG, Tayebi N, Sidransky E. Next-Generation Sequencing Analysis of GBA1: The Challenge of Detecting Complex Recombinant Alleles. Front Genet. (2021). 12:684067. doi:10.3389/fgene.2021.684067. PMCID: PMC8255797.

Report and increase independent differentiations (N = biological replicates) and present per-differentiation summary statistics.

This was addressed above [see response to Weaknesses (1)-b, (1)-c]. 

(2) Mechanistic validation is insufficient

(a) RNA-seq pathways (Wnt, mTOR, lysosome) are not functionally probed. The manuscript shows pathway enrichment and some protein markers (p-4E-BP1) but lacks perturbation/rescue experiments to link these pathways causally to the DA phenotype.

(b) Autophagy analysis lacks flux assays. LC3-II and LAMP1 are informative, but without flux assays (e.g., bafilomycin A1 or chloroquine), one cannot distinguish increased autophagosome formation from decreased clearance.

(c) Dopaminergic dysfunction is superficially assessed. Dopamine in the medium and TH protein are shown, but no neuronal electrophysiology, synaptic marker co-localization, or viability measures are provided to demonstrate functional recovery after therapy.

(d) Suggested fixes/experiments

Perform targeted functional assays:

(i) Wnt reporter assays (TOP/FOP flash) and/or treat organoids with Wnt agonists/antagonists to test whether Wnt modulation rescues DA differentiation.

(ii) Test mTOR pathway causality using mTOR inhibitors (e.g., rapamycin) or 4E-BP1 perturbation and assay effects on DA markers and autophagy.

Include autophagy flux assessment (LC3 turnover with bafilomycin), and measure cathepsin activity where relevant.

Add at least one functional neuronal readout: calcium imaging, MEA recordings, or synaptic marker quantification (e.g., SYN1, PSD95) together with TH colocalization.

We thank the reviewer for these valuable suggestions. We agree that the suggested experiments could provide additional mechanistic insights into this study and will consider them in future work. Importantly, the primary conclusions of our manuscript, that GBA1 mutations in nGD MLOs resulted in nGD pathologies such as diminished enzymatic function, accumulation of lipid substrates, widespread transcriptomic changes, and impaired dopaminergic neuron differentiation, which can be corrected by several therapeutic strategies in this study, are supported by the evidence presented. The suggested experiments represent an important direction for future research using brain organoids.

(3) Therapeutic evaluation needs greater depth and standardization

(a) Short windows and limited durability data. SapC-DOPS and AAV9 experiments range from 48 hours to 3 weeks; longer follow-up is needed to assess durability and whether biochemical rescue translates into restored neuronal function.

We agree with the reviewer. Because this is a proof-of-principle study, the treatment was designed within a short time window. Long-term studies with more comprehensive outcome assessments will be conducted in future work.

(b) Dose-response and biodistribution are under-characterized. AAV injection sites/volumes are described, but transduction efficiency, vg copies per organoid, cell-type tropism quantification, and SapC-DOPS penetration/distribution are not rigorously quantified.

We appreciate the reviewer’s concerns. This study was intended to demonstrate the feasibility and initial response of MLOs to AAV therapy. A comprehensive evaluation of AAV biodistribution will be considered in future studies.

The penetration and distribution of SapC-DOPS have been extensively characterized in prior studies. In vivo biodistribution of SapC–DOPS coupled CellVue Maroon, a fluorescent cargo, was examined in mice bearing human tumor xenografts using real-time fluorescence imaging, where CellVue Maroon fluorescence in tumor remained for 48 hours (Ref. #3: Fig. 4B, mouse 1), 100 hours (Ref. #4: Fig. 5), up to 216 hours (Ref. #5: Fig. 3). Uptake kinetics were also demonstrated in cells, with flow cytometry quantification showing that fluorescent cargo coupled SapC-DOPS nanovesicles, were incorporated into human brain tumor cell membranes within minutes and remained stably incorporated into the cells for up to one hour (Ref. # 6: Fig. 1a and Fig. 1b). Building on these findings, the present study focuses on evaluating the restoration of GCase function rather than reexamining biodistribution and uptake kinetics.

References:

(3) X. Qi, Z. Chu, Y.Y. Mahller, K.F. Stringer, D.P. Witte, T.P. Cripe. Cancer-selective targeting and cytotoxicity by liposomal-coupled lysosomal saposin C protein. Clin. Cancer Res. (2009) 15, 5840-5851. PMID: 19737950.

(4) Z. Chu, S. Abu-Baker, M.B. Palascak, S.A. Ahmad, R.S. Franco, and X. Qi. Targeting and cytotoxicity of SapC-DOPS nanovesicles in pancreatic cancer. PLOS ONE (2013) 8, e75507. PMID: 24124494.

(5) Z. Chu, K. LaSance, V.M. Blanco, C.-H. Kwon, B., Kaur, M., Frederick, S., Thornton, L., Lemen, and X. Qi. Multi-angle rotational optical imaging of brain tumors and arthritis using fluorescent SapC-DOPS nanovesicles. J. Vis. Exp. (2014) 87, e51187, 17. PMID: 24837630.

(6) J. Wojton, Z. Chu, C-H. Kwon, L.M.L. Chow, M. Palascak, R. Franco, T. Bourdeau, S. Thornton, B. Kaur, and X. Qi. Systemic delivery of SapC-DOPS has antiangiogenic and antitumor effects against glioblastoma. Mol. Ther. (2013) 21, 1517-1525. PMID: 23732993.

(c) Specificity controls are missing. For SapC-DOPS, inclusion of a non-functional enzyme control (or heat-inactivated fGCase) would rule out non-specific nanoparticle effects. For AAV, assessment of off-target expression and potential cytotoxicity is needed.

Including inactive fGCase would confound the assessment of fGCase in MLOs by immunoblot and immunofluorescence; therefore, saposin C–DOPS was used as the control instead. 

We agree that assessment of Off-target expression and potential cytotoxicity for AAV is important; this will be included in future studies.

(d) Comparative efficacy lacking. It remains unclear which modality is most effective in the long term and in which cellular compartments.

To address this comment, we have added a new table (Supplementary Table 2) comparing the four therapeutic modalities and summarizing their respective outcomes. While this study focused on short-term responses as a proof-of-principle, future work will explore long-term therapeutic effects. 

(e) Suggested fixes/experiments

Extend follow-up (e.g., 6+ weeks) after AAV/SapC dosing and evaluate DA markers, electrophysiology, and lipid levels over time.

We appreciate the reviewer’s suggestions. The therapeutic testing in patient-derived MLOs was designed as a proof-of-principle study to demonstrate feasibility and the primary response (rescue of GCase function) to the treatment. A comprehensive, long-term therapeutic evaluation of AAV and SapC-DOPS-fGCase is indeed important for a complete assessment; however, this represents a separate therapeutic study and is beyond the scope of the current work.

Quantify AAV transduction by qPCR for vector genomes and by cell-type quantification of GFP+ cells (neurons vs astrocytes vs progenitors).

For the AAV-treated experiments, we agree that measuring AAV copy number and GFP expression would provide additional information. However, the primary goal of this study was to demonstrate the key therapeutic outcome, rescue of GCase function by AAV-delivered normal GCase, which is directly relevant to the treatment objective.

Include SapC-DOPS control nanoparticles loaded with an inert protein and/or fluorescent cargo quantitation to show distribution and uptake kinetics.

As noted above [see response to Weakness (3)-c], using inert GCase would confound the assessment of fGCase uptake in MLOs; therefore, it was not suitable for this study. See response above for the distribution and uptake kinetics of SapC-DOPS [see response to Weaknesses (3)-b].

Provide head-to-head comparative graphs (activity, lipid clearance, DA restoration, and durability) with statistical tests.

We have added a new table (Supplementary Table 2) providing a head-to-head comparison of the treatment effects. 

(4) Model limitations not fully accounted for in interpretation

(a) Absence of microglia and vasculature limits recapitulation of neuroinflammatory responses and drug penetration, both of which are important in nGD. These absences could explain incomplete phenotypic rescues and must be emphasized when drawing conclusions about therapeutic translation.

We agree that the absence of microglia and vasculature in midbrain-like organoids represents a limitation, as we have discussed in the manuscript. In this revision, we highlighted this limitation in the Discussion section and clarified that it may contribute to incomplete phenotyping and phenotypic rescue observed in our therapeutic experiments. Additionally, we have outlined future directions to incorporate microglia and vascularization into the organoid system to better recapitulate the in vivo environment and improve translational relevance (see 7th paragraph in the Discussion).

(b) Developmental vs degenerative phenotype conflation. Many phenotypes appear during differentiation (patterning defects). The manuscript sometimes interprets these as degenerative mechanisms; the distinction must be clarified.

We appreciate the reviewer’s comments. In the revised manuscript, we have clarified that certain abnormalities, such as patterning defects observed during early differentiation, likely reflect developmental consequences of GBA1 mutations rather than degenerative processes. Conversely, phenotypes such as substrate accumulation, lysosomal dysfunction, and impaired dopaminergic maturation at later stages are interpreted as degenerative features. We have updated the Results and Discussion sections to avoid conflating developmental defects with neurodegenerative mechanisms.

(c) Suggested fixes

Tone down the language throughout (Abstract/Results/Discussion) to avoid overstatement that MLOs fully recapitulate nGD neuropathology.

The manuscript has been revised to avoid overstatements.

Add plans or pilot data (if available) for microglia incorporation or vascularization to indicate how future work will address these gaps.

The manuscript now includes further plans to address the incorporation of microglia and vascularization, described in the last two paragraphs in the Discussion. Pilot study of microglia incorporation will be reported when it is completed.

(5) Statistical and presentation issues

(a) Missing or unclear sample sizes (n). For organoid-level assays, report the number of organoids and the number of independent differentiations.

We have clarified biological replicates and differentiation in the figure legend [see response to Weaknesses (1)-b, (1)-c]. 

(b) Statistical assumptions not justified. Tests assume normality; where sample sizes are small, consider non-parametric tests and report exact p-values.

We have updated Statistical analysis in the methods as described below:

“For comparisons between two groups, data were analyzed using unpaired two-tailed Student’s t-tests when the sample size was ≥6 per group and normality was confirmed by the Shapiro-Wilk test. When the normality assumption was not met or when sample sizes were small (n < 6), the non-parametric Mann-Whitney U test was used instead. For comparisons involving three or more groups, one-way ANOVA followed by Tukey’s multiple comparison test was applied when data were normally distributed; otherwise, the nonparametric Dunn’s multiple comparison test was used. Exclusion of outliers was made based on cut-offs of the mean ±2 standard deviations. All statistical analyses were performed using GraphPad Prism 10 software. Exact p-values are reported throughout the manuscript and figures where feasible. A p-value < 0.05 was considered statistically significant.”

(c) Quantification scope. Many image quantifications appear to be from selected fields of view, which are then averaged across organoids and differentiations.

In this work, quantitative immunofluorescence analyses (e.g., cell counts for FOXP1+, FOXG1+, SOX2+ and Ki67+ cells, as well as marker colocalization) were performed on at least 3–5 randomly selected non-overlapping fields of view (FOVs) per organoid section, with a minimum of 3 organoids per differentiation batch. Each FOV was imaged at consistent magnification (60x) and z-stack depth to ensure comparable sampling across conditions. Data from individual FOVs were first averaged within each organoid to obtain an organoid-level mean, and then biological replicates (independent differentiations, n ≥ 3) were averaged to generate the final group mean ± SEM. This multilevel averaging approach minimizes bias from regional heterogeneity within organoids and accounts for variability across differentiations. Representative confocal images shown in the figures were selected to accurately reflect the quantified data. We believe this standardized quantification strategy ensures robust and reproducible results while appropriately representing the 3D architecture of the organoids.

In the revision, we have clarified the method used for image analysis of sectioned MLOs as below:

“Quantitative immunofluorescence analyses (e.g., cell counts for FOXP1+, FOXG1+, SOX2+ and Ki67+ cells, as well as marker colocalization) were performed using ImageJ (NIH) on at least 3–5 randomly selected non-overlapping fields of view (FOVs) per organoid section, with a minimum of 3 organoids per differentiation batch. Each FOV was imaged at consistent magnification (60x) and z-stack depth to ensure comparable sampling across conditions. Data from individual FOVs were first averaged within each organoid to obtain an organoid-level mean, and then biological replicates (independent differentiations, n ≥ 3) were averaged to generate the final group mean ± SEM.”

(d) RNA-seq QC and deposition. Provide mapping rates, batch correction details, and ensure the GEO accession is active. Include these in Methods/Supplement.

RNA-seq data are from the same batch. The mapping rate is >90%. GEO accession will be active upon publication. These were included in the Methods.

(e) Suggested fixes

Add a table summarizing biological replicates, technical replicates, and statistical tests used for each figure panel.

We have revised the figure legends to include replicates for each figure and statistical tests [see response in weaknesses (1)-b, (1)-c].

Recompute statistics where appropriate (non-parametric if N is small) and report effect sizes and confidence intervals.

Statistical analysis method is provided in the revision [see response in Weaknesses (5)-b].

(6) Minor comments and clarifications

(a) The authors should validate midbrain identity further with additional regional markers (EN1, OTX2) and show absence/low expression of forebrain markers (FOXG1) across replicates.

We validated the MLO identity by 1) FOXG1 and 2) EN1. FOXG1 was barely detectable in Wk8 75.1_MLO but highly present in ‘age-matched’ cerebral organoid (CO), suggesting our culturing method is midbrain region-oriented. In nGD MLO, FOXG1 expression is significantly higher than 75.1_MLO, indicating that there was aberrant anterior-posterior brain specification, consistent with the transcriptomic dysregulation observed in our RNA-seq data.

To further confirm midbrain identity, we examined the expression of EN1, an established midbrain-specific marker. Quantitative RT-PCR analysis demonstrated that EN1 expression increased progressively during differentiation in both WT-75.1 and nGD2-1260 MLOs at weeks 3 and 8 (Author response image 1). EN1 reached 34-fold and 373-fold higher levels than in WT-75.1 iPSCs at weeks 3 and 8, respectively, in WT-75.1 MLOs. In nGD MLOs, although EN1 expression showed a modest reduction at week 8, the levels were not significantly different from those observed in age-matched WT-75.1 MLOs (p > 0.05, ns).

Author response image 1.

qRT-PCR quantification of midbrain progenitor marker EN1 expression in WT-75.1 and GD2-1260 MLOs at Wk3 and Wk8. Data was normalized to WT-75.1 hiPSC cells and presented as mean ± SEM (n = 3-4 MLOs per group).ns, not significant.<br />

(b) Extracellular dopamine ELISA should be complemented with intracellular dopamine or TH+ neuron counts normalized per organoid or per total neurons.

We quantified TH expression at both the mRNA level (Fig. 3F) and the protein level (Fig. 3G/H) from whole-organoid lysates, which provides a more consistent and integrative measure across samples. These TH expression levels correlated well with the corresponding extracellular (medium) dopamine concentrations for each genotype. In contrast, TH⁺ neuron counts may not reliably reflect total cellular dopamine levels because the number of cells captured on each organoid section varies substantially, making normalization difficult. Measuring intracellular dopamine is an alternative approach that will be considered in future studies.

(c) For CRISPR editing: the authors should report off-target analysis (GUIDE-seq or targeted sequencing of predicted off-targets) or at least in-silico off-target score and sequencing coverage of the edited locus. (off-target analysis (GUIDE-seq or targeted sequencing of predicted off-targets) or at least in-silico off-target score and sequencing coverage of the edited locus). 

The off-target effect was analyzed during gene editing and the chance to target other off-targets is low due to low off-target scores ranked based on the MIT Specificity Score analysis. The related method was also updated as stated below:

“The chance to target other Off-targets is low due to low Off-target scores ranked based on the MIT Specificity Score analysis (Hsu, P., Scott, D., Weinstein, J. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol 31, 827–832 (2013).https://doi.org/10.1038/nbt.2647).”

(d) It should be clarified as to whether lipidomics normalization is to total protein per organoid or per cell, and include representative LC-MS chromatograms or method QC.

The normalization was to the protein of the organoid lysate. This was clarified in the Methods section in the revision as stated below:

“The GluCer and GluSph levels in MLO were normalized to total MLO protein (mg) that were used for glycosphingolipid analyses. Protein mass was determined by BCA assay and glycosphingolipid was expressed as pmol/mg protein. Additionally, GluSph levels in the culture medium were quantified and normalized to the medium volume (pmol/mL).”

Representative LC-MS chromatograms for both normal and GD MLOs have been included in a new figure, Supplementary Figure 2.

(e) Figure legends should be improved in order to state the number of organoids, the number of differentiations, and the exact statistical tests used (including multiplecomparison corrections).

This was addressed above [see response to Weaknesses (1)-b and (5)-b].

(f) In the title, the authors state "reveal disease mechanisms", but the studies mainly exhibit functional changes. They should consider toning down the statement.

The title was revised to: Patient-Specific Midbrain Organoids with CRISPR Correction Recapitulate Neuronopathic Gaucher Disease Phenotypes and Enable Evaluation of Novel Therapies

(7) Recommendations

This reviewer recommends a major revision. The manuscript presents substantial novelty and strong potential impact but requires additional experimental validation and clearer, more conservative interpretation. Key items to address are:

(a) Strengthening genetic and biological replication (additional lines or replicate differentiations).

This was addressed above [see response to Weaknesses (1)-a, (1)-b, (1)-c].

(b) Adding functional mechanistic validation for major pathways (Wnt/mTOR/autophagy) and providing autophagy flux data.

(c) Including at least one neuronal functional readout (calcium imaging/MEA/patch) to demonstrate functional rescue.

As addressed above [see response to Weaknesses (2)], the suggested experiments in b) and c) would provide additional insights into this study and we will consider them in future work. 

(d) Deepening therapeutic characterization (dose, biodistribution, durability) and including specificity controls.

This was addressed above [see response to Weaknesses (3)-a to e].

(e) Improving statistical reporting and explicitly stating biological replicate structure.

This was addressed above [see response to Weaknesses (1)-b, (5)-b].

Reviewer #2 (Public review):

Sun et al. have developed a midbrain-like organoid (MLO) model for neuronopathic Gaucher disease (nGD). The MLOs recapitulate several features of nGD molecular pathology, including reduced GCase activity, sphingolipid accumulation, and impaired dopaminergic neuron development. They also characterize the transcriptome in the MLO nGD model. CRISPR correction of one of the GBA1 mutant alleles rescues most of the nGD molecular phenotypes. The MLO model was further deployed in proof-of-principle studies of investigational nGD therapies, including SapC-DOPS nanovesicles, AAV9-mediated GBA1 gene delivery, and substrate-reduction therapy (GZ452). This patient-specific 3D model provides a new platform for studying nGD mechanisms and accelerating therapy development. Overall, only modest weaknesses are noted.

We thank the reviewer for the supportive remarks.

Reviewer #3 (Public review):

Summary:

In this study, the authors describe modeling of neuronopathic Gaucher disease (nGD) using midbrain-like organoids (MLOs) derived from hiPSCs carrying GBA1 L444P/P415R or L444P/RecNciI variants. These MLOs recapitulate several disease features, including GCase deficiency, reduced enzymatic activity, lipid substrate accumulation, and impaired dopaminergic neuron differentiation. Correction of the GBA1 L444P variant restored GCase activity, normalized lipid metabolism, and rescued dopaminergic neuronal defects, confirming its pathogenic role in the MLO model. The authors further leveraged this system to evaluate therapeutic strategies, including: (i) SapC-DOPS nanovesicles for GCase delivery, (ii) AAV9-mediated GBA1 gene therapy, and (iii) GZ452, a glucosylceramide synthase inhibitor. These treatments reduced lipid accumulation and ameliorated autophagic, lysosomal, and neurodevelopmental abnormalities.

Strengths:

This manuscript demonstrates that nGD patient-derived MLOs can serve as an additional platform for investigating nGD mechanisms and advancing therapeutic development.

Comments:

(1) It is interesting that GBA1 L444P/P415R MLOs show defects in midbrain patterning and dopaminergic neuron differentiation (Figure 3). One might wonder whether these abnormalities are specific to the combination of L444P and P415R variants or represent a 

general consequence of GBA1 loss. Do GBA1 L444P/RecNciI (GD2-10-257) MLOs also exhibit similar defects?

We observed reduced dopaminergic neuron marker TH expression in GBA1 L444P/RecNciI (GD2-10-257) MLOs, suggesting that this line also exhibits defects in dopaminergic neuron differentiation. These data are provided in a new Supplementary Fig. 4E, and are summarized in new Supplementary Table 2 in the revision.

(2) In Supplementary Figure 3, the authors examined GCase localization in SapC-DOPSfGCase-treated nGD MLOs. These data indicate that GCase is delivered to TH⁺ neurons, GFAP⁺ glia, and various other unidentified cell types. In fruit flies, the GBA1 ortholog, Gba1b, is only expressed in glia (PMID: 35857503; 35961319). Neuronally produced GluCer is transferred to glia for GBA1-mediated degradation. These findings raise an important question: in wild-type MLOs, which cell type(s) normally express GBA1? Are they dopaminergic neurons, astrocytes, or other cell types?

All cell types in wild-type MLOs are expected to express GBA1, as it is a housekeeping gene broadly expressed across neurons, astrocytes, and other brain cell types. Its lysosomal function is essential for cellular homeostasis and is therefore not restricted to any specific lineage. (https://www.proteinatlas.org/ENSG00000177628GBA1/brain/midbrain). 

(3) The authors may consider switching Figures 2 and 3 so that the differentiation defects observed in nGD MLOs (Figure 3) are presented before the analysis of other phenotypic abnormalities, including the various transcriptional changes (Figure 2).

We appreciate the reviewer’s suggestion; however, we respectfully prefer to retain the current order of Figures 2 and 3, as we believe this structure provides the clearest narrative flow. Figure 2 establishes the core biochemical hallmarks: reduced GCase activity, substrate accumulation, and global transcriptomic dysregulation (1,429 DEGs enriched in neural development, WNT signaling, and lysosomal pathways), which together provide essential molecular context for studying the specific cellular differentiation defects presented in Figure 3. Presenting the broader disease landscape first creates a coherent mechanistic link to the subsequent analyses of midbrain patterning and dopaminergic neuron impairment.

To enhance readability, we have added a brief transitional sentence at the start of the Figure 3 paragraph: “Building on the molecular and transcriptomic hallmarks of GCase deficiency observed in nGD MLOs (Figure 2), we next investigated the impact on midbrain patterning and dopaminergic neuron differentiation (Figure 3).”

This is 100% important for me to keep in mind. Notes need to be written down in order for me to be able to expand on them later on. Using descriptive notes in the beginning will only distract me and lead to weaker observations.

Authority reframed as responsibility, not power.

Reviewer #2 (Public review):

Summary:

The manuscript entitled "Mitochondrial Protein FgDML1 Regulates DON Toxin Biosynthesis and Cyazofamid Sensitivity in Fusarium graminearum by affecting mitochondrial homeostasis" identified the regulatory effect of FgDML1 in DON toxin biosynthesis and sensitivity of Fusarium graminearum to cyazofamid. The manuscript provides a theoretical framework for understanding the regulatory mechanisms of DON toxin biosynthesis in F. graminearum and identifies potential molecular targets for Fusarium head blight control. The paper in innovative, but there are issues in the writing that need to be added and corrected.

Comments on revisions:

The author has addressed my questions.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1:

Summary:

In their study, the authors investigated the F. graminearum homologue of the Drosophila Misato-Like Protein DML1 for a function in secondary metabolism and sensitivity to fungicides.

Strengths:

Generally, the topic of the study is interesting and timely, and the manuscript is well written, albeit in some cases, details on methods or controls are missing.

Weaknesses:

However, a major problem I see is with the core result of the study, the decrease in the DON content associated with the deletion of FgDML1. Although some growth data are shown in Figure 6, indicating a severe growth defect, the DON production presented in Figure 3 is not related to biomass. Also, the method and conditions for measuring DON are not described. Consequently, it could well be concluded that the decreased amount of DON detected is simply due to decreased growth, and the specific DON production of the mutant remains more or less the same.

To alleviate this concern, it is crucial to show the details on the DON measurement and growth conditions and to relate the biomass formation under the same conditions to the DON amount detected. Only then can a conclusion as to an altered production in the mutant strains be drawn.

We appreciate it very much that you spent much time on my paper and give me good suggestions, we tried our best to revise the manuscript. I have revised my manuscript according to your suggestions. The point to point responds to the reviewer’s comments are listed as following. Our method for DON quantification was based on the amount per unit of mycelium. After obtaining the absorbance value from the ELISA reaction, the concentration of DON was calculated according to a standard curve and a formula, then divided by the dry weight of the mycelium to obtain the DON content per unit of mycelium, with the results finally expressed in µg/g.

(1) Line 139f

... FgDML1 is a critical positive regulator of virulence ....

Clearly, the deletion of FgDML1 impacts virulence, but it is too much of a general effect to say it is a regulator. DML1 acts high up in the cascade, impacting numerous processes, one of which is virulence. Generally, it has to be considered that deletion of DML1 causes a severe growth defect, which in turn is likely to lead to a plethora of effects. Besides discussing this fact, please also revise the manuscript to avoid references to "direct effects" or "regulator".

Thank you very much for your advice. Our method for determining the amount of DON is based on the amount of mycelium per unit. After obtaining the absorbance value through Elisa reaction, we calculate the concentration of DON toxin according to the established standard curve and formula. Then, we divide it by the dry weight of mycelium to obtain the DON toxin content per unit mycelium, and finally present the results in µg/g. In summary, we conclude that the decrease in DON production by ΔFgDML is not due to slower hyphal growth, but rather a decrease in the ability of unit hyphae to produce DON toxins compared to the wild type. Given the decrease in DON toxin synthesis caused by FgDML1 deficiency, we believe that using a regulator is reasonable.

(2) Line 143

Please define "toxin-producing conditions".

Thank you very much for your advice. We have accurately defined the conditions for toxin-producing conditions in the manuscript' toxin-inducing conditions '(28°C, 145 ×g, 7 days incubation)' (in L163-164)

(3) Line 149

A brief intro on toxisomes should be provided in the introduction to better integrate this into the manuscript's results.

Thank you very much for your advice. We have added corresponding content about toxin producing bodies in the introduction section 'The biosynthesis of DON entails a reorganization of the endoplasmic reticulum into a specialized compartment termed the "toxisome" (Tang et al., 2018). The assembly of the toxisome coincides with the aggregation of key biosynthetic enzymes, which in turn enhances the efficiency of DON production. Concurrently, this compartmentalization serves as a self-defense mechanism, protecting the fungus from the autotoxicity of TRI pathway intermediates (Boenisch et al., 2017). The proteins TRI1, TRI4, TRI14, and Hmr1 are confirmed constituents of this structure(Kistler and Broz, 2015; Menke et al., 2013).' (in L86-93)

(4) Line 153

DON production decreases by about 80 %, but not to 0. Consequently, DML1 is important, but NOT essential for DON production.

Thank you very much for your advice. We have made changes to the wording of the corresponding sections based on your suggestions. 'FgDML1 is essential for the biosynthesis of the DON toxin. '(in L161)

(5) Line 168ff

Please provide a reference for FgDnm1 being critical for mitochondrial fission and state whether such an interaction has been shown in other organisms.

Thank you very much for your advice. We have made changes to the wording of the corresponding sections based on your suggestions. 'FgDnm1 is a key dynamin-related protein mediating mitochondrial fission(Griffin et al., 2005; Kang et al., 2023), suggesting that FgDML1 may form a complex with FgDnm1 to regulate mitochondrial fission and fusion processes. To our knowledge, this is the first report documenting an interaction between DML1 and Dnm in any fungal species, including model organisms such as S. cerevisiae. This novel finding provides new insights into the molecular mechanisms underlying mitochondrial dynamics in filamentous fungi. '(in L277-283)

(6) Line 178

Please specify whether Complex III activity was related to biomass and provide a p-value or standard deviation for the value.

Thank you very much for your question. The activity determination of complex III was completed using a complex III enzyme activity kit (Solarbio, Beijing, China) (Li, et al 2022; Wang, et al 2022). Take 0.1 g of standardized mycelium as the sample for the experiment. Given that the mycelium has been homogenized, we believe that there is no necessary correlation between the activity and biomass of complex III. And we also refined the specific measurement steps in the article. ' Briefly, 0.1 g of mycelia was homogenized with 1 mL of extraction buffer in an ice bath. The homogenate was centrifuged at 600 ×g for 10 min at 4°C. The resulting supernatant was then subjected to a second centrifugation at 11,100 ×g for 10 min at 4°C. The pellet was resuspended in 200 μL of extraction buffer and disrupted by ultrasonication (200 W, 5 s pulses with 10 s intervals, 15 cycles). Complex III enzyme activity was finally measured by adding the working solution as per the manufacturer's protocol. Each treatment group contains three biological replicates and three technical replicates. '(in L511-517)

Li C, et al. Amino acid catabolism regulates hematopoietic stem cell proteostasis via a GCN2-eIF2 axis. Cell Stem Cell. 2022 Jul 7; 29(7):1119-1134.e7. doi: 10.1016/j.stem.2022.06.004. PMID: 35803229.

Wang K, et al. Locally organised and activated Fth1hi neutrophils aggravate inflammation of acute lung injury in an IL-10-dependent manner. Nat Commun. 2022 Dec 13;13(1):7703. doi: 10.1038/s41467-022-35492-y. PMID: 36513690; PMCID: PMC9745290

(7) Line 185

Albeit this headline is a reasonable hypothesis, you actually did not show that the conformation is altered. Please reword accordingly.

Please also add references for cyazofamid acting on the QI site versus other fungicides acting on the QO site.

Thank you very much for your advice. We have made changes to the wording of the corresponding sections based on your suggestions. 'Overexpression of FgQCR2, FgQCR8, and FgQCR9 may alters the conformation of the QI site, resulting in reduced sensitivity to cyazofamid. '(in L212-213). For fungicides targeting Qi and QO sites, we have added corresponding descriptions in the respective sections 'Numerous fungicides have been developed to inhibit the Qo site (e.g., pyraclostrobin, azoxystrobin)(Nuwamanya et al., 2022; Peng et al., 2022) and the Qi site (e.g., cyazofamid)(Mitani et al., 2001) of the cytochrome bc1 complex. '(in L327-329)

(8) Line 200

This section on growth should be moved up right after introducing the mutant strain.

Thank you very much for your advice. We have advanced the part of nutritional growth and sexual asexual development before DON toxin to promote better reading and understanding. We arranged the sequence in the previous way to emphasize the new discovery between mitochondria and DON toxin. We found a significant decrease in DON toxin in ΔFgDML1, defects in the formation of toxin producing bodies, and downregulation of FgTRis at both the gene and protein levels. In summary, we believe that the absence of FgDML1 does indeed lead to a decrease in the content of DON toxin, and FgDML1 plays a regulatory role in the synthesis of DON toxin. In addition, our measurements of DON toxin, acetyl CoA, ATP and other indicators are all based on the amount per unit hyphae, excluding differences caused by hyphal biomass or growth. We have further refined the materials and methods to facilitate better reading and understanding.

(9) Line 203

"... significantly reduced growth rates ..."

This is not what was measured here. Figure 6A shows a plate assay that can be used to assess hyphal extension. In the figure, it is also visible that the mycelium of the deletion mutant is much denser, maybe due to increased hyphal branching. Please reword.

Additionally, it is important to include a biomass measurement here under the conditions used for DON assessment. Hyphal extension measurements cannot be used instead of biomass.

Thank you very much for your advice. We have made changes to the wording of the corresponding sections based on your suggestions. 'The ΔFgDML1 strain displayed a distinct growth phenotype characterized by retardation in radial growth and the formation of more compact, denser hyphal networks on all tested media compared to the PH-1 and ΔFgDML-C strains. '(in L136-138).

(10) Line 217

Please include information on how long the cultures were monitored. Given the very slow growth of the mutant, perithecia formation may be considerably delayed beyond 14 days.

Thank you very much for your advice. Based on your suggestion, we have extended the incubation time for sexual reproduction to 21 days to more accurately evaluate its sexual reproduction ability. Our results show that even after 21 days, Δ FgDML1 still cannot produce ascospores and ascospores, which proves that the absence of FgDML1 does indeed cause sexual reproduction defects in F. graminearum.

Author response image 1.

Discussion

(11) Please mention your summary Figure 8 early on in the discussion, and explain conclusions with this figure in mind. Please avoid repetition of the results section as much as possible.

Also, please state clearly what was already known from previous research and is in agreement with your results, and what is new (in fungi or generally).

Thank you very much for your advice. Based on your suggestion, we mentioned Fig8 earlier in the first half of the discussion and provided guidance for the following text. We also conducted a more comprehensive discussion by analyzing our research results and comparing them with previous studies. 'Our study defines a novel mechanism through which FgDML1 governs mitochondrial homeostasis. We demonstrate that FgDML1 directly interacts with the key mitochondrial fission regulator FgDnm1 and positively modulates cellular bioenergetic metabolism, as evidenced by elevated ATP and acetyl-CoA levels (Fig. 8). '(in L250-253). 'The Misato/DML1 protein family is evolutionarily conserved from yeast to humans and plays a critical role in mitochondrial regulation. In S. cerevisiae, DML1 is an essential gene; its deletion is lethal, while its overexpression results in fragmented mitochondrial networks and aberrant cellular morphology, underscoring its necessity for normal mitochondrial function (Gurvitz et al., 2002). Similarly, in Homo sapiens, the homolog Misato localizes to the mitochondrial outer membrane, and both its depletion and overexpression are sufficient to disrupt mitochondrial morphology and distribution (Kimura and Okano, 2007). '(in L241-244).

(12) Line 262ff

Please specify if this interaction was shown previously in other organisms and provide references.

Thank you very much for your advice. We have clearly stated in the corresponding section that the interaction between FgDML and FgDnm is the first reported, and to our knowledge, no relevant reports have been found in other species so far. ' Notably, FgDML1 was found to interact with FgDnm1 (Fig. 5E), FgDnm1 is a key dynamin-related protein mediating mitochondrial fission(Griffin et al., 2005; Kang et al., 2023), suggesting that FgDML1 may form a complex with FgDnm1 to regulate mitochondrial fission and fusion processes. To our knowledge, this is the first report documenting an interaction between DML1 and Dnm in any fungal species, including model organisms such as S. cerevisiae. This novel finding provides new insights into the molecular mechanisms underlying mitochondrial dynamics in filamentous fungi. '(in L276-283)

(13) Line 287ff

There is no result that would justify this speculation. Please remove.

Thank you very much for your advice. We have modified the corresponding wording in the corresponding section. 'In conclusion, our findings suggest that the overexpression of assembly factors FgQCR2, FgQCR7, and FgQCR8 in ΔFgDML1 potentially modifies the conformation of the Qi site, which specifically modulates the sensitivity of F. graminearum to cyazofamid. '(in L352-355)

Materials and methods

(14) A table with all primer sequences used in the study and their purpose is missing. For every experiment, the number of technical and biological replicates needs to be stated.

Thank you very much for your advice. We have presented all the primers used in this study in Supplementary Table 1 (in Table S1) .We added the number of technical and biological replicates in the material and method descriptions for each experiment. 'For each sample, a total of 200 conidia were counted. The experiment included three biological replicates with three technical replicates each.'(in L434-436). 'Each treatment group contains three biological replicates. '(in L444-445). 'Each treatment group contains three biological replicates and three technical replicates. ' (in L463-464). 'Each treatment group contains three biological replicates and three technical replicates. '(in L474-475). 'Each treatment group contains three biological replicates. '(in L483). 'Each treatment group contains three biological replicates and three technical replicates.'(in L501-502). 'Each treatment group contains three biological replicates and three technical replicates. '(in L516-517). 'The experiment was independently repeated three times. '(in L533-534).

(15) Line 369ff

Please provide final concentrations used for assays here.

Thank you very much for your advice. The final concentration has been displayed in the Figure (in Fig6. A, B) (in Fig. S3). And we have provided supplementary Table 2 to reflect the concentration in a more intuitive way.(in Table. S2)

(16) Line 383

Please provide a reference or data on the use of F2du for transformant selection and explain the abbreviation.

Thank you very much for your advice. Based on your suggestion, we have provided the full name and references of F2du. 'Transformants were selected on PDA plates containing either 100 μg/mL Hygromycin B (Yeasen, Shanghai, China) or 0.2 μmol/mL 5-Fluorouracil 2'-deoxyriboside (F2du) (Solarbio, Beijing, China)(Zhao et al., 2022). '(in L405-407).

(17) Line 407

Please provide a reference for the method and at least a brief description.

Thank you very much for your advice. Based on your suggestion, we have added references and provided a brief introduction to the method. 'As previously described (Tang et al., 2020; Wang et al., 2025), Specifically, coleoptiles were inoculated with conidial suspensions and incubated for 14 days, while leaves were inoculated with fresh mycelial plugs and incubated for 5 days, followed by observation and quantification of disease symptoms. DON toxin was measured using a Wise Science ELISA-based kit (Wise Science, Jiangsu, China) (Li et al., 2019; Zheng et al., 2018). '(in L466-471)

(18) Line 414ff

Also, here, the amount of biomass has to be considered for the measurement to be able to distinguish if actually less of the compounds were produced or if the effect seen was merely due to an altered amount of biomass present.

Thank you very much for your advice. We believe that biomass is not within the scope of our measurement indicators, as we have measured and calculated based on unit hyphae. Therefore, we have ruled out experimental bias caused by a decrease in biomass.

RNA and RT-qPCR

(19) Line 461

When the strains were transferred to AEA medium, was the biomass measured, at least wet weight, and in which culture volume was it done? It makes a big difference if the amount of (wet) biomass dilutes a small amount of fungicide-containing culture or if biomass is added in at least roughly equal amounts in sufficient growth medium to ensure equal conditions.

Thank you very much for your question. Our sample processing controlled the wet weight of the samples before dosing, ensuring that the wet weight of the mycelium obtained from each sample before dosing was 0.2g. The mycelium was obtained through AEA with a volume of 100mL. This ensured consistency in the initial biomass between groups before dosing, and also ensured the accuracy of the drug concentration.

(20) Line 466

Please provide the name and supplier of the kit.

Thank you very much for your advice. We have added corresponding content in the corresponding location. 'Mycelium was collected and total RNA was extracted following the instructions provided by the Total RNA Extraction Kit (Tiangen, Beijing, China).' (in L523-524).

(21) All primer sequences must be provided in a table.

Thank you very much for your advice. We have presented all the primers used in this study in Supplementary Table 1. (in Table S1).

(22) For RT qPCR it is essential to check the RNA quality to be sure that the obtained results are not artifacts due to varying quality, which may exceed differences. Please state how quality control was done and which threshold was applied for high-quality RNA to be used in RTqPCR (like RIN factor, etc).

Thank you very much for your question. We performed stringent quality control on the extracted total RNA. First, a micro-spectrophotometer was used to measure RNA concentration and purity, confirming that the A260/A280 ratio was between 1.8 and 2.0 and the A260/A230 ratio was greater than 2.0, indicating good RNA purity without significant protein or organic solvent contamination.Subsequently, verification by agarose gel electrophoresis revealed distinct 28S and 18S rRNA bands, demonstrating good RNA integrity and absence of degradation.

Author response image 2.

(B): Minor Comments:

(1) Please increase the font size of the labels and annotations of the figures; it is hard to read as it is now.

Thank you very much for your advice. We have increased the size of annotations or numerical labels in the corresponding images for better reading.

(2) Throughout the manuscript: Please check that all abbreviations are explained at first use.

Thank you very much for your advice. We have checked the entire text to ensure that abbreviations have their full names when they first appear.

(3) I do hope that the authors can clarify all concerns and provide an amended manuscript of this interesting story.

Thank you very much for your advice. Sincerely thank you for your suggestions and questions, which have been very helpful to us.

Reviewer #2:

The manuscript entitled "Mitochondrial Protein FgDML1 Regulates DON Toxin Biosynthesis and Cyazofamid Sensitivity in Fusarium graminearum by affecting mitochondrial homeostasis" identified the regulatory effect of FgDML1 in DON toxin biosynthesis and sensitivity of Fusarium graminearum to cyazofamid. The manuscript provides a theoretical framework for understanding the regulatory mechanisms of DON toxin biosynthesis in F. graminearum and identifies potential molecular targets for Fusarium head blight control. The paper is innovative, but there are issues in the writing that need to be addressed and corrected.

We appreciate it very much that you spent much time on my paper and give me good suggestions, we tried our best to revise the manuscript. I have revised my manuscript according to your suggestions with red words. In the response comments, to highlight the specific positions of the revised parts in the manuscript with red line number. The point to point responds to the reviewer’s comments are listed as following.

Weaknesses:

(1) The authors speculate that cyazofamid treatment caused upregulation of the assembly factors, leading to a change in the conformation of the Qi protein, thus restoring the enzyme activity of complex III. But no speculation was given in the discussion as to why this would lead to the upregulation of assembly factors, and how the upregulation of assembly factors would change the protein conformation, and is there any literature reporting a similar phenomenon? I would suggest adding this to the discussion.

Thank you very much for your advice. Based on your suggestion, we have added content related to the assembly factor of complex III in the discussion section and made modifications to the corresponding wording. 'Previous studies have reported that mutations in the Complex III assembly factors TTC19, UQCC2, and UQCC3 impair the assembly and activity of Complex III (Feichtinger et al., 2017; Wanschers et al., 2014). '(in L345-347). 'In conclusion, our findings suggest that the overexpression of assembly factors FgQCR2, FgQCR7, and FgQCR8 in ΔFgDML1 potentially modifies the conformation of the Qi site, which specifically modulates the sensitivity of F. graminearum to cyazofamid. '(in L352-355).

(2) Would increased sensitivity of the mutant to cell wall stress be responsible for the excessive curvature of the mycelium?

Thank you very much for your question. We believe that the sensitivity of ΔFgDML1 to osmotic stress is reduced, which may not be related to hyphal bending, as shown in the Author response image 3. During the conidia stage, ΔFgDML1 cannot germinate in YEPD, while the application of 1M Sorbitol promotes its germination. But it is caused by internal unknown mechanisms, which is also the focus of our future research.

Author response image 3.

(3) The vertical coordinates of Figure 7B need to be modified with positive inhibition rates for the mutants.

Thank you very much for your advice. The display in Figure 7B truly reflects its inhibition rate. In the Δ FgDML1 mutant, when subjected to osmotic stress treatment, the inhibition rate becomes negative, indicating that the colony growth is greater than that of the CK. Therefore, the negative inhibition rate is shown in Figure 7B.

(1) In Figure 1B, Figure 3C, and Figure 6C, the scale below the picture is not clear. In Figure 5D, the histogram is unclear, and it is recommended to redraw the graph.

Thank you very much for your advice. The issue with the above images may be due to Word compression. We have changed the settings and enlarged the images as much as possible to better display them.

(2) The full Latin name of the strain should be used in the title of figures and tables.

Thank you very much for your advice. Based on your suggestion, we have used the full names of the strains appearing in the title of figures and tables.

(3) Proteins in line 117 should be abbreviated.

Thank you very much for your advice. Based on your suggestion, we have abbreviated the corresponding positions. 'The DML1 protein from S. cerevisiae was used as a query for a BLAST search against the Fusarium genome database, resulting in the identification of the putative DML1 gene FgDML1 (FGSG_05390) in F. graminearum. '(in L118-120).

(4) The sentence in lines 187-189, which is supposed to introduce why the test is sensitive to the three drugs, is currently illogical.

Thank you very much for your advice. Based on your suggestion, we have made modifications to the corresponding sections. 'Since Complex III is involved in the action of both cyazofamid (targeting the QI site) and pyraclostrobin (targeting the QO site), the sensitivity of ΔFgDML1 to cyazofamid and pyraclostrobin was investigated. ' (in L214-216).

(5) The expression of FgQCR2, FgQCR7, and FgQCR8 was significantly upregulated in ΔFgDML1 at transcription levels. Do FgQCR2, FgQCR8, and FgQCR9 show upregulated expression at the protein level?

Thank you very much for your question. Based on your suggestion, we evaluated the protein expression levels of FgQCR2, FgQCR7, and FgQCR8 in PH-1 and ΔFgDML1, and we found that the protein expression levels of FgQCR2, FgQCR7, and FgQCR8 in ΔFgDML1 were higher than those in PH-1. (in Fig. 6F).

(6) In Figure 7B, it is recommended to adjust the position of the horizontal axis labels in the histogram.

Thank you very much for your advice. Based on your suggestion, we have made modifications to the corresponding sections.(in Fig. 7B)

(7) There are numerous errors in the writing of gene names in the text. Please check the full text and change the writing of gene names and mutant names to italic.

Thank you very much for your advice. We have checked the entire text to ensure that all genes have been italicized.

(8) All acronyms should be spelled out in figure and table captions. e.g., F. graminearum.

Thank you very much for your advice. Based on your suggestion, we have used the full names of the strains appearing in the title of figures and tables.

(9) In line 492, P should be lowercase and italic.

Thank you very much for your advice. Based on your suggestion, we have made adjustments to the corresponding content.

Reviewer #3:

Summary:

The manuscript "Mitochondrial 1 protein FgDML1 regulates DON toxin biosynthesis and cyazofamid sensitivity in Fusarium graminearum by affecting mitochondrial homeostasis" describes the construction of a null mutant for the FgDML1 gene in F. graminearum and assays characterising the effects of this mutation on the pathogen's infection process and lifecycle. While FgDML1 remains underexplored with an unclear role in the biology of filamentous fungi, and although the authors performed several experiments, there are fundamental issues with the experimental design and execution, and interpretation of the results.

Strengths:

FgDML1 is an interesting target, and there are novel aspects in this manuscript. Studies in other organisms have shown that this protein plays important roles in mitochondrial DNA (mtDNA) inheritance, mitochondrial compartmentalisation, chromosome segregation, mitochondrial distribution, mitochondrial fusion, and overall mitochondrial dynamics. Indeed, in Saccharomyces cerevisiae, the mutation is lethal. The authors have carried out multi-faceted experiments to characterise the mutants.

Weaknesses:

However, I have concerns about how the study was conceived. Given the fundamental importance of mitochondrial function in eukaryotic cells and how the absence of this protein impacts these processes, it is unsurprising that deletion of this gene in F. graminearum profoundly affects fungal biology. Therefore, it is misleading to claim a direct link between FgDML1 and DON toxin biosynthesis (and virulence), as the observed effects are likely indirect consequences of compromised mitochondrial function. In fact, it is reasonable to assume that the production of all secondary metabolites is affected to some extent in the mutant strains and that such a strain would not be competitive at all under non-laboratory conditions. The order in which the authors present the results can be misleading, too. The results on vegetative growth rate appeared much later in the manuscript, which should have come first, as the FgDML1 mutant exhibited significant growth defects, and subsequent results should be discussed in that context. Moreover, the methodologies are not described properly, making the manuscript hard to follow and difficult to replicate.

We appreciate it very much that you spent much time on my paper and give me good suggestions, we tried our best to revise the manuscript. I have revised my manuscript according to your suggestions with red words. In the response comments, to highlight the specific positions of the revised parts in the manuscript with red line number. The point to point responds to the reviewer’s comments are listed as following.

For weaknesses,we arranged the sequence in this way to emphasize the novel discovery between mitochondria and DON toxin. We found a significant decrease in DON toxin in Δ FgDML1, defects in the formation of toxin producing bodies, and downregulation of FgTRis at both the gene and protein levels. In summary, we believe that the absence of FgDML1 does indeed lead to a decrease in the content of DON toxin, and FgDML1 plays a regulatory role in the synthesis of DON toxin. In addition, our measurements of DON toxin, acetyl CoA, ATP and other indicators are all based on the amount per unit hyphae, excluding differences caused by hyphal biomass or growth. We have further refined the materials and methods to facilitate better reading and understanding.

(1) Lines 37-39: The disease itself does not produce toxins; it is the fungus that causes the disease that produces toxins. Moreover, the disease symptoms observed are likely caused by the toxins produced by the fungus.

Thank you very much for your advice. We have made modifications to the wording of the corresponding sections. 'Studies have shown that increased DON levels are positively correlated with the pathogenicity rate of F. graminearum.'(in L36-37).

(2) Lines 82-87: While it is challenging to summarise the role of ATP in just a few words, this section needs improvement for clarity and accuracy. Additionally, I do not believe that drawing a direct link between mitochondrial defects and toxin production is an appropriate strategy in this case.

Thank you very much for your advice. Based on your suggestion, we have added corresponding descriptions in the corresponding positions to provide more information on the relationship between ATP and toxins, in order to better prepare for the following text. 'Pathogen-intrinsic ATP homeostasis is recognized as a critical, rate-limiting determinant for toxin biosynthesis. Previous studies indicate that dual-target inhibition of ATP synthase (AtpA) and adenine deaminase (Ade) by a specific small-molecule probe effectively depletes intracellular ATP, consequently suppressing the synthesis of key virulence factors TcdA and TcdB transcriptionally and translationally(Marreddy et al., 2024). The systemic toxicity of Anthrax Edema Toxin (ET) is primarily attributed to its catalytic activity, which depletes the host cell's ATP reservoir, thereby triggering a bioenergetic collapse that culminates in cell lysis and death(Liu et al., 2025). '(in L78-86).

(3) Lines 125-126: The manuscript does not clearly describe how subcellular localisation was determined. This methodology needs to be properly detailed.

Thank you very much for your advice. The subcellular localization was validated through co-localization analysis with MitoTracker Red CMXRos, a mitochondrial-specific dye. The observed overlap between the FgDML1-GFP signal and the mitochondrial marker confirmed mitochondrial localization. Based on these results, we determined that FgDML1 is definitively localized to the mitochondria.We have incorporated this description in the appropriate section of the manuscript. 'Furthermore, subcellular localization studies confirmed that FgDML1 localizes to mitochondria, as demonstrated by colocalization with a mitochondria-specific dye MitoTracker Red CMXRos (Fig. 1B). '(in L125-127).

(4) Regarding the organisation of the Results section, it needs to be revised. While I understand the authors' intention to emphasise the impact on virulence, the results showing how FgDML1 deletion affects vegetative growth, asexual and sexual reproduction, and sensitivity to stressors should be presented before the virulence assays and effects on DON production. Additionally, the authors do not provide any clear evidence that FgDML1 directly interacts with proteins involved in asexual or sexual reproduction, stress responses, or virulence. Therefore, it is misleading to suggest that FgDML1 directly regulates these processes. The observed phenotypes are, rather, a consequence of severely impaired mitochondrial function. Without functional mitochondria, the cell cannot operate properly, leading to widespread physiological defects. In this regard, statements such as those in lines 139-140 and 343-344 are misleading.

Thank you very much for your advice. We have adjusted the order of the images based on your suggestion, placing the characterization of ΔFgDML1 in nutritional growth, sexual reproduction, and other aspects before DON toxin. And we have made adjustments to the corresponding statements. 'These findings demonstrate that FgDML1 is a positive regulator of virulence in F. graminearum. '(in L140-141).

(5) Lines 185-186: The authors do not provide sufficient evidence to support the claim that FgQCR2, FgQCR8, and FgQCR9 overexpression is the main cause of reduced cyazofamid sensitivity. Although expression of these genes is altered, reduced sensitivity may result from changes in other proteins or pathways. To strengthen this claim, overexpression of FgQCR2, 8, and 9 in the wild-type background, followed by assessment of cyazofamid resistance, would be necessary. As it stands, there is no support for the claim presented in lines 329-332.

Thank you very much for your advice. To establish a causal link between the overexpression of FgQCR2, FgQCR7, and FgQCR8 and the observed reduction in cyazofamid sensitivity, we first quantified the protein levels of these assembly factor. Western blot analysis confirmed their elevated expression in the ΔFgDML1 mutant compared to the wild-type PH-1. We further generated individual overexpression strains for FgQCR2, FgQCR7, and FgQCR8 in the wild-type PH-1 background. Fungicide sensitivity assays revealed that all three overexpression mutants displayed significantly reduced sensitivity to cyazofamid compared to the parental strain. These genetic complementation experiments confirm that upregulation of FgQCR2, FgQCR7, and FgQCR8 is sufficient to confer reduced cyazofamid sensitivity.We have incorporated these explanations and provided supporting images in the appropriate section of the manuscript. 'To further clarify whether the upregulated expression of FgQCR2, FgQCR7, and FgQCR8 genes affects their protein expression levels, we measured the protein levels. The results showed that the protein expression levels of FgQCR2, FgQCR7, and FgQCR8 in ΔFgDML1 were higher than those in PH-1(Fig. 6F). Subsequently, we overexpressed FgQCR2, FgQCR7, and FgQCR8 in the wild-type background, and the corresponding overexpression mutants exhibited reduced sensitivity to cyazofamid(Fig. 6E). '(in L205-211)(in Fig. 6E, F)

(6) Lines 187-190: This segment is confusing and difficult to follow. It requires rewriting for clarity.

Thank you very much for your advice. Based on your suggestion, we have made corresponding modifications in the corresponding locations. 'Since Complex III is involved in the action of both cyazofamid (targeting the QI site) and pyraclostrobin (targeting the QO site), the sensitivity of ΔFgDML1 to cyazofamid and pyraclostrobin was investigated. ''(in L214-216)

(7) Lines 345-346: The authors state that in this study, FgDML1 is localised in mitochondria, which implies that in other studies, its localisation was different. Is this accurate? Clarification is needed.

Thank you very much for your question. In previous studies, the localization of this protein was not clearly defined, and its function was only emphasized to be related to mitochondria. Whether in yeast or in Drosophila melanogaster. (Miklos et al., 1997; Gurvitz et al., 2002)

Miklos GLG, Yamamoto M-T, Burns RG, Maleszka R. 1997. An essential cell division gene of drosophila, absent from saccharomyces, encodes an unusual protein with  tubulin-like and myosin-like peptide motifs. Proc Natl Acad Sci 94:5189–5194. doi:10.1073/pnas.94.10.5189

Gurvitz A, Hartig A, Ruis H, Hamilton B, de Couet HG. 2002. Preliminary characterisation of DML1, an essential saccharomyces cerevisiae gene related to misato of drosophila melanogaster. FEMS Yeast Res 2:123–135. doi:10.1016/S1567-1356(02)00083-1

Material and Methods Section

(8) In general, the methods require more detailed descriptions, including the brands and catalog numbers of reagents and kits used. Simply stating that procedures were performed according to manufacturers' instructions is insufficient, particularly when the specific brand or kit is not identified.

Thank you very much for your advice. We have added corresponding content based on your suggestion to more comprehensively display the reagent brand and complete product name. 'Transformants were selected on PDA plates containing either 100 μg/mL Hygromycin B (Yeasen, Shanghai, China) or 0.2 μmol/mL 5-Fluorouracil 2'-deoxyriboside (F2du) (Solarbio, Beijing, China)(Zhao et al., 2022). ' (in L405-407). 'DON toxin was measured using a Wise Science ELISA-based kit (Wise Science, Jiangsu, China) (Li et al., 2019; Zheng et al., 2018) '. (in L469-471)

(9) Line 364: What do CM and MM stand for? Please define.

Thank you very much for your advice. Based on your suggestion, we have made modifications in the corresponding locations. 'To evaluate vegetative growth, complete medium (CM), minimal medium (MM), and V8 Juice Agar (V8) media were prepared as described previously(Tang et al., 2020). '(in L385-387)

Generation of Deletion and Complemented Mutants:

(10) This section lacks detail. For example, were PCR products used directly for PEG-mediated transformation, or were the fragments cloned into a plasmid?

Thank you very much for your question. We directly use the fused fragments for protoplast transformation after sequencing confirmation. We have clearly defined the fragment form used for transformation at the corresponding location. 'The resulting fusion fragment was transformed into the wild-type F. graminearum PH-1 strain via polyethylene glycol (PEG)-mediated protoplast transformation. '(in L403-405).

(11) PCR and Southern blot validation results should be included as supplementary material, along with clear interpretations of these results.

Thank you very much for your advice. In the supplementary material we submitted, Supplementary Figure 2 already includes the results of PCR and Southern blot validation.(in Fig. S2)

(12) There is almost no description of how the mutants mentioned in lines 388-390 were generated.

Thank you very much for your advice. Based on your suggestions, we have added relevant content in the appropriate sections to more comprehensively and clearly reflect the experimental process. 'Specifically, FgDML1, including its native promoter region and open reading frame (ORF) (excluding the stop codon), was amplified.The PCR product was then fused with the XhoI -digested pYF11 vector. After transformation into E. coli and sequence verification, the plasmid was extracted and subsequently introduced into PH-1 protoplasts. For FgDnm1-3×Flag, the 3×Flag tag was added to the C-terminus of FgDnm1 by PCR, fused with the hygromycin resistance gene and the FgDnm1 downstream arm, and then introduced into PH-1 protoplasts. The overexpression mutant was constructed according to a previously described method. Specifically, the ORF of FgDML1 was amplified and the PCR product was ligated into the SacII-digested pSXS overexpression vector. The resulting plasmid was then transformed into PH-1 protoplasts (Shi et al., 2023). For the construction of PH-1::FgTri1+GFP and ΔFgDML1::FgTri1+GFP, the ORF of FgTri1 was amplified and ligated into the XhoI-digested pYF11 vector as described above. The resulting vectors were then transformed into protoplasts of PH-1 or ΔFgDML1, respectively.'(in L413-426).

Vegetative Growth and Conidiation Assays:

(13) There is no information about how long the plates were incubated before photos were taken. Judging by the images, it appears that different incubation times may have been used.

Thank you very much for your advice. Due to the slower growth of ΔFgDML1, we adopted different incubation periods and have supplemented the relevant content in the corresponding section. 'All strains were incubated at 25°C in darkness; however, due to ΔFgDML1 slower growth, the ΔFgDML1 mutant required a 5-day incubation period compared to the 3 days used for PH-1 and ΔFgDML1-C. '(in L490-493).

(14) There is no description of the MBL medium.

Thank you very much for your advice. Based on your suggestion, we have supplemented the corresponding content in the corresponding positions. 'Mung bean liquid (MBL) medium was used for conidial production, while carrot agar (CA) medium was utilized to assess sexual reproduction(Wang et al., 2011). '(in L387-389).

DON Production and Pathogenicity Assays:

(15) Were DON levels normalised to mycelial biomass? The vegetative growth assays show that FgDML1 null mutants exhibit reduced growth on all tested media. If mutant and wild-type strains were incubated for the same period under the same conditions, it is reasonable to assume that the mutants accumulated significantly less biomass. Therefore, results related to DON production, as well as acetyl-CoA and ATP levels, must be normalised to biomass.

Thank you very much for your question. We have taken into account the differences in mycelial biomass. Therefore, when measuring DON, acetyl-CoA, and ATP levels, all data were normalized to mycelial mass and calculated as amounts per unit of mycelium, thereby avoiding discrepancies arising from variations in biomass.

Sensitivity Assays:

(16) While the authors mention that gradient concentrations were used, the specific concentrations and ranges are not provided. Importantly, have the plates shown in Figure 5 been grown for different periods or lengths? Given the significantly reduced growth rate shown in Figure 6A, the mutants should not have grown to the same size as the WT (PH-1) as shown in Figures 5A and 5B unless the pictures have been taken on different days. This needs to be explained.

Thank you very much for your question. Due to the slower growth of ΔFgDML1, we adopted different incubation periods and have supplemented the relevant content in the corresponding section. 'All strains were incubated at 25°C in darkness; however, due to ΔFgDML1 slower growth, the ΔFgDML1 mutant required a 5-day incubation period compared to the 3 days used for PH-1 and ΔFgDML1-C. '(in L490-493).

(17) Additionally, was inhibition measured similarly for both stress agents and fungicides? This should be clarified.

Thank you very much for your question. We have supplemented the specific concentration gradient of fungicides. 'The concentration gradients for each fungicide in the sensitivity assays were set up according to Supplementary Table S2. '(in L493-494)(in Table. S2).

Complex III Enzyme Activity:

(18) A more detailed description of how this assay was performed is needed.

Thank you very much for your advice. We have provided further detailed descriptions of the corresponding sections. 'Briefly, 0.1 g of mycelia was homogenized with 1 mL of extraction buffer in an ice bath. The homogenate was centrifuged at 600 ×g for 10 min at 4°C. The resulting supernatant was then subjected to a second centrifugation at 11,000 ×g for 10 min at 4°C. The pellet was resuspended in 200 μL of extraction buffer and disrupted by ultrasonication (200 W, 5 s pulses with 10 s intervals, 15 cycles). Complex III enzyme activity was finally measured by adding the working solution as per the manufacturer's protocol. '(in L511-517)

(19) Were protein concentrations standardised prior to the assay?

Thank you very much for your question. Protein concentrations for all Western blot samples were quantified using a BCA assay kit to ensure equal loading.

(20) Line 448: Are ΔFgDML1::Tri1+GFP and ΔFgDML1+GFP the same strain? ΔFgDML1::Tri1+GFP has not been previously described.

Thank you very much for your question. These two strains are not the same strain, and we have supplemented their construction process in the corresponding section. 'For the construction of PH-1::FgTri1+GFP and ΔFgDML1::FgTri1+GFP, the ORF of FgTri1 was amplified and ligated into the XhoI-digested pYF11 vector as described above. The resulting vectors were then transformed into protoplasts of PH-1 or ΔFgDML1, respectively. '(in L423-426)

(21) Lines 460 and 468: Please adopt a consistent nomenclature, either RT-qPCR or qRT-PCR.

Thank you very much for your advice. We have unified it and modified the corresponding content in the corresponding sections. 'Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) was carried out using the QuantStudio 6 Flex real-time PCR system (Thermo, Fisher Scientific, USA) to assess the relative expression of three subunits of Complex III (FgCytb, FgCytc1, FgISP), five assembly factors (FgQCR2, FgQCR6, FgQCR7, FgQCR8, FgQCR9), and DON biosynthesis-related genes (FgTri5 and FgTri6). '(in L526-531)

(22) Lines 472-473: Why was FgCox1 used as a reference for FgCytb? Clarification is needed.

Thank you very much for your question. FgCytb (cytochrome b) and FgCOX1 (cytochrome c oxidase subunit I) are both encoded by the mitochondrial genome and serve as core components of the oxidative phosphorylation system (Complex III and Complex IV, respectively). Their transcription is co-regulated by mitochondrial-specific mechanisms in response to cellular energy status. Consequently, under experimental conditions that perturb energy homeostasis, FgCOX1 expression exhibits relative, context-dependent stability with FgCytb, or at least co-varies directionally, making it a superior reference for normalizing target gene expression. In contrast, FgGapdh operates within a distinct genetic and regulatory system. Using FgCOX1 ensures that both reference and target genes reside within the same mitochondrial compartment and functional module, thereby preventing normalization artifacts arising from independent variation across disparate pathways.

(23) Lines 476-477: This step requires a clearer and more detailed explanation.

Thank you very much for your advice. We provided detailed descriptions of them in their respective positions. 'For FgDnm1-3×Flag, the 3×Flag tag was added to the C-terminus of FgDnm1 by PCR, fused with the hygromycin resistance gene and the FgDnm1 downstream arm, and then introduced into PH-1 protoplasts. '(in L417-419). 'The FgDnm1-3×Flag fragment was introduced into PH-1 and FgDML1+GFP protoplasts, respectively, to obtain single-tagged and double-tagged strains. '(in L541-543)

Western blotting:

(24) Uncropped Western blot images should be provided as supplementary material.

Thank you very much for your advice. All Western blot images will be submitted to the supplementary material package.

(25) Lines 485-489: A more thorough description of the antibodies used (including source, catalogue number, and dilution) is necessary.

Thank you very much for your advice. The antibodies used are clearly stated in terms of brand, catalog number, and dilution. We have added the dilution ratio. 'All antibodies were diluted as follows: primary antibodies at 1:1000 and secondary antibodies at 1:10000. '(in L550-551)

(26) The Western blot shown in Figure 3D appears problematic, particularly the anti-GAPDH band for FgDML1::FgTri1+GFP. Are both anti-GAPDH bands derived from the same gel?

Thank you very much for your advice. We are unequivocally certain that these data derive from the same gel. Therefore, we are providing the original image for your inspection.

Author response image 4.

Reviewer #2 (Public review):

Summary:

Xu et al. used fMRI to examine the neural correlates associated with retrieving temporal information from an external compared to internal perspective ('mental time watching' vs. 'mental time travel'). Participants first learned a fictional religious ritual composed of 15 sequential events of varying durations. They were then scanned while they either (1) judged whether a target event happened in the same part of the day as a reference event (external condition); or (2) imagined themselves carrying out the reference event and judged whether the target event occurred in the past or will occur in the future (internal condition). Behavioural data suggested that the perspective manipulation was successful: RT was positively correlated with sequential distance in the external perspective task, while a negative correlation was observed between RT and sequential distance for the internal perspective task. Neurally, the two tasks activated different regions, with the external task associated with greater activity in the supplementary motor area and supramarginal gyrus, and the internal condition with greater activity in default mode network regions. Of particular interest, only a cluster in the posterior parietal cortex demonstrated a significant interaction between perspective and sequential distance, with increased activity in this region for longer sequential distances in the external task but increased activity for shorter sequential distances in the internal task. Only a main effect of sequential distance was observed in the hippocampus head, with activity being positively correlated with sequential distance in both tasks. No regions exhibited a significant interaction between perspective and duration, although there was a main effect of duration in the hippocampus body with greater activity for longer durations, which appeared to be driven by the internal perspective condition. On the basis of these findings, the authors suggest that the hippocampus may represent event sequences allocentrically, whereas the posterior parietal cortex may process event sequences egocentrically.

Strengths:

The topic of egocentric vs. allocentric processing has been relatively under-investigated with respect to time, having traditionally been studied in the domain of space. As such, the current study is timely and has the potential to be important for our understanding of how time is represented in the brain in the service of memory. The study is well thought out and the behavioural paradigm is, in my opinion, a creative approach to tackling the authors' research question. A particular strength is the implementation of an imagination phase for the participants while learning the fictional religious ritual. This moves the paradigm beyond semantic/schema learning and is probably the best approach besides asking the participants to arduously enact and learn the different events with their exact timings in person. Importantly, the behavioural data point towards successful manipulation of internal vs. external perspective in participants, which is critical for the interpretation of the fMRI data. The use of syllable length as a sanity check for RT analyses as well as neuroimaging analyses is also much appreciated.

Suggestions:

The authors have done a commendable job addressing my previous comments. In particular, the additional analyses elucidating the potential contribution of boundary effects to the behavioural data, the impact of incorporating RT into the fMRI GLMs, and the differential contributions of RT and sequential distance to neural activity (i.e., in PPC) are valuable and strengthen the authors' interpretation of their findings.

My one remaining suggestion pertains to the potential contribution of boundary effects. While the new analyses suggest that the RT findings are driven by sequential distance and duration independent of a boundary effect (i.e., Same vs. Different factor), I'm wondering whether the same applies to the neural findings? In other words, have the authors run a GLM in which the Same vs. Different factor is incorporated alongside distance and duration?

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this fMRI study, the authors wished to assess neural mechanisms supporting flexible "temporal construals". For this, human participants learned a story consisting of fifteen events. During fMRI, events were shown to them, and they were instructed to consider the event from "an internal" or from "an external" perspective. The authors found opposite patterns of brain activity in the posterior parietal cortex and the anterior hippocampus for the internal and the external viewpoint. They conclude that allocentric sequences are stored in the hippocampus, whereas egocentric sequences are used in the parietal cortex. The claims align with previous fMRI work addressing this question.

We appreciate the reviewer's concise summary of our research. We would like to offer two clarifications to prevent any potential misunderstandings.

First, the activity patterns in the parietal cortex and hippocampus are not entirely opposite across internal and external perspectives. Specifically, the activation level in the posterior parietal cortex shows a positive correlation with sequential distance during external-perspective tasks, but a negative correlation during internal-perspective tasks. In contrast, the activation level in the anterior hippocampus positively correlates with sequential distance, irrespective of the observer's perspective. Therefore, our results suggest that the parietal cortex, with its perspective-dependent activity, supports egocentric representation; the hippocampus, with its consistent activity across perspectives, supports allocentric representation.

Second, while some of our findings align with previous fMRI studies, to our knowledge, no prior research has explicitly investigated how the neural representation of time may vary depending on the observer's viewpoint. This gap in the literature is the primary motivation for our current study.

Strengths:

The research topic is fascinating, and very few labs in the world are asking the question of how time is represented in the human brain. Working hypotheses have been recently formulated, and this work seems to want to tackle some of them.

We appreciate the reviewer's acknowledgment of the theoretical significance of our study.

Weaknesses:

The current writing is fuzzy both conceptually and experimentally. I cannot provide a sufficiently well-informed assessment of the quality of the experimental work because there is a paucity of details provided in the report. Any future revisions will likely improve transparency.

(1) Improving writing and presentation:

The abstract and the introduction make use of loaded terms such as "construals", "mental timeline", "panoramic views" in very metaphoric and unexplained ways. The authors do not provide a comprehensive and scholarly overview of these terms, which results in verbiage and keywords/name-dropping without a clear general framework being presented. Some of these terms are not metaphors. They do refer to computational concepts that the authors should didactically explain to their readership. This is all the more important that some statements in the Introduction are misattributed or factually incorrect; some statements lack attributions (uncited published work). Once the theory, the question, and the working hypothesis are clarified, the authors should carefully explain the task.

We appreciate the reviewer's critics.

The formulation of the scientific question in the introduction is grounded in the spatial construals of time hypothesis and conceptual metaphor theory (e.g., Traugott, 1978; Lakoff & Johnson, 1980; see recent reviews by Núñez & Cooperrider, 2013; Bender & Beller, 2014). These frameworks were originally developed through analyses of how spatial metaphors are used to describe temporal concepts in natural language. Consequently, it is theoretically motivated and largely unavoidable to introduce the two primary temporal construals—mental time travel and mental time watching— using metaphorical expressions.

However, we do agree with the reviewer that the introduction in the original manuscript was overly long and that the working hypothesis was not clearly stated. In the revised manuscript, we have streamlined the introduction and substantially revised the following two paragraphs to clarify the formulation of our working hypothesis (Pages 5-6):

“Recent studies have already begun to investigate the neural representation of the memorized event sequence (e.g., Deuker et al., 2016; Thavabalasingam et al., 2018; Bellmund et al., 2019, 2022; see reviews by Cohn-Sheehy & Ranganath, 2017; Bellmund et al., 2020). Yet, the neural mechanisms that enable the brain to construct distinct construals of an event sequence remain largely unknown. Valuable insights may be drawn from research in the spatial domain, which diPerentiates the neural representation in allocentric and egocentric reference frames. According to an influential neurocomputational model (Byrne et al., 2007; Bicanski & Burgess, 2018; Bicanski & Burgess, 2020), allocentric and egocentric spatial representations are dissociable in the brain—they are respectively implemented in the medial temporal lobe (MTL)—including the hippocampus—and the parietal cortex. Various egocentric representations in the parietal cortex derived from diPerent viewpoints can be transformed and integrated into a unified allocentric representation and stored in the MTL (i.e., bottom-up process). Conversely, the allocentric representation in the MTL can serve as a template for reconstructing diverse egocentric representations across diPerent viewpoints in the parietal cortex (i.e., top-down process).”

“In line with the spatial construals of time hypothesis, several authors have recently suggested that such mutually engaged egocentric and allocentric reference frames (in the parietal cortex and the medial temporal lobe, respectively) proposed in the spatial domain might also apply to the temporal one (e.g., Gauthier & van Wassenhove, 2016ab; Gauthier et al., 2019, 2020; Bottini & Doeller, 2020). If this hypothesis holds, it could explain how the brain flexibly generates diverse construals of the same event sequence. Specifically, the hippocampus may encode a consistent representation of an event sequence that is independent of whether an individual adopts an internal or external perspective, reflecting an allocentric representation of time. In contrast, parietal cortical representations are expected to vary flexibly with the adopted perspective that is shaped by task demands, reflecting an egocentric representation of time.”

In the revised manuscript, we also corrected statements in the Introduction that may have been misattributed (see Reviewer 2, comment 4(ii)) and added several relevant and important publications.

(2) The experimental approach lacks sufficient details to be comprehensible to a general audience. In my opinion, the results are thus currently uninterpretable. I highlight only a couple of specific points (out of many). I recommend revision and clarification.

(a) No explanation of the narrative is being provided. The authors report a distribution of durations with no clear description of the actual sequence of events. The authors should provide the text that was used, how they controlled for low-level and high-level linguistic confounds.

We thank the reviewer for the suggestions. The event sequence for the odd-numbered participants is shown in the original Figure 1. In the revised manuscript, we added to Figure 1 the figure supplement 1 to illustrate the actual sequence of events for the participants with both odd and even numbers. We also added the narratives used in the reading phase of the learning procedures for the participants with both odd and even numbers (Figure 1—source data 1).

To control for low-level linguistic confounds, we included the number of syllables as a covariate in the first-level general linear model in the fMRI analysis. To address high-level linguistic confounds, such as semantic information (which is difficult to quantify), we randomly assigned event labels to the 15 events twice, creating two counterbalanced versions for participants with even and odd numbers (see Comment 2b below).

(b) The authors state, "we randomly assigned 15 phrases to the events twice". It is impossible to comprehend what this means. Were these considered stimuli? Controls? IT is also not clear which event or stimulus is part of the "learning set" and whether these were indicated to be such to participants.

We apologize for any confusion in the Results section and the legend of Figure 1. Our motivation was explained in the "Stimuli" section of the Methods. In the revised manuscript, we have clarified this by adding an explanation to the legend of Figure 1 and including the supplementary Figure 1: " To minimize potential confounds between the semantic content of the event phrases and the temporal structure of the events, we randomly assigned the phrases to the events, creating two versions for participants with even and odd ID numbers. Both versions can be seen in Figure1—figure supplement 1 and Figure 1—source data 1."

(c) The left/right counterbalancing is not being clearly explained. The authors state that there is counterbalancing, but do not sufficiently explain what it means concretely in the experiment. If a weak correlation exists between sequential position and distance, it also means that the position and the distance have not been equated within. How do the authors control for these?

We thank the reviewer for highlighting this point and apologize for the lack of clarity in the original manuscript. In the current version (Page 40), we have provided further clarification: “We carefully selected two sets of 20 event pairs from the 210 possible combinations, assigning them to the odd and even runs of the fMRI experiment. Using a brute-force search, we identified 20 pairs in which sequential distance showed only weak correlations with positional information for both reference and target events (ranging from 1 to 15), as well as with behavioral responses (Same vs. Different or Future vs. Past, coded as 0 and 1), with all correlation coefficients below 0.2. At the same time, we balanced the proportion of correct responses across conditions: for the external-perspective task, Same/Different = 11/9 and 12/8; for the internal-perspective task, Future/Past = 12/8 and 8/12. Under these constraints, the sequential distances in both sets ranged from 1 to 5. To further mitigate spatial response biases, we pseudorandomized the left/right on-screen positions of the two response options within each task block, while ensuring an equal number of correct responses mapped to the left and right buttons (i.e., 10 per block).”

The event pairs we selected already represent the best possible choice given all the criteria we aimed to satisfy. It is impossible to completely eliminate all potential correlations. For instance, if the target event occurs near the beginning of the day, it will tend to fall in the past, whereas if it occurs near the end of the day, it is more likely to fall in the future. To further ensure that the significant results were not driven by these weak confounding factors, we constructed another GLM that included three additional parametric modulators: the sequence position of the target event (ranging from 1 to 15) and the behavioral responses (Future vs. Past in the internal-perspective task; Same vs. Different in the external-perspective task, coded as 0 and 1). The significant findings were unaffected.

(d) The authors used two tasks. In the "external perspective" one, the authors asked participants to report whether events were part of the same or a different part of the day. In the "internal perspective one", the authors asked participants to project themselves to the reference event and to determine whether the target event occurred before or after the projected viewpoint. The first task is a same/different recognition task. The second task is a temporal order task (e.g., Arzy et al. 2009). These two asks are radically different and do not require the same operationalization. The authors should minimally provide a comprehensive comparison of task requirements, their operationalization, and, more importantly, assess the behavioral biases inherent to each of these tasks that may confound brain activity observed with fMRI.

We understand the reviewer’s concern. We agree that there is a substantial difference between the two tasks. However, the primary goal of this study was not to directly compare these tasks to isolate a specific cognitive component. Rather, the neural correlates of temporal distance were first identified as brain regions showing a significant correlation between neural activity and temporal distance using the parametric modulation analysis. We then compared these neural correlates between the two tasks. Therefore, any general differences between the tasks should not be a confound for our main results. Our aim was to examine whether the hippocampal representation of temporal distance remains consistent across different perspectives, and whether the parietal representation of temporal distance varies as a function of the perspective adopted.

Therefore, the main aim of our task manipulation was to ensure that participants adopted either an external or an internal perspective on the event sequence, depending on the task condition. In the Introduction (Pages 6–7), we clarify this manipulation as follows: “In the externalperspective task, participants localized events with respect to external temporal boundaries, judging whether the target event occurred in the same or a different part of the day as the reference event. In the internal-perspective task, participants were instructed to mentally project themselves into the reference event and localize the target event relative to their own temporal point, judging whether the target event happened in the future or the past of the reference event (see Methods for details of the scanning procedure).”

We believe this task manipulation was successful. Behaviorally, the two tasks showed opposite correlations between reaction time and temporal distance, resembling the symbolic distance versus mental scanning effect. Neurally, contrasting the internal- and external-perspective tasks revealed activation of the default mode network, which is known to play a central role in self-projection (Buckner et al., 2017).

(e) The authors systematically report interpreted results, not factual data. For instance, while not showing the results on behavioral outcomes, the authors directly interpret them as symbolic distance effects.

Thank you for this comment. In the original paper, we reported the relevant statistics before our interpretation: “Sequential Distance was correlated positively with RT in the external-perspective task (z = 3.80, p < 0.001) but negatively in the internal-perspective task (z = -3.71, p < 0.001).” However, they may have been difficult to notice, and we are including a figure for the RT analysis in the revised manuscript.

Crucially, the authors do not comment on the obvious differences in task difficulty in these two tasks, which demonstrates a substantial lack of control in the experimental design. The same/different task (task 1 called "external perspective") comes with known biases in psychophysics that are not present in the temporal order task (task 2 called " internal perspective"). The authors also did not discuss or try to match the performance level in these two tasks. Accordingly, the authors claim that participants had greater accuracy in the external (same/different) task than in the internal task, although no data are shown and provided to support this report. Further, the behavioral effect is trivialized by the report of a performance accuracy trade off that further illustrates that there is a difference in the task requirements, preventing accurate comparison of the two tasks.

As noted in Question 2d, we acknowledge the substantial difference between the two tasks. However, the primary goal of this study was not to directly compare these tasks to isolate a specific cognitive component. Instead, we first identified the neural correlates of temporal distance as brain regions showing a significant correlation between neural activity and temporal distance, independent of task demands. We then compared these neural correlates across the two task conditions, which were designed to engage different temporal perspectives. Therefore, any general differences between the tasks should not be a confound for our main findings and interpretation.

Our aim was to investigate whether the hippocampal representation of temporal distance remains consistent across different perspectives and whether the parietal representation of temporal distance varies as a function of the perspective adopted. We do not see how this doubledissociation pattern could be explained by differences in task difficulty.

While we do not consider the overall difference in task difficulty between the two tasks to be a confounding factor, we acknowledge the potential confound posed by variations in task difficulty across temporal distances (1 to 5). This concern arises from the similarity between the activity patterns in the posterior parietal cortex and reaction time across temporal distances. To address this, we conducted control analyses to test this hypothesis (see the second and third points from Reviewer 2 for details).

On page 8, we present the behavioral accuracy data: “Participants showed significantly higher accuracy in the external-perspective task than in the internal-perspective task (external-perspective task: M = 93.5%, SD = 4.7%; internal-perspective task: M = 89.5%, SD = 8.1%; paired t(31) = 3.33, p = 0.002).”

All fMRI contrasts are also confounded by this experimental shortcoming, seeing as they are all reported at the interaction level across a task. For instance, in Figure 4, the authors report a significant beta difference between internal and external tasks. It is impossible to disentangle whether this effect is simply due to task difference or to an actual processing of the duration that differs across tasks, or to the nature of the representation (the most difficult to tackle, and the one chosen by the authors).

We thank the reviewer for pointing out this important issue. Like temporal distance, the neural correlates of duration were not derived from a direct contrast between the two tasks. Instead, they were identified by detecting brain regions showing a significant correlation between neural activity and the implied duration of each event using the parametric modulation analysis. Therefore, what is shown in Figure 4 reflects the significant differences in these neural correlations with duration between the two tasks.

The observed difference in the neural representation of duration between the two tasks was unexpected. In the original manuscript, we provided a post hoc explanation: “Since the externalperspective task in the current study encouraged the participants to compare the event sequence with the external parallel temporal landmarks, duration representation in the hippocampus may be dampened.”

However, we agree that this difference might also arise from other factors distinguishing the two tasks. In the revised manuscript, we have clarified this possibility as follows: “The difference in duration representation between the two tasks remains open to interpretation. One possible explanation is that the hippocampus is preferentially involved in memory for durations embedded within event sequences (see review by Lee et al., 2020). In the internal-perspective task, participants indeed localized events within the event sequence itself. In contrast, the externalperspective task encouraged participants to compare the event sequence with external temporal landmarks, which may have attenuated the hippocampal representation of duration.”

Conclusion:

In conclusion, the current experimental work is confounded and lacks controls. Any behavioral or fMRI contrasts between the two proposed tasks can be parsimoniously accounted for by difficulty or attentional differences, not the claim of representational differences being argued for here.

We hope that our explanations and clarifications above adequately address the reviewer’s concerns. We would like to reiterate that we did not directly compare the two tasks. Rather, we first identified the neural representations of sequential distance and duration, and then examined how these representations differed across tasks. It is unclear to us how the overall difference in task difficulty or attentional demands could lead to the observed pattern of results.

By determining where the neural representations were consistent and where they diverged, we were able to differentiate brain regions that encode temporal information allocentrically from those that represent temporal information in a perspective-dependent manner, modulated by task demands.

Reviewer #2 (Public review):

Summary:

Xu et al. used fMRI to examine the neural correlates associated with retrieving temporal information from an external compared to internal perspective ('mental time watching' vs. 'mental time travel'). Participants first learned a fictional religious ritual composed of 15 sequential events of varying durations. They were then scanned while they either (1) judged whether a target event happened in the same part of the day as a reference event (external condition); or (2) imagined themselves carrying out the reference event and judged whether the target event occurred in the past or will occur in the future (internal condition). Behavioural data suggested that the perspective manipulation was successful: RT was positively correlated with sequential distance in the external perspective task, while a negative correlation was observed between RT and sequential distance for the internal perspective task. Neurally, the two tasks activated different regions, with the external task associated with greater activity in the supplementary motor area and supramarginal gyrus, and the internal condition with greater activity in default mode network regions. Of particular interest, only a cluster in the posterior parietal cortex demonstrated a significant interaction between perspective and sequential distance, with increased activity in this region for longer sequential distances in the external task, but increased activity for shorter sequential distances in the internal task. Only a main effect of sequential distance was observed in the hippocampus head, with activity being positively correlated with sequential distance in both tasks. No regions exhibited a significant interaction between perspective and duration, although there was a main effect of duration in the hippocampus body with greater activity for longer durations, which appeared to be driven by the internal perspective condition. On the basis of these findings, the authors suggest that the hippocampus may represent event sequences allocentrically, whereas the posterior parietal cortex may process event sequences egocentrically.

We sincerely appreciate the reviewers for providing an accurate, comprehensive, and objective summary of our study.

Strengths:

The topic of egocentric vs. allocentric processing has been relatively under-investigated with respect to time, having traditionally been studied in the domain of space. As such, the current study is timely and has the potential to be important for our understanding of how time is represented in the brain in the service of memory. The study is well thought out, and the behavioural paradigm is, in my opinion, a creative approach to tackling the authors' research question. A particular strength is the implementation of an imagination phase for the participants while learning the fictional religious ritual. This moves the paradigm beyond semantic/schema learning and is probably the best approach besides asking the participants to arduously enact and learn the different events with their exact timings in person. Importantly, the behavioural data point towards successful manipulation of internal vs. external perspective in participants, which is critical for the interpretation of the fMRI data. The use of syllable length as a sanity check for RT analyses, as well as neuroimaging analyses, is also much appreciated.

We thank the reviewer for the positive and encouraging comments.

Weaknesses/Suggestions:

Although the design and analysis choices are generally solid, there are a few finer details/nuances that merit further clarification or consideration in order to strengthen the readers' confidence in the authors' interpretation of their data.

(1) Given the known behavioural and neural effects of boundaries in sequence memory, I was wondering whether the number of traversed context boundaries (i.e., between morning-afternoon, and afternoon-evening) was controlled for across sequential length in the internal perspective condition? Or, was it the case that reference-target event pairs with higher sequential numbers were more likely to span across two parts of the day compared to lower sequential numbers? Similarly, did the authors examine any potential differences, whether behaviourally or neurally, for day part same vs. day part different external task trials?

We thank the reviewer for the thoughtful comments. When we designed the experiment, we minimized the correlation between the sequential distance between the target and reference events and whether the reference and target events occurred within the same or different parts of the day (coded as Same = 0, Different = 1). The point-biserial correlation coefficient between these two variables across all the trials within the same run were controlled below 0.2.

To investigate the effect of day-part boundaries on behavior, as well as the contribution of other factors, we conducted a new linear mixed-effects model analysis incorporating four additional variables. They are whether the target and the reference events are within the same or different parts of the day (i.e., Same vs. Different), whether the target event is in the future or the past of the reference event (i.e., Future vs. Past), and the interactions of the two factors with Task Type (i.e., internal- vs. external-perspective task).

The results are largely the same as the original one in the table: There was a significant main effect of Syllable Length, and the interaction effects between Task Type and Sequence Distance and between Task Type and Duration remain significant. What's new is we also found a significant interaction effect between Task Type and Same vs. Different.

As shown in the Figure 2—figure supplement 1, this Same vs. Different effect was in line with the effect of Sequential Distance, with two events in the same and different parts of the day corresponding to the short and long sequential distances. Given that Sequential Distance had already been considered in the model, the effect of parts of the day should result from the boundary effect across day parts or the chunking effect within day parts, i.e., the sequential distance across different parts of the day was perceived longer while the sequential distance within the same parts of the day was perceived shorter. We have incorporated these findings into the manuscript.

Neurally, to further verify that the significant effects of sequential distance were not driven by its weak correlation with the Same/Different judgment or other potential confounding factors, we constructed another GLM that incorporated three additional parametric modulators: the sequence position of the target event (ranging from 1 to 15) and the behavioral responses (Future vs. Past in the internal-perspective task; Same vs. Different in the external-perspective task, coded as 0 and 1). The significant findings were unaffected.

(2) I would appreciate further insight into the authors' decision to model their task trials as stick functions with duration 0 in their GLMs, as opposed to boxcar functions with varying durations, given the potential benefits of the latter (e.g., Grinband et al., 2008). I concur that in certain paradigms, RT is considered a potential confound and is taken into account as a nuisance covariate (as the authors have done here). However, given that RTs appear to be critical to the authors' interpretation of participant behavioural performance, it would imply that variations in RT actually reflect variations in cognitive processes of interest, and hence, it may be worth modelling trials as boxcar functions with varying durations.

We appreciate the reviewer’s insightful comment on this important issue. Whether to control for RT’s influence on fMRI activation is indeed a long-standing paradox. On the one hand, RT reflects underlying cognitive processes and therefore should not be fully controlled for. On the other hand, RT can independently influence neural activity, as several brain networks vary with RT irrespective of the specific cognitive process involved—a domain-general effect. For example, regions within the multiple-demand network are often positively correlated with RT across different cognitive domains.

Our strategy in the manuscript is to first present the results without including RT as a control variable and then examine whether the effects are preserved after controlling for RT. In the revised manuscript, we have clarified this approach (Page 13): “Here, changes in activity levels within the PPC were found to align with RT. Whether to control for RT’s influence on fMRI activation represents a well-known paradox. On the one hand, RT reflects underlying cognitive processes and therefore should not be fully controlled for. On the other hand, RT can independently influence neural activity, as several brain networks vary with RT irrespective of the specific cognitive process involved—a domain-general effect. For instance, regions within the multiple-demand network are often positively correlated with RT and task difficulty across diverse cognitive domains (e.g., Fedorenko et al., 2013; Mumford et al., 2024). To evaluate the second possibility, we conducted an additional control analysis by including trial-by-trial RT as a parametric modulator in the first-level model (see Methods). Notably, the same PPC region remained the only area in the entire brain showing a significant interaction between Task Type and Sequential Distance (voxel-level p < 0.001, clusterlevel FWE-corrected p < 0.05). This finding indicates that PPC activity cannot be fully attributed to RT. Furthermore, we do not interpret the effect as reflecting a domain-general RT influence, as regions within the multiple-demand system—typically sensitive to RT and task difficulty—did not exhibit significant activation in our data.”

The reason we did not use boxcar functions with varying durations in our original manuscript is that we also applied parametric modulation in the same model. In the parametric modulation, all parametric modulators inherit the onsets and durations of the events being modulated. Consequently, the modulators would also take the form of boxcar functions rather than stick functions—the height of each boxcar reflecting the parameter value and its length reflecting the RT. We were uncertain whether this approach would be appropriate, as we have not encountered other studies implementing parametric modulation in this manner.

For exploratory purposes, we also conducted a first-level analysis using boxcar functions with variable durations. The same PPC region remained the strongest area in the entire brain that shows an interaction effect between Task Type and Sequential Distance. However, the cluster size was slightly reduced (voxel-level p < 0.001, cluster-level FWE-corrected p = 0.0610; see the Author response image 1 below). The cross indicates the MNI coordinates at [38, –69, 35], identical to those shown in the main results (Figure 4A).

Author response image 1.

(3) The activity pattern across tasks and sequential distance in the posterior parietal cortex appears to parallel the RT data. Have the authors examined potential relationships between the two (e.g., individual participant slopes for RT across sequential distance vs. activity betas in the posterior parietal cortex)?

We thank the reviewer for this helpful suggestion. As shown in the Author response image 2, the interaction between Task Type and Sequential Distance was a stronger predictor of PPC activation than of RT. Because PPC activation and RT are measured on different scales, we compared their standardized slopes (standardized β) measuring the change in a dependent variable in terms of standard deviations for a one-standard-deviation increase in an independent variable. The standardized β for the Task Type × Sequential Distance interaction was −0.30 (95% CI [−0.42, −0.19]) for PPC activation and −0.21 (95% CI [−0.30, −0.13]) for RT. The larger standardized effect for PPC activation indicates that the Task Type × Sequential Distance interaction was a stronger predictor of neural activation than of behavioral RT.

Author response image 2.

A more relevant question is whether PPC activation can be explained by temporal information (i.e., the sequential distance) independently of RT. To test this, we included both Sequential Distance and RT in the same linear mixed-effects model predicting PPC Activation Level. As shown in the Author response table 1, although RT independently influenced PPC activation (F(1, 288) = 4.687, p = 0.031), the interaction between Task Type and Sequential Distance was a much stronger independent predictor (F(1, 290) = 19.319, p < 0.001).

Author response table 1.

PPC Activation Level Predicted by Sequential Distance and RT

(3) Linear Mixed Model Formula: PPC Activation Level ~ 1 + Task Type * (Sequential Distance + RT) + (1 | Participant)

(4) There were a few places in the manuscript where the writing/discussion of the wider literature could perhaps be tightened or expanded. For instance:

(i) On page 16, the authors state 'The negative correlation between the activation level in the right PPC and sequential distance has already been observed in a previous fMRI study (Gauthier & van Wassenhove, 2016b). The authors found a similar region (the reported MNI coordinate of the peak voxel was 42, -70, 40, and the MNI coordinate of the peak voxel in the present study was 39, -70, 35), of which the activation level went up when the target event got closer to the self-positioned event. This finding aligns with the evidence suggesting that the posterior parietal cortex implements egocentric representations.' Without providing a little more detail here about the Gauthier & van Wassenhove study and what participants were required to do (i.e., mentally position themselves at a temporal location and make 'occurred before' vs. 'occurred after' judgements of a target event), it could be a little tricky for readers to follow why this convergence in finding supports a role for the posterior parietal cortex in egocentric representations.

We appreciate the reviewer’s comments. In the revised manuscript, we have provided a more detailed explanation of Gauthier and van Wassenhove’s study (Page 17): “The negative correlation between the activation level in the right PPC and sequential distance has already been observed in a previous fMRI study by Gauthier & van Wassenhove (2016b). In their study, the participants were instructed to mentally position themselves at a specific time point and judge whether a target event occurred before or after that time point. The authors identified a similar brain region (reported MNI coordinates of the peak voxel: 42, −70, 40), closely matching the activation observed in the present study (MNI coordinates of the peak voxel: 39, −70, 35). In both studies, activation in this region increased as the target event approached the self-positioned time point, which aligns with the evidence suggesting that the posterior parietal cortex implements egocentric representations.”

(ii) Although the authors discuss the Lee et al. (2020) review and related studies with respect to retrospective memory, it is critical to note that this work has also often used prospective paradigms, pointing towards sequential processing being the critical determinant of hippocampal involvement, rather than the distinction between retrospective vs. prospective processing.

We sincerely thank the reviewer for highlighting these important points. In response, we have revised the section of the Introduction discussing the neural underpinnings of duration (Pages 3-4). “Neurocognitive evidence suggests that the neural representation of duration engages distinct brain systems. The motor system—particularly the supplementary motor area—has been associated with prospective timing (e.g., Protopapa et al., 2019; Nani et al., 2019; De Kock et al., 2021; Robbe, 2023), whereas the hippocampus is considered to support the representation of duration embedded within an event sequence (e.g., Barnett et al., 2014; Thavabalasingam et al., 2018; see also the comprehensive review by Lee et al., 2020).”

(iii) The authors make an interesting suggestion with respect to hippocampal longitudinal differences in the representation of event sequences, and may wish to relate this to Montagrin et al. (2024), who make an argument for the representation of distant goals in the anterior hippocampus and immediate goals in the posterior hippocampus.

We thank the reviewer for bringing this intriguing and relevant study to our attention. In the Discussion of the manuscript, we have incorporated it into our discussion (Page 21): “Evidence from the spatial domain has suggested that the anterior hippocampus (or the ventral rodent hippocampus) implements global and gist-like representations (e.g., larger receptive fields), whereas the posterior hippocampus (or the dorsal rodent hippocampus) implements local and detailed ones (e.g., finer receptive fields) (e.g., Jung et al., 1994; Kjelstrup et al., 2008; Collin et al., 2015; see reviews by Poppenk et al., 2013; Robin & Moscovitch, 2017; see Strange et al., 2014 for a different opinion). Recent evidence further shows that the organizational principle observed along the hippocampal long axis may also extend to the temporal domain (Montagrin et al., 2024). In that study, the anterior hippocampus showed greater activation for remote goals, whereas the posterior hippocampus was more strongly engaged for current goals, which are presumed to be represented in finer detail.”

Reviewing Editor Comments:

While both reviewers acknowledged the significance of the topic, they raised several important concerns. We believe that providing conceptual clarification, adding important methodological details, as well as addressing potential confounds will further strengthen this paper.

We thank the editor for the suggestions.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Please, provide the actual ethical approval #.

We have added the ethical approval number in the revised manuscript (P 36): “The ethical committee of the University of Trento approved the experimental protocol (Approval Number 2019-018),”

(2) Thirty-two participants were tested. Please report how you estimated the sample size was sufficient to test your working hypothesis.

We thank the editor for pointing out this omission. In the revised manuscript, we have added an explanation for our choice of sample size (p. 36): “The sample size was chosen to align with the upper range of participant numbers reported in previous fMRI studies that successfully detected sequence or distance effects in the hippocampus (N = 15–34; e.g., Morgan et al., 2011; Howard et al., 2014; Deuker et al., 2016; Garvert et al., 2017; Theves et al., 2019; Park et al., 2021; Cristoforetti et al., 2022).”

(3) All MRI figures: please orient the reader; left/right should be stated.

In the revised manuscript, we have added labels to all MRI figures to indicate the left and right hemispheres.

(4) In Figure 3A-B, the clear lateralization of the activation is not discussed in the Results or in the Discussion. Was it predicted?

We thank the editors for highlighting this important point regarding hemispheric lateralization. The right-lateralization observed in our findings is indeed consistent with previous literature. In the revised manuscript, we have expanded our discussion to emphasize this aspect more clearly.

For the parietal cortex, we now note (Page 17-18): “The negative correlation between activation in the right posterior parietal cortex (PPC) and sequential distance has previously been reported in an fMRI study by Gauthier and van Wassenhove (2016b). In their paradigm, participants were instructed to mentally position themselves at a specific time point and judge whether a target event occurred before or after that point. The authors identified a similar region (peak voxel MNI coordinates: 42, −70, 40), closely corresponding to the activation observed in the present study (peak voxel MNI coordinates: 39, −70, 35). In both studies, activation in this region increased as the target event approached the self-positioned time point, consistent with evidence suggesting that the posterior parietal cortex supports egocentric representations. Neuropsychological studies have further shown that patients with lesions in the bilateral or right PPC exhibit ‘egocentric disorientation’ (Aguirre & D’Esposito, 1999), characterized by an inability to localize objects relative to themselves (e.g., Case 2: Levine et al., 1985; Patient DW: Stark, 1996; Patients MU: Wilson et al., 1997, 2005).”

For the hippocampus, we have added (Page 19): “Previous research has shown that hippocampal activation correlates with distance (e.g., Morgan et al., 2011; Howard et al., 2014; Garvert et al., 2017; Theves et al., 2019; Viganò et al., 2023), and that distributed hippocampal activity encodes distance information (e.g., Deuker et al., 2016; Park et al., 2021). Most studies have reported hippocampal ePects either bilaterally or predominantly in the right hemisphere, whereas only one study (Morgan et al., 2011) found the ePect localized to the left hippocampus.”

This passage explains that emulsions are mixtures of two normally immiscible liquids, like oil and water, stabilized by emulsifying agents. Soaps and detergents work because they have both a hydrophilic (water-attracting) charged head and a hydrophobic (oil-attracting) nonpolar tail. This dual structure allows them to interact with both water and oil, helping keep the mixture stable.

via Todd Young https://www.facebook.com/groups/TypewriterCollectors/posts/10163194812329678/

I feel a disturbance in the force, almost like a man cut off the heads of the ruling party for so long that people got fed up and cut off his head.

Reviewer #1 (Public review):

Summary:

Hoverflies are known for a striking sexual dimorphism in eye morphology and early visual system physiology. Surprisingly, the male and female flight behaviors show only subtle differences. Nicholas et al. investigate the sensori-motor transformation of sexually dimorphic visual information to flight steering commands via descending neurons. The authors combined intra- and extracellular recordings, neuroanatomy, and behavioral analysis. They convincingly demonstrate that descending neurons show sexual dimorphisms - in particular at high optic flow velocities - while wing steering responses seem relatively monomorphic. The study highlights a very interesting discrepancy between neuronal and behavioral response properties.

More specifically, the authors focused on two types of descending neurons that receive inputs from well-characterized wide-field sensitive tangential cells: OFS DN1, which receives inputs from so-called HS cells, and OFS DN2, which receives input from a set of VS cells. Their likely counterparts in Drosophila connect to the neck, wing, and haltere neuropils. The authors characterized the visual response properties of these two neuronal classes in both male and female hoverflies and identified several interesting differences. They then presented the same set of stimuli, tracked wing beat amplitude, and analyzed the sum and the difference of right and left wing beat amplitude as a readout of lift or thrust, and yaw turning, respectively. Behavioral responses showed little to no sexual dimorphism, despite the observed neuronal differences.

Strengths:

I find the question very interesting and the results both convincing and intriguing. A fundamental goal in neuroscience is to link neuronal responses and behavior. The current study highlights that the transformations - even at the level of descending neurons to motoneurons - are complex and less straightforward than one might expect.

Weaknesses:

The authors investigated two types of descending neurons, but it was not clear to me how many other descending neurons are thought to be involved in wing steering responses to wide-field motion. I would suggest providing a more in-depth overview of what is known about hoverflies and Drosophila, since the conclusions drawn from the study would be different if these two types were the only descending neurons involved, as opposed to representing a subset of the neurons conveying visual information to the wing neuropil.

Both neuronal classes have counterparts in Drosophila that also innervate neck motor regions. The authors filled the hoverfly DNs in intracellular recordings to characterize their arborization in the ventral nerve cord. In my opinion, these anatomical data could be further exploited and discussed a bit more: is the innervation in hoverflies also consistent with connecting to the neck and haltere motor regions? Are there any obvious differences and similarities to the Drosophila neurons mentioned by the authors? If the arborization also supports a role in neck movements, the authors could discuss whether they would expect any sexual dimorphism in head movements.

Author response:

eLife Assessment

Hoverflies are known for their sexually dimorphic visual systems and exquisite flight behaviors. This valuable study reports how two types of visual descending neurons differ between males and females in their motion- and speed-dependent responses, yet surprisingly, the behavior they control lacks any sexual dimorphism. The results convincingly support these findings, which will be of interest for studies of visuomotor transformations and network-level brain organization.

This statement perfectly recapitulates our findings.

Public Reviews:

Reviewer #1 (Public review):  

Summary: 

Hoverflies are known for a striking sexual dimorphism in eye morphology and early visual system physiology. Surprisingly, the male and female flight behaviors show only subtle differences. Nicholas et al. investigate the sensori-motor transformation of sexually dimorphic visual information to flight steering commands via descending neurons. The authors combined intra- and extracellular recordings, neuroanatomy, and behavioral analysis. They convincingly demonstrate that descending neurons show sexual dimorphisms - in particular at high optic flow velocities - while wing steering responses seem relatively monomorphic. The study highlights a very interesting discrepancy between neuronal and behavioral response properties.

Thank you for this summary. Most of the statement perfectly recapitulates the main findings of our paper. However, we want to emphasize that some hoverfly flight behaviors are strongly sexually dimorphic, especially those related to courtship and mating. Indeed, only male hoverflies pursue targets at high speed, chase away territorial intruders, and pursue females for mating. However, other flight behaviours, such as those related to optomotor responses and flights between flowers when feeding, are not sexually dimorphic. We will amend the Introduction to make the difference between flight behaviors clear.

More specifically, the authors focused on two types of descending neurons that receive inputs from well-characterized wide-field sensitive tangential cells: OFS DN1, which receives inputs from so-called HS cells, and OFS DN2, which receives input from a set of VS cells. Their likely counterparts in Drosophila connect to the neck, wing, and haltere neuropils. The authors characterized the visual response properties of these two neuronal classes in both male and female hoverflies and identified several interesting differences. They then presented the same set of stimuli, tracked wing beat amplitude, and analyzed the sum and the difference of right and left wing beat amplitude as a readout of lift or thrust, and yaw turning, respectively. Behavioral responses showed little to no sexual dimorphism, despite the observed neuronal differences.

Thank you for this very nice summary of our work. We want to clarify that LPTC input to DN1 and DN2 has not been shown directly in hoverflies using e.g. dye coupling, or dual recordings. Instead, the presumed HS and VS input is inferred from morphological and physiological DN evidence, and comparisons to similar data in Drosophila and blowflies. We will amend the Introduction to clarify this. The rest of the paragraph perfectly recapitulates the main findings of our paper.

Strengths:

I find the question very interesting and the results both convincing and intriguing. A fundamental goal in neuroscience is to link neuronal responses and behavior. The current study highlights that the transformations - even at the level of descending neurons to motoneurons - are complex and less straightforward than one might expect.

Thank you.

Weaknesses:

The authors investigated two types of descending neurons, but it was not clear to me how many other descending neurons are thought to be involved in wing steering responses to wide-field motion. I would suggest providing a more in-depth overview of what is known about hoverflies and Drosophila, since the conclusions drawn from the study would be different if these two types were the only descending neurons involved, as opposed to representing a subset of the neurons conveying visual information to the wing neuropil.

This is a great point. There are around 1000 fly DNs, of which many could respond to widefield motion, without being specifically tuned to widefield motion. For example, many looming sensitive neurons also respond to widefield motion, and could therefore be involved in the WBA movements that we measured here. In addition, there are many multimodal neurons that could be involved in optomotor responses in free flight, but these may not have been stimulated when we only provided visual input. Furthermore, many visual neurons are modulated by proprioceptive feedback, which is lacking in immobilized physiology preps. Finally, in blowflies, up to 5 optic flow sensitive DNs have been identified morphologically, and in Drosophila 3 have been identified morphologically and physiologically. In summary, it is more than likely that other neurons project visual widefield motion information to the wing neuropil. We will amend our Introduction and Discussion to make this important point clear to the readers.

Both neuronal classes have counterparts in Drosophila that also innervate neck motor regions. The authors filled the hoverfly DNs in intracellular recordings to characterize their arborization in the ventral nerve cord. In my opinion, these anatomical data could be further exploited and discussed a bit more: is the innervation in hoverflies also consistent with connecting to the neck and haltere motor regions? Are there any obvious differences and similarities to the Drosophila neurons mentioned by the authors? If the arborization also supports a role in neck movements, the authors could discuss whether they would expect any sexual dimorphism in head movements.

These are all great points. We did not see any clear arborizations to the frontal nerve, where we would expect to find the neck motor neurons (NMNs). In addition, while we did see fine arborizations throughout the length of the thoracic ganglion, we saw no strong outputs projecting directly to the haltere nerve (HN). In the revised version of the MS we will modify figure 4 (morphological characterization) to clarify.

There are important differences between the morphology of DN1 and DN2 in hoverflies and DNHS1 and DNOVS2 in Drosophila, in terms of their projections in the thoracic ganglion. For example, In Drosophila DNOVS2, there are several fine branches along the length of the neuron in the thoracic ganglia. Similarly, we found fine branches in Eristalis tenax DN2, however, in addition, we found a wide branch projecting to the area of the thoracic ganglion where the prothoracic and pterothoracic nerves likely get their inputs (Figure 4), suggesting that the neuron could contribute to controlling the wings and/or the forelegs (which is why we quantified the WBA). In Drosophila DNHS1, there is a similar fat branch to the prothoracic and pterothoracic nerves, which we also found in Eristalis tenax OFS DN1 (Figure 4). Indeed, while Drosophila DNHS1 and DNOVS2 have quite strikingly different morphology, DN1 and DN2 in Eristalis looked quite similar. We will modify the Results section to make this clear.

In addition, to investigate this further, in the revised version of the MS we will include analysis of the movement of different body parts (including the head) to investigate the presence of any potential sexual dimorphism. Unfortunately, however, this will not include the halteres, as they cannot be seen well in the videos.

Reviewer #2 (Public review):

Summary:

Many fly species exhibit male-specific visual behaviors during courtship, while little is known about the circuit underlying the dimorphic visuomotor transformations. Nicholas et al focus on two types of visual descending neurons (DNs) in hoverflies, a species in which only males exhibit high-speed pursuit of conspecifics. They combined electrophysiology and behavior analysis to identify these DNs and characterize their response to a variety of visual stimuli in both male and female flies. The results show that the neurons in both sexes have similar receptive fields but exhibit speed-dependent dimorphic responses to different optic flow stimuli.

This statement perfectly recapitulates the main findings of our paper. However, as mentioned above, while hoverfly flight behaviors related to courtship and mating are strongly sexually dimorphic, other flight behaviours, such as those related to optomotor responses and flights between flowers when feeding, are not. We will amend the Introduction to make the difference between flight behaviors clear.

Strengths:

Hoverflies, though not a common model system, show very interesting dimorphic behaviors and provide a unique and valuable entry point to explore the brain organization behind sexual dimorphism. The findings here are not only interesting on their own right but will also likely inspire those working in other systems, particularly Drosophila.

Thank you.

The authors employed rigorous morphology, electrophysiology, and behavior methods to deliver a comprehensive characterization of the neurons in question. The precision of the measurements allowed for identifying a subtle and nuanced neuronal dimorphism and set a standard for future work in this area.

Thank you.

Weaknesses:

Cell-typing using receptive field preferred directions (RFPDs): if I understood correctly, this classification method mostly relies on the LPDs near the center of the receptive field (median within the contour in Fig.1). I have two concerns here. First, this method is great if we are certain there are only two types of visual DNs as described in the manuscript. But how certain is this? Given the importance of vision in flight control, I would expect many DNs that transmit optic flow information to the motor center. I'd also like to point out that there are other lobula plate tangential cells (LPTCs) than HS and VS cells, which are much less studied and could potentially contribute to dimorphic behaviors.

This is very true, and an important point. As mentioned above, in blowflies, up to 5 optic flow sensitive DNs have been identified morphologically, however, if these correspond to 5 different physiological types remain unclear. In both blowflies and Drosophila 3 have been identified morphologically and physiologically (DNHS1, DNOVS1, DNOVS2). Importantly, in both blowflies and fruitflies DNOVS1 gives graded responses, and no action potentials, meaning that we would not be able to record from it using extracellular electrophysiology.

We previously used clustering techniques to show that in Eristalis, we can reliably distinguish two types of optic flow sensitive DNs from extracellular electrophysiological data, based on a range of receptive field parameters, and we think that these correspond to DNHS1 and DNOVS2 in Drosophila (Nicholas et al, J Comp Physiol A, 2020, cited in paper). As mentioned above in response to Reviewer 1, this does not mean that there are no other neurons that could respond to widefield optic flow, and which might be involved in the WBA we recorded in the paper. However, the point of this paper was not to conclusively show that there are only two optic flow sensitive descending neurons. The point was to say that there are two quite distinct optic flow sensitive neurons that have similar receptive fields in males and females, while the responses to widefield motion show differences between males and females.

We will modify the Introduction and Discussion to make these important points clear to the Reader, including the discussion of the 45-60 LPTCs that exist in the lobula plate, and what their role might be.

Second, this method feels somewhat impoverished given the richness of the data. The authors have nicely mapped out the directional tuning for almost the entire visual field. Instead of reducing this measurement to 2 values (center and direction), I was wondering if there is a better method to fully utilize the data at hand to get a better characterization of these DNs. As the authors are aware, local features alone can be ambiguous in characterizing optic flows. What's more, taking into account more global features can be useful for discovering potentially new cell types.

This is a great point, and we did an extensive analysis of other receptive field properties in this study (shown in supp fig 1). In addition, and as mentioned above, we have published a clustering analysis across receptive field properties of these neurons (Nicholas et al, J Comp Physiol A, 2020, cited in paper). The point that we attempted to make in this paper was that by using two strikingly simple metrics, we can reliably distinguish which of the two neuron types we are recording from (if we accept that there are two main types that we are likely to record from) simply based on location and overall directional preference. This makes automated analysis very easy and straightforward. Indeed, we now use this routinely to ID what neuron we are recording from, rather than making a human-based assumption.

However, we agree that further in depth analysis is warranted. Therefore, to address this, we will provide additional receptive field analysis and clustering in the revised version of the MS. In addition, we want to highlight that all data is uploaded to DataDryad for anyone interested in doing additional in-depth analyses.

Line 131, it wasn't clear to me why full-screen stimuli were used for comparison here, instead of the full receptive field maps. Male flies exhibit sexual dimorphic behaviors only during courtship, which would suggest that small-sized visual stimuli (mimicking an intruder or female conspecific) would be better suited to elicit dimorphic neuronal responses. A similar comment applies to the later results as well. Based on the receptive field mapping in Figure 1, I'm under the impression that these 2 DN types are more suited to detect wide-field optic flows, those induced by self-motion as mentioned in the manuscript. The results are still very interesting, but it's good to make this point clear early on to help set appropriate expectations. Conversely, this would also suggest that there are other visual DN types that are responsible for the courtship-related sexually dimorphic behaviors.

Thank you for mentioning these important points. Our reasoning for using full-screen stimuli for the analysis on line 131 was that since we used the small sinusoidal gratings for mapping the receptive fields, and to subsequently classify the neurons, it would be unfair to use the same data to investigate potential sexual dimorphism. I.e., we selected neurons that fulfilled certain criteria, and then we cannot rightfully use the same criteria to determine differences. This was not explicitly mentioned in the paper, so we will modify the text to make this clear to the Reader.

However, in Supp Figure 1d/e we show that there are no striking receptive field differences between males and females in terms of receptive field center nor directional preference. In Supp Figure 1f we show that there is no difference between male and female receptive field height and width. We will modify the text to draw the Reader’s attention to this figure, and also mention the additional analysis done in response to the comment above.

As a side note, I personally expected at least DNHS1 to have a smaller receptive field in males, as the hoverfly HSN is strikingly sexually dimorphic (Nordström et al, Curr Biol 2008), and also very sensitive to small objects. However, while optic flow sensitive DNs do respond to small objects (see e.g. the J Comp Physiol paper mentioned above) we did not detect any obvious sexual dimorphism in receptive field properties. Indeed, we think that a different subset of DNs control target pursuit behavior (target selective DNs (TSDNs)). This will be addressed in the modified version of the paper.

eLife Assessment

This valuable study reports results showing how different neurons in the dysgranular retrosplenial cortex code spatial orientation. Specifically, the paper reports that some neurons maintain tuning for a single head direction across multi-compartmental environments, while other neurons are tuned to different head directions that reflect the geometry within each compartment. The study was viewed as likely to expand the field's understanding of directional tuning of neurons, but incomplete evidence was provided to support the conclusions.

Reviewer #1 (Public review):

Summary:

The dysgranular retrosplenial cortex (RSD) and hippocampus both encode information related to an animal's navigation through space. Here, the authors study the different ways in which these two brain regions represent spatial information when animals navigate through interconnected rooms. Most importantly, they find that the RSD contains a small fraction of neurons that encode properties of interconnected rooms by firing in different head directions within each room. This direction is shifted by 180 degrees in 2-room environments, and by 90 degrees in 4-room environments. While it cannot be definitively proven that this encoding is not just related to the presence of exits (doors) in each room, this is a noteworthy finding and will motivate further study in more complex and well-controlled environments to understand this coding scheme in the RSD. The recordings and analyses used to identify these multi-directional cells are mostly solid. Additional conclusions regarding the rotational symmetry across rooms seen in the RSD neurons that do not encode direction (representing the majority of RSD neurons) remain incomplete, given the evidence presented thus far. The differences between RSD and hippocampus encoding of space are clear and consistent with prior observations.

Strengths:

(1) Use of tetrode recordings from the RSD to identify multi-direction cells that only encode one direction in each room, but shift the preferred direction by either 180 or 90 degrees depending on the number of rooms in the environment.

(2) Solid controls to show that this multi-direction encoding is stable over time and across some environmental manipulations.

(3) Convincing evidence that these multi-direction cells can co-exist with single-direction head direction cells in the RSD (as both cell types can be simultaneously recorded).

(4) Convincing evidence for clear differences between directional and spatial encoding in the RSD versus hippocampus, consistent with prior observations.

Weaknesses:

(1) The paper mostly uses the term "retrosplenial cortex", but it is important to clarify that the study is only focused on the dysgranular retrosplenial cortex (RSD; Brodmann Area 30) and not the granular retrosplenial cortex (Brodmann Area 29). These are two distinct regions (despite the similar names), each with distinct connectivity and distinct behavioral encoding and function, so it is important to clarify in the abstract and title that the present study is solely about the RSD to prevent confusion in the literature.

(2) The proportion of each observed cell type is not clearly stated, although it is clear that the multi-directional cells are in the minority. Having the proportion of well-isolated neurons in distinct sessions that encode each type of information (e.g., multi vs single direction encoding) would greatly aid the interpretation of the result and help the field know how common each cell type is in the RSD.

(3) The authors state that "MDCs [multi-directional cells] never exhibited multidirectional activity within a single room" - but many of the single room examples from the 4-room environment (shown in Figures 2E and 2F) reveal multi-peaked directional encoding. This suggests that the multi-direction encoding may be more compatible with encoding some property of the number of exits rather than relative room orientations.

(4) The spatial rotation analyses of non-directional cell analyses are considered incomplete. This is impacted by the slower speed at the doors and hence altered firing rates (as evidenced in spatial rate plots). The population rate is not relevant as the correlational analyses are done on a single cell level. Since some cells fire more with increasing speed and others fire less, that will necessarily result in a population rate map that minimizes firing rate differences near the doorway, where the animals move more slowly. But on a single cell level, that reduced speed is having a big effect, as evidenced by individual rate map examples, and the rooms will need to be rotated to obtain a higher correlation by overlapping the doorway regions. This does not necessarily say anything about spatial coding across the two or four interconnected rooms being rotationally symmetric, and it would appear difficult to draw any conclusions related to spatial encoding from those analyses.

Reviewer #2 (Public review):

Summary:

Laurent et al. perform in vivo electrophysiological recordings in the retrosplenial cortex of rats foraging in multi-compartment environments with either identical or unique visual features. The authors characterize two types of directional signals in the area that they have previously reported: classic head direction cells anchored to the global allocentric reference frame and multi-direction cells (MDCs), which have a rotationally preserved directional field anchored to local compartments. The primary finding of this work is that MDCs seem sensitive to local environmental geometry rather than visual context. They also show that MDC tuning persists in the absence of hippocampal place field repetition, further dissociating the RSC local directional signal from the broader allocentric representation of space. A novel observation is that RSC non-directional spatial signals are anchored to the local environment, which could and should be explored further. While the data is solid and the analyses are mostly appropriate, the primary findings are incremental, and more interesting novel claims are not explored in detail or not explicitly tested.

Strengths:

The environmental manipulations clearly demonstrate that tuning is not modulated by complex visual information.

The finding that RSC two-dimensional spatial responses are stable and anchored to environmental features is novel and can be further explored in future work.

Weaknesses:

The observation that BDCs and MDCs are insensitive to visual context builds upon the author's previous work (and replicates aspects of Zhang et al., 2022) but leaves many open questions that are not addressed with the current set of experiments. Specifically, what exactly are MDCs anchoring to? The primary theory is that they anchor to environmental geometry, but there are no explicit experimental manipulations to test this theory. It is important to note that 2- and 4-compartment environments share many features, including the same cardinal axes, making any differences/similarities in these two conditions difficult to interpret.

The main finding presented with respect to BDC/MDs tuning is that they are not sensitive to visual context as manipulated by distinct visual patterns on the wall and floor in multicompartment environments. One could argue that the individual rooms are, in actuality, quite similar in low-level visual features - each possesses a large white background square visual feature on a single wall with a fixed relationship to the door(s). How can the authors rule out that i) BDC/MDC responses are modulated by these low-level features rather than geometry and/or ii) that the rats are not paying attention to any visual features at all? There is no task requiring them to indicate which room they are in. Furthermore, the doorways themselves are prominent visual features that are present in each context. It would be interesting to see if MDC/BDC tuning persisted in a square room where the number of doorways was manipulated to rule out this possibility.

A strong possibility is that the rotational symmetry of both MDCs and non-directional spatial neurons is related to i) door-related firing, 2) stereotyped movement, and 3) stereotyped directional sampling. In Supplemental Figure 8, the authors begin to address this by comparing a 'population ratemap' to a 'population speed map.' I do not think this is sufficient and is difficult to interpret. Instead, the authors should assess whether MDC and BDCs fire more at doorways and what the overlap is with the speed-modulated cells they report. Moreover, they should assess whether the spatial speed profile itself is rotationally symmetric within each session. It would also be useful to look at the confluence of the variables simultaneously using some form of regression analysis. The authors could generate a directional predictor that captures the main response property of these cells and see if it accounts for greater variability in spiking than speed or x,y position. Finally, rotationally symmetric directional sampling biases could arise from the doors being present on the same two walls in each room. The authors should assess whether MDC tuning is still present if directional sampling is randomly downsampled to match directional observations in each compartment.

Recent work has demonstrated that neurons with egocentric corner or boundary tuning are observed in RSC. The authors do not address whether egocentric tuning contributes to MDC signals. An explicit analysis of the relationship and potential overlap of MDC and egocentric populations is warranted.

Many of the MDCs presented in the main figures are not especially compelling. This includes alterations to MDC tuning in Figure 2, which is a key datapoint. The authors should show significantly more (if not all) examples of MDCs in each environment. It would similarly be useful to see all/more examples of non-directional spatially tuned neurons with rotationally symmetric firing patterns.

"One might hypothesize that specific environmental cues, such as door orientation or landmark positioning, drive these tuning shifts. However, our results argue against this interpretation. In four-room environments, each room had multiple entry points, yet MDCs never exhibited multidirectional activity within a single room."

I do not understand the logic here. Can the authors unpack this? Also, it is clear that some of the example cells have more than one peak in individual compartments. How is this quantified?

Reviewer #3 (Public review):

Summary:

The authors examine firing of dysgranular retrosplenial cortex (dRSC) neurons in relation to head orientation and location for rats exploring open-field environments. One environment utilized was a square arena with high walls that is split into two rectangular spaces connected by a doorway. Another environment is a square arena split into quadrants connected by doors near the center. For each, the different sub-spaces of the environments are either identical in terms of visual and tactile cues or different. For head direction neurons, the authors present one population where each neuron maintains a single tuning direction for the two or four sub-compartments of the two environments. A second population exhibits what is termed multi-directional firing, wherein neurons exhibit (overall) two or four head direction peaks in firing. For such neurons, firing in each of the sub-compartments is associated with only a single preferred direction, but the directions across compartments are shown to be at 180-degree (two-compartment environment) or 90-degree offsets. The offsets evidence tuning to the "same" orientation for the sub-compartments that are, in the global reference frame, oriented at 180 or 90 degree offsets. The results are similar whether or not the sub-compartments have the same or different tactile and visual cues. Thus, the first population is said to be global in its head direction tuning, while the second relates to each local environment in a way that is systematic across sub-compartments. Spatially-specific activity of another population of non-direction-tuned RSC neurons is examined, and comparisons of sub-compartment spatial firing maps suggest that spatial tuning in RSC also repeats across compartments when the firing maps for the compartments are rotated to match each other (as in physical space). Finally, a population of hippocampal "place" cells exhibited different location mapping across sub-compartments. The findings are interpreted to indicate that RSC can simultaneously map orientation in both local and global reference frames, possibly forming a mechanism whereby the sub-compartments' shared geometry (given by the boundary shapes and the door locations) can be related to each other and to the global space they share.

Strengths:

This paper addresses an interesting problem and expands how the field will think about directional tuning.

Weaknesses:

It is not clear that the experimental design allows for a clear interpretation of the data. Rates for preferred turning are low, as are ratemap correlations for spatially-tuned neurons.

(1) It is concerning that the neurons with head direction tuning have fairly low peak firing rates (mean close to 5 Hz), where prior studies examining head direction tuning in dRSC found head direction-tuned neurons with peak rates more than an order of magnitude higher (100 Hz or more). Under circumstances where neurons are tuned well to variables other than head direction (for example, angular velocity of movement), weak head direction tuning may be observed if those other variables are not sampled equally across head directions. The manuscript contains no rigorous control for this possibility. One place to start to address this issue would be to map out variables such as angular velocity by head orientation, and to test whether such relationships also carry 90 and 180 degree offsets.

(2) There is some question as to whether dRSC neurons (spatial or directional) following the sub-compartment "geometry" is appropriate in terms of interpreting the data. In the condition with sub-compartments carrying different tactile and visual cues, it seems that such cues pertain only to the floor of the environments. The distal visual space of the boundaries appears to be identical. One is left to wonder whether distinguishing environments according to boundary wall visual cues would lead to different results. The CA1 data does not help to rule this possibility out. A second reason to doubt the "shared geometry" interpretation is that there is no condition where sub-compartment geometry is varied. It is also the case that the sub-compartment doorways may stand as the only salient distal visual cue linking the environments. Local sensory cues and geometry seem not so disentangled in this study, but this is a major claim in the abstract.

(3) There is some concern with the interpretation that the spatial tuning of some dRSC neurons repeats in rotated form across sub-compartments. The firing rate map correlations are very low on average (~0.2), and far lower than the population of CA1 having repeating fields across the same vs different visual/tactile cue conditions. The authors should define the chance level of ratemap correlation by shuffling neuron identities. Apologies if this is indeed the current approach, but it seems not to be (I was left a bit lost by the description in the methods). For any population of hippocampal place cells, the cross-neuron correlations of firing rate maps are typically not zero, and correlations at 0.2 would normally be evidence for remapping.

(4) A somewhat picky point here that is not meant to claim that multi-compartment studies are not useful - the introduction states that real-world environments typically consist of multi-compartment rooms. This is certainly not true for rodents and is only sometimes true in humans.

(5) The discussion lacks a consideration of how such dRSC output might impact the target structures of dRSC.

(6) The discussion speaks to the idea that multi-directional neurons may aid in transitioning between contexts (sub-compartments). But it is notable that none of the multidirectional neurons have multi-directional tuning in all sub-compartments, but such firing was seen in the 2017 Nature Neuroscience study by Jacob/Jeffery. The discussion should address this difference and perhaps posit a means by which the firing of global and local head direction neurons can be related to each other to yield navigation that depends on both scales.

(7) The authors should provide the size of the smoothing function for spatial firing rate maps.

(8) The authors should devise a measure to define directional tuning in 4 directions (with 90-degree offsets).

(9) Figures 2D and 2H - The offsets in preferred tuning across sub-compartments are rather variable.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript uses primarily simulation tools to probe the pathway of cholesterol transport with the smoothened (SMO) protein. The pathway to the protein and within SMO is clearly discovered, and interactions deemed important are tested experimentally to validate the model predictions.

Strengths:

The authors have clearly demonstrated how cholesterol might go from the membrane through SMO for the inner and outer leaflets of a symmetrical membrane model. The free energy profiles, structural conformations, and cholesterol-residue interactions are clearly described.

We thank the reviewer for their kind words.

(1) Membrane Model: The authors decided to use a rather simple symmetric membrane with just cholesterol, POPC, and PSM at the same concentration for the inner and outer leaflets. This is not representative of asymmetry known to exist in plasma membranes (SM only in the outer leaflet and more cholesterol in this leaflet). This may also be important to the free energy pathway into SMO. Moreover, PE and anionic lipids are present in the inner leaflet and are ignored. While I am not requesting new simulations, I would suggest that the authors should clearly state that their model does not consider lipid concentration leaflet asymmetry, which might play an important role.

We thank the reviewer for their comment. Membrane asymmetry is inherent in endogenous systems; we acknowledge that as a limitation of our current model. We have addressed the comment by adding this limitation to our discussion in the manuscript.

Added lines: (End of paragraph 6, Results subsection 2):

“One possibility that might alter the thermodynamic barriers is native membrane asymmetry, particularly the anionic lipid-rich inner leaflet. This presents as a limitation of our current model.”

(2) Statistical comparison of barriers: The barriers for pathways 1 and 2 are compared in the text, suggesting that pathway 2 has a slightly higher barrier than pathway 1. However, are these statistically different? If so, the authors should state the p-value. If not, then the text in the manuscript should not state that one pathway is preferred over the other.

We thank the reviewer for their comment. We have added statistical t-tests for the barriers.

Changes made: (Paragraph 6, Results subsection 2)

“However, we also observe that pathway 1 shows a lower thermodynamic barrier (5.8 ± 0.7 kcal/mol v/s 6.5 ± 0.8 kcal/mol, p = 0.0013)”

(3) Barrier of cholesterol (reasoning): The authors on page 7 argue that there is an enthalpy barrier between the membrane and SMO due to the change in environment. However, cholesterol lies in the membrane with its hydroxyl interacting with the hydrophilic part of the membrane and the other parts in the hydrophobic part. How is the SMO surface any different? It has both characteristics and is likely balanced similarly to uptake cholesterol. Unless this can be better quantified, I would suggest that this logic be removed.

We thank the reviewer for this suggestion. We have removed the line to avoid confusion.

Reviewer #2 (Public review):

Summary:

In this work, the authors applied a range of computational methods to probe the translocation of cholesterol through the Smoothened receptor. They test whether cholesterol is more likely to enter the receptor straight from the outer leaflet of the membrane or via a binding pathway in the inner leaflet first. Their data reveal that both pathways are plausible but that the free energy barriers of pathway 1 are lower, suggesting this route is preferable. They also probe the pathway of cholesterol transport from the transmembrane region to the cysteine-rich domain (CRD).

Strengths:

(1) A wide range of computational techniques is used, including potential of mean force calculations, adaptive sampling, dimensionality reduction using tICA, and MSM modelling. These are all applied rigorously, and the data are very convincing. The computational work is an exemplar of a well-carried out study.

(2) The computational predictions are experimentally supported using mutagenesis, with an excellent agreement between their PMF and mRNA fold change data.

(3) The data are described clearly and coherently, with excellent use of figures. They combine their findings into a mechanism for cholesterol transport, which on the whole seems sound.

(4) The methods are described well, and many of their analysis methods have been made available via GitHub, which is an additional strength.

Weaknesses:

(1) Some of the data could be presented a little more clearly. In particular, Figure 7 needs additional annotation to be interpretable. Can the position of the cholesterol be shown on the graph so that we can see the diameter change more clearly?

We thank the reviewer for this suggestion. We have added the cholesterol positions as requested.

Changes made: (Caption, Figure 7)

“The tunnel profile during cholesterol translocation in SMO. (a) Free energy plot of the zcoordinate v/s the tunnel diameter when cholesterol is present in the core TMD. The tunnel shows a spike in the radius in the TMD domain, indicating the presence of a cholesterol-accommodating cavity. (b) Representative figure for the tunnel when a cholesterol molecule is in the TMD. (c) Same as (a), when cholesterol is at the TMD-CRD interface. (e) same as (b), when cholesterol is at the TMD-CRD interface. (e) same as (a), when cholesterol is at the CRD binding site. (f) same as (b), when cholesterol is at the CRD binding site. Tunnel diameters shown as spheres. Cholesterol positions marked on plots using dotted lines. All snapshots presented are frames taken from MD simulations.”

(2) In Figure 3C, it doesn’t look like the Met is constricting the tunnel at all. What residue is constricting the tunnel here? Can we see the Ala and Met panels from the same angle to compare the landscapes? Or does the mutation significantly change the tunnel? Why not A283 to a bulkier residue? Finally, the legend says that the figure shows that cholesterol can still pass this residue, but it doesn’t really show this. Perhaps if the HOLE graph was plotted, we could see the narrowest point of the tunnel and compare it to the size of cholesterol.

We thank the reviewer for this suggestion. A283 was mutated to methionine as it presents with a longer heavy tail containing sulfur. We have plotted the tunnel radii for both WT and A283M mutants and added them as a supplemental figure. As shown in the figure, the presence of methionine doesn’t completely block the tunnel, but occludes it, thereby increasing the barrier for cholesterol transport slightly.

Changes made: (End of Results subsection 1)

“When we calculated the PMF for cholesterol entry, A^2.60fM mutant showed restricted tunnel but it did not fully block the tunnel (Figure 3—figure Supplement 3).”

(3) The PMF axis in 3b and d confused me for a bit. Looking at the Supplementary data, it’s clear that, e.g., the F455I change increases the energy barrier for chol entering the receptor. But in 3d this is shown as a -ve change, i.e., favourable. This seems the wrong way around for me. Either switch the sign or make this clearer in the legend, please.

We thank the reviewer for this suggestion. We measured ∆PMF as PMF_WT PMF_mutant, hence the negative values. We have added additional text to the legend to clarify this.

Changes made: (Caption, Figure 3)

“(b) ∆Gli1 mRNA fold change (high SHH vs untreated) and ∆ PMF (difference of peak PMF , calculated as PMF_WT - PMF_mutant) plotted for the mutants in Pathway 1. (c) Example mutant A^2_._60fM shows that cholesterol can enter SMO through Pathway 1 even on a bulky mutation. (d) Same as (b) but for Pathway 2 (e) Example mutant L^5.62fA shows that cholesterol can enter SMO through Pathway 2 due to lesser steric hindrance. All snapshots presented are frames taken from MD simulations.”

Changes made: (Caption, Figure 6)

“(b) ∆Gli1 mRNA fold change (high SHH vs untreated) and ∆ PMF (difference of peak PMF, calculated as PMF_WT - PMF_mutant) plotted for mutants along the TMD-CRD pathway. (c, d) Example mutants Y^LDA and F^5.65fA show that cholesterol is unable to translocate through this pathway because of the loss of crucial hydrophobic contacts provided by Y207 and F484 and along the solvent-exposed pathway.”

(4) The impact of G280V is put down to a decrease in flexibility, but it could also be a steric hindrance. This should be discussed.

We thank the reviewer for this suggestion. We have added it as a possible mechanism of the decrease in activity of SMO.

Changes made: (Paragraph 5, Results subsection 1)

“We mutated G280^2.57f  to valine - G^2.57fV to test whether reducing the flexibility of TM2 prevents cholesterol entry into the TMD. Consequently, the activity of mSMO showed a decrease. However, this decrease could also be attributed to steric hindrance added by the presence of a bulky propyl group in valine.”

(5) Are the reported energy barriers of the two pathways (5.8plus minus0.7 and 6.5plus minus0.8 kcal/mol) significantly and/or substantially different enough to favour one over the other? This could be discussed in the manuscript.

We thank the reviewer for this suggestion. We have added statistical t-tests for the barriers.

Changes made: (Paragraph 6, Results subsection 2)

“However, we also observe that pathway 1 shows a lower thermodynamic barrier (5.8 ± 0.7 kcal/mol v/s 6.5 ± 0.8 kcal/mol, p = 0.001)”

(6) Are the energy barriers consistent with a passive diffusion-driven process? It feels like, without a source of free energy input (e.g., ion or ATP), these barriers would be difficult to overcome. This could be discussed.

We thank the reviewer for this suggestion. We have added a discussion to further clarify this point.

Discussion: (Paragraph 6, Results subsection 2)

“These values are comparable to ATP-Binding Cassette (ABC) transporters of membrane lipids, which use ATP hydrolysis (-7.54 ± 0.3 kcal/mol) (Meurer et al., 2017) to drive lipid transport from the membrane to an extracellular acceptor. Some of these transporters share the same mechanism as SMO, where the lipid from the inner leaflet is flipped and transported to the extracellular acceptor protein (Tarling et al., 2013). Additionally, for secondary active transporters that do not use ATP for the transport of substrates, a thermodynamic barrier of 5-6 kcal/mol has been reported in literature. (Chan et al., 2022; Selvam et al., 2019; McComas et al., 2023; Thangapandian et al., 2025).”

(7) Regarding the kinetics from MSM, it is stated that the values seen here are similar to MFS transporters, but this then references another MSM study. A comparison to experimental values would support this section a lot.

We thank the reviewer for this suggestion. We have added a discussion discussing millisecond-scale timescales measured for MFS transporters.

Changes made: (Paragraph 2, Results subsection 5)

“These timescales are comparable to the substrate transport timescales of Major Facilitator Superfamily (MFS) transporters (Chan et al., 2022). Furthermore, several experimental studies have also resolved the millisecond-scale kinetics of MFS transporters (Blodgett and Carruthers, 2005; Körner et al., 2024; Bazzone et al., 2022; Smirnova et al., 2014; Zhu et al., 2019), further corroborating the results from our study.”

Reviewer #2 (Recommendations for the authors):

(1) The heatmaps in Figures 2a and 4a are great. On these, an arrow denotes what looks like a minimum energy path. Is it possible to see this plotted, as this might show the height of the energy barriers more clearly?

We thank the reviewer for this suggestion. We have computed the minimum energy paths for both pathways and presented them in a supplementary figure.

Added lines: (Paragraph 4, Results subsection 1):

For further clarity, we have plotted the minimum energy path taken by cholesterol as it translocates along this pathway (Figure 2—figure Supplement 3)a,b)

Added lines: (Paragraph 4, Results subsection 2):

For further clarity, we have plotted the minimum energy path taken by cholesterol as it translocates along this pathway (Figure 2—figure Supplement 3)c,d)

(2) The tiCA data in S15 is first referred to on line 137, but the technique isn’t introduced until line 222. This makes understanding the data a little confusing. Reordering this might improve readability.

We thank the reviewer for this suggestion. We have reordered the text to make it clearer.

Changes made: (Paragraph 2, Results subsection 1) This provides evidence for multiple stable poses along the pathway as observed in the multiple stable poses of cholesterol in Cryo-EM structures of SMO bound to sterols (Deshpande et al., 2019; Qi et al., 2019b, 2020). A reliable estimate of the barriers comes from using the time-lagged Independent Components (tICs), which project the entire dataset along the slowest kinetic degrees of freedom. Overall, the highest barrier along Pathway 1 is ∼ 5.8 ± 0.7 kcal/mol, and it is associated with the entry of cholesterol into the TMD (Figure 2—Figure Supplement 2).

Changes made: (Paragraph 3, Results subsection 2)

“On plotting the first two components of tICs, (Figure 2—Figure Supplement 2), we observe that the energetic barrier between η and θ is ∼6.5 ± 0.8 kcal/mol.”

(3) Missing bracket on line 577.

We thank the reviewer for this suggestion. The typo has been fixed.

(4) Line 577: Fig. S2nd?

We thank the reviewer for this suggestion. This typo has been fixed.

Reviewer #3 (Public review):

Summary:

This manuscript presents a study combining molecular dynamics simulations and Hedgehog (Hh) pathway assays to investigate cholesterol translocation pathways to Smoothened (SMO), a G protein-coupled receptor central to Hedgehog signal transduction. The authors identify and characterize two putative cholesterol access routes to the transmembrane domain (TMD) of SMO and propose a model whereby cholesterol traverses through the TMD to the cysteine-rich domain (CRD), which is presented as the primary site of SMO activation. The MD simulations and biochemical experiments are carefully executed and provide useful data.

Weaknesses:

However, the manuscript is significantly weakened by a narrow and selective interpretation of the literature, overstatement of certain conclusions, and a lack of appropriate engagement with alternative models that are well-supported by published data-including data from prior work by several of the coauthors of this manuscript. In its current form, the manuscript gives a biased impression of the field and overemphasizes the role of the CRD in cholesterol-mediated SMO activation. Below, I provide specific points where revisions are needed to ensure a more accurate and comprehensive treatment of the biology.

(1) Overstatement of the CRD as the Orthosteric Site of SMO Activation

The manuscript repeatedly implies or states that the CRD is the orthosteric site of SMO activation, without adequate acknowledgment of alternative models. To give just a few examples (of many in this manuscript):

(a) “PTCH is proposed to modulate the Hh signal by decreasing the ability of membrane cholesterol to access SMO’s extracellular cysteine-rich domain (CRD)” (p. 3).

(b) “In recent years, there has been a vigorous debate on the orthosteric site of SMO” (p. 3).

(c) “cholesterol must travel through the SMO TMD to reach the orthosteric site in the CRD” (p. 4).

(d) “we observe cholesterol moving along TM6 to the TMD-CRD interface (common pathway, Fig. 1d) to access the orthosteric binding site in the CRD” (p. 6).

While the second quote in this list at least acknowledges a debate, the surrounding text suggests that this debate has been entirely resolved in favor of the CRD model. This is misleading and not reflective of the views of other investigators in the field (see, for example, a recent comprehensive review from Zhang and Beachy, Nature Reviews Molecular and Cell Biology 2023, which makes the point that both the CRD and 7TM sites are critical for cholesterol activation of SMO as well as PTCH-mediated regulation of SMO-cholesterol interactions).

In contrast, a large body of literature supports a dual-site model in which both the CRD and the TMD are bona fide cholesterol-binding sites essential for SMO activation. Examples include:

(a) Byrne et al., Nature 2016: point mutation of the CRD cholesterol binding site impairs-but does not abolish-SMO activation by cholesterol (SMO D99A, Y134F, and combination mutants - Fig 3 of the 2016 study).

(b) Myers et al., Dev Cell 2013 and PNAS 2017: CRD deletion mutants retain responsiveness to PTCH regulation and cholesterol mimetics (similar Hh responsiveness of a CRD deletion mutant is also observed in Fig. 4 Byrne et al, Nature 2016).

(c) Deshpande et al., Nature 2019: mutation of residues in the TMD cholesterol binding site blocks SMO activation entirely, strongly implicating the TMD as a required site, in contrast to the partial effects of mutating or deleting the CRD site.

Qi et al., Nature 2019, and Deshpande et al., Nature 2019, both reported cholesterol binding at the TMD site based on high-resolution structural data. Oddly, Deshpande et al., Nature 2019, is not cited in the discussion of TMD binding on p. 3, despite being one of the first papers to describe cholesterol in the TMD site and its necessity for activation (the authors only cite it regarding activation of SMO by synthetic small molecules).

Kinnebrew et al., Sci Adv 2022 report that CRD deletion abolished PTCH regulation, which is seemingly at odds with several studies above (e.g., Byrne et al, Nature 2016; Myers et al, Dev Cell 2013); but this difference may reflect the use of an N-terminal GFP fusion to SMO in the Kinnebrew et al 2022, which could alter SMO activation properties by sterically hindering activation at the TMD site by cholesterol (but not synthetic SMO agonists like SAG); in contrast, the earlier work by Byrne et al is not subject to this caveat because it used an untagged, unmodified form of SMO.

Although overexpression of PTCH1 and SMO (wild-type or mutant) has been noted as a caveat in studies of CRD-independent SMO activation by cholesterol, this reviewer points out that several of the studies listed above include experiments with endogenous PTCH1 and low-level SMO expression, demonstrating that SMO can clearly undergo activation by cholesterol (as well as regulation by PTCH1) in a manner that does not require the CRD.

Recommendation: The authors should revise the manuscript to provide a more balanced overview of the field and explicitly acknowledge that the CRD is not the sole activation site. Instead, a dual-site model is more consistent with available structural, mutational, and functional data. In addition, the authors should reframe their interpretation of their MD studies to reflect this broader and more accurate view of how cholesterol binds and activates SMO.

We thank the reviewer for this comprehensive overview of the existing literature. We agree that cholesterol binding to both the TMD and CRD sites is required for full activation of SMO. As described below in responses to comments, we have made changes to the manuscript to make this point clear. For instance, in the revised manuscript, we refrain from calling the CRD cholesterol binding site the “orthosteric site”. Instead, we highlight that the goal of the manuscript is not to resolve the debate over whether the TMD or CRD site is more important for PTCH1 regulation by SMO but rather to use molecular dynamics to understand the fascinating question of how cholesterol in the membrane can reach the CRD, located at a significant distance above the outer leaflet of the membrane. We believe that this is an important goal since there is an abundance of evidence that supports the view that PTCH1 inhibits SMO by reducing cholesterol access to the CRD. This evidence is now summarized succinctly in the introduction:

Changes made: (Paragraph 4, Introduction)

“While cholesterol binding to both the TMD and CRD sites is required for full SMO activation, our work focuses on how cholesterol gains access to the CRD site, perched above the outer leaflet of the membrane (Luchetti et al., 2016; Kinnebrew et al., 2022). Multiple lines of evidence suggest that PTCH1-regulated cholesterol binding to the CRD plays an instructive role in SMO regulation both in cells and animals. Mutations in residues predicted to make hydrogen bonds with the hydroxyl group of cholesterol bound to the CRD reduced both the potency and efficacy of SHH in cellular signaling assays (Kinnebrew et al., 2022; Byrne et al., 2016) and, more importantly, eliminated HH signaling in mouse embryos (Xiao et al., 2017). Experiments using both covalent and photocrosslinkable sterol probes in live cells directly show that PTCH1 activity reduces sterol access to the CRD (Kinnebrew et al., 2022; Xiao et al., 2017). Notably, our simulations evaluate a path of cholesterol translocation that includes both the TMD and CRD sites: cholesterol first enters the 7-transmembrane domain bundle from the membrane; it then engages the TMD site before continuing along a conduit to the CRD site. Thus, we analyze translocation energetics and residue-level contacts along a path that includes both the TMD and the CRD.”

However, Reviewer 3 makes several comments below that are biased, inaccurate, or selective. We feel it is important to address these so readers can approach the literature from a balanced perspective. Indeed, the eLife review forum provides an ideal venue to present contrasting views on a scientific model. We encourage the editors to publish both Reviewer 3’s comments and our response in full so readers can read the original papers and reach their own conclusions. It is important to note these issues are not relevant to the quality of the computational and experimental data presented in this paper.

We have now removed the term “orthosteric” to describe the CRD site throughout the paper and clearly state in the introduction that “both the CRD and TMD sites are required for SMO activation” but that our focus is on how cholesterol moves from the membrane to the CRD site. There is no doubt that cholesterol binding to the CRD plays a key role in SMO activation– our focus on this path is justified and does not devalue the importance of the TMD site. Our prior models (see Figure 7 of Kinnebrew 2022 explicitly include contributions of both sites).

Now we respond to some of the concerns outlined, individually:

(1) Byrne et al., Nature 2016: point mutation of the CRD cholesterol binding site impairs-but does not abolish-SMO activation by cholesterol (SMO D99A, Y134F, and combination mutants - Fig 3 of the 2016 study)

The fact that a point mutation dramatically diminishes (but does not abolish signaling) does not mean that the CRD cholesterol binding site is not important for SMO regulation. Indeed, the reviewer fails to mention that Song et. al. (Molecular Cell, 2017) found that a SMO protein carrying a subtle mutation at D99 (D95/99N, a residue that makes a hydrogen bond with the cholesterol hydroxyl) completely abolishes SMO signaling in mouse embryos. Thus, the CRD site is critical for SMO activation in an intact animal, justifying our focus on evaluating the path of cholesterol translocation to the CRD site.

(2) Myers et al., Dev Cell 2013 and PNAS 2017: CRD deletion mutants retain responsiveness to PTCH regulation and cholesterol mimetics (similar Hh responsiveness of a CRD deletion mutant is also observed in Fig 4 Byrne et al, Nature 2016).

The Reviewer fails to note that CRD-deleted versions of SMO have markedly (>10-fold) higher basal (i.e. ligand-independent) activity compared to full-length SMO. The response to SHH is minimal (∼2-fold), compared to >50-100-fold with full-length SMO. Thus, CRD-deleted SMO is likely in a non-native conformation. Local changes in cholesterol accessibility caused by PTCH1 inactivation or cholesterol loading can cause small fluctuations in delta-CRD activity, but this cannot be used to infer meaningful insights about how native, full-length SMO (with >10-fold lower basal activity) is regulated. We encourage the reviewer to read our previous paper (Kinnebrew et. al. 2022), which presents a unified view of how the TMD and CRD sites together regulate SMO activation.

A more physiological experiment, reported in Kinnebrew et. al. 2022, tested mutations in residues that make hydrogen bonds with cholesterol at the CRD and TMD sites in the context of full-length SMO. These mutants were stably expressed at moderate levels in Smo^−/− cells. Mutations at the CRD site reduced the fold-increase in signaling output in response to SHH, as would be expected for a PTCH1-regulated site. In contrast, analogous mutations in the TMD site reduced the magnitude of both basal and maximal signaling, without affecting the fold-change in response to SHH. In signaling assays, the key parameter in evaluating the impact of a mutation is whether it impacts the change in output in response to a signal (in this case PTCH1 inactivation by SHH). A mutation in SMO that affects PTCH1 regulation is expected to decrease the fold-change in signaling in response to SHH, a criterion that is fulfilled by mutations in the CRD site. Accordingly, mutations in the CRD site abolish SMO signaling in mouse embryos (Xiao et al., 2017).

(3) Deshpande et al., Nature 2019: mutation of residues in the TMD cholesterol binding site blocks SMO activation entirely, strongly implicating the TMD as a required site, in contrast to the partial effects of mutating or deleting the CRD site.

Introduction of bulky mutations at the TMD site (V333F) that abolish SMO activity were first reported by Byrne et. al. 2016 and were used to markedly increase the stability of SMO for protein expression. These mutations indeed stabilize the inactive state of SMO, increasing protein abundance and completely preventing its localization at primary cilia. SMO variants carrying such bulky mutations cannot be used to infer the importance of the TMD site since they do not distinguish between the following possibilities: (1) SMO is inactive because the sterol cannot bind, or (2) SMO is inactive because it is locked in an inactive conformation, or (3) SMO is inactive because it cannot localize to primary cilia (where it must be localized to activate downstream signaling).

As described in Response 3.3, a better evaluation of the importance of the TMD site is the use of mutations in residues that make hydrogen bonds with the hydroxyl group of TMD cholesterol. These mutations do not markedly increase protein stability or prevent ciliary localization (Kinnebrew 2022, Fig.S2). While a TMD site mutation decreases the magnitude of maximal (and basal) SMO signaling, it does not impact the fold-increase in signal output in response to Hh ligands (the key parameter that should be used to evaluate PTCH1 activity).

(4) Qi et al., Nature 2019, and Deshpande et al., Nature 2019, both reported cholesterol binding at the TMD site based on high-resolution structural data. Oddly, Deshpande et al., Nature 2019 not cited in the discussion of TMD binding on p. 3, despite being one of the first papers to describe cholesterol in the TMD site and its necessity for activation (the authors only cite it regarding activation of SMO by synthetic small molecules)

The reference has now been added at this location in the manuscript.

(5) Kinnebrew et al., Sci Adv 2022 report that CRD deletion abolished PTCH regulation, which is seemingly at odds with several studies above (e.g., Byrne et al, Nature 2016; Myers et al, Dev Cell 2013); but this difference may reflect the use of an N-terminal GFP fusion to SMO in the Kinnebrew et al 2022, which could alter SMO activation properties by sterically hindering activation at the TMD site by cholesterol (but not synthetic SMO agonists like SAG); in contrast, the earlier work by Byrne et al is not subject to this caveat because it used an untagged, unmodified form of SMO.

The reviewer fails to note that CRD deleted versions of SMO have markedly (>10-fold) higher basal activity than full-length SMO. The response to SHH is minimal (∼2fold), compared to >50-fold with full-length SMO. Thus, CRD-deleted SMO is likely in a non-native conformation. Local changes in cholesterol accessibility caused by PTCH1 inactivation or cholesterol loading can cause small fluctuations in delta-CRD activity, but this cannot be used to infer meaningful insights about how native, full-length SMO (with >10-fold lower basal activity) is regulated. Please see Response 3.3 for further details.

Reviewer 3 presents an incomplete picture of the extensive experiments reported in Kinnebrew et. al. to establish the functionality of YFP-tagged delta-CRD SMO. Most importantly, a TMDselective sterol analog (KK174) can fully activate YFP-tagged delta-CRD, showing conclusively that the YFP fusion does not block sterol access to the TMD site. The fact that this protein is nearly unresponsive to SHH highlights the critical role of the CRD-bound cholesterol in SMO regulation by PTCH1. Indeed, the YFP-tagged, CRD-deleted SMO was made purposefully to test the requirement of the CRD in a construct that had normal basal activity. Again, this data justifies the value of investigating the path of cholesterol movement from the membrane via the TMD site to the CRD.

(6) Although overexpression of PTCH1 and SMO (wild-type or mutant) has been noted as a caveat in studies of CRD-independent SMO activation by cholesterol, this reviewer points out that several of the studies listed above include experiments with endogenous PTCH1 and low-level SMO expression, demonstrating that SMO can clearly undergo activation by cholesterol (as well as regulation by PTCH1) in a manner that does not require the CRD.

This comment is inaccurate. The data presented in Deshpande et. al. (and prior work in Myers et. al.) used transient transfection to overexpress SMO in Smo^−/− cells. At the individual cell level transient transfection produces expression levels that are markedly higher (10-1000-fold) than stable expression (in addition to being more variable). Most scientists would agree that stable expression (as used in Kinnebrew 2022) at a moderate expression level is a better system to compare mutant phenotypes, assess basal and activated signaling, and provide an accurate measure of the fold-change in signal output in response to SHH. Notably, introduction of a mutation in the CRD cholesterol binding site at the endogenous mouse Smo locus (an even better experiment than stable expression) leads to complete loss of SMO activity (PMID 28344083). This result again justifies our investigation of the pathway of cholesterol movement from the membrane to the CRD site.

We have changed the initial discussion and reflect a more general outlook.

Changes made: (Paragraph 1, Introduction)

“PTCH modulates the availability of accessible cholesterol at the primary cilium and thereby regulates SMO, with models invoking effects on both the CRD and 7TM pockets.”

Changes made: (Results subsection 3, paragraph 1)

“According to the dual-site model, to reach the binding site in the CRD (ζ), cholesterol translocate along the TMD-CRD interface from the TM binding site (α∗) is required.”

Added lines: (Paragraph 5, Results subsection 3):

“The computational investigation showed here covers the dual-site model, where cholesterol reaches the CRD site via binding to the TM binding site first. In comparison to the CRD site, the TM site is more stable by ∼ 2 kcal/mol (Figure 2—Figure Supplement 3b, d).”

Added lines: (Paragraph 2, Conclusions):

“Here we have explored the role the CRD-site plays in SMO activation. In addition, through simulating the CRD site-dependent SMO activation hypothesis, we have also simulated the TMD site-dependent activation. We show that the overall stability of cholesterol is higher than the CRD site by ∼ 2 kcal/mol.”

(2) Bias in Presentation of Translocation Pathways

The manuscript presents the model of cholesterol translocation through SMO to the CRD as the predominant (if not sole) mechanism of activation. Statements such as: "Cholesterol traverses SMO to ultimately reach the CRD binding site" (p. 6) suggest an exclusivity that is not supported by prior literature in the field. Indeed, the authors’ own MD data presented here demonstrate more stable cholesterol binding at the TMD than at the CRD (p 17), and binding of cholesterol to the TMD site is essential for SMO activation. As such, it is appropriate to acknowledge that cholesterol may activate SMO by translocating through the TM5/6 tunnel, then binding to the TMD site, as this is a likely route of SMO activation in addition to the CRD translocation route they highlight in their discussion.

The authors describe two possible translocation pathways (Pathway 1: TM2/3 entry to TMD; Pathway 2: TM5/6 entry and direct CRD transfer), but do not sufficiently acknowledge that their own empirical data support Pathway 2 as more relevant. Indeed, because their experimental data suggest Pathway 2 is more strongly linked to SMO activation, this pathway should be weighted more heavily in the authors’ discussion. In addition, Pathway 2 is linked to cholesterol binding to both the TMD and CRD sites (the former because the TMD binding site is at the terminus of the hydrophobic tunnel, the latter via the translocation pathway described in the present manuscript), so it is appropriate that Pathway 2 figures more prominently than Pathway 1 in the authors’ discussion.

The authors also claim that "there is no experimental structure with cholesterol in the inner leaflet region of SMO TMD" (p 16). However, a structural study of apo-SMO from the Manglik and Cheng labs (Zhang et al., Nat Comm, 2022) identified a cholesterol molecule docked at the TM5/6 interface and also proposed a "squeezing" mechanism by which cholesterol could enter the TM5/6 pocket from the membrane. The authors do not consider this SMO conformation in their models, nor do they discuss the possibility that conformational dynamics at the TM5/6 interface could facilitate cholesterol flipping and translocation into the hydrophobic conduit, despite both possibilities having precedent in the 2022 empirical cryoEM structural analysis.

Recommendation: The authors should avoid oversimplifying the SMO cholesterol activation process, either by tempering these claims or broadening their discussion to better reflect the complexity and multiplicity of cholesterol access and activation routes for SMO. They should also consider the 2022 apo-SMO cryoEM structure in their analysis of the TM5/6 translocation pathway.

We thank the reviewer for this comprehensive overview of the existing literature and parts we have missed to include in the discussion. We agree with the reviewer, since our data shows that both pathways are probable. Through our manuscript, we have avoided using a competitive approach (that one pathway dominates over the other). Instead, we have evaluated both pathways independently and presented a comparative rather than competitive overview of both pathways from our observations. While we agree that experimental evidence suggests the inner leaflet pathway is possible, we cannot discount the observations made in previous studies that support the outer leaflet pathway, particularly Hedger et al. (2019), Bansal et al. (2023), and Kinnebrew et al. (2021). Therefore, considering the reviewer’s comments have made the following changes:

(1) Added lines: (Paragraph 3, Conclusions):

“We show that the barriers associated with the pathway starting from the outer leaflet are lower by ∼0.7 kcal, (p=0.0013). We also provide evidence that cholesterol can enter SMO via both leaflets, considering that multiple computational and experimental studies have found cholesterol entry sites and activation modulation via the outer leaflet, between TM2TM3. This is countered by evidence from multiple experimental and computational studies corroborating entry via the inner leaflet, between TM5-TM6, including this study. Overall, we posit that cholesterol translocation from either pathway is feasible.”

(2)nChanges made: (Paragraph 6, Results subsection 2)

“Based on our experimental and computational data, we conclude that cholesterol translocation can happen via either pathway. This is supported on the basis of the following observations: mutations along pathway 2 affect SMO activity more significantly, and the presence of a direct conduit that connects the inner leaflet to the TMD binding site. In addition, a resolved structure of SMO in the presence of cholesterol shows a cholesterol situated at the entry point from the membrane into the protein between TM5 and TM6, in the inner leaflet. However, we also observe that pathway 1 shows a lower thermodynamic barrier (5.8 ± 0.7 kcal/mol vs. 6.5 ± 0.8 kcal/mol, p \= 0.0013). Additionally, PTCH1 controls cholesterol accessibility in the outer leaflet. This shows that there is a possibility for transport from both leaflets. One possibility that might alter the thermodynamic barriers is native membrane asymmetry, particularly the anionic lipid-rich inner leaflet. This presents as a limitation of our current model.”

(3)nChanges made: (Paragraph 1, Results subsection 2)

“In a structure resolved in 2022, cholesterol was observed at the interface between the protein and the membrane, in the inner leaflet, between TMs 5 and 6. However, cholesterol in the inner leaflet has a downward orientation, with the polar hydroxyl group pointing intracellularly (η). A striking observation is that this cholesterol binding site pose was never used as a starting point for simulations and was discovered independent of the pose described in Zhang et al. (2022) (Figure 4—Figure Supplement 1).”

(3) Alternative Possibility: Direct Membrane Access to CRD

The possibility that the CRD extracts cholesterol directly from the membrane outer leaflet is not considered. While the crystal structures place the CRD in a stable pose above the membrane, multiple cryo-EM studies suggest that the CRD is dynamic and adopts a variety of conformations, raising the possibility that the stability of the CRD in the crystal structures is a result of crystal packing and that the CRD may be far more dynamic under more physiological conditions.

Recommendation: The authors should explicitly acknowledge and evaluate this potential mechanism and, if feasible, assess its plausibility through MD simulations.

We thank the reviewer for the suggestion. We have addressed this comment by calculating the distance from the lipid headgroups for each lipid in the membrane to the cholesterol binding site. We show that in our study, we do not observe any bending of the CRD over the membrane, precluding any cholesterol from being extracted from the membrane directly.

Added lines: (Paragraph 3, Conclusions):

“An alternative possibility states that the flexibility associated with the CRD would allow it to directly access the membrane, and consequently, cholesterol. In the extensive simulations reported in this study, the binding site of cholesterol in the CRD remains at least 20 Å away from the nearest lipid head group in the membrane, suggesting that such direct extraction and the bending of the CRD do not occur within the timescales sampled (Appendix 2 – Figure 6).

The mechanistic details of this process are still unexplored and form the basis of future work.”

(4) Inconsistent Framing of Study Scope and Limitations

The discussion contains some contradictory and misleading language. For example, the authors state that "In this study we only focused on the cholesterol movement from the membrane to the CRD binding site," and then several sentences later state that "We outline the entire translocation mechanism from a kinetic and thermodynamic perspective." These statements are at odds. The former appropriately (albeit briefly) notes the limited scope of the modeling, while the latter overstates the generality of the findings.

In addition, the authors’ narrow focus on the CRD site constitutes a major caveat to the entire work. It should be acknowledged much earlier in the manuscript, preferably in the introduction, rather than mentioned as an aside in the penultimate paragraph of the conclusion.

Recommendation: The authors should clarify the scope of the study and expand the discussion of its limitations. They should explicitly acknowledge that the study models one of several cholesterol access routes and that the findings do not rule out alternative pathways.

We thank the reviewer for the suggestion. We have addressed this comment by explicitly mentioning the scope of the study.

Changes made: (Paragraph 3, Conclusions)

“We outline the entire translocation mechanism from a kinetic and thermodynamic perspective for one of the leading hypotheses for the activation mechanism of SMO.”

(5) Summary:

This study has the potential to make a useful contribution to our understanding of cholesterol translocation and SMO activation. However, in its current form, the manuscript presents an overly narrow and, at times, misleading view of the literature and biological models; as such, it is not nearly as impactful as it could be. I strongly encourage the authors to revise the manuscript to include:

(1) A more balanced discussion of the CRD vs. TMD binding sites.

(2) Acknowledgment of alternative cholesterol access pathways.

(3) More comprehensive citation of prior structural and functional studies.

(4) Clarification of assumptions and scope.

Of note, the above suggestions require little to no additional MD simulations or experimental studies, but would significantly enhance the rigor and impact of the work.

We thank the reviewer for the suggestions. We have taken into account the literature and diverse viewpoints. We have changed the initial discussion and reflected a more general outlook. In the revised version of the manuscript, we have refrained from referring to the CRD site as the orthosteric site. Instead, we refer to it as the CRD sterol-binding site. To better represent the dual-site model, we add further discussion in the Introduction. Through our manuscript, we have avoided using a competitive approach (that one pathway dominates over the other). Instead, we have evaluated both pathways independently and presented a comparative rather than competitive overview of both pathways from our observations. We explicitly mention the scope of the study.

via u/Forge_Le_Femme

I think the suggestion to set aside your writing for a day is the most underrated part of this chapter. When you're too close to a draft, your brain automatically fills in the logical gaps because you already know what you meant to say, so taking that break is actually a mechanical necessity to help you read the words that are actually on the page rather than the ones in your head.

something to really consider. people even put their headset on with music playing when they are reading, i wonder how they assimilate or even understand what they are reading, talk less of to retain and remember the information that they read about, someone like me, the music lyrics will just be ringing in my head and my mind.

This is a good point to begin with. For my Women's Literature class, I recently was assigned to read an essay that I only barely grasped. My head hurt from trying to piece together what the incredibly intellectual author was saying. There were several passages I had to reread out loud. Which brings up the point of how interesting it is that rereading something, especially out loud, can help us "see" what the author wants us to see, or at least our interpretation of it.

I've actually never thought about that before! Then again, i cannot visualize anything in my head, so maybe that is why. But hearing out loud is such an interesting concept. I wonder why, in theory, our brain has to visualize each and every word on the page?

As someone with little experience going into this reading, I would have appreciated an inclusion of what CRP stands for, as it would help me visualize how to reverse the steps better in my head.

These fears are so real and so relatable. My initial fears were around judgement. Judgement that I was too old, or whats the point of going to college now at 63. A bit of shame that I had not gone to college after high School, which was always a thought in the back of my head. It made me feel less than, even though I had acheived great financial and professional success. I am now loving taking classes, and have a 4.0. My current anxiety is can I cut it at U.C. Berkeley or UC Davis one of which I plan to transfer to next year.

This scene shocks through the contrast between a child’s innocent fear and the extreme resolve it awakens in his mother. The question “you won’t let him get me?” reveals how the threat of being sold has invaded even the safety of sleep, underscoring slavery’s reach into the most intimate spaces of family life. This pure and genuine question awakens something in Eliza- a strength to continue the fight to escape and bring Harry to freedom no matter what. Stowe intensifies the emotional impact by showing maternal love as a source of physical power, framing motherhood as both sacred and revolutionary in the face of slavery’s violence.

The importance of this description of Rachel and her treatment of the slaves cannot be underscored for the juxtaposition it provides between the torture slaves were typically forced to endure and the warm hospitality she instead offers them. It presents to the audiences that the privileged and those who have been enslaved can live harmoniously as equals and not be oppositions to one another.

This sentence reveals the master's jealousy and how threatened he feels by George’s confidence, skill, and intelligence. Seeing George’s accomplishments at the factory makes the master view him as more than just a worker, so he acts quickly to reduce George back to a piece of property by taking him home and forcing him into hard labor.

the conversation shift too romeo and lady montaque is asking benvolioo where he is and is explained by montaque that he is always withdrawn and sad

Benvolio explains that the servants of both houses started fighting first. He tried to stop them, but then Tybalt being the hot head he is, came and attacked him he later gets asked by Lady Montague if he was involved in the fight which he wasnt

Montague is asking who started the fight

I like this! Our world runs based on what we pay attention to, and what is attention and a willingness to do something about what is noticed but care? If we don't care about something, only the sensational or head turning is revealed to us-- it seems like we lose the everyday

Now we know its 100 percent confirmed that Omakakiins was the girl in the beginning

Harold used his blog as source for creating ebooks on diff topics. #idea Vgl jaarboeken v blogarchief

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1

Chen et al. engineered and characterized a suite of next-generation GECIs for the Drosophila NMJ that allow for the visualization of calcium dynamics within the presynaptic compartment, at presynaptic active zones, and in the postsynaptic compartment. These GECIs include ratiometric presynaptic Scar8m (targeted to synaptic vesicles), ratiometric active zone localized Bar8f (targeted to the scaffold molecule BRP), and postsynaptic SynapGCaMP8m. The authors demonstrate that these new indicators are a large improvement on the widely used GCaMP6 and GCaMP7 series GECIs, with increased speed and sensitivity. They show that presynaptic Scar8m accurately captures presynaptic calcium dynamics with superior sensitivity to the GCaMP6 and GCaMP7 series and with similar kinetics to chemical dyes. The active-zone targeted Bar8f sensor was assessed for the ability to detect release-site-specific nanodomain changes, but the authors concluded that this sensor is still too slow to accurately do so. Lastly, the use of postsynaptic SynapGCaMP8m was shown to enable the detection of quantal events with similar resolution to electrophysiological recordings. Finally, the authors developed a Python-based analysis software, CaFire, that enables automated quantification of evoked and spontaneous calcium signals. These tools will greatly expand our ability to detect activity at individual synapses without the need for chemical dyes or electrophysiology.

We thank this Reviewer for the overall positive assessment of our manuscript and for the incisive comments.

(1) The role of Excel in the pipeline could be more clearly explained. Lines 182-187 could be better worded to indicate that CaFire provides analysis downstream of intensity detection in ImageJ. Moreover, the data type of the exported data, such as .csv or .xlsx, should be indicated instead of 'export to graphical program such as Microsoft Excel'.

We thank the Reviewer for these comments, many of which were shared by the other reviewers. In response, we have now 1) more clearly explained the role of Excel in the CaFire pipeline (lines 677-681), 2) revised the wording in lines 676-679 to indicate that CaFire provides analysis downsteam of intensity detection in ImageJ, and 3) Clarified the exported data type to Excel (lines 677-681). These efforts have improved the clarity and readability of the CaFire analysis pipeline.

(2) In Figure 2A, the 'Excel' step should either be deleted or included as 'data validation' as ImageJ exports don't require MS Excel or any specific software to be analysed. (Also, the graphic used to depict Excel software in Figure 2A is confusing.)

We thank the reviewer for this helpful suggestion. In the Fig. 2A, we have changed the Excel portion and clarified the processing steps in the revised methods. Specifically, we now indicate that ROIs are first selected in Fiji/ImageJ and analyzed to obtain time-series data containing both the time information and the corresponding imaging mean intensity values. These data are then exported to a spreadsheet file (e.g., Excel), which is used to organize the output before being imported into CaFire for subsequent analysis. These changes can be found in the Fig. 2A and methods (lines 676-681).

(3) Figure 2B should include the 'Partition Specification' window (as shown on the GitHub) as well as the threshold selection to give the readers a better understanding of how the tool works.

We absolutely agree with this comment, and have made the suggested changes to the Fig. 2B. In particular, we have replaced the software interface panels and now include windows illustrating the Load File, Peak Detection, and Partition functions. These updated screenshots provide a clearer view of how CaFire is used to load the data, detect events, and perform partition specification for subsequent analysis. We agree these changes will give the readers a better understanding of how the tool works, and we thank the reviewer for this comment.

(4) The presentation of data is well organized throughout the paper. However, in Figure 6C, it is unclear how the heatmaps represent the spatiotemporal fluorescence dynamics of each indicator. Does the signal correspond to a line drawn across the ROI shown in Figure 6B? If so, this should be indicated.

We apologize that the heatmaps were unclear in Fig panel 6C (Fig. 7C in the Current revision). Each heatmap is derived from a one-pixel-wide vertical line within a miniature-event ROI. These heatmaps correspond to the fluorescence change in the indicated SynapGCaMP variant of individual quantal events and their traces shown in Fig. 7C, with a representative image of the baseline and peak fluorescence shown in Fig. 7B. Specifically, we have added the following to the revised Fig. 7C legend:

The corresponding heatmaps below were generated from a single vertical line extracted from a representative miniature-event ROI, and visualize the spatiotemporal fluorescence dynamics (ΔF/F) along that line over time.

(5) In Figure 6D, the addition of non-matched electrophysiology recordings is confusing. Maybe add "at different time points" to the end of the 6D legend, or consider removing the electrophysiology trace from Figure 6D and referring the reader to the traces in Figure 7A for comparison (considering the same point is made more rigorously in Figure 7).

This is a good point, one shared with another reviewer. We apologize this was not clear, and have now revised this part of the figure to remove the electrophysiological traces in what is now Fig. 7 while keeping the paired ones still in what is now Fig. 8A as suggested by the reviewer. We agree this helps to clarify the quantal calcium transients.

(6) In GitHub, an example ImageJ Script for analyzing the images and creating the inputs for CaFire would be helpful to ensure formatting compatibility, especially given potential variability when exporting intensity information for two channels. In the Usage Guide, more information would be helpful, such as how to select ∆R/R, ideally with screenshots of the application being used to analyze example data for both single-channel and two-channel images.

We agree that additional details added to the GitHub would be helpful for users of CaFire. In response, we have now added the following improvements to the GitHub site: 

- ImageJ operation screenshots

Step-by-step illustrations of ROI drawing and Multi Measure extraction.

- Example Excel file with time and intensity values

Demonstrates the required data format for CaFire import, including proper headers.

- CaFire loading screenshots for single-channel and dual-channel imaging

Shows how to import GCaMP into Channel 1 and mScarlet into Channel 2.

- Peak Detection and Partition setting screenshots

Visual examples of automatic peak detection, manual correction, and trace partitioning.

- Instructions for ROI Extraction and CaFire Analysis

A written guide describing the full workflow from ROI selection to CaFire data export.

These changes have improved the usability and accessibility of CaFire, and we thank the reviewer for these points.

Reviewer #2

Calcium ions play a key role in synaptic transmission and plasticity. To improve calcium measurements at synaptic terminals, previous studies have targeted genetically encoded calcium indicators (GECIs) to pre- and postsynaptic locations. Here, Chen et al. improve these constructs by incorporating the latest GCaMP8 sensors and a stable red fluorescent protein to enable ratiometric measurements. In addition, they develop a new analysis platform, 'CaFire', to facilitate automated quantification. Using these tools, the authors demonstrate favorable properties of their sensors relative to earlier constructs. Impressively, by positioning postsynaptic GCaMP8m near glutamate receptors, they show that their sensors can report miniature synaptic events with speed and sensitivity approaching that of intracellular electrophysiological recordings. These new sensors and the analysis platform provide a valuable tool for resolving synaptic events using all-optical methods.

We thank the Reviewer for their overall positive evaluation and comments.

Major comments:

(1) While the authors rigorously compared the response amplitude, rise, and decay kinetics of several sensors, key parameters like brightness and photobleaching rates are not reported. I feel that including this information is important as synaptically tethered sensors, compared to freely diffusible cytosolic indicators, can be especially prone to photobleaching, particularly under the high-intensity illumination and high-magnification conditions required for synaptic imaging. Quantifying baseline brightness and photobleaching rates would add valuable information for researchers intending to adopt these tools, especially in the context of prolonged or high-speed imaging experiments.

This is a good point made by the reviewer, and one we agree will be useful for researchers to be aware. First, it is important to note that the photobleaching and brightness of the sensors will vary depending on the nature of the user’s imaging equipment, which can vary significantly between widefield microscopes (with various LED or halogen light sources for illumination), laser scanning systems (e.g., line scans with confocal systems), or area scanning systems using resonant scanners (as we use in our current study). Under the same imaging settings, GCaMP8f and 8m exhibit comparable baseline fluorescence, whereas GCaMP6f and 6s are noticeably dimmer; because our aim is to assess each reagent’s potential under optimal conditions, we routinely adjust excitation/camera parameters before acquisition to place baseline fluorescence in an appropriate dynamic range. As an important addition to this study, motivated by the reviewer’s comments above, we now directly compare neuronal cytosolic GCaMP8m expression with our Scar8m sensor, showing higher sensitivity with Scar8m (now shown in the new Fig. 3F-H).

Regarding photobleaching, GCaMP signals are generally stable, while mScarlet is more prone to bleaching: in presynaptic area scanned confocal recordings, the mScarlet channel drops by ~15% over 15 secs, whereas GCaMP6s/8f/8m show no obvious bleaching over the same window (lines 549-553). In contrast, presynaptic widefield imaging using an LED system (CCD), GCaMP8f shows ~8% loss over 15 secs (lines 610-611). Similarly, for postsynaptic SynapGCaMP6f/8f/8m, confocal resonant area scans show no obvious bleaching over 60 secs, while widefield shows ~2–5% bleaching over 60 secs (lines 634-638). Finally, in active-zone/BRP calcium imaging (confocal), mScarlet again bleaches by ~15% over 15 s, while GCaMP8f/8m show no obvious bleaching. The mScarlet-channel bleaching can be corrected in Huygens SVI (Bleaching correction or via the Deconvolution Wizard), whereas we avoid applying bleaching correction to the green GCaMP channel when no clear decay is present to prevent introducing artifacts. This information is now added to the methods (lines 548-553).

(2) In several places, the authors compare the performance of their sensors with synthetic calcium dyes, but these comparisons are based on literature values rather than on side-by-side measurements in the same preparation. Given differences in imaging conditions across studies (e.g., illumination, camera sensitivity, and noise), parameters like indicator brightness, SNR, and photobleaching are difficult to compare meaningfully. Additionally, the limited frame rate used in the present study may preclude accurate assessment of rise times relative to fast chemical dyes. These issues weaken the claim made in the abstract that "...a ratiometric presynaptic GCaMP8m sensor accurately captures .. Ca²⁺ changes with superior sensitivity and similar kinetics compared to chemical dyes." The authors should clearly acknowledge these limitations and soften their conclusions. A direct comparison in the same system, if feasible, would greatly strengthen the manuscript.

We absolutely agree with these points made the reviewer, and have made a concerted effort to address them through the following:

We have now directly compared presynaptic calcium responses on the same imaging system using the chemical dye Oregon Green Bapta-1 (OGB-1), one of the primary synthetic calcium indicators used in our field. These experiments reveal that Scar8f exhibits markedly faster kinetics and an improved signal-to-noise ratio compared to OGB-1, with higher peak fluorescence responses (Scar8f: 0.32, OGB-1: 0.23). The rise time constants of the two indicators are comparable (both ~3 msecs), whereas the decay of Scar8f is faster than that of OGB-1 (Scar8f: ~40, OGB-1: ~60), indicating more rapid signal recovery. These results now directly demonstrate the superiority of the new GCaMP8 sensors we have engineered over conventional synthetic dyes, and are now presented in the new Fig. 3A-E of the manuscript.

We agree with the reviewer that, in the original submission, the relatively slow resonant area scans (~115 fps) limited the temporal resolution of our rise time measurements. To address this, we have re-measured the rise time using higher frame-rate line scans (kHz). For Scar8f, the rise time constant was 6.736 msec at ~115 fps resonant area scanned, but shortened to 2.893 msec when imaged at ~303 fps, indicating that the original protocol underestimated the true kinetics. In addition, for Bar8m, area scans at ~118 fps yielded a rise time constant of 9.019 msec, whereas line scans at ~1085 fps reduced the rise time constant to 3.230 msec. These new measurements are now incorporated into the manuscript ( Figs. 3,4, and 6) to more accurately reflect the fast kinetics of these indicators.

(3) The authors state that their indicators can now achieve measurements previously attainable with chemical dyes and electrophysiology. I encourage the authors to also consider how their tools might enable new measurements beyond what these traditional techniques allow. For example, while electrophysiology can detect summed mEPSPs across synapses, imaging could go a step further by spatially resolving the synaptic origin of individual mEPSP events. One could, for instance, image MN-Ib and MN-Is simultaneously without silencing either input, and detect mEPSP events specific to each synapse. This would enable synapse-specific mapping of quantal events - something electrophysiology alone cannot provide. Demonstrating even a proof-of-principle along these lines could highlight the unique advantages of the new tools by showing that they not only match previous methods but also enable new types of measurements.

These are excellent points raised by the reviewer. In response, we have done the following: 

We have now included a supplemental video as “proof-of-principle” data showing simultaneous imaging of SynapGCaMP8m quantal events at both MN-Is and -Ib, demonstrating that synapse-specific spatial mapping of quantal events can be obtained with this tool (see new Supplemental Video 1). 

We have also included an additional discussion of the potential and limitations of these tools for new measurements beyond conventional approaches. This discussion is now presented in lines 419-421 in the manuscript.

(4) For ratiometric measurements, it is important to estimate and subtract background signals in each channel. Without this correction, the computed ratio may be skewed, as background adds an offset to both channels and can distort the ratio. However, it is not clear from the Methods section whether, or how, background fluorescence was measured and subtracted.

This is a good point, and we agree more clarification about how ratiometric measurements were made is needed. In response, we have now added the following to the Methods section (lines 548-568):

Time-lapse videos were stabilized and bleach-corrected prior to analysis, which visibly reduced frame-toframe motion and intensity drift. In the presynaptic and active-zone mScarlet channel, a bleaching factor of ~1.15 was observed during the 15 sec recording. This bleaching can be corrected using the “Bleaching correction” tool in Huygens SVI. For presynaptic and active-zone GCaMP signals, there was minimal bleaching over these short imaging periods. Therefore, the bleaching correction step for GCaMP was skipped. Both GCaMP and mScarlet channels were processed using the default settings in the Huygens SVI “Deconvolution Wizard” (with the exception of the bleaching correction option). Deconvolution was performed using the CMLE algorithm with the Huygens default stopping criterion and a maximum of 30 iterations, such that the algorithm either converged earlier or, if convergence was not reached, was terminated at this 30iteration limit; no other iteration settings were used across the GCaMP series. ROIs were drawn on the processed images using Fiji ImageJ software, and mean fluorescence time courses were extracted for the GCaMP and mScarlet channels, yielding F_GCaMP(t) and F_mScarlet(t). F(t)s were imported into CaFire with GCaMP assigned to Channel #1 (signal; required) and mScarlet to Channel #2 (baseline/reference; optional). If desired, the mScarlet signal could be smoothed in CaFire using a user-specified moving-average window to reduce high-frequency noise. In CaFire’s ΔR/R mode, the per-frame ratio was computed as R(t)=F_GCaMP(t) and F_mScarlet(t); a baseline ratio R0 was estimated from the pre-stimulus period, and the final response was reported as ΔR/R(t)=[R(t)−R0]/R0, which normalizes GCaMP signals to the co-expressed mScarlet reference and thereby reduces variability arising from differences in sensor expression level or illumination across AZs.

(5) At line 212, the authors claim "... GCaMP8m showing 345.7% higher SNR over GCaMP6s....(Fig. 3D and E) ", yet the cited figure panels do not present any SNR quantification. Figures 3D and E only show response amplitudes and kinetics, which are distinct from SNR. The methods section also does not describe details for how SNR was defined or computed.

This is another good point. We define SNR operationally as the fractional fluorescence change (ΔF/F). Traces were processed with CaFire, which estimates a per-frame baseline F₀(t) with a user-configurable sliding window and percentile. In the Load File panel, users can specify both the length of the moving baseline window and the desired percentile; the default settings are a 50-point window and the 30th percentile, representing a 101-point window centered on each time point (previous 50 to next 50 samples) and took the lower 30% of values within that window to estimate F₀(t). The signal was then computed as ΔF/F=[F(t)−F0(t)]/F0(t). This ΔF/F value is what we report as SNR throughout the manuscript and is now discussed explicitly in the revised methods (lines 686-693).

(6) Lines 285-287 "As expected, summed ΔF values scaled strongly and positively with AZ size (Fig. 5F), reflecting a greater number of Cav2 channels at larger AZs". I am not sure about this conclusion. A positive correlation between summed ΔF values and AZ size could simply reflect more GCaMP molecules in larger AZs, which would give rise to larger total fluorescence change even at a given level of calcium increase.

The reviewer makes a good point, one that we agree should be clarified. The reviewer is indeed correct that larger active zones should have more abundant BRP protein, which in turn will lead to a higher abundance of the Bar8f sensor, which should lead to a higher GCaMP response simply by having more of this sensor. However, the inclusion of the ratiometric mScarlet protein should normalize the response accurately, correcting for this confound, in which the higher abundance of GCaMP should be offset (normalized) by the equally (stoichiometric) higher abundance of mScarlet. Therefore, when the ∆R/R is calculated, the differences in GCaMP abundance at each AZ should be corrected for the ratiometric analysis. We now use an improved BRP::mScarlet3::GCaMP8m (Bar8m) and compute ΔR/R with R(t)=F_GCaMP8m/F_mScarlet3. ROIs were drawn over individual AZs (Fig. 6B). CaFire estimated R0 with a sliding 101-point window using the lowest 10% of values, and responses were reported as ΔR/R=[R−R0]/R0. Area-scan examples (118 fps) show robust ΔR/R transients (peaks ≈1.90 and 3.28; tau rise ≈9.0–9.3 ms; Fig. 6C, middle).

We have now made these points more clearly in the manuscript (lines 700-704) and moved the Bar8f intensity vs active zone size data to Table S1. Together, these revisions improve the indicator-abundance confound (via mScarlet normalization). 

(6) Lines 313-314: "SynapGCaMP quantal signals appeared to qualitatively reflect the same events measured with electrophysiological recordings (Fig. 6D)." This statement is quite confusing. In Figure 6D, the corresponding calcium and ephys traces look completely different and appear to reflect distinct sets of events. It was only after reading Figure 7 that I realized the traces shown in Figure 6D might not have been recorded simultaneously. The authors should clarify this point.

Yes, we absolutely agree with this point, one shared by Reviewer 1. In response, we have removed the electrophysiological traces in Fig. 6 to clarify that just the calcium responses are shown, and save the direct comparison for the Fig. 7 data (now revised Fig. 8).

(8) Lines 310-313: "SynapGCaMP8m .... striking an optimal balance between speed and sensitivity", and Lines 314-316: "We conclude that SynapGCaMP8m is an optimal indicator to measure quantal transmission events at the synapse." Statements like these are subjective. In the authors' own comparison, GCaMP8m is significantly slower than GCaMP8f (at least in terms of decay time), despite having a moderately higher response amplitude. It is therefore unclear why GCaMP8m is considered 'optimal'. The authors should clarify this point or explain their rationale for prioritizing response amplitude over speed in the context of their application.

This is another good point that we agree with, as the “optimal” sensor will of course depend on the user’s objectives. Hence, we used the term “an optimal sensor” to indicate it is what we believed to be the best one for our own uses. However, this point should be clarified and better discussed. In response, we have revised the relevant sections of the manuscript to better define why we chose the 8m sensors to strike an optimal balance of speed and sensitivity for our uses, and go on to discuss situations in which other sensor variants might be better suited. These are now presented in lines 223-236 in the revised manuscript, and we thank the reviewer for making these comments, which have improved our study.

Minor comments

(1)  Please include the following information in the Methods section:

(a) For Figures 3 and 4, specify how action potentials were evoked. What type of electrodes were used, where were they placed, and what amount of current or voltage was applied?

We apologize for neglecting to include this information in the original submission. We have now added this information to the revised Methods section (lines 537-543).

(b) For imaging experiments, provide information on the filter sets used for each imaging channel, and describe how acquisition was alternated or synchronized between the green and red channels in ratiometric measurements. Additionally, please report the typical illumination intensity (in mW/mm²) for each experimental condition.

We thank the reviewer for this helpful comment. We have now added detailed information about the imaging configuration to the Methods (lines 512-528) with the following:

Ca2+ imaging was conducted using a Nikon A1R resonant scanning confocal microscope equipped with a 60x/1.0 NA water-immersion objective (refractive index 1.33). GCaMP signals were acquired using the FITC/GFP channel (488-nm laser excitation; emission collected with a 525/50-nm band-pass filter), and mScarlet/mCherry signals were acquired using the TRITC/mCherry channel (561-nm laser excitation; emission collected with a 595/50-nm band-pass filter). ROIs focused on terminal boutons of MN-Ib or -Is motor neurons. For both channels, the confocal pinhole was set to a fixed diameter of 117.5 µm (approximately three Airy units under these conditions), which increases signal collection while maintaining adequate optical sectioning. Images were acquired as 256 × 64 pixel frames (two 12-bit channels) using bidirectional resonant scanning at a frame rate of ~118 frames/s; the scan zoom in NIS-Elements was adjusted so that this field of view encompassed the entire neuromuscular junction and was kept constant across experiments. In ratiometric recordings, the 488-nm (GCaMP) and 561-nm (mScarlet) channels were acquired in a sequential dual-channel mode using the same bidirectional resonant scan settings: for each time point, a frame was first collected in the green channel and then immediately in the red channel, introducing a small, fixed frame-to-frame temporal offset while preserving matched spatial sampling of the two channels.

Directly measuring the absolute laser power at the specimen plane (and thus reporting illumination intensity in mW/mm²) is technically challenging on this resonant-scanning system, because it would require inserting a power sensor into the beam path and perturbing the optical alignment; consequently, we are unable to provide reliable absolute mW/mm² values. Instead, we now report all relevant acquisition parameters (objective, numerical aperture, refractive index, pinhole size, scan format, frame rate, and fixed laser/detector settings) and note that laser powers were kept constant within each experimental series and chosen to minimize bleaching and phototoxicity while maintaining an adequate signal-to-noise ratio. We have now added the details requested in the revised Methods section (lines 512-535), including information about the filter sets, acquisition settings, and typical illumination intensity.

(2) Please clarify what the thin versus thick traces represent in Figures 3D, 3F, 4C, and 4E. Are the thin traces individual trials from the same experiment, or from different experiments/animals? Does the thick trace represent the mean/median across those trials, a fitted curve, or a representative example?

We apologize this was not more clear in the original submission. Thin traces are individual stimulus-evoked trials (“sweeps”) acquired sequentially from the same muscle/NMJ in a single preparation; the panel is shown as a representative example of recordings collected across animals. The thick colored trace is the trialaveraged waveform (arithmetic mean) of those thin traces after alignment to stimulus onset and baseline subtraction (no additional smoothing beyond what is stated in Methods). The thick black curve over the decay phase is a single-exponential fit used to estimate τ. Specifically, we fit the decay segment by linear regression on the natural-log–transformed baseline-subtracted signal, which is equivalent to fitting y = y_peak·e^−t/τdecay over the decay window (revised Fig.4D and Fig.5C legends).

(3) Please clarify what the reported sample size (n) represents. Does it indicate the number of experimental repeats, the number of boutons or PSDs, or the number of animals?

Again, we apologize this was not clear. (n) refers to the number of animals (biological replicates), which is reported in Supplementary Table 1. All imaging was performed at muscle 6, abdominal segment A3. Per preparation, we imaged 1-2 NMJs in total, with each imaging targeting 2–3 terminal boutons at the target NMJ and acquired 2–3 imaging stacks choosing different terminal boutons per NMJ. For the standard stimulation protocol, we delivered 1 Hz stimulation for 1ms and captured 14 stimuli in a 15s time series imaging (lines 730-736).

Reviewer #3

Genetically encoded calcium indicators (GECIs) are essential tools in neurobiology and physiology. Technological constraints in targeting and kinetics of previous versions of GECIs have limited their application at the subcellular level. Chen et al. present a set of novel tools that overcome many of these limitations. Through systematic testing in the Drosophila NMJ, they demonstrate improved targeting of GCaMP variants to synaptic compartments and report enhanced brightness and temporal fidelity using members of the GCaMP8 series. These advancements are likely to facilitate more precise investigation of synaptic physiology.

This is a comprehensive and detailed manuscript that introduces and validates new GECI tools optimized for the study of neurotransmission and neuronal excitability. These tools are likely to be highly impactful across neuroscience subfields. The authors are commended for publicly sharing their imaging software.

This manuscript could be improved by further testing the GECIs across physiologically relevant ranges of activity, including at high frequency and over long imaging sessions. The authors provide a custom software package (CaFire) for Ca2+ imaging analysis; however, to improve clarity and utility for future users, we recommend providing references to existing Ca2+ imaging tools for context and elaborating on some conceptual and methodological aspects, with more guidance for broader usability. These enhancements would strengthen this already strong manuscript.

We thank the Reviewer for their overall positive evaluation and comments. 

Major comments:

(1) Evaluation of the performance of new GECI variants using physiologically relevant stimuli and frequency. The authors took initial steps towards this goal, but it would be helpful to determine the performance of the different GECIs at higher electrical stimulation frequencies (at least as high as 20 Hz) and for longer (10 seconds) (Newman et al, 2017). This will help scientists choose the right GECI for studies testing the reliability of synaptic transmission, which generally requires prolonged highfrequency stimulation.

We appreciate this point by the reviewer and agree it would be of interest to evaluate sensor performance with higher frequency stimulation and for a longer duration. In response, we performed a variety of stimulation protocols at high intensities and times, but found the data to be difficult to separate individual responses given the decay kinetics of all calcium sensors. Hence, we elected not to include these in the revised manuscript. However, we have now included an evaluation of the sensors with 20 Hz electrical stimulation for ~1 sec using a direct comparison of Scar8f with OGB-1. These data are now presented in a new Fig. 3D,E and discussed in the manuscript (lines 396-403).

(2) CaFire.

The authors mention, in line 182: 'Current approaches to analyze synaptic Ca2+ imaging data either repurpose software designed to analyze electrophysiological data or use custom software developed by groups for their own specific needs.' References should be provided. CaImAn comes to mind (Giovannucci et al., 2019, eLife), but we think there are other software programs aimed at analyzing Ca2+ imaging data that would permit such analysis.

Thank you for the thoughtful question. At this stage, we’re unable to provide a direct comparison with existing analysis workflows. In surveying prior studies that analyze Drosophila NMJ Ca²⁺ imaging traces, we found that most groups preprocess images in Fiji/ImageJ and then rely on their own custom-made MATLAB or Python scripts for downstream analysis (see Blum et al. 2021; Xing and Wu 2018). Because these pipelines vary widely across labs, a standardized head-to-head evaluation isn’t currently feasible. With CaFire, our goal is to offer a simple, accessible tool that does not require coding experience and minimizes variability introduced by custom scripts. We designed CaFire to lower the barrier to entry, promote reproducibility, and make quantal event analysis more consistent across users. We have added references to the sentence mentioned above.

Regarding existing software that the reviewer mentioned – CaImAn (Giovannucci et al. 2019): We evaluated CaImAn, which is a powerful framework designed for large-scale, multicellular calcium imaging (e.g., motion correction, denoising, and automated cell/ROI extraction). However, it is not optimized for the per-event kinetics central to our project - such as extracting rise and decay times for individual quantal events at single synapses. Achieving this level of granularity would typically require additional custom Python scripting and parameter tuning within CaImAn’s code-centric interface. This runs counter to CaFire’s design goals of a nocode, task-focused workflow that enables users to analyze miniature events quickly and consistently without specialized programming expertise.

Regarding Igor Pro (WaveMetrics), (Müller et al. 2012): Igor Pro is another platform that can be used to analyze calcium imaging signals. However, it is commercial (paid) software and generally requires substantial custom scripting to fit the specific analyses we need. In practice, it does not offer a simple, open-source, point-and-click path to per-event kinetic quantification, which is what CaFire is designed to provide.

The authors should be commended for making their software publicly available, but there are some questions:

How does CaFire compare to existing tools?

As mentioned above, we have not been able to adapt the custom scripts used by various labs for our purposes, including software developed in MatLab (Blum et al. 2021), Python (Xing and Wu 2018), and Igor (Müller et al. 2012). Some in the field do use semi-publically available software, including Nikon Elements (Chen and Huang 2017) and CaImAn (Giovannucci et al. 2019). However, these platforms are not optimized for the per-event kinetics central to our project - such as extracting rise and decay times for individual quantal events at single synapses. We have added more details about CaFire, mainly focusing on the workflow and measurements, highlighting the superiority of CaFire, showing that CaFire provides a no-code, standardized pipeline with automated miniature-event detection and per-event metrics (e.g., amplitude, rise time τ, decay time τ), optional ΔR/R support, and auto-partition feature. Collectively, these features make CaFire simpler to operate without programming expertise, more transparent and reproducible across users, and better aligned with the event-level kinetics required for this project.

Very few details about the Huygens deconvolution algorithms and input settings were provided in the methods or text (outside of MLE algorithm used in STED images, which was not Ca2+ imaging). Was it blind deconvolution? Did the team distill the point-spread function for the fluorophores? Were both channels processed for ratiometric imaging? Were the same settings used for each channel? Importantly, please include SVI Huygens in the 'Software and Algorithms' Section of the methods.

We thank the reviewer for raising this important point. We have now expanded the Methods to describe our use of Huygens in more detail and have added SVI Huygens Professional (Scientific Volume Imaging, Hilversum, The Netherlands) to the “Software and Algorithms” section. For Ca²⁺ imaging data, time-lapse stacks were processed in the Huygens Deconvolution Wizard using the standard estimation algorithm (CMLE). This is not a blind deconvolution procedure. Instead, Huygens computes a theoretical point-spread function (PSF) from the full acquisition metadata (objective NA, refractive index, voxel size/sampling, pinhole, excitation/emission wavelengths, etc.); if refractive index values are provided and there is a mismatch, the PSF is adjusted to account for spherical aberration. We did not experimentally distill PSFs from bead measurements, as Huygens’ theoretical PSFs are sufficient for our data.

Both green (GCaMP) and red (mScarlet) channels were processed for ratiometric imaging using the same workflow (stabilization, optional bleaching correction, and deconvolution within Huygens). For each channel, the PSF, background, and SNR were estimated automatically by the same built-in algorithms, so the underlying procedures were identical even though the numerical values differ between channels because of their distinct wavelengths and noise characteristics. Importantly, Huygens normalizes each PSF to unit total intensity, such that the deconvolution itself does not add or remove signal and therefore preserves intensity ratios between channels; only background subtraction and bleaching correction can change absolute fluorescence values. For the mScarlet channel, where we observed modest bleaching (~1.10 over 15 sec), we applied Huygens’ bleaching correction and visually verified that similar structures maintained comparable intensities after correction. For presynaptic GCaMP signals, bleaching over these short recordings was negligible, so we omitted the bleaching-correction step to avoid introducing multiplicative artifacts. This workflow ensures that ratiometric ΔR/R measurements are based on consistently processed, intensity-conserving deconvolved images in both channels.

The number of deconvolution iterations could have had an effect when comparing GCAMP series; please provide an average number of iterations used for at least one experiment. For example, Figure 3, Syt::GCAMP6s, Scar8f & Scar8m, and, if applicable, the maximum number of permissible iterations.

We thank the reviewer for this comment. For all Ca²⁺ imaging datasets, deconvolution in Huygens was performed using the recommended default settings of the CMLE algorithm with a maximum of 30 iterations. The stopping criterion was left at the Huygens default, so the algorithm either converged earlier or, if convergence was not reached, terminated at this 30-iteration limit. No other iteration settings were used across the GCaMP series (lines 555-559).

Please clarify if the 'Express' settings in Huygens changed algorithms or shifted input parameters.

We appreciate the reviewer’s question regarding the Huygens “Express” settings. For clarity, we note that all Ca²⁺ imaging data reported in this manuscript were deconvolved using the “Deconvolution Wizard”, not the “Deconvolution Express” mode. In the Wizard, we explicitly selected the CMLE algorithm (or GMLE in a few STED-related cases as recommended by SVI), using the recommended maximum of 30 iterations, and other recommended settings while allowing Huygens to auto-estimate background and SNR for each channel.Bleaching correction was toggled manually per channel (applied to mScarlet when bleaching was evident, omitted for GCaMP when bleaching was negligible), as described in the revised Methods (lines 553-559).

By contrast, the Deconvolution Express tool in Huygens is a fully automated front-end that can internally adjust both the choice of deconvolution algorithm (e.g., CMLE vs. GMLE/QMLE) and key input parameters such as SNR, number of iterations, and quality threshold based on the selected “smart profile” and the image metadata. In preliminary tests on our datasets, Express sometimes produced results that were either overly smoothed or showed subtle artifacts, so we did not use it for any data included in this study. Instead, we relied exclusively on the Wizard with explicitly controlled settings to ensure consistency and transparency across all GCaMP series and ratiometric analyses.

We suggest including a sample data set, perhaps in Excel, so that future users can beta test on and organize their data in a similar fashion.

We agree that this would be useful, a point shared by R1 above. In response, we have added a sample data set to the GitHub site and included sample ImageJ data along with screenshots to explain the analysis in more detail. These improvements are discussed in the manuscript (lines 705-708).

(3) While the challenges of AZ imaging are mentioned, it is not discussed how the authors tackled each one. What is defined as an active zone? Active zones are usually identified under electron microscopy. Arguably, the limitation of GCaMP-based sensors targeted to individual AZs, being unable to resolve local Ca2+ changes at individual boutons reliably, might be incorrect. This could be a limitation of the optical setup being used here. Please discuss further. What sensor performance do we need to achieve this performance level, and/or what optical setup would we need to resolve such signals?

We appreciate the reviewer’s thoughtful comments and agree that the technical challenges of active zone (AZ) Ca²⁺ imaging merit further clarification. We defined AZs, as is the convention in our field, as individual BRP puncta at NMJs. These BRP puncta co-colocalize with individual puncta of other AZ components, including CAC, RBP, Unc13, etc. ROIs were drawn tightly over individual BRP puncta and only clearly separable spots were included.

To tackle the specific obstacles of AZ imaging (small signal volume, high AZ density, and limited photon budget at high frame rates), we implemented both improved sensors and optimized analysis (Fig. 6). First, we introduced a ratiometric AZ-targeted indicator, BRP::mScarlet3::GCaMP8m (Bar8m), and computed ΔR/R with ΔR/R with R(t)=F_GCaMP8m/F_mScarlet3. ROIs were drawn over individual AZs (Fig. 6B). Under our standard resonant area-scan conditions (~118 fps), Bar8m produces robust ΔR/R transients at individual AZs (example peaks ≈ 3.28; τ_rise≈9.0 ms; Fig. 6C, middle), indicating that single-AZ signals can be detected reproducibly when AZs are optically resolvable.

Second, we increased temporal resolution using high-speed Galvano line-scan imaging (~1058 fps), which markedly sharpened the apparent kinetics (τ_rise≈3.23 ms) and revealed greater between-AZ variability (Fig. 6C, right; 6D–E). Population analyses show that line scans yield much faster rise times than area scans (Fig. 6D) and a dramatically higher fraction of significantly different AZ pairs (8.28% and 4.14% in 8f and 8m areascan vs 78.62% in 8m line-scan, lines 721-725), uncovering pronounced AZ-to-AZ heterogeneity in Ca²⁺ signals. Together, these revisions demonstrate that under our current confocal configuration, AZ-targeted GCaMP8m can indeed resolve local Ca²⁺ changes at individual, optically isolated boutons.

We have revised the Discussion to clarify that our original statement about the limitations of AZ-targeted GCaMPs refers specifically to this combination of sensor and optical setup, rather than an absolute limitation of AZ-level Ca²⁺ imaging. In our view, further improvements in baseline brightness and dynamic range (ΔF/F or ΔR/R per action potential), combined with sub-millisecond kinetics and minimal buffering, together with optical configurations that provide smaller effective PSFs and higher photon collection (e.g., higher-NA objectives, optimized 2-photon or fast line-scan modalities, and potentially super-resolution approaches applied to AZ-localized indicators), are likely to be required to achieve routine, high-fidelity Ca²⁺ measurements at every individual AZ within a neuromuscular junction.

(4) In Figure 5: Only GCAMP8f (Bar8f fusion protein) is tested here. Consider including testing with GCAMP8m. This is particularly relevant given that GCAMP8m was a more successful GECI for subcellular post-synaptic imaging in Figure 6.

We appreciate this point and request by Reviewer 3. The main limitation for detecting local calcium changes at AZs is the speed of the calcium sensor, and hence we used the fastest available (GCaMP8f) to test the Bar8f sensor. While replacing GCaMP8f with GCaMP8m would indeed be predicted to enhance sensitivity (SNR), since GCaMP8m does not have faster kinetics relative to GCaMP8f, it is unlikely to be a more successful GECI for visualizing local calcium differences at AZs. 

That being said, we agree that the Bar8m tool, including the improved mScarlet3 indicator, would likely be of interest and use to the field. Fortunately, we had engineered the Bar8m sensor while this manuscript was in review, and just recently received transgenic flies. We have evaluated this sensor, as requested by the reviewer, and included our findings in Fig. 1 and 6. In short, while the sensitivity is indeed enhanced in Bar8m compared to Bar8f, the kinetics remain insufficient to capture local AZ signals. These findings are discussed in the revised manuscript (lines 424-442, 719-730), and we appreciate the reviewer for raising these important points.

In earlier experiments, Bar8f yielded relatively weak fluorescence, so we traded frame rate for image quality during resonant area scans (~60 fps). After switching to Bar8m, the signal was bright enough to restore our standard 118 fps area-scan setting. Nevertheless, even with dual-channel resonant area scans and ratiometric (GCaMP/mScarlet) analysis, AZ-to-AZ heterogeneity remained difficult to resolve. Because Ca²⁺ influx at individual active zones evolves on sub-millisecond timescales, we adopted a high-speed singlechannel Galvano line-scan (~1 kHz) to capture these rapid transients. We first acquired a brief area image to localize AZ puncta, then positioned the line-scan ROI through the center of the selected AZ. This configuration provided the temporal resolution needed to uncover heterogeneity that was under-sampled in area-scan data. Consistent with this, Bar8m line-scan data showed markedly higher AZ heterogeneity (significant AZ-pair rate ~79%, vs. ~8% for Bar8f area scans and ~4% for Bar8m area scans), highlighting Bar8m’s suitability for quantifying AZ diversity. We have updated the text, Methods, and figure legend accordingly (tell reviewer where to find everything).

(5) Figure 5D and associated datasets: Why was Interquartile Range (IQR) testing used instead of ZScoring? Generally, IQR is used when the data is heavily skewed or is not normally distributed. Normality was tested using the D'Agostino & Pearson omnibus normality test and found that normality was not violated. Please explain your reasoning for the approach in statistical testing. Correlation coefficients in Figures 5 E & F should also be reported on the graph, not just the table. In Supplementary Table 1. The sub-table between 4D-F and 5E-F, which describes the IQR, should be labeled as such and contain identifiers in the rows describing which quartile is described. The table description should be below. We would recommend a brief table description for each sub-table.

Thank you for this helpful suggestion. We have updated the analysis in two complementary ways. First, we now perform paired two-tailed t-tests between every two AZs within the same preparation (pairwise AZ–AZ comparisons of peak responses). At α<0.05, the fraction of significant AZ pairs is ~79% for Bar8m line-scan data versus ~8% for Bar8f area-scan data, indicating markedly greater AZ-to-AZ diversity when measured at high temporal resolution. Second, for visually marking the outlying AZs, we re-computed the IQR (Q1–Q3) based on the individual values collected from each AZs(15 data points per AZ, 30 AZs for each genotype), and marked AZs whose mean response falls above Q3 or below Q1; IQR is used here solely as a robust dispersion reference rather than for hypothesis testing. Both analyses support the same observation: Bar8m line-scan data reveal substantially higher AZ heterogeneity than Bar8f and Bar8m area-scan data. We have revised the Methods, figure panels, and legends accordingly (t-test details; explicit “IQR (Q1–Q3)” labeling; significant AZ-pair rates reported on the plots) (lines 719-730).

(6) Figure 6 and associated data. The authors mention: ' SynapGCaMP quantal signals appeared to qualitatively reflect the same events measured with electrophysiological recordings (Fig. 6D).' If that was the case, shouldn't the ephys and optical signal show some sort of correlation? The data presented in Figure 6D show no such correlation. Where do these signals come from? It is important to show the ROIs on a reference image.

We apologize this was not clear, as similar points were raised by R1 and R2. We were just showing separate (uncorrelated) sample traces of electrophysiological and calcium imaging data. Given how confusing this presentation turned out to be, and the fact that we show the correlated ephys and calcium imaging events in Fig. 7, we have elected to remove the uncorrelated electrophysiological events in Fig. 6 to just focus on the calcium imaging events (now Figures 7 and 8).

Figure 7B: Were Ca2+ transients not associated with mEPSPs ever detected? What is the rate of such events?

This is an astute question. Yes indeed, during simultaneous calcium imaging and current clamp electrophysiology recordings, we occasionally observed GCaMP transients without a detectable mEPSP in the electrophysiological trace. This may reflect the detection limit of electrophysiology for very small minis; with our noise level and the technical limitation of the recording rig, events < ~0.2 mV cannot be reliably detected, whereas the optical signal from the same quantal event might still be detected. The fraction of calcium-only events was ~1–10% of all optical miniature events, depending on genotype (higher in lines with smaller average minis). These calcium-only detections were low-amplitude and clustered near the optical threshold (lines 361-365).

Minor comments

(1) It should be mentioned in the text or figure legend whether images in Figure 1 were deconvolved, particularly since image pre-processing is only discussed in Figure 2 and after.

We thank the reviewer for pointing this out. Yes, the confocal images shown in Figure 1 were also deconvolved in Huygens using the CMLE-based workflow described in the revised Methods. We applied deconvolution to improve contrast, reduce out-of-focus blur, and better resolve the morphology of presynaptic boutons, active zones, and postsynaptic structures, so that the localization of each sensor is more clearly visualized. We have now explicitly stated in the Fig. 1 legend and Methods (lines 575-577) that these images were deconvolved prior to display. 

(2) The abbreviation, SNR, signal-to-noise ratio, is not defined in the text.

We have corrected this error and thank the reviewer for pointing this out.

(3) Please comment on the availability of fly stocks and molecular constructs.

We have clarified that all fly stocks and molecular constructs will be shared upon request (lines 747-750). We are also in the process of depositing the new Scar8f/m, Bar8f/m, and SynapGCaMP sensors to the Bloomington Drosophila Stock Center for public dissemination.

(4) Please add detection wavelengths and filter cube information for live imaging experiments for both confocal and widefield.

We thank the reviewer for this helpful suggestion. We have now added the detection wavelengths and filter cube configurations for both confocal and widefield live imaging to the Methods.

For confocal imaging, GCaMP signals were acquired on a Nikon A1R system using the FITC/GFP channel (488-nm laser excitation; emission collected with a 525/50-nm band-pass filter), and mScarlet signals were acquired using the TRITC/mCherry channel (561-nm laser excitation; emission collected with a 595/50-nm band-pass filter). Both channels were detected with GaAsP detectors under the same pinhole and scan settings described above (lines 512-517).

For widefield imaging, GCaMP was recorded using a GFP filter cube (LED excitation ~470/40 nm; emission ~525/50 nm), which is now explicitly described in the revised Methods section (lines 632-633).

(5) Please include a mini frequency analysis in Supplemental Figure S1.

We apologize for not including this information in the original submission. This is now included in the Supplemental Figure S1.

(6) In Figure S1B, consider flipping the order of EPSP (currently middle) and mEPSP (currently left), to easily guide the reader through the quantification of Figure S1A (EPSPs, top traces & mEPSPs, bottom traces).

We agree these modifications would improve readability and clarity. We have now re-ordered the electrophysiological quantifications in Fig. S1B as requested by the reviewer.

(7) Figure 6C: Consider labeling with sensor name instead of GFP.

We agree here as well, and have removed “GFP” and instead added the GCaMP variant to the heatmap in Fig. 7C.

(8) Figure 6E, 7B, 7E: Main statistical differences highlighting sensor performance should be represented on the figures for clarity.

We did not show these differences in the original submission in an effort to keep the figures “clean” and for clarity, putting the detailed statistical significance in Table S1. However, we agree with the reviewer that it would be easier to see these in the Fig. 6E and 7B,E graphs. This information has now been added the Figs. 7 and 8.

(9) Please report if the significance tested between the ephys mini (WT vs IIB-/-, WT vs IIA-/-, IIB-/- vs IIA-/-) is the same as for Ca2+ mini (WT vs IIB-/-, WT vs IIA-/-, IIB-/- vs IIA-/-). These should also exhibit a very high correlation (mEPSP (mV) vs Ca2+ mini deltaF/F). These tests would significantly strengthen the final statement of "SynapGCaMP8m can capture physiologically relevant differences in quantal events with similar sensitivity as electrophysiology."

We agree that adding the more detailed statistical analysis requested by the reviewer would strengthen the evidence for the resolution of quantal calcium imaging using SynapGCaMP8m. We have included the statistical significance between the ephys and calcium minis in Fig. 8 and included the following in the revised methods (lines 358-361), the Fig. 8 legend and Table S1:

Using two-sample Kolmogorov–Smirnov (K–S) tests, we found that SynapGCaMP8m Ca²⁺ minis (ΔF/F, Fig. 8E) differ significantly across all genotype pairs (WT vs IIB^-/-, WT vs IIA^-/-, IIB^-/- vs IIA^-/-; all p < 0.0001). The genotype rank order of the group means (±SEM) is IIB^-/- > WT > IIA^-/- (0.967 ± 0.036; 0.713 ± 0.021; 0.427 ± 0.017; n=69, 65, 59). For electrophysiological minis (mEPSP amplitude, Fig. 8F), K–S tests likewise show significant differences for the same comparisons (all p < 0.0001) with D statistics of 0.1854, 0.3647, and 0.4043 (WT vs IIB^-/-, WT vs IIA^-/-, IIB^-/- vs IIA^-/-, respectively). Group means (±SEM) again follow IIB^-/- > WT > IIA^-/- (0.824 ± 0.017 mV; 0.636 ± 0.015 mV; 0.383 ± 0.007 mV; n=41 each). These K–S results demonstrate identical significance and rank order across modalities, supporting our conclusion that SynapGCaMP8m resolves physiologically relevant quantal differences with sensitivity comparable to electrophysiology.

References

Blum, Ian D., Mehmet F. Keleş, El-Sayed Baz, Emily Han, Kristen Park, Skylar Luu, Habon Issa, Matt Brown, Margaret C. W. Ho, Masashi Tabuchi, Sha Liu, and Mark N. Wu. 2021. 'Astroglial Calcium Signaling Encodes Sleep Need in Drosophila', Current Biology, 31: 150-62.e7.

Chen, Y., and L. M. Huang. 2017. 'A simple and fast method to image calcium activity of neurons from intact dorsal root ganglia using fluorescent chemical Ca(2+) indicators', Mol Pain, 13: 1744806917748051.

Giovannucci, Andrea, Johannes Friedrich, Pat Gunn, Jérémie Kalfon, Brandon L. Brown, Sue Ann Koay, Jiannis Taxidis, Farzaneh Najafi, Jeffrey L. Gauthier, Pengcheng Zhou, Baljit S. Khakh, David W. Tank, Dmitri B. Chklovskii, and Eftychios A. Pnevmatikakis. 2019. 'CaImAn an open source tool for scalable calcium imaging data analysis', eLife, 8: e38173.

Müller, M., K. S. Liu, S. J. Sigrist, and G. W. Davis. 2012. 'RIM controls homeostatic plasticity through modulation of the readily-releasable vesicle pool', J Neurosci, 32: 16574-85.

Wu, Yifan, Keimpe Wierda, Katlijn Vints, Yu-Chun Huang, Valerie Uytterhoeven, Sahil Loomba, Fran Laenen, Marieke Hoekstra, Miranda C. Dyson, Sheng Huang, Chengji Piao, Jiawen Chen, Sambashiva Banala, Chien-Chun Chen, El-Sayed Baz, Luke Lavis, Dion Dickman, Natalia V. Gounko, Stephan Sigrist, Patrik Verstreken, and Sha Liu. 2025. 'Presynaptic Release Probability Determines the Need for Sleep', bioRxiv: 2025.10.16.682770.

Xing, Xiaomin, and Chun-Fang Wu. 2018. 'Unraveling Synaptic GCaMP Signals: Differential Excitability and Clearance Mechanisms Underlying Distinct Ca²⁺ Dynamics in Tonic and Phasic Excitatory, and Aminergic Modulatory Motor Terminals in Drosophila', eneuro, 5: ENEURO.0362-17.2018.

Nicely acid

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study presents a system for delivering precisely controlled cutaneous stimuli to freely moving mice by coupling markerless real-time tracking to transdermal optogenetic stimulation, using the tracking signal to direct a laser via galvanometer mirrors. The principal claims are that the system achieves sub-mm targeting accuracy with a latency of <100 ms. The nature of mouse gait enables accurate targeting of forepaws even when mice are moving.

Strengths:

The study is of high quality and the evidence for the claims is convincing. There is increasing focus in neurobiology in studying neural function in freely moving animals, engaged in natural behaviour. However, a substantial challenge is how to deliver controlled stimuli to sense organs under such conditions. The system presented here constitutes notable progress towards such experiments in the somatosensory system and is, in my view, a highly significant development that will be of interest to a broad readership.

Weaknesses:

(1) "laser spot size was set to 2.00 } 0.08 mm2 diameter (coefficient of variation = 3.85)" is unclear. Is the 0.08 SD or SEM? (not stated). Also, is this systematic variation across the arena (or something else)? Readers will want to know how much the spot size varies across the arena - ie SD. CV=4 implies that SD~7 mm. ie non-trivial variation in spot size, implying substantial differences in power delivery (and hence stimulus intensity) when the mouse is in different locations. If I misunderstood, perhaps this helps the authors to clarify. Similarly, it would be informative to have mean & SD (or mean & CV) for power and power density. In future refinements of the system, would it be possible/useful to vary laser power according to arena location?

We thank the reviewer for their comments and for identifying areas needing more clarity. The previous version was ambiguous: 0.08 refers to the standard deviation (SD). We have removed the ambiguity by stating mean ± SD and reporting a unitless coefficient of variation (CV).

The revised text reads “laser spot size was set to 2.00 ± 0.08 mm² (mean ± SD; coefficient of variation = 0.039).” This makes clear that the variability in spot size is minimal: it is 0.08 mm² SD (≈0.03 mm SD in diameter). This should help clarify that spot size variability across the arena is minute and unlikely to contribute meaningfully to differences in stimulus intensity across locations. The power was modulated depending on the experiment, so we provide the unitless CV here in “The absolute optical power and power density were uniform across the glass platform (coefficient of variation 0.035 and 0.029, respectively; Figure 2—figure supplement)”. We are grateful to the reviewer for spotting these omissions.

The reviewer also asks whether, in the future, it is “possible/useful to vary laser power according to arena location”. This is already possible in our system for infrared cutaneous stimulation using analog modulation (Figure 4). We have added the following sentence to make this clearer: “Laser power could be modulated using the analog control.”

(2) "The video resolution (1920 x 1200) required a processing time higher than the frame interval (33.33 ms), resulting in real-time pose estimation on a sub-sample of all frames recorded". Given this, how was it possible to achieve 84 ms latency? An important issue for closed-loop research will relate to such delays. Therefore please explain in more depth and (in Discussion) comment on how the latency of the current system might be improved/generalised. For example, although the current system works well for paws it would seem to be less suited to body parts such as the snout that do not naturally have a stationary period during the gait cycle.

We captured and stored video with a frame-to-frame interval of 33.33 ms (30 fps). DeepLabCut-live! was run in a latency-optimization mode, meaning that new frames are not processed while the network is busy - only the most recent frame is processed when free. The processing latency is measured per processed frame, and intermediate frames are thus skipped while the network is busy. Although a wide field of view and high resolution is required to capture the large environment, increasing the per-frame compute time, the processing latency remained small enough to track and stimulate moving mice. This processing latency of 84 ± 12 ms (mean ± SD) was calculated using the timestamps stored in the output files from DeepLabCut-live!: subtracting the frame acquisition timestamp from the frame processing timestamp across 16,000 processed frames recorded across four mice (4,000 each). In addition, there is a small delay to move the galvanometers and trigger the laser, calculated as 3.3 ± 0.5 ms (mean ± SD; 245 trials). This is described in the manuscript, but can be combined with the processing latency to indicate a total closed-loop delay of ≈87 ms so we have expanded on the ‘Optical system characterization’ subsection in the Methods, adding “We estimated a processing latency of 84 ± 12 ms (mean ± SD) by subtracting…” and that “In the current configuration the end-to-end closed-loop delay is ≈87 ms from the combination of the processing latency and other delays”. To the Discussion, we now comment on how this latency can be reduced and how this can allow for generalization to more rapidly moving body parts.

Reviewer #2 (Public review):

Parkes et al. combined real-time keypoint tracking with transdermal activation of sensory neurons to examine the effects of recruitment of sensory neurons in freely moving mice. This builds on the authors' previous investigations involving transdermal stimulation of sensory neurons in stationary mice. They illustrate multiple scenarios in which their engineering improvements enable more sophisticated behavioral assessments, including (1) stimulation of animals in multiple states in large arenas, (2) multi-animal nociceptive behavior screening through thermal and optogenetic activation, and (3) stimulation of animals running through maze corridors. Overall, the experiments and the methodology, in particular, are written clearly. However, there are multiple concerns and opportunities to fully describe their newfound capabilities that, if addressed, would make it more likely for the community to adopt this methodology:

The characterization of laser spot size and power density is reported as a coefficient of variation, in which a value of ~3 is interpreted as uniform. My interpretation would differ - data spread so that the standard deviation is three times larger than the mean indicates there is substantial variability in the data. The 2D polynomial fit is shown in Figure 2 - Figure Supplement 1A and, if the fit is good, this does support the uniformity claim (range of spot size is 1.97 to 2.08 mm2 and range of power densities is 66.60 to 73.80 mW). The inclusion of the raw data for these measurements and an estimate of the goodness of fit to the polynomials would better help the reader evaluate whether these parameters are uniform across space and how stable the power density is across repeated stimulations of the same location. Even more helpful would be an estimate of whether the variation in the power density is expected to meaningfully affect the responses of ChR2-expressing sensory neurons.

We thank the reviewer for their comments. As also noted in response to Reviewer 1, the coefficient of variation (CV) is now reported in unitless form (rather than a percentage) to ensure clarity. For avoidance of doubt, the CV is 0.039 (3.9%), so the variation in laser spot size is minimal – there is negligible spot size variability across the system. The ranges are indeed consistent with uniformity. We have included the goodness-of-fit estimates in the appropriate figure legend “fit with a two-dimensional polynomial (area R² = 0.91; power R² = 0.75)”. This indicates that the polynomials fit well overall.

The system already allows for control of spot size. To examine whether the variation in the power density affects the responses of ChR2-expressing sensory neurons, we examined this in our previous work that focused more on input-output relationships, demonstrating a steep relationship between spot size (range of 0.02 mm² to 2.30 mm²) and the probability of paw response, demonstrating a meaningful change in response probability (Schorscher-Petcu et al. eLife, 2021). In future studies, we aim to use this approach to “titrate” cutaneous inputs as mice move through their environments.

While the error between the keypoint and laser spot error was reported as ~0.7 to 0.8 mm MAE in Figure 2L, in the methods, the authors report that there is an additional error between predicted keypoints and ground-truth labeling of 1.36 mm MAE during real-time tracking. This suggests that the overall error is not submillimeter, as claimed by the authors, but rather on the order of 1.5 - 2.5 mm, which is considerable given the width of a hind paw is ~5-6 mm and fore paws are even smaller. In my opinion, the claim for submillimeter precision should be softened and the authors should consider that the area of the paw stimulated may differ from trial to trial if, for example, the error is substantial enough that the spot overlaps with the edge of the paw.

We thank the reviewer for identifying a discrepancy in these reported errors. We clarify this below and in the manuscript

The real-time tracking error is the mean absolute Euclidean distance (MAE) between ground truth and DLC on the left hind paw where likelihood was relatively high. More specifically, ground truth was obtained by manual annotation of the left hind paw center. The corresponding DLC keypoint was evaluated in frames with likelihood >0.8 (the stimulation threshold). Across 1,281 frames from five videos of freely exploring mice (30 fps), the MAE was 1.36 mm.

The targeting error is the MAE between ground truth and the laser spot location, so should reflect the real-time tracking error plus errors from targeting the laser. More specifically, this metric was determined by comparing the manually determined ground truth keypoint of the left hind paw and the actual center of the laser spot. Importantly, this metric was calculated using four five-minute high-speed videos recorded at 270 fps of mice freely exploring the open arena (463 frames) and frames were selected with a likelihood threshold >0.8. This allowed us to resolve the brief laser pulses but inadvertently introduced a difference in spatial scaling. After rescaling, the values give a targeting error MAE now in line with the real-time tracking error  (see corrected Figure 2L). This is approximately 1.3 mm across all locomotion speeds categories. These errors are small and are limited by the spatial resolution of the cameras. We thank the reviewer for noting this discrepancy and prompting us to get to its root cause.

We have amended the subtitle on Figure 2L as “Ground truth keypoint to laser spot error” and have avoided the use of submillimeter throughout. We have added the following sentence to clarify this point: “As laser targeting relies on real-time tracking to direct the laser to the specified body part, this metric includes any errors introduced by tracking and targeting”.

As the major advance of this paper is the ability to stimulate animals during ongoing movement, it seems that the Figure 3 experiment misses an opportunity to evaluate state-dependent whole-body reactions to nociceptor activation. How does the behavioral response relate to the animal's activity just prior to stimulation?

The reviewers suggest analysis of state-dependent responses. In the Figure 3 experiment, mice were stimulated up to five times when stationary. Analysis of whole body reactions in stationary mice has been described in (Schorscher-Petcu et al. eLife, 2021) and doing this here would be redundant, so instead we now analyse the responses of moving mice in Figure 5. This new analysis shows robust state-dependent responses during movement as suggested by the reviewer. We find two behavioral clusters: one that is for faster, direct (coherent) movement and the other that is for slower assessment (incoherent) movement. Stimulation during the former results in robust and consistent slowing and shift towards assessment, whereas stimulation during the former results in a reduction in assessment. We describe and interpret these new data in the Results and Discussion sections and add information in the Methods and Figure legend, as given below. We believe that demonstrating movement statedependence is a valuable addition to the paper and thank the reviewer for suggesting this.

Given the characterization of full-body responses to activation of TrpV1 sensory neurons in Figure 4 and in the authors' previous work, stimulation of TrpV1 sensory neurons has surprisingly subtle effects as the mice run through the alternating T maze. The authors indicate that the mice are moving quickly and thus that precise targeting is required, but no evidence is shared about the precision of targeting in this context beyond images of four trials. From the characterization in Figure 2, at max speed (reported at 241 +/- 53 mm/s, which is faster than the high speeds in Figure 2), successful targeting occurs less than 50% of the time. Is the initial characterization consistent with the accuracy in this context? To what extent does inaccuracy in targeting contribute to the subtlety of affecting trajectory coherence and speed? Is there a relationship between animal speed and disruption of the trajectory?

We thank the reviewer for pointing out the discrepancy in the reported maximum speed. We have corrected the error in the main text: the average maximum speed is 142 ± 26 mm/s (four mice).

The self-paced T-maze alternation task in Figure 5 demonstrates that mice running in a maze can be stimulated using this method. We did not optimize the particular experimental design to assess the hit accuracy, as this was determined in Figure 2. Instead, we optimized for the pulse frequencies, meaning the galvanometers tracked with processed frames but the laser was triggered whether or not the paw was actually targeted. However, even in this case with the system pulsing in the free-run mode, the laser hit rate was 54 ± 6% (mean ± sem, n = 7 mice). We have weakened references to submillimeter as it was only inferred from other experiments and was not directly measured here. We find in this experiment that stimulation in freely moving mice can cause them to briefly halt and evaluate. In the future, we will use experimental designs to more optimally examine learning.

The reviewer also asks if there is a relationship between speed and disruption of the trajectory. We find that this is the case as described above with our additional analysis.

Reviewer #3 (Public review):

Summary:

To explore the diverse nature of somatosensation, Parkes et al. established and characterized a system for precise cutaneous stimulation of mice as they walk and run in naturalistic settings. This paper provides a framework for real-time body part tracking and targeted optical stimuli with high precision, ensuring reliable and consistent cutaneous stimulation. It can be adapted in somatosensation labs as a general technique to explore somatosensory stimulation and its impact on behavior, enabling rigorous investigation of behaviors that were previously difficult or impossible to study.

Strengths:

The authors characterized the closed-loop system to ensure that it is optically precise and can precisely target moving mice. The integration of accurate and consistent optogenetic stimulation of the cutaneous afferents allows systematic investigation of somatosensory subtypes during a variety of naturalistic behaviors. Although this study focused on nociceptors innervating the skin (Trpv1::ChR2 animals), this setup can be extended to other cutaneous sensory neuron subtypes, such as low-threshold mechanoreceptors and pruriceptors. This system can also be adapted for studying more complex behaviors, such as the maze assay and goal-directed movements.

Weaknesses:

Although the paper has strengths, its weakness is that some behavioral outputs could be analyzed in more detail to reveal different types of responses to painful cutaneous stimuli. For example, paw withdrawals were detected after optogenetically stimulating the paw (Figures 3E and 3F). Animals exhibit different types of responses to painful stimuli on the hind paw in standard pain assays, such as paw lifting, biting, and flicking, each indicating a different level of pain. Improving the behavioral readouts from body part tracking would greatly strengthen this system by providing deeper insights into the role of somatosensation in naturalistic behaviors. Additionally, if the laser spot size could be reduced to a diameter of 2 mm², it would allow the activation of a smaller number of cutaneous afferents, or even a single one, across different skin types in the paw, such as glabrous or hairy skin.

We thank the reviewer for highlighting how our system can be combined with improved readouts of coping behavior to provide deeper insights. Optogenetic and infrared cutaneous stimulation are well established generators of coping behaviors (lifting, flicking, licking, biting, guarding). Detection of these behaviors is an active and evolving field with progress being made regularly (e.g. Jones et al., eLife 2020 [PAWS];  Wotton et al., Mol Pain 2020; Zhang et al., Pain 2022; Oswell et al., bioRxiv 2024 [LUPE]; Barkai et al., Cell Reports Methods 2025 [BAREfoot], along with more general tools like Hsu et al., Nature Communications 2021 [B-SOiD]; Luxem et al., Communications Biology 2022 [VAME]; Weinreb et al,. Nature Methods 2024 [Keypoints-MoSeq]). One output of our system is bodypart keypoints, which are the typical input to many of these tools. We will leave the readers and users of the system to decide which tools are appropriate for their experimental designs - the focus of this current manuscript is describing the novel stimulation approach in moving animals.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) It is hard to see how the rig is arranged from the render of Figure 2AB due to the components being black on black. A particularly useful part of Fig2AB is the aerial view in panel B that shows the light paths. I suggest adding the labelling of Figure 2A also to that. The side/rear views could perhaps be deleted, allowing the aerial view to be larger.

We appreciate this suggestion and have revised Figure 2B to improve the visibility of the optomechanical components. We have enlarged the side and aerial views, removed the rear view, and added further labels to the aerial view.

(2) MAE - to interpret the 0.54 result, it would be useful to state the arena size in this paragraph.

Thank you. We have added the arena size in this paragraph and also added scales in the relevant figure (Figure 2).

(3) "pairwise correlations of R = 0.999 along both x- and y-axes". Is this correlation between hindpaw keypoint and galvo coordinates?

Yes, we have added the following to clarify: “...between galvanometer coordinates and hind paw keypoints”

(4) Latency was 84 ms. Is this mainly/entirely the delay between DLC receiving the camera image and outputting key point coordinates?

Yes, we hope that the additional detail in the Methods and Discussion described above will now clarify the current closed-loop latencies.

(5) "Mice move at variable speeds": in this sentence, spell out when "speed" refers to mouse and when it refers to hindpaw. Similarly, Fig 2i. The sentence is potentially confusing to general readers (paws stationary although the mouse is moving). Presumably, it's due to gait. I suggest explaining this here.

The speed values that relate to the mouse body and paws are now clearer in the main text and in the legend for Figure 2I.

(6) Figure 2k and associated main text. It is not clear what "success/hit rate" means here.

We have added the following sentence in the main text: “Hit accuracy refers to the percentage of trials in which the laser successfully targeted (‘hit’) the intended hind paw.” and use hit accuracy throughout instead of success rate.

(7) Figure 2L. All these points are greater than the "average" 0.54 reported in the text. How is this possible?

The MAE of 0.54 mm refers to the “predicted and actual laser spot locations” (that is, the difference between where the calibration map should place the laser spot and where it actually fell), while Figure 2L MAE values refers to the error between the ground truth keypoint to laser spot (that is, the error between the human-observed paw target and where the laser spot fell). The latter error will include the former error so is expected to be larger. We have clarified this point throughout the text, for example, stating “As laser targeting relies on real-time tracking to direct the laser to the specified body part, this metric inherently accounts for any errors introduced by the tracking and targeting.”. This is also discussed above in response to Reviewer 2.

(8) "large circular arena". State the size here

We have added this to the Figure 2 legend.

(9) Figure 3c-left. Can the contrast between the mouse and floor be increased here?

We have improved the contrast in this image.

(10) Figure 5c. It is unclear what C1, C2, etc refers to. Mice?

Yes, these refer to mice. We have removed reference to these now as they are not needed.

(11) Discussion. A comment. There is scope for elaborating on the potential for new research by combining it with new methods for measurements of neural activity in freely moving animals in the somatosensory system.

Thank you. We agree and have added more detail on this in the discussion stating “The system may be combined with existing tools to record neural activity in freely-moving mice, such as fiber photometry, miniscopes, or large-scale electrophysiology, and manipulations of this neural activity, such as optogenetics and chemogenetics. This can allow mechanistic dissection of cell and circuit biology in the context of naturalistic behaviors.”

Reviewer #3 (Recommendations for the authors):

(1) Include the number of animals for behavior assays for the panels (e.g., Figures 4G).

Where missing, we now state the number of animals in panels.

(2) If representative responses are shown, such as in Figures 3E and 4F, include the average response with standard deviation so readers can appreciate the variation in the responses.

We appreciate the suggestion to show variability in the responses. We have made several changes to Figures 3 and 4. Specifically, to illustrate the variability across multiple trials more clearly, Figure 3E now shows representative keypoint traces for each body part from two mice during their 5 trials. For Figure 4, we have re-analyzed the thermal stimulation trials and shown a raster plot of keypoint-based local motion energy (Figure 4E) sorted by response latency for hundreds of trials. Figure 4G now presents the cumulative distribution for all trials and animals for thermal (18 wild-type mice, 315 trials) and optogenetic stimulation trials (9 Trpv1::ChR2 mice, 181 trials). We also now provide means ± SD for the key metrics for optogenetic and thermal stimulation trials in Figure 4 in the Results section. This keeps the manuscript focused on the methodological advances while showing the trial variability.

(3) "optical targeting of freely-moving mice in a large environments" should be "optical targeting of freely-moving mice in a large environment".

Corrected

(4) Define fps when you first mention this in the manuscript.

Added

(5) Data needs to be shown for the claim "Mice concurrently turned their heads toward the stimulus location while repositioning their bodies away from it".

We state this observation to qualify that the stimulation of stationary mice resulted in behavioral responses “consistent with previous studies”. It would be redundant to repeat our full analysis and might distract from the novelty of the current manuscript. We have restricted this sentence to make it clearer: “Consistent with previous studies, we observed the whole-body behaviors like head orienting concurrent with local withdrawal (Browne et al., Cell Reports 2017; Blivis et al., eLife, 2017.)”

.

Reviewer #2 (Public review):

Summary:

The authors aimed to dissect the plasticity of circadian outputs by combining evolutionary biology with chronobiology. By utilizing Drosophila strains selected for "Late" and "Early" adult emergence, they sought to investigate whether selection for developmental timing co-evolves with plasticity in daily locomotor activity. Specifically, they examined how these diverse lines respond to complex, desynchronized environmental cues (temperature and light cycles) and investigated the molecular role of the splicing factor Psi and timeless isoforms in mediating this plasticity.

Major strengths and weaknesses:

The primary strength of this work is the novel utilization of long-term selection lines to address fundamental questions about how organisms cope with complex environmental cues. The behavioral data are compelling, clearly demonstrating that "Late" and "Early" flies possess distinct capabilities to track temperature cycles when they are desynchronized from light cycles.

However, a significant weakness lies in the causal links proposed between the molecular findings and these behavioral phenotypes. The molecular insights (Figures 2, 4, 5, and 6) rely on mRNA extracted from whole heads. As head tissue is dominated by photoreceptor cells and glia rather than the specific pacemaker neurons (LNv, LNd) driving these behaviors, this approach introduces a confound. Differential splicing observed here may reflect the state of the compound eye rather than the central clock circuit, a distinction highlighted by recent studies (e.g., Ma et al., PNAS 2023).

Furthermore, while the authors report that Psi mRNA loses rhythmicity under out-of-sync conditions, this correlation does not definitively prove that Psi oscillation is required for the observed splicing patterns or behavioral plasticity. The amplitude of the reported Psi rhythm is also low (~1.5 fold) and variable, raising questions about its functional significance in the absence of manipulation experiments (such as constitutive expression) to test causality.

Appraisal of aims and conclusions:

The authors successfully demonstrate the co-evolution of emergence timing and activity plasticity, achieving their aim on the behavioral level. However, the conclusion that the specific molecular mechanism involves the loss of Psi rhythmicity driving timeless splicing changes is not yet fully supported by the data. The current evidence is correlative, and without spatial resolution (specific clock neurons) or causal manipulation, the mechanistic model remains speculative.

This study is likely to be of significant interest to the chronobiology and evolutionary biology communities as it highlights the "enhanced plasticity" of circadian clocks as an adaptive trait. The findings suggest that plasticity to phase lags - common in nature where temperature often lags light - may be a key evolutionary adaptation. Addressing the mechanistic gaps would significantly increase the utility of these findings for understanding the molecular basis of circadian plasticity.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript addresses an important question: how do circadian clocks adjust to a complex rhythmic environment with multiple daily rhythms? The focus is on the temperature and light cycles (TC and LD) and their phase relationship. In nature, TC usually lags the LD cycle, but the phase delay can vary depending on seasonal and daily weather conditions. The authors present evidence that circadian behavior adjusts to different TC/LD phase relationships, that temperature-sensitive tim splicing patterns might underlie some of these responses, and that artificial selection for preferential evening or morning eclosion behavior impacts how flies respond to different LD/TC phase relationship

Strength:

Experiments are conducted on control strains and strains that have been selected in the laboratory for preferential morning or evening eclosion phenotypes. This study is thus quite unique as it allows us to probe whether this artificial selection impacted how animals respond to different environmental conditions, and thus gives hints on how evolution might shape circadian oscillators and their entrainment. The authors focused on circadian locomotor behavior and timeless (tim) splicing because warm and cold-specific transcripts have been described as playing an important role in determining temperature-dependent circadian behavior. Not surprisingly, the results are complex, but there are interesting observations. In particular, the "late" strain appears to be able to adjust more efficiently its evening peak in response to changes in the phase relationship between temperature and light cycles, but the morning peak seems less responsive in this strain. Differences in the circadian pattern of expression of different tim mRNA isoforms are found under specific LD/TC conditions.

We sincerely thank the reviewer for this generous assessment and for recognizing several key strengths of our study. We are particularly gratified that the reviewer values our use of long-term laboratory-selected chronotype lines (350+ generations), which provide a unique evolutionary perspective on how artificial selection reshapes circadian responses to complex LD/TC phase relationships—precisely our core research question.

Weaknesses:

These observations are interesting, but in the absence of specific genetic manipulations, it is difficult to establish a causative link between tim molecular phenotypes and behavior. The study is thus quite descriptive. It would be worth testing available tim splicing mutants, or mutants for regulators of tim splicing, to understand in more detail and more directly how tim splicing determines behavioral adaptation to different phase relationships between temperature and light cycles. Also, I wonder whether polymorphisms in or around tim splicing sites, or in tim splicing regulators, were selected in the early or late strains.

We thank the reviewer for this insightful comment. We agree that our current data do not establish a direct causal link between tim splicing (or Psi) and behaviour, and we appreciate that some of our wording (e.g. “linking circadian gene splicing to behavioural plasticity” or describing tim splicing as a “pivotal node”) may have suggested unintended causal links. In the revision, we will (i) explicitly state in the Abstract, Introduction, and early Discussion that the main aim was to test whether selection for timing of eclosion is accompanied by correlated evolution of temperature‑dependent tim splicing patterns and evening activity plasticity under complex LD/TC regimes, and (ii) consistently describe the molecular findings as correlational and hypothesis‑generating rather than causal. We will also add phrases throughout the text to point the reader more clearly to existing passages where we already emphasize “correlated evolution” and explicitly label our mechanistic ideas as “we speculate” / “we hypothesize” and as future experiments.

We fully agree that studies using tim splicing mutants or manipulations of splicing regulators under in‑sync and out‑of‑sync LD/TC regimes will be essential to ascertain what role tim variants play under such environmental conditions, and we will highlight this as a key future direction. At the same time, we emphasize that the long‑term selection lines provide a complementary perspective to classical mutant analyses by revealing how behavioural and molecular phenotypes can exhibit correlated evolution under a specific, chronobiologically relevant selection pressure (timing of emergence).

Finally, we appreciate the suggestion regarding polymorphisms. Whole‑genome analyses of these lines in a PhD thesis from our group (Ghosh, 2022, unpublished, doctoral dissertation) reveal significant SNPs in intronic regions of timeless in both Early and Late populations, as well as SNPs in CG7879, a gene implicated in alternative mRNA splicing, in the Late line. Because these analyses are ongoing and not yet peer‑reviewed, we do not present them as main results.

I also have a major methodological concern. The authors studied how the evening and morning phases are adjusted under different conditions and different strains. They divided the daily cycle into 12h morning and 12h evening periods, and calculated the phase of morning and evening activity using circular statistics. However, the non-circadian "startle" responses to light or temperature transitions should have a very important impact on phase calculation, and thus at least partially obscure actual circadian morning and evening peak phase changes. Moreover, the timing of the temperature-up startle drifts with the temperature cycles, and will even shift from the morning to the evening portion of the divided daily cycle. Its amplitude also varies as a function of the LD/TC phase relationship. Note that the startle responses and their changes under different conditions will also affect SSD quantifications.

We thank the reviewer for this perceptive methodological concern, which we had anticipated and systematically quantified but had not included in the original submission. The reviewer is absolutely correct that non-circadian startle responses to zeitgeber transitions could confound both circular phase (CoM) calculations and SSD quantifications, particularly as TC drift creates shifting startle locations across morning/evening windows.

We will be including startle response quantification (previously conducted but unpublished) as new a Supplementary figure, systematically measuring SSD in 1-hour windows immediately following each of the four environmental transitions (lights-ON, lights-OFF, temperature rise and temperature fall) across all six LDTC regimes (2-12hr TC-LD lags) for all 12 selection lines (early_1-4, control_1-4, late_1-4).

Author response image 1.

Startle responses in selection lines under LDTC regimes: SSD calculated to assess startle response to each of the transitions (1-hour window after the transition used for calculations). Error bars are 95% Tukey’s confidence intervals for the main effect of selection in a two-factor ANOVA design with block as a random factor. Non-overlapping error bars indicate significant differences among the values. SSD values between in-sync and out-of-sync regimes for a range of phase relationships between LD and TC cycles (A) LDTC 2-hr, (B) LDTC 4-hr, (C) LDTC 6-hr, (D) LDTC 8-hr, (E) LDTC 10-hr, (F) LDTC 12-hr.

Key findings directly addressing the reviewer's concerns:

(1) Morning phase advances in LDTC 8-12hr regimes are explained by quantified nocturnal startle activity around temperature rise transitions occurring within morning windows. Critically, these startles show no selection line differences, confirming they represent equivalent non-circadian confounds across lines.

(2) Early selection lines exhibit significantly heightened startle responses specifically to temperature rise in LDTC 4hr and 6hr regimes (early > control ≥ late), demonstrating that startle responses themselves exhibit correlated evolution with emergence timing—an important novel finding that strengthens our evolutionary story.

(3) Startle responses differed among selection lines only for the temperature rise transition under two of the regimes used, LDTC 4 hr and 6 hr regimes. Under LDTC 4 hr, temperature rise transition falls in the morning window and despite early having significantly greater startle than late, the overall morning SSD (over 12 hours morning window) did not differ significantly among the selection lines for this regime. Thus, eliminating the startle window would make the selection lines more similar to one another. On the other hand, under LDTC 6 hour regime, the startle response to temperature rise falls in the evening 12 hour window. In this case too, early showed higher startle than control and late. A higher startle in early would thus, contribute to the observed differences among selection lines. We agree with the reviewer that eliminating this startle peak would lead to a clearer interpretation of the change in circadian evening activity.

We deliberately preserved all behavioural data without filtering out startle windows since it would require arbitrary cutoffs like 1 hr, 2 hr or 3 hours post transitions or until the startle peaks declines in different selection lines under different regimes. In the revised version, we will add complementary analyses excluding the startle windows to obtain mean phase and SSD values which are unaffected by the startle responses.

For the circadian phase, these issues seem, for example, quite obvious for the morning peak in Figure 1. According to the phase quantification on panel D, there is essentially no change in the morning phase when the temperature cycle is shifted by 6 hours compared to the LD cycle, but the behavior trace on panel B clearly shows a phase advance of morning anticipation. Comparison between the graphs on panels C and D also indicates that there are methodological caveats, as they do not correlate well.

Because of the various masking effects, phase quantification under entrainment is a thorny problem in Drosophila. I would suggest testing other measurements of anticipatory behavior to complement or perhaps supersede the current behavior analysis. For example, the authors could employ the anticipatory index used in many previous studies, measure the onset of morning or evening activity, or, if more reliable, the time at which 50% of anticipatory activity is reached. Termination of activity could also be considered. Interestingly, it seems there are clear effects on evening activity termination in Figure 3. All these methods will be impacted by startle responses under specific LD/TC phase relationships, but their combination might prove informative.

We agree that phase quantification under entrained conditions in Drosophila is challenging and that anticipatory indices, onset/offset measures, and T50 metrics each have particular strengths and weaknesses. In designing our analysis, we chose to avoid metrics that require arbitrary or subjective criteria (e.g. defining activity thresholds or durations for anticipation, or visually marking onset/offset), because these can substantially affect the estimated phase and reduce comparability across regimes and genotypes. Instead, we used two fully quantitative, parameter-free measures applied to the entire waveform within defined windows: (i) SSD to capture waveform change in shape/amplitude and (ii) circular mean phase of activity (CoM) restricted to the 12 h morning and 12 h evening windows. By integrating over the entire window, these measures are less sensitive to the exact choice of threshold and to short-lived, high-amplitude startles at transitions, and they treat all bins within the window in a consistent, reproducible way across all LDTC regimes and lines. Panels C (SSD) and D (CoM) are intentionally complementary, not redundant: SSD reflects how much the waveform changes in shape and amplitude, whereas CoM reflects the timing of the center of mass of activity. Under conditions where masking alters amplitude and introduces short-lived bouts without a major shift of the main peak, it is expected that SSD and CoM will not correlate linearly across regimes.

We will be including a detailed calculation of how CoM is obtained in our methods for the revised version.  

Reviewer #2 (Public review):

Summary:

The authors aimed to dissect the plasticity of circadian outputs by combining evolutionary biology with chronobiology. By utilizing Drosophila strains selected for "Late" and "Early" adult emergence, they sought to investigate whether selection for developmental timing co-evolves with plasticity in daily locomotor activity. Specifically, they examined how these diverse lines respond to complex, desynchronized environmental cues (temperature and light cycles) and investigated the molecular role of the splicing factor Psi and timeless isoforms in mediating this plasticity.

Major strengths and weaknesses:

The primary strength of this work is the novel utilization of long-term selection lines to address fundamental questions about how organisms cope with complex environmental cues. The behavioral data are compelling, clearly demonstrating that "Late" and "Early" flies possess distinct capabilities to track temperature cycles when they are desynchronized from light cycles.

We sincerely thank the reviewer for this enthusiastic recognition of our study's core strengths. We are particularly gratified that the reviewer highlights our novel use of long-term selection lines (350+ generations) as the primary strength, enabling us to address fundamental evolutionary questions about circadian plasticity under complex environmental cues. We thank them for identifying our behavioral data as compelling (Figs 1, 3), which robustly demonstrate selection-driven divergence in temperature cycle tracking.

However, a significant weakness lies in the causal links proposed between the molecular findings and these behavioral phenotypes. The molecular insights (Figures 2, 4, 5, and 6) rely on mRNA extracted from whole heads. As head tissue is dominated by photoreceptor cells and glia rather than the specific pacemaker neurons (LNv, LNd) driving these behaviors, this approach introduces a confound. Differential splicing observed here may reflect the state of the compound eye rather than the central clock circuit, a distinction highlighted by recent studies (e.g., Ma et al., PNAS 2023).

We thank the reviewer for highlighting this important methodological consideration. We fully agree that whole-head extracts do not provide spatial resolution to distinguish central pacemaker neurons (~100-200 total) from compound eyes and glia, and that cell-type-specific profiling represents the critical next experimental step. As mentioned in our response to Reviewer 1, we appreciate the issue with our phrasing and will be revising it accordingly to more clearly describe that we do not claim any causal connections between expression of the tim splice variants in particular circadian neurons and their contribution of the phenotype observed.

We chose whole-head extracts for practical reasons aligned with our study's specific goals:

(1) Fly numbers: Our artificially selected populations are maintained at large numbers (~1000s per line). Whole-head extracts enabled sampling ~150 flies per time point = ~600 flies per genotype per environmental, providing means to faithfully sample the variation that may exist in such randomly mating populations.

(2) Established method for characterizing splicing patterns: The majority of temperature-dependent period/timeless splicing studies have successfully used whole-head extracts (Majercak et al., 1999; Shakhmantsir et al., 2018; Martin Anduaga et al., 2019) to characterize splicing dynamics under novel conditions.

(3) Novel environmental regimes: Our primary molecular contribution was documenting timeless splicing patterns under previously untested LDTC phase relationships (TC 2-12hr lags relative to LD) and testing whether these exhibit selection-dependent differences consistent with behavioral divergence.

Furthermore, while the authors report that Psi mRNA loses rhythmicity under out-of-sync conditions, this correlation does not definitively prove that Psi oscillation is required for the observed splicing patterns or behavioral plasticity. The amplitude of the reported Psi rhythm is also low (~1.5 fold) and variable, raising questions about its functional significance in the absence of manipulation experiments (such as constitutive expression) to test causality.

We thank the reviewer for this insightful comment and appreciate that our phrasing has been misleading. We will especially pay attention to this issue, raised by two reviewers, and clearly highlight our results as correlated evolution and hypothesis-generating.

We appreciate the reviewer highlighting these points and would like to draw attention to the following points in our Discussion section:

“Psi and levels of tim-cold and tim-sc (Foley et al., 2019). We observe that this correlation is most clearly upheld under temperature cycles wherein tim-medium and Psi peak in-phase while the cold-induced transcripts start rising when Psi falls (Figure 8A1&2). Under LDTC in-sync conditions this relationship is weaker, even though Psi is rhythmic, potentially due to light-modulated factors influencing timeless splicing (Figure 8B1&2). This is in line with Psi’s established role in regulating activity phasing under TC 12:12 but not LD 12:12 (Foley et al., 2019). This is also supported by the fact that while tim-medium and tim-cold are rhythmic under LD 12:12 (Shakhmantsir et al., 2018), Psi is not (datasets from Kuintzle et al., 2017; Rodriguez et al., 2013). Assuming this to be true across genetic backgrounds and sexes and combined with our similar findings for these three transcripts under LDTC out-of-sync (Figure 2B3, D3&E3), we speculate that Psi rhythmicity may not be essential for tim-medium or tim-cold rhythmicity especially under conditions wherein light cycles are present along with temperature cycles (Figure 8C1&2). Our study opens avenues for future experiments manipulating PSI expression under varying light-temperature regimes to dissect its precise regulatory interactions. We hypothesize that flies with Psi knocked down in the clock neurons should exhibit a less pronounced shift of the evening activity under the range LDTC out-of-sync conditions for which activity is assayed in our study. On the other hand, its overexpression should cause larger delays in response to delayed temperature cycles due to the increased levels of tim-medium translating into delay in TIM protein accumulation.”

Appraisal of aims and conclusions:

The authors successfully demonstrate the co-evolution of emergence timing and activity plasticity, achieving their aim on the behavioral level. However, the conclusion that the specific molecular mechanism involves the loss of Psi rhythmicity driving timeless splicing changes is not yet fully supported by the data. The current evidence is correlative, and without spatial resolution (specific clock neurons) or causal manipulation, the mechanistic model remains speculative.

This study is likely to be of significant interest to the chronobiology and evolutionary biology communities as it highlights the "enhanced plasticity" of circadian clocks as an adaptive trait. The findings suggest that plasticity to phase lags - common in nature where temperature often lags light - may be a key evolutionary adaptation. Addressing the mechanistic gaps would significantly increase the utility of these findings for understanding the molecular basis of circadian plasticity.

Thank you for this thoughtful appraisal affirming our successful demonstration of co-evolution between emergence timing and circadian activity plasticity.

Reviewer #3 (Public review):

Summary:

This study attempts to mimic in the laboratory changing seasonal phase relationships between light and temperature and determine their effects on Drosophila circadian locomotor behavior and on the underlying splicing patterns of a canonical clock gene, timeless. The results are then extended to strains that have been selected over many years for early or late circadian phase phenotypes.

Strengths:

A lot of work, and some results showing that the phasing of behavioural and molecular phenotypes is slightly altered in the predicted directions in the selected strains.

We thank the reviewer for acknowledging the substantial experimental effort across 7 environmental regimes (6 LDTC phase relationships + LDTC in-phase), 12 replicate populations (early_1-4, control_1-4, late_1-4), and comprehensive behavioural + molecular phenotyping.

Weaknesses:

The experimental conditions are extremely artificial, with immediate light and temperature transitions compared to the gradual changes observed in nature. Studies in the wild have shown how the laboratory reveals artifacts that are not observed in nature. The behavioural and molecular effects are very small, and some of the graphs and second-order analyses of the main effects appear contradictory. Consequently, the Discussion is very speculative as it is based on such small laboratory effects.

We thank the reviewer for these important points regarding ecological validity, effect sizes, and interpretation scope.

(1) Behavioural effects are robust across population replicates in selection lines (not small/weak)

Our study assayed 12  populations total (4 replicate populations each of early, control, and late selection lines) under 7 LDTC regimes. Critically, selection effects were consistent across all 4 replicate populations within each selection line for every condition tested. In these randomly mating large populations, the mixed model ANOVA reveals highly significant selection×regime interactions [F(5,45)=4.1, p=0.003; Fig 3E, Table S2], demonstrating strong, replicated evolutionary divergence in evening temperature sensitivity.

(2) Molecular effects test critical evolutionary hypothesis

As stated in our Introduction, "selection can shape circadian gene splicing and temperature responsiveness" (Low et al., 2008, 2012). Our laboratory-selected chronotype populations—known to exhibit evolved temperature responsiveness (Abhilash et al., 2019, 2020; Nikhil et al., 2014; Vaze et al., 2012)—provide an apt system to test whether selection for temporal niche leads to divergence in timeless splicing. With ~600 heads per environmental regime per selection line, we detect statistically robust, selection line-specific temporal profiles [early4 advanced timeless phase (Fig 4A4); late4 prolonged tim-cold (Fig 5A4); significant regime×selection×time interactions (Tables S3-S5)], providing initial robust evidence of correlated molecular evolution under novel LDTC regimes.

(3) Systematic design fills critical field gap

Artificial conditions like LD/DD have been useful in revealing fundamental zeitgeber principles. Our systematic 2-12hr TC-LD lags directly implement Pittendrigh & Bruce (1959) + Oda & Friesen (2011) validated design, which discuss how such experimental designs can provide a more comprehensive understanding of zeitgeber integration compared to studies with only one phase jump between two zeitgebers.

(4) Ramping regimes as essential next step

Gradual ramping regimes better mimic nature and represent critical future experiments. New Discussion addition in the revised version: "Ramping LDTC regimes can test whether selection-specific zeitgeber hierarchy persists under naturalistic gradients." While ramping experiments are essential, we would like to emphasize that we aimed to use this experimental design as a tool to test if evening activity exhibits greater temperature sensitivity and if this property of the circadian system can undergo correlated evolution upon selection for timing of eclosion/emergence.

(5) New startle quantification addresses masking

Our startle quantification (which will be added as a new supplementary figure) confirms circadian evening tracking persists despite quantified, selection-independent masking in most of the regimes.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript by Hao Jiang et al described a systematic approach to identify proline hydroxylation proteins. The authors implemented a proteomic strategy with HILIC-chromatographic separation and reported an identification of 4993 sites from HEK293 cells (4 replicates) and 3247 sites from RCC4 sites (3 replicates) with 1412 sites overlapping between the two cell lines. From the analysis, the authors identified 225 sites and 184 sites respectively from 293 and RCC4 cells with HyPro diagnostic ion. The identifications were validated by analyzing a few synthetic peptides, with a specific focus on Repo-man (CDCA2) through comparing MS/MS spectra, retention time, and diagnostic ions. With SILAC analysis and recombinant enzyme assay, the study showed that Repo-man HyPro604 is a target of the PHD1 enzyme.

Strengths:

The study involved extensive LC-MS analysis and was carefully implemented. The identification of over 4000 confident proline hydroxylation sites would be a valuable resource for the community. The characterization of Repo-man proline hydroxylation is a novel finding.

Weaknesses:

However, as a study mainly focused on methodology, the findings from the experimental data did not convincingly demonstrate the sensitivity and specificity of the workflow for site-specific identification of proline hydroxylation in global studies.

Proline hydroxylation is an enzymatic post translational protein modification, catalysed by prolyl Hydroxylases (PHDs), which can have profound biological significance, e.g. altering protein half-life and/or the stability of protein-protein interactions. Furthermore, there has been controversy in the field as to the true number of protein targets for PHDs in cells. Thus, there is a clear need for methods to enable the robust identification of genuine PHD targets and to reliably map sites of PHD-catalysed proline hydroxylation in proteins. We believe, therefore, that our methodology, as reported here in Jiang et al., is an important contribution towards this goal. We note that our methodology has already been used successfully by others

(https://doi.org/10.1016/j.mcpro.2025.100969). While further improvements in this methodology may of course be developed in future, we are not currently aware of any superior methods that have been reported previously in the literature. The criticism made by the reviewer notably does not include reference to any such alternative published methodology that interested researchers can use which would offer superior results to the approach we document in this study.

Major concerns:

(1) The study applied HILIC-based chromatographic separation with a goal of enriching and separating hydroxyproline-containing peptides. However, as the authors mentioned, such an approach is not specific to proline hydroxylation. In addition, many other chromatography techniques can achieve deep proteome fractionation such as high pH reverse phase fractionation, strong-cation exchange etc. There was no data in this study to demonstrate that the strategy offered improved coverage of proline hydroxylation proteins, as the identifications of the HyPro sites could be achieved through deep fractionation and a highly sensitive LCMS setup. The data of Figure 2A and S1A were somewhat confusing without a clear explanation of the heat map representations. 

The data we present in this study demonstrate clearly that peptides with hydroxylated prolines are enriched in specific HILIC fractions (F10-F18), in comparison with total unfractionated peptides derived from cell extracts. We also refer the reviewer to our previously published study by Bensaddek et al (International Journal of Mass Spectrometry: doi:10.1016/j.ijms.2015.07.029), which was reference 41 in this study, in which we compared directly the performance of both HILIC and strong anionic exchange chromatography, (hSAX). This showed that HILIC provided superior enrichment to hSAX for enrichment of peptides containing hydroxylated proline residues. To clarify this point for readers, we have now included a specific reference to our previous study at the start of the Results section in our current revision. Currently, we use HILIC to provide a degree of enrichment for proline hydroxylated peptides because we are not aware of alternative chromatographic methods that in our hands provide better results.

We have included descriptions of the information shown in the heatmaps in the associated figure legends and captions.

(2) The study reported that the HyPro immonium ion is a diagnostic ion for HyPro identification. However, the data showed that only around 5% of the identifications had such a diagnostic ion. In comparison, acetyl-lysine immonium ion was previously reported to be a useful marker for acetyllysine peptides (PMID: 18338905), and the strategy offered a sensitivity of 70% with a specificity of 98%. In this study, the sensitivity of HyPro immonium ion was quite low. The authors also clearly demonstrated that the presence of immonium ion varied significantly due to MS settings, peptide sequence, and abundance. With further complications from L/I immonium ions, it became very challenging to implement this strategy in a global LC-MS analysis to either validate or invalidate HyPro identifications.

The reviewer appears to have misunderstood the point we make with regard to the identification of the immonium ion and its use as a diagnostic marker for proline hydroxylation in MS analyses. We do not claim that this immonium ion is an essential diagnostic marker for proline hydroxylation. As the reviewer notes, with respect to the acetyl-lysine modification, the corresponding immonium ion is often used in MS studies as a diagnostic for identification of specific post translational modifications. Previous studies have reported that the immonium ion for hydroxylated proline is detected when the transcription factor HIF is analysed, but is often absent with other putative PHD targets, which has been used as an argument that these targets are not genuine proline hydroxylation sites. We are not, therefore, introducing the idea in this study that the hydroxy-proline immonium ion is a required diagnostic marker for proline hydroxylation, but instead demonstrating that detection of this ion, at least in some peptide sequences, may require the use of higher MS collision energies than are typically required for routine peptide identification. We believe that this is an interesting observation that can help to clear up discussions in the literature regarding the true prevalence of PHD-catalysed proline hydroxylation in different target proteins. Our data suggest that, in future MS studies analysing suspected PHD target proteins, two different collision energy might need to be used, i.e., normal collision energy for the routine identification of a peptide, combined with use of a higher collision energy if the hydroxy-proline immonium ion was not already detected.

(3) The study aimed to apply the HILIC-based proteomics workflow to identify HyPro proteins regulated by the PHD enzyme. However, the quantification strategy was not rigorous. The study just considered the HyPro proteins not identified by FG-4592 treatment as potential PHD targeted proteins. There are a few issues. First, such an analysis was not quantitative without reproducibility or statistical analysis. Second, it did not take into consideration that data-dependent LC-MS analysis was not comprehensive and some peptide ions may not be identified due to background interferences. Lastly, FG-4592 treatment for 24 hrs could lead to wide changes in gene expressions and protein abundances. Therefore, it is not informative to draw conclusions based on the data for bioinformatic analysis.

We refer the reviewer to the data we present in this study using SILAC analysis, combined with our MS workflow. to achieve a more accurate quantitative picture of proline hydroxylation levels. While we agree that the point the reviewer makes is valid, regarding our data dependent LC-MS/MS analysis potentially not being comprehensive, this means, however, that we are potentially underestimating the true prevalence of proline hydroxylated peptides, not overestimating the level of these modified peptides. We also refer the reviewer to the accompanying study by Druker et al., (eLife 2025; doi.org/10.7554/eLife.108131.1)  in which we present a detailed follow-on study demonstrating the functional significance of the novel proline hydroxylation site we detected in the protein RepoMan (CDCA2). Therefore, even if we have not achieved a fully comprehensive analysis of all proline hydroxylated peptides catalysed by PHD enzymes, we believe that we have advanced the field by documenting a workflow that is able to identify and validate novel PHD targets.

(4) The authors performed an in vitro PHD1 enzyme assay to validate that Repo-man can be hydroxylated by PHD1. However, Figure 9 did not show quantitatively PHD1-induced increase in Repo-man HyPro abundance and it is difficult to assess its reaction efficiency to compare with HIF1a HyPro.

The analysis shown in Figure 9 was not intended to quantify the efficiency of in vitro hydroxylation of RepoMan by PHD1, but rather to answer the question, ‘Can recombinant PHD1 alone hydroxylate P604 on RepoMan in vitro, yes or no?’. The data show that the answer here is ‘yes’. Clearly, the HIF peptide is a more efficient substrate in vitro for recombinant PHD1 than the RepoMan peptide and we have now included a statement in the Discussion that addresses the significance of this observation more directly.

Reviewer #2 (Public review):

Summary:

In this manuscript, Jiang et al. developed a robust workflow for identifying proline hydroxylation sites in proteins. They identified proline hydroxylation sites in HEK293 and RCC4 cells, respectively. The authors found that the more hydrophilic HILIC fractions were enriched in peptides containing hydroxylated proline residues. These peptides showed differences in charge and mass distribution compared to unmodified or oxidized peptides. The intensity of the diagnostic hydroxyproline iminium ion depended on parameters including MS collision energy, parent peptide concentration, and the sequence of amino acids adjacent to the modified proline residue. Additionally, they demonstrate that a combination of retention time in LC and optimized MS parameter settings reliably identifies proline hydroxylation sites in peptides, even when multiple proline residues are present.

Strengths:

Overall, the manuscript presents an advanced, standardized protocol for identifying proline hydroxylation. The experiments were well designed, and the developed protocol is straightforward, which may help resolve confusion in the field.

Weaknesses:

(1) The authors should provide a summary of the standard protocol for identifying proline hydroxylation sites in proteins that can easily be followed by others.

This is a good suggestion and we have now included a figure (Figure 10) with a summary of our workflow in the current revision.

(2) Cockman et al. proposed that HIF-α is the only physiologically relevant target for PHDs. Their approach is considered the gold standard for identifying PHD targets. Therefore, the authors should discuss the major progress they made in this manuscript that challenges Cockman's conclusion.

While we had mentioned the Cockman et al., paper in the Introduction, we had not focussed on this somewhat controversial issue. However, in response to the Reviewer’s request, we have now added a comment in the Discussion section in the current revision of how our new data address the proposal discussed previously by Cockman et al. In brief, we believe that our findings are not consistent with a model in which PHDs have no protein targets other than HIFs.

Reviewer #3 (Public review): 

Summary:

The authors present a new method for detecting and identifying proline hydroxylation sites within the proteome. This tool utilizes traditional LC-MS technology with optimized parameters, combined with HILIC-based separation techniques. The authors show that they pick up known hydroxy-proline sites and also validate a new site discovered through their pipeline.

Strengths:

The manuscript utilizes state-of-the-art mass spectrometric techniques with optimized collision parameters to ensure proper detection of the immonium ions, which is an advance compared to other similar approaches before. The use of synthetic control peptides on the HILIC separation step clearly demonstrates the ability of the method to reliably distinguish hydroxy-proline from oxidized methionine - containing peptides. Using this method, they identify a site on CDCA2, which they go on to validate in vitro and also study its role in regulation of mitotic progression in an associated manuscript.

Weaknesses:

Despite the authors' claim about the specificity of this method in picking up the intended peptides, there is a good amount of potential false positives that also happen to get picked (owing to the limitations of MS-based readout), and the authors' criteria for downstream filtering of such peptides require further clarification. In the same vein, greater and more diverse cell-based validation approach will be helpful to substantiate the claims regarding enrichment of peptides in the described pathway analyses.

We of course agree that false positives may arise, as is true for essentially all PTM studies. There are two issues here; first, are identified sites technically correct? (i.e. not misidentifications from the MS data) and second, are the identified modifications of biological significance? We have addressed this using the popular MaxQuant software suite to evaluate the modifications identified and to control the false discovery rate (FDR) at both the precursor and protein level, as described in the manuscript. We are aware that false positives could arise from confusing oxidation of methionine with hydroxylation of proline. Therefore, to address the issue as to whether we could identify bona fide PHD protein targets outside of the HIF family, we adopted a conservative approach by simply filtering out peptides where there was a methionine residue within three amino acids of the predicted proline hydroxylation site. This was a pragmatic decision made to reduce the likelihood of false positives in our dataset and we recognise that this likely results in us overlooking some genuine proline hydroxylation sites that occur nearby methionine residues. To address the potential biological relevance of the proline hydroxylation sites identified, we analysed extracts from cells treated with FG inhibitors. Of course a detailed understanding of biological significance relies upon follow-on experimental analyses for each site, which we have performed for P604 on RepoMan in accompanying study by Druker et al., (eLife 2025; doi.org/10.7554/eLife.108131.1).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The finding that the immonium ion intensities of L/I did not increase with increasing collision energy was surprising. Was this specific to this synthetic peptide?

We agree this is an interesting and unexpected finding. We have no reason to believe that it is specific to synthetic peptides per se, but rather think this reflects an effect of amino acid composition in the peptides analysed. It will be interesting to explore this phenomenon in more detail in future.

(2) The sequence logos in Figure 4 seemed to lack any amino acid enrichment in most positions except for collagen peptides. Have these findings been tested with statistical analysis?

The results we show for sequence logo analysis were generated using WebLogo (10.1101/gr.849004) and correspond to an analysis of all proline hydroxylated peptides we detected across all cell lines and replicates analysed. The fact that collagens are highly abundant proteins with very high levels of proline hydroxylation likely explains why collagen peptides dominated the outcome of the sequence logo analysis. There is clearly scope for more detailed follow up analysis in future of the sequence specificity of proline hydroxylation sites in no- collagen proteins that are validated PHD targets.

(3) Overall figure quality was not ideal. The resolution and font sizes of figures should be carefully evaluated and adjusted. The figure legend should contain a title for the figure. Annotations of the figures were somewhat confusing. 

We agree with the criticism of the figure resolution in the review copies - the lower resolution appears to have been generated after we had uploaded higher resolution original images. We are providing again higher resolution versions of all figures for the current revision.

Reviewer #3 (Recommendations for the authors):

Certain concerns regarding portions of the manuscript that need addressing:

(1) " These data show that two different cell lines show unique profiles of proteins with hydroxylated peptides." - It is difficult to conclusively say this statement after profiling the prolyl hydroxy proteome from just two cell lines, especially since the amino acids with the highest frequency in the most enriched peptides are similar in both cell lines.

We agree with this point and have changed the current revision to state instead, “This shows that each of the two cell lines analysed have distinct profiles.”

(2) "We noted that there was a high frequency of a methionine residues appearing either at the first, second, or even third positions after the HyPro site.." - according to the authors, claim, the advantage of their method was that they were able to overcome the limitation of older methods that couldn't separate methionine oxidation from proline hydroxylation. However, in this statement, they say that the high frequency of methionine residues may be because of the similar mass shift. These statements are contradictory. The authors should either tone down the claim or prove that those are indeed hydroxyproline sites. Is it possible that in the filtering step of excluding these high-frequency of methionine - containing peptides, we are losing potential positive hits for hydroxy-proline sites? What is the authors' take on this?

We respectfully do not agree that our, “statements are contradictory”, with respect to the potential confusion between identification of methionine oxidation and proline hydroxylation, but acknowledge that we have not explained this issue clearly enough. It is a fact that the similar mass shift resulting from proline hydroxylation and methionine oxidation is a technical challenge that can potentially lead to misidentifications in MS studies and that is what we state clearly in the manuscript. We have addressed this issue head on experimentally in this study and show using synthetic peptides how detailed analysis of specific proline hydroxylation sites in target proteins can be distinguished from methionine oxidation, based upon differential chromatographic behaviour of peptides with either hydroxylated proline or oxidised methionine, as well as by detailed analysis of fragmentation spectra. However, in the case of our global analysis, as we were not able to perform synthetic peptide comparisons for every putative site identified, we took the pragmatic approach of filtering out examples of peptides where a methionine residue was present within three residues of a potential proline hydroxylation site. This was done simply to reduce the possibility of misidentification in the set of novel proline hydroxylated peptides identified and we accept that as a consequence we are likely filtering out peptides that include bona fide proline hydroxylation sites. We have clarified this point in the current revision and hope to be able to address this issue more comprehensively in future studies.

(3) "Accordingly, a score cut-off of 40 for hydroxylated peptides and a localisation probability cut-off of more than 0.5 for hydroxylated peptides was performed." Could the authors shed more light and clarify what was the basis for this value of cut-off to be used in this filtering step? Is this sample dependent? What should be the criteria to determine this value?

We used MaxQuant software (10.1016/j.cell.2006.09.026), for PTM analysis, in which a localization probability score of 0.75 and score cut-off of 40 is a commonly used threshold to define high confidence. The reason that we used 0.5 at the first step was to investigate how likely it might be that the misassignment of delta m/z +16 Da (oxidation) on Methionine would affect the identification of hydroxylation on Proline. However, we note that in the final results set used for analysis, all putative proline hydroxylated peptides that had a Methionine residue near to the hydroxylated proline were disregarded as a pragmatic step to reduce the probability of false identifications.

(4) The authors are requested to kindly make the HPLC and MS traces more legible and use highresolution images, with clearly labeled values on the peaks. Kindly extract coordinates from the underlying data files to plot the curves if needed to make it clearer.

We have reviewed the clarity of all images and figures in the current revision.

(5) There seems to be no error bars in Figure 3, Figure 7E, and panels of Figure 8 with bar graphs. Are those single replicate data?

These specific figures are from single replicate data.

(6) For Figure 9C, the control with only PHD1 (no peptide) is missing. 

The ‘no peptide control’ was not included in the figure because it is simply a blank lane and there is nothing to see.

I like this visual. Having wet clothes drying on a line outside is something everyone recognizes, whether it is something they grew up doing, or they simply saw in multiple pieces of media. Thus it is very easy to picture that scene in your head. At the same time, the author manages to make it hint at the location by specifying that kimonos are drying as opposed to just leaving it at a general statement of clothes are drying.

The descriptions of the scenes and the people the author talks to are so well done. The dialogue adds to it as well. When I read this part, I could picture Mr. Watanabe in my head and could almost hear him talking.

I'm hoping the long sentences are just done on purpose now that I'm reading more. I'm reading this and feeling the same way I do whenever a child tries to tell me what they did on the weekend. A few times feels purposeful, but the lack of a break makes my head spin.

"Da Capo" from Italy.

With so much weighing on a college student , I think it not fair to have this thought of finances lingering over your head. Focusing on classes, studying and just surviving is enough.

wrong size head

Illustrates the feasibility of the phenomenon

Symptoms

April 3 letter - Indian victory and accelerating catastrophe: the Pinnace capture arms/ammunition, kill the captain, and muster 200 canoes with 1000 warriors. The English are outnumbered and outgunned. This escalation shows the colony is losing control and facing near-total annihilation.

Reviewer #1 (Public review):

Summary:

Renard, Ukrow et al. applied their recently published computational pipeline (CHROMAS) to the skin of Euprymna berryi and Sepia officinalis to track the dynamics of cephalopod chromatophore expansion. By segmenting each chromatophore into radial slices and analyzing the co-expansion of slices across regions of the skin, they inferred the motor control underlying chromatophore groups.

Strengths:

The authors demonstrate that most motor units of cephalopod skin include a subregion of multiple chromatophores, creating "virtual chromatophores" in between the fixed chromatophores. This is an interesting concept that challenges prevailing models of chromatophore organization, and raises interesting possibilities for how chromatophore arrays may be patterned during development.

This study introduces new analyses of cephalopod skin that will be valuable for the quantitative study of cephalopod behavior.

Weaknesses:

The authors chose to image spontaneous skin changes in sedated animals, rather than visually-evoked skin changes in awake, freely-moving animals. Spontaneous chromatophore changes tend to be small shimmers of expansion and contraction, rather than obvious, sizable expansions. This may make it more challenging to distinguish truly co-occurring expansions from background activity. The authors don't provide any raw data (videos) of the skin, so it is difficult to independently assess the robustness of the inferred chromatophore groupings.

The patch-clamp experiments in E. berryi are used to test the validity of their approach for inferring motor units. The stimulations evoke expansions of sub-regions of each chromatophore, creating "virtual chromatophores" as predicted from the behavioral analysis. However, the authors were not able to predict these specific motor units from behavioral analysis before confirming them with patch-clamp, limiting the strength of the validation. It would be informative to quantify the results of the patch-clamp experiments - are the inferred motor units of similar sizes to those predicted from behavior?

The authors report testing multiple experimental conditions (e.g., age, size, behavioral stimuli, sedation, head-fixation, and lighting), but only a small subset of these data are presented. It is difficult to determine which conditions were used for which experiments, and the manuscript would benefit from pooling data from multiple experiments to draw general conclusions about the motor control of cephalopod skin.

The authors use a different clustering algorithm for E. berryi and S. officinalis, but do not discuss why different clustering approaches were required for the two species.

Impact:

The authors use their computational pipeline to generate a number of interesting predictions about chromatophore control, including motor unit size, their spatial distribution within the skin, and the independent control of subregions within individual chromatophores by putatively distinct motor neurons. While these observations are interesting, the current data do not yet fully support them.

The CHROMAS tool is likely to be valuable to the field, given the need for quantitative frameworks in cephalopod biology. The predictions outlined here provide a useful foundation for future experimental investigation.

This part really played with my emotions because at first I thought her father was supportive, but later I realized he only saw college as a tool for her to find someone to marry instead of getting an education.

Is showing that this type of action from other does affect her like she has cross some of her own dialogue even in her own head to make it make sense to the others.

Why did hitchhikers guide to the galaxy flash immediately in my head. I think I need some sleep.

Calendar planners and to-do lists help students organize their time and avoid forgetting important dates. Writing schedules down is more reliable than keeping everything in your head, because everyone forgets things sometimes. Planners are not only for deadlines but also for scheduling study time in advance so work is spread out and less stressful. To-do lists are useful for smaller daily tasks that don’t fit into a planner, helping you stay organized without feeling overwhelmed.

Procrastination is a common habit where people delay important tasks by making excuses to do something else. It affects many students for different reasons, such as stress, fear of failure, or poor time management, and can hurt academic performance. However, with planning, positive thinking, and the right strategies, procrastination can be reduced and overcome over time.

Why did New York City attract such a high Caribbean population when Miami is much closer geographically and seemingly more convenient? Perhaps it is because NYC is well-known as an immigrant city/hub, with a much more diverse scene and also many economic opportunities.

Through the medium of a game, agency is limited but the environment is engineered in a way that makes helplessness as just another step or another obstacle adding to the enjoyment. Much like Odysseus, you're placed in a situation where there's not much to work with but through your agency you can make what there is of the situation to get out of it.

this just goes to show that freedom of speech isnt all over the world and people die everyday for what they say

I find Malala to be a huge inspiration to me, and I feel that she is strong for what she has done and I look up to her.

I think this is Luther trying to express the corruption and authority the catholic church by comparing it to slavery.

I appreciate how the importance of both fast and slow thinking is mentioned. Fast thinking is important in busy environments and setting require you to have commonsense or knowledge on the top of your head. While slow thing is needed when you encounter a situation that has high risk, give you time to thing, and require research.

[[Cory Doctorow p]] on AI en the economic dynamic behind the hype that bigtech is pursuing to remain growth stocks even as monopolists. 'tech centaur' me in a car, a human head on something else vs 'reverse centaur' a meat puppet doing the machines bidding, e.g. an Amazon delivery driver

Children have difficulty imagining the world outside of their perspective. E.g a child nodding or shaking their head while on the phone with someone who cannot see them.

Lady Susan is an absolutely intriguing character, albeit heavily dislikeable to me personally. That being said, she's spectacularly written; the characterization within this passage alone is outstanding! It goes without saying that Susan does not like being played with, and that is exactly what is being described here. She already has a large amount of unwarranted distaste for her own daughter, of which she convinced Reginald to believe, and is now infuriated by the mere thought of her daughter going behind her back and, in her head, twisting what she has convinced Reginald of. In short, she does not like being made out to be the bad guy. Though a very heated letter, I did sense some interesting contesting feelings towards her daughter, Frederica. Though Susan's dislike of her child is palpable, there seems to still be a sense of caring. After all, she is knowledgeable enough to realize Frederica is romantically pursuing Reginald, but Susan is angered by Reginald toying with this likely one-sided romantic interest from her daughter. It is an incredibly interesting family dynamic between mother and daughter, especially with Reginald involved.

I am very deep into fandom spaces, and I see very frequently parasocial relationships festering. Typically, I see parasocial relationships forming in children and teenagers the most in these spaces, typically also consisting of fandoms with a large neurodivergent population. Usually this happens with creators that have their face on screen or a lot of time to communicate with their viewers, the main example being streamers and their chats. Being able to receive a reply in real time is like a conversation, which can lead to someone believing they are friends with the streamer they are typing to. I'm sure I have some parasocial relationships currently and don't really realize it. I know in the past I had a few with streamers I watched, however I always kept a degree of separation in my head: I do not truly know these people.

https://globalgrowthforum.com/saturn-devouring-his-son-by-goya-an-analysis-of-a-haunting-masterpiece/

Reviewer #3 (Public review):

The goal of the work is to establish the linkage between the spatial transcription factors (STF's) that function transiently to establish the identities of the individual NBs and the terminal selector genes (typically homeodomain genes) that appear in the new-born post-mitotic neurons. How is the identity of the NB maintained and carried forward after the spatial genes have faded away? Focusing on a single neuroblast (NB 7-1), the authors present evidence that the fork-head transcription factor, fd4, provides a bridge linking the transient spatial cues that initially specified neuroblast identity with the terminal selector genes that establish and maintain the identity of the stem cell's progeny.

The study is systematic, concise and takes full advantage of 40+ years of work on the molecular players that establish neuronal identities in the Drosophila CNS. In the embryonic VNC, fd4 is expressed only in the NB 7-1 and its lineage. They show that Fd4 appears in the NB while the latter is still expressing the Spatial Transcription Factors and continues after the expression of the latter fades out. Fd4 is maintained through the early life of the neuronal progeny but then declines as the neurons turn on their terminal selector genes. Hence, fd4 expression is compatible with it being a bridging factor between the two sets of genes.

Experimental support for the "bridging" role of Fd4 comes from set of loss-of-function and gain-of-function manipulations. The loss of function of fd4, and the partially redundant gene fd5, from lineage 7-1 does not affect the size of the lineage, but terminal markers of late-born neuronal phenotypes, like Eve and Dbx, are reduced or missing. By contrast, ectopic expression of fd4, but not fd5, results in ectopic expression of the terminal markers eve and dbx throughout diverse VNC lineages.

A detailed test of fd4's expression was then carried out using lineages 7-3 and 5-6, two well characterized lineages in Drosophila. Lineage 7-3 is much smaller that 7-1 and continues to be so when subjected to fd4 misexpression. However, under the influence of ectopic fd4 expression, the lineage 7-3 neurons lost their expected serotonin and corazonin expression and showed Eve expression as well as motoneuron phenotypes that partially mimic the U motoneurons of lineage 7-1.

Ectopic expression of Fd4 also produced changes in the 5-6 lineage. Expression of apterous, a feature of lineage 5-6 was suppressed, and expression of the 7-1 marker, Eve, was evident. Dbx expression was also evident in the transformed 5-6 lineages but extremely restricted as compared to a normal 7-1 lineage. Considering the partial redundancy of fd4 and fd5, it would have been interesting to express both genes in the 5-6 lineage. The anatomical changes that are exhibited by motoneurons in response to fd4 expression confirms that these cells do, indeed, show a shift in their cellular identity.

Comments on revisions:

The authors adequately addressed all of the issues that I had with the original submission.

Their responses to the other reviewers are also well-reasoned

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Lai and Doe address the integration of spatial information with temporal patterning and genes that specify cell fate. They identify the Forkhead transcription factor Fd4 as a lineage-restricted cell fate regulator that bridges transient spatial transcription factors to terminal selector genes in the developing Drosophila ventral nerve cord. The experimental evidence convincingly demonstrates that Fd4 is both necessary for lateborn NB7-1 neurons, but also sufficient to transform other neural stem cell lineages toward the NB7-1 identity. This work addresses an important question that will be of interest to developmental neurobiologists: How can cell identities defined by initial transient developmental cues be maintained in the progeny cells, even if the molecular mechanism remains to be investigated? In addition, the study proposes a broader concept of lineage identity genes that could be utilized in other lineages and regions in the Drosophila nervous system and in other species.

Thanks for the accurate summary and positive comments!

While the spatial factors patterning the neuroepithelium to define the neuroblast lineages in the Drosophila ventral nerve cord are known, these factors are sometimes absent or not required during neurogenesis. In the current work, Lai and Doe identified Fd4 in the NB7-1 lineage that bridges this gap and explains how NB7-1 neurons are specified after Engrailed (En) and Vnd cease their expression. They show that Fd4 is transiently co-expressed with En and Vnd and is present in all nascent NB7-1 progenies. They further demonstrate that Fd4 is required for later-born NB7-1 progenies and sufficient for the induction of NB7-1 markers (Eve and Dbx) while repressing markers of other lineages when force-expressed in neural progenitors, e.g., in the NB56 lineage and in the NB7-3 lineage. They also demonstrate that, when Fd4 is ectopically expressed in NB7-3 and NB5-6 lineages, this leads to the ectopic generation of dorsal muscle-innervating neurons. The inclusion of functional validation using axon projections demonstrates that the transformed neurons acquire appropriate NB7-1 characteristics beyond just molecular markers. Quantitative analyses are thorough and well-presented for all experiments.

Thanks for the positive comments!

(1) While Fd4 is required and sufficient for several later-born NB7-1 progeny features, a comparison between early-born (Hb/Eve) and later-born (Run/Eve) appears missing for pan-progenitor gain of Fd4 (with sca-Gal4; Figure 4) and for the NB7-3 lineage (Figure 6). Having a quantification for both could make it clearer whether Fd4 preferentially induces later-born neurons or is sufficient for NB7-1 features without temporal restriction.

We quantified the percentage of Hb+ and Runt+ cells among Eve+ cells with sca-gal4, and the results are shown in Figure 4-figure supplement 1. We found that the proportion of early-born cells is slightly reduced but the proportion of later-born cells remain similar. Interestingly, we also found a subset of Eve+ cells with a mixed fate (Hb+Runt+) but the reason remains unclear.

(2) Fd4 and Fd5 are shown to be partially redundant, as Fd4 loss of function alone does not alter the number of Eve+ and Dbx+ neurons. This information is critical and should be included in Figure 3.

Because every hemisegment in an fd4 single mutant is normal, we just added it as the following text: “In fd4 mutants, we observe no change in the number of Eve+ neurons or Dbx+ neurons (n=40 hemisegments).”

(3) Several observations suggest that lineage identity maintenance involves both Fd4dependent and Fd4-independent mechanisms. In particular, the fact that fd4-Gal4 reporter remains active in fd4/fd5 mutants even after Vnd and En disappear indicates that Fd4's own expression, a key feature of NB7-1 identity, is maintained independently of Fd4 protein. This raises questions about what proportion of lineage identity features require Fd4 versus other maintenance mechanisms, which deserves discussion.

We agree, thanks for raising this point. We add the following text to the Discussion. “Interestingly, the fd4 fd5 mutant maintains expression of fd4:gal4, suggesting that the fd4/fd5 locus may have established a chromatin state that allows “permanent” expression in the absence of Vnd, En, and Fd4/Fd5 proteins.”

(4) Similarly, while gain of Fd4 induces NB7-1 lineage markers and dorsal muscle innervation in NB5-6 and NB7-3 lineages, drivers for the two lineages remain active despite the loss of molecular markers, indicating some regulatory elements retain activity consistent with their original lineage identity. It is therefore important to understand the degree of functional conversion in the gain-of-function experiments. Sparse labeling of Fd4 overexpressing NB5-6 and NB7-3 progenies, as was done in Seroka and Doe (2019), would be an option.

We agree it is interesting that the NB7-3 and NB5-6 drivers remain on following Fd4 misexpression. To explore this, we used sca-gal4 to overexpress Fd4 and observed that Lbe expression persisted while Eg was largely repressed (Author response image 1). The results show that Lbe and Eg respond differently to Fd4. A non-mutually exclusive possibility is that the continued expression of lbe-Gal4 UAS-GFP or eg-Gal4 UAS-GFP may be due to the lengthy perdurance of both Gal4 and GFP.

Author response image 1.

(5) The less-penetrant induction of Dbx+ neurons in NB5-6 with Fd4-overexpression is interesting. It might be worth the authors discussing whether it is an Fd4 feature or an NB56 feature by examining Dbx+ neuron number in NB7-3 with Fd4-overexpression.

In the NB7-3 lineages misexpressing Fd4, only 5 lineages generated Dbx+ cells (0.1±0.4, n=64 hemisegments), suggesting that the low penetrance of Dbx+ induction is an intrinsic feature of Fd4 rather than lineage context. We have added this information in the results section.

(6) It is logical to hypothesize that spatial factors specify early-born neurons directly, so only late-born neurons require Fd4, but it was not tested. The model would be strengthened by examining whether Fd4-Gal4-driven Vnd rescues the generation of laterborn neurons in fd4/fd5 mutants.

When we used en-gal4 driver to express UAS-vnd in the fd4/fd5 mutant background, we found an average 7.4±2.2 Eve+ cells per hemisegment (n=36), significantly higher than fd4/fd5 mutant alone (3.9±0.8 cells, n=52, p=2.6x10^-11) (Figure 3J). In addition, 0.2±0.5 Eve+ cells were ectopic Hb+ (excluding U1/U2), indicating that Vnd-En integration is sufficient to generate both early-born and late-born Eve+ cells in the fd4/fd5 mutants. We have added the results to the text.

(7) It is mentioned that Fd5 is not sufficient for the NB7-1 lineage identity. The observation is intriguing in how similar regulators serve distinct roles, but the data are not shown. The analysis in Figure 4 should be performed for Fd5 as supplemental information.

Thanks for the suggestion. Because the results are exactly the same as the wild type, we don’t think it is necessary to provide an additional images or analysis as supplemental information.

Reviewer #2 (Public review):

Via a detailed expression analysis, they find that Fd4 is selectively expressed in embryonic NB7-1 and newly born neurons within this lineage. They also undertake a comprehensive genetic analysis to provide evidence that fd4 is necessary and sufficient for the identity of NB7-1 progeny.

Thanks for the accurate summary!

The analysis is both careful and rigorous, and the findings are of interest to developmental neurobiologists interested in molecular mechanisms underlying the generation of neuronal diversity. Great care was taken to make the figures clear and accessible. This work takes great advantage of years of painstaking descriptive work that has mapped embryonic neuroblast lineages in Drosophila.

Thanks for the positive comments!

The argument that Fd4 is necessary for NB7-1 lineage identity is based on a Fd4/Fd5 double mutant. Loss of fd4 alone did not alter the number of NB7-1-derived Eve+ or Dbx+ neurons. The authors clearly demonstrate redundancy between fd4 and fd5, and the fact that the LOF analysis is based on a double mutant should be better woven through the text.The authors generated an Fd5 mutant. I assume that Fd5 single mutants do not display NB7-1 lineage defects, but this is not stated. The focus on Fd4 over Fd5 is based on its highly specific expression profile and the dramatic misexpression phenotypes. But the LOF analysis demonstrates redundancy, and the conclusions in the abstract and through the results should reflect the existence of Fd5 in the conclusions of this manuscript.

We agree, and have added new text to clarify the single mutant phenotypes (there are none) and the double mutant phenotype (loss of NB7-1 molecular and morphological features. The following text is added to the manuscript: “Not surprisingly, we found that fd4 single mutants or fd5 single mutants had no phenotype (Eve+ neurons were all normal). Thus, to assess their roles, we generated a fd4 and fd5 double mutant. Because many Eve+ and Dbx+ cells are generated outside of NB7-1 lineage, it was also essential to identify the Eve+ or Dbx+ cells within NB7-1 lineage in wild type and fd4 mutant embryos. To achieve this, we replaced the open reading frame of fd4 with gal4 (called fd4-gal4) (see Methods); this stock simultaneously knocked out both fd4 and fd5 (called fd4/fd5 mutant hereafter) while specifically labeling the NB7-1 lineage. For the remainder of this paper we use the fd4/fd5 double mutant to assay for loss of function phenotypes.”

It is notable that Fd4 overexpression can rewire motor circuits. This analysis adds another dimension to the changes in transcription factor expression and, importantly, demonstrates functional consequences. Could the authors test whether U4 and U5 motor axon targeting changes in the fd4/fd5 double mutant? To strengthen claims regarding the importance of fd4/fd5 for lineage identity, it would help to address terminal features of U motorneuron identity in the LOF condition.

Thanks for raising this important point. We examined the axon targeting on body wall muscles in both wild type and in fd4/fd5 mutant background and added the results in Figure 3-figure supplement 2. We found that the axon targeting in the late-born neuron region (LL1) is significantly reduced, suggesting that the loss of late-born neurons in fd4/fd5 mutant leads to the absence of innervation of corresponding muscle targets.

Reviewer #3 (Public review):

The goal of the work is to establish the linkage between the spatial transcription factors (STFs) that function transiently to establish the identities of the individual NBs and the terminal selector genes (typically homeodomain genes) that appear in the newborn postmitotic neurons. How is the identity of the NB maintained and carried forward after the spatial genes have faded away? Focusing on a single neuroblast (NB 7-1), the authors present evidence that the fork-head transcription factor, fd4, provides a bridge linking the transient spatial cues that initially specified neuroblast identity with the terminal selector genes that establish and maintain the identity of the stem cell's progeny.

Thanks for the positive comments!

The study is systematic, concise, and takes full advantage of 40+ years of work on the molecular players that establish neuronal identities in the Drosophila CNS. In the embryonic VNC, fd4 is expressed only in the NB 7-1 and its lineage. They show that Fd4 appears in the NB while the latter is still expressing the Spatial Transcription Factors and continues after the expression of the latter fades out. Fd4 is maintained through the early life of the neuronal progeny but then declines as the neurons turn on their terminal selector genes. Hence, fd4 expression is compatible with it being a bridging factor between the two sets of genes.

Thanks for the accurate summary!

Experimental support for the "bridging" role of Fd4 comes from a set of loss-of-function and gain-of-function manipulations. The loss of function of Fd4, and the partially redundant gene Fd5, from lineage 7-1 does not aoect the size of the lineage, but terminal markers of late-born neuronal phenotypes, like Eve and Dbx, are reduced or missing. By contrast, ectopic expression of fd4, but not fd5, results in ectopic expression of the terminal markers eve and Dbx throughout diverse VNC lineages.

Thanks for the accurate summary!

A detailed test of fd4's expression was then carried out using lineages 7-3 and 5-6, two well-characterized lineages in Drosophila. Lineage 7-3 is much smaller than 7-1 and continues to be so when subjected to fd4 misexpression. However, under the influence of ectopic Fd4 expression, the lineage 7-3 neurons lost their expected serotonin and corazonin expression and showed Eve expression as well as motoneuron phenotypes that partially mimic the U motoneurons of lineage 7-1.

Thanks for the positive comments!

Ectopic expression of Fd4 also produced changes in the 5-6 lineage. Expression of apterous, a feature of lineage 5-6, was suppressed, and expression of the 7-1 marker, Eve, was evident. Dbx expression was also evident in the transformed 5-6 lineages, but extremely restricted as compared to a normal 7-1 lineage. Considering the partial redundancy of fd4 and fd5, it would have been interesting to express both genes in the 5-6 lineage. The anatomical changes that are exhibited by motoneurons in response to Fd4 expression confirm that these cells do, indeed, show a shift in their cellular identity.

We appreciate the positive comments. We agree double misexpression of Fd4 and Fd5 might give a stronger phenotype (as the reviewer says) but the lack of this experiment does not change the conclusions that Fd4 can promote NB7-1 molecular and morphological aspects at the expense of NB5-6 molecular markers.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

The title of Figure 4 may be intended to include the term "Widespread", not "Wild spread". (Though the expansion of the Eve and Dbx with Fd4 is quite remarkable…).

Done!

Reviewer #3 (Recommendations for the authors):

(1) Line 138. Is part of the sentence missing? Did the authors mean to say "that fd5 is coexpressed with fd4 in NB7-1 and its .....".

Done!

(2) ln 237: In trying to explain the "U-like" phenotype of the transformed motoneurons in lineage 7-3, the authors speculate that "perhaps their late birth did not give them time to extend to the most distant dorsal muscles ". It is very difficult to convince a motoneuron to stop growing in the absence of a target! An alternate possibility is that since there is only one or two U neurons made instead of the normal five, the growing motoneuron has enough information to direct them to the dorsal domain, but they lack the specification that allows them to recognize a specific muscle target.

We agree there are additional possibilities, and now update the text to say: “We observed that these transformed neurons did not innervate the dorsal muscles, perhaps their late birth did not give them time to extend to the most distant dorsal muscles, or they were incompletely specified.”

(3) In the References, I think that the Anderson et al. reference should also include "BioRxiv" before the DOI.

Done!

(4) Figure 6A for wild-type 7-3 lineage. The corazonin expression appears to be expressed in EW2 as well as EW3. This should be explained.

We agree it looks that way, due to the 3D rotation used; we now replace it with a more representative image. Note that our quantification always shows a single Cor+ neuron per hemisegment.

(5) Figure 7: Issues of terminology. The designation of "longitudinal" for muscles is traditionally in reference to the body axis, such as the Dorsal Longitudinal Muscles (DLM) of the adult thorax. The "longitudinal" muscles in the figure are really "transverse" muscles. I also suggest using "axon" or "neurites" rather than "filament". For the middle and bottom parts of E and F, are these lateral and ventral views? They should be designated as such.

Thanks, we agree and have made the changes, using Axon instead of Filament, and labeling the views (lateral and ventro-lateral).

My message may impact the way the receiver views me, however, it gives them a head start on finding an alternative way of getting the meeting set up as well as the other responsibilities that were assigned to me.

Moving from the theme of equality in the United States to its foreign policy, Killer Mike directly accuses the Presidents of the United States, from Reagan to Obama, of being actors and puppets of the country real masters.

His argument in support of this is the common political position shared by presidents on opposite sides of the political spectrum, such as Reagan and Obama, toward North Africa and the Middle East, and especially Gaddafi.

Gaddafi was the authoritarian leader of Lybia for 42 years, from 1969 to 2011, and was bombed by the United States both in 1986 and in 2011 in attempts to kill him, with the latter being succesful.

In his arguments against U.S. foreing policy, he also refers to the wars in the Middle East. He argues that the wars in Iraq and Afghanistan during the 2000s and 2010s were actually motivated by a colonial quest for oil and natural resources, adding how Ahmadinejad, then the 6th President of Iran, warned that they coming for Iran.

In his second verse, Killer Mike essentially exposes the same theory as Michelle Alexander in "The New Jim Crow". According to him, the War on Drugs and the War on Terror were instruments to control the population and let the police terrorize whoever.

The main targets, though, were Black boys, many of whom ended up as victims of police brutality or, in the worst case, as "new slaves" in the prison system boosted by Reaganomics. Under Reagan, in fact, many prisons ended up privatized and became a profitable business; and, according to the 13th Amendment:

Neither slavery nor involuntary servitude, except as a punishment for crime whereof the party shall have been duly convicted, shall exist within the United States, or any place subject to their jurisdiction.

In the words of Killer Mike, this is the reason why they givin' drug offenders time in double digits. Harsh punishments for drug trafficking and possession, allow the 13th Amendment to become a way to enfroce a new form of slavery through mass incarceration, of which, Black boys are the main and disproportionate targets.

In the America that Killer Mike and Alexander are painting, equality is prevented at every turn by a system designed to keep a racial caste order in place while, at the same time, profiting from it in an indirect and subtle way.

In the closing lines of the third verse, Nas is expressing his hope for the future, in which he envisions the possibility of ghetto children changing the world.

Nas is arguing for the freedom of the African-American people from the darker aspects of their history, by remembering their forgotten one. In doing so, even their future can shine brightly through the means of education and ambition to reclaim control of their life.

I enjoy how they describe a what i assume to be a dangerous storm,

The invention of Email can be seen as one of the great step of how people communicate. The time to transport an information was significantly reduced within few minutes. This reminds me a fun fact that Her Majesty Queen Elizabeth II, the queen of Great Britain was one of the earliest adopters of email, probably the first ever email from a head of the state!

Could have just been scared and/or lying.

Author highlighting how Della's love was greater than her hair (of which the author gives great importance)

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer # 1 (Public review):

Significance:

While most MAVEs measure overall function (which is a complex integration of biochemical properties, including stability), VAMP-seqtype measurements more strongly isolate stability effects in a cellular context. This work seeks to create a simple model for predicting the response for a mutation on the "abundance" measurement of VAMPseq.

We thank the reviewer for their evaluation of our work and for their comments and feedback below.

Of course, there is always another layer of the onion, VAMP-seq measures contributions from isolated thermodynamic stability, stability conferred by binding partners (small molecule and protein), synthesis/degradation balance (especially important in "degron" motifs), etc. Here the authors' goal is to create simple models that can act as a baseline for two main reasons:

(1) how to tell when adding more information would be helpful for a global model;

(2) how to detect when a residue/mutation has an unusual profile indicative of an unbalanced contribution from one of the factors listed above.

As such, the authors state that this manuscript is not intended to be a state-of-the-art method in variant effect prediction, but rather a direction towards considering static structural information for the VAMP-seq effects. At its core, the method is a fairly traditional asymmetric substitution matrix (I was surprised not to see a comparison to BLOSUM in the manuscript) - and shows that a subdivision by burial makes the model much more predictive. Despite only having 6 datasets, they show predictive power even when the matrices are based on a smaller number. Another success is rationalizing the VAMPseq results on relevant oligomeric states.

We thank the reviewer for their summary of the main points of our work. Based on the suggestion by the reviewer, we have added a comparison to predictions with BLOSUM62 to our revised manuscript, noting that we have previously compared the BLOSUM62 matrix to a broader and more heterogeneous set of scores generated by MAVEs (Høie et al, 2022).

Specific Feedback:

Major points:

The authors spend a good amount of space discussing how the six datasets have different distributions in abundance scores. After the development of their model is there more to say about why? Is there something that can be leveraged here to design maximally informative experiments?

We believe that these effects arise from a combination of intrinsic differences between the systems and assay-specific effects. For example, biophysical differences between the systems, such as differences in absolute folding stabilities or melting temperatures, will play a role, as will the fact that some proteins contain multiple domains.

Also, the sequencing-based score for an individual variant in a sort-seq experiment (such as VAMP-seq) depends both on the properties of that variant and on the composition of the entire FACS-sorted cell library. This is because cells are sorted into bins depending on the composition of the entire library, which means that library-to-library composition differences can contribute to the differences between VAMP-seq score distributions. 

From our developed models and outliers in predictions from these, it is difficult to tell which of the several possible underlying reasons cause the differences. We have briefly expanded the discussion of these points in the manuscript, and we have moreover elaborated on this in subsequent work (Schulze et al., 2025).

They compare to one more "sophisticated model" - RosettaddG - which should be more correlated with thermodynamic stability than other factors measured by VAMP-seq. However, the direct head-tohead comparison between their matrices and ddG is underdeveloped. How can this be used to dissect cases where thermodynamics are not contributing to specific substitution patterns OR in specific residues/regions that are predicted by one method better than the other? This would naturally dovetail into whether there is orthogonal information between these two that could be leveraged to create better predictions.

We thank the reviewer for this suggestion and indeed had spent substantial effort trying to gain additional biological insights from variants for which MAVE scores or MAVE predictions do not match predicted ∆∆G values. One major caveat in this analysis is that the experimental MAVE scores, MAVE predictions and the predicted ∆∆G values are rather noisy, making it difficult to draw conclusions based on individual variants or even small subsets of variants.

In our revised manuscript, we have added an analysis to discover residue substitution profiles that are predicted most accurately either by a ∆∆G model or by our substitution matrix model, thereby avoiding analysis of individual variant effect scores. 

We find that many substitution profiles are predicted equally well by the two model types, but also that there are residues for which one method predicts substitution effects better than the other method. We have added an analysis of the characteristics of the residues and variants for which either the ∆∆G model or the substitution matrix model is most useful to rank variants. Since we only find relatively few residues for which this is the case, we do not expect a model that leverages predicted scores from both methods to perform better than ThermoMPNN across variants. 

Perhaps beyond the scope of this baseline method, there is also ThermoMPNN and the work from Gabe Rocklin to consider as other approaches that should be more correlated only with thermodynamics.

We acknowledge that there are other approaches to predict ∆∆G beyond Rosetta including for example ThermoMPNN and our own method called RaSP (Blaabjerg et al, eLIFE, 2023), and we have added comparisons to ThermoMPNN and RaSP in the revised manuscript. We are unsure how one would use the data from Rocklin and colleagues directly, but we note that e.g. RaSP has been benchmarked on this data and other methods have been trained on this data. We originally used Rosetta since the Rosetta model is known to be relatively robust and because it has never seen large databases during training (though we do not think that training of ThermoMPNN and RaSP would be biased towards the VAMP-seq data). We note also that we have previously compared both Rosetta calculations and RaSP with VAMP-seq data for TPMT, PTEN and NUDT15 (Blaabjerg et al, eLIFE, 2023)

I find myself drawn to the hints of a larger idea that outliers to this model can be helpful in identifying specific aspects of proteostasis. The discussion of S109 is great in this respect, but I can't help but feel there is more to be mined from Figure S9 or other analyses of outlier higher than predicted abundance along linear or tertiary motifs.

We agree with these points and have previously spent substantial time trying to make sense of outliers in Figure S9 and Figure S18 (Figure S8 and Figure S18 of revised manuscript). The outlier analysis was challenging, in part due to the relatively high noise levels in both experimental data and predictions, and we did not find any clear signals. Some outliers in e.g. Figure S9 are very likely the result of dataset-specific abundance score distributions, which further complicates the outlier analysis. We now note this in the revised paper and hope others will use the data to gain additional insights on proteostasis-specific effects.  

Reviewer # 2 (Public review):

Summary:

This study analyzes protein abundance data from six VAMP-seq experiments, comprising over 31,000 single amino acid substitutions, to understand how different amino acids contribute to maintaining cellular protein levels. The authors develop substitution matrices that capture the average effect of amino acid changes on protein abundance in different structural contexts (buried vs. exposed residues). Their key finding is that these simple structure-based matrices can predict mutational effects on abundance with accuracy comparable to more complex physics-based stability calculations (ΔΔG).

Major strengths:

(1) The analysis focuses on a single molecular phenotype (abundance) measured using the same experimental approach (VAMP-seq), avoiding confounding factors present when combining data from different phenotypes (e.g., mixing stability, activity, and fitness data) or different experimental methods.

(2) The demonstration that simple structural features (particularly solvent accessibility) can capture a significant portion of mutational effects on abundance.

(3) The practical utility of the matrices for analyzing protein interfaces and identifying functionally important surface residues.

We thank the reviewer for the comments above and the detailed assessment of our work.

Major weaknesses:

(1) The statistical rigor of the analysis could be improved. For example, when comparing exposed vs. buried classification of interface residues, or when assessing whether differences between prediction methods are significant.

We agree with the reviewer that it is useful to determine if interface residues (or any of the residues in the six proteins) can confidently be classified as buried- or exposed-like in terms of their substitution profiles. Thus, we have expanded our approach to compare individual substitution profiles to the average profiles of buried and exposed residues to now account for the noise in the VAMP-seq data. In our updated approach, we resample the abundance score substitution profile for every residue several thousand times based on the experimental VAMP-seq scores and score standard deviations, and we then compare every resampled profile to the average profiles for buried and exposed residues, thereby obtaining residue-specific distributions of RMSD_buried and RMSD_exposed values. These RMSD distributions are typically narrow, since many variants in several datasets have small standard deviations. In the revised manuscript, we report a residue to have e.g. a buried-like substitution profile if RMSD_buried <RMSD_exposed for at least 95% of the resampled profiles. We do not recalculate average scores in substitution matrices for this analysis. 

Moreover, to illustrate potential overlap in predictive performance between prediction methods more clearly than in our preprint, we have added confidence intervals in Fig. 2 and Fig. 3 of the revised manuscript. We note that the analysis in Fig. 2 is performed using a leave-one-protein-out approach, which we believe provides the cleanest assessment of how well the different models perform.

(2) The mechanistic connection between stability and abundance is assumed rather than explained or investigated. For instance, destabilizing mutations might decrease abundance through protein quality control, but other mechanisms like degron exposure could also be at play.

We agree that we have not provided much description of the relation between stability and abundance in our original preprint. In the revised manuscript, we provide some more detail as well as references to previous literature explaining the ways in which destabilising mutations can cause degradation. We have moreover performed and added additional analyses of the relationship between thermodynamic stability and abundance through comparisons of stability predictions and predictions performed with our substitution matrix models.

(3) The similar performance of simple matrix-based and complex physics-based predictions calls for deeper analysis. A systematic comparison of where these approaches agree or differ could illuminate the relationship between stability and abundance. For instance, buried sites showing exposed-like behavior might indicate regions of structural plasticity, while the link between destabilization and degradation might involve partial unfolding exposing typically buried residues. The authors have all the necessary data for such analysis but don't fully exploit this opportunity.

This is similar to a point made by reviewer 1, and our answer is similar. We were indeed hoping that our analyses would have revealed clearer differences between effects on thermodynamic protein stability and cellular abundance and have tried to find clear signals. One major caveat in performing the suggested analysis is that both the experimental MAVE scores, ∆∆G predictions and our simple matrix-based predictions are rather noisy, making it difficult to make conclusions based on individual variants or even small subsets of variants. 

To address this point, we have added an analysis to discover residue substitution profiles that are predicted most accurately either by a ∆∆G model or by our substitution matrix model, thereby avoiding analysis of individual variant effect scores. We find that many substitution profiles are predicted equally well by the two model types, but we also, in particular, find solvent-exposed residues for which the substitution matrix model is the better predictor. These residues are often aspartate, glutamate and proline, suggesting that surface-level substitutions of these amino acid types often can have effects that are not captured well by a thermodynamical model, either because this model does not describe thermodynamic effects perfectly, or because in-cell effects are necessary to account for to provide an accurate description.

(4) The pooling of data across proteins to construct the matrices needs better justification, given the observed differences in score distributions between proteins (for example, PTEN's distribution is shifted towards high abundance scores while ASPA and PRKN show more binary distributions).

We agree with the reviewer that the differences between the score distributions are important to investigate further and keep in mind when analysing e.g. prediction outliers. However, our results show that the pooling of VAMP-seq scores across proteins does result in substitution matrices that make sense biochemically and can identify outlier residues with proteostatic functions. As we also respond to a related point by reviewer 1, the differences in score distributions likely have complex origins. In that sense, we also hope that our results can inspire experimentalists to design methods to generate data that are more comparable across proteins.

For example, biophysical differences between the systems, such as differences in absolute folding stabilities or melting temperatures will play a role, as will the fact that some proteins contain multiple domains. Also, the sequence-based score for an individual variant in a sort-seq experiment (such as VAMP-seq) depends both on the properties of that variant and from the composition of the entire FACS-sorted cell library. This is because cells are sorted into bins depending on the composition of the entire library, which means that library-to-library composition can contribute to the differences between VAMP-seq score distributions. From our developed models and outliers in predictions from these, it is difficult to tell which of the several possible underlying reasons cause the differences.

Thus, even when experiments on different proteins are performed using the same technique (VAMP-seq), quantifying the same phenomenon (cellular abundance) and done in similar ways (saturation mutagenesis, sort-seq using four FACS bins), there can still be substantial differences in the results across different systems. An interesting side result of our work is to highlight this including how such variation makes it difficult to learn across experiments. We now elaborate on these points in the revised manuscript.

(5) Some key methodological choices require better justification. For example, combining "to" and "from" mutation profiles for PCA despite their different behaviors, or using arbitrary thresholds (like 0.05) for residue classification.

We hope we have explained our methodological choices clearer in the revised paper.

We removed the dependency of the threshold of 0.05 used for residue classification in Fig. S19 of the original manuscript; in the revised manuscript we only report a residue to have e.g. a buried-like substitution profile if RMSD_buried <RMSD_exposed for at least 95% of the abundance score profiles that we resampled according to VAMP-seq score noise levels, as explained above.

With respect to combining “to” and “from” mutational profiles for PCA, we could have also chosen to analyse these two sets of profiles separately to take potentially different behaviours along the two mutational axes into account. We do not think that there should be anything wrong with concatenating the two sets of profiles in a single analysis, since the analysis on the concatenated profiles simply expresses amino acid similarities and differences in a more general manner.

The authors largely achieve their primary aim of showing that simple structural features can predict abundance changes. However, their secondary goal of using the matrices to identify functionally important residues would benefit from more rigorous statistical validation. While the matrices provide a useful baseline for abundance prediction, the paper could offer deeper biological insights by investigating cases where simple structure-based predictions differ from physics-based stability calculations.

This work provides a valuable resource for the protein science community in the form of easily applicable substitution matrices. The finding that such simple features can match more complex calculations is significant for the field. However, the work's impact would be enhanced by a deeper investigation of the mechanistic implications of the observed patterns, particularly in cases where abundance changes appear decoupled from stability effects.

We agree that disentangling stability and other effects on cellular abundance is one of the goals of this work. As discussed above, it has been difficult to find clear cases where amino acid substitutions affect abundance without stability beyond for example the (rare) effects of creating surface exposed degrons. Our new analysis, in which we compare substitution matrix-based predictions to stability predictions, does offer deeper insight into the relationship between the two predictor types and hence possibly between folding stability and abundance. 

Reviewer #3 (Public review): 

"Effects of residue substitutions on the cellular abundance of proteins" by Schulze and Lindorff-Larsen revisits the classical concept of structure-aware protein substitution matrices through the scope of modern protein structure modelling approaches and comprehensive phenotypic readouts from multiplex assays of variant effects (MAVEs). The authors explore 6 unique protein MAVE datasets based on protein abundance (and thus stability) by utilizing structural information, specifically residue solvent accessibility and secondary structure type, to derive combinations of context-specific substitution matrices predicting variant abundance. They are clear to outline that the aim of the study is not to produce a new best abundance predictor but to showcase the degree of prediction afforded simply by utilizing information on residue accessibility. The performance of their matrices is robustly evaluated using a leave-one-out approach, where the abundance effects for a single protein are predicted using the remaining datasets. Using a simple classification of buried and solvent-exposed residues, and substitution matrices derived respectively for each residue group, the authors convincingly demonstrate that taking structural solvent accessibility contexts into account leads to more accurate performance than either a structureunaware matrix, secondary structure-based matrix, or matrices combining both solvent accessibility or secondary structure. Interestingly, it is shown that the performance of the simple buried and exposed residue substitution matrices for predicting protein abundance is on par with Rosetta, an established and specialized protein variant stability predictor. More importantly, the authors finish off the paper by demonstrating the utility of the two matrices to identify surface residues that have buried-like substitution profiles, that are shown to correspond to protein interface residues, posttranslational modification sites, functional residues, or putative degrons.

Strengths:

The paper makes a strong and well-supported main point, demonstrating the utility of the authors' approach through performance comparisons with alternative substitution matrices and specialized methods alike. The matrices are rigorously evaluated without introducing bias, exploring various combinations of protein datasets. Supplemental analyses are extremely comprehensive and detailed. The applicability of the substitution matrices is explored beyond abundance prediction and could have important implications in the future for identifying functionally relevant sites.

We thank the reviewer for the supportive comments on our work. 

Comments:

(1) A wider discussion of the possible reasons why matrices for certain proteins seem to correlate better than others would be extremely interesting, touching upon possible points like differences or similarities in local environments, degradation pathways, posttranslation modifications, and regulation. While the initial data structure differences provide a possible explanation, Figure S17A, B correlations show a more complicated picture.

We agree with the reviewer that biochemical and biophysical differences between the proteins might contribute to the fact that some matrices correlate better than others. We also agree that it would be very interesting to understand these differences better. While it might be possible to examine some of the suggested causes of the differences, like differences or similarities in local environments, we have generally found that noise and differences in score distributions make such analyses difficult (see also responses to reviewers 1 and 2). For now, we will defer additional analyses to future work.

(2) The performance analysis in Figure 2D seems to show that for particular proteins "less is more" when it comes to which datasets are best to derive the matrix from (CYP2C9, ASPA, PRKN). Are there any features (direct or proxy), that would allow to group proteins to maximize accuracy? Do the authors think on top of the buried vs exposed paradigm, another grouping dimension at the protein/domain level could improve performance?

We don’t currently know if any protein- or domain-level features could be used to further split residues into useful categories for constructing new substitution matrices, but it is an interesting suggestion. We note that every substitution matrix consists of 380 averages, and creating too many residue groupings will cause some matrix entries to be averaged over very few abundance scores, at least with the current number of scores in the pooled VAMP-seq dataset. For example, while previous work has shown different mutational effects e.g. in helices and sheets (as one would expect), we find that a model with six matrices ({buried,exposed}x{helix,sheet,other}) does not lead to improved predictions (Fig. 2C), presumably because of an unfavourable balance between parameters and data.

(3) While the matrices and Rosetta seem to show similar degrees of correlation, do the methods both fail and succeed on the same variants? Or do they show a degree of orthogonality and could potentially be synergistic?

These are good questions and are related to similar questions from reviewers 1 and 2. In the revised manuscript, we have added additional analyses of differences between predictions from our substitution matrix model and a stability model, and we indeed find that the two methods show a degree of orthogonality. However, since we identify only relatively few residues for which one method performs better than the other, we don’t expect a synergistic model to outperform the stability predictor across all variants in any of the six proteins.  

Overall, this work presents a valuable contribution by creatively utilizing a simple concept through cutting-edge datasets, which could be useful in various.

Reviewing Editor:

As discussed in more detail below, to strengthen the assessment, the authors are encouraged to:

(1) Include more thorough statistical analyses, such as confidence intervals or standard errors, to better validate key claims (e.g., RMSD comparisons).

(2) Perform a deeper comparison between substitution response matrices and ΔΔG-based predictions to uncover areas of agreement or orthogonality

(3) Clarify the relationship between structural features, stability, and abundance to provide more mechanistic insights.

As discussed above and below, we have added new analyses and clarifications to the revised manuscript.

Reviewer #1 (Recommendations for the authors):

Minor points:

Why is a continuous version of the contact number used here, instead of a discrete count of neighbouring residues? WCN values of the residues in the core domain can be affected by residues far away (small contribution but not strictly zero; if there are many of them, it adds up).

We have previously found WCN, which quantifies residue contact numbers in a continuous manner, to be a useful input feature for a classifier that determines whether individual residues are important for maintaining protein abundance or function (Cagiada et al, 2023). We have also found WCN and the cellular abundance of single substitution variants to correlate well in individual analyses of different proteins (Grønbæk-Thygesen et al., 2024; Gersing et al., 2024; Clausen et al., 2024).

We have calculated the WCN as well as a contact number based on discrete counts of neighbouring residues for the six proteins in our dataset. When distances between residues are evaluated in the same way (i.e. using the shortest distance between any pair of heavy atoms in the side chains), and when the cutoff value used for the discrete count is equal to the r₀ of the WCN function, the continuous and discrete evaluations of residue contact numbers are highly and linearly correlated, and their rank correlation with the VAMP-seq data are very similar. We only observe minor contributions from residues far away in the structure on the WCN.

Typos in SI figure captions e.g. Figure S8-11 "All predictions were performed using using...."

Thank you for pointing this out. We have corrected the typos in Figure S8-11 (Figure S7-S10 in the revised manuscript).

Personally, I'd appreciate a definition of these new substitution matrices under the constraints of rASA/WCN values. It was unclear to me until I read the code but we think that the definition is averaging the substitution matrix based on the clusters they are assigned to. If so, this could be straightforwardly defined in the method section with a heaviside step function.

We have added a definition of the “buried” and “exposed” substitution matrices as a function of rASA in the methods section (“Definitions of buried and exposed residues” and “Definition of substitution matrices”) of the manuscript, as well as a definition of how we classified residues as either buried or exposed using both rASA and WCN as input. Our final substitution matrices, as shown in e.g. Fig. 2, do not depend on the WCN; only the substitution matrix results in Figure S6 (Figure S20 in the revised manuscript) depend on both WCN and rASA.

Reviewer #2 (Recommendations for the authors):

The following suggestions aim to strengthen the analysis and clarify the presentation of your findings:

(1) Specific analyses to consider:

(1.1) Analyze buried positions where the exposed matrix performs better. Understanding these cases might reveal properties of protein core regions that show unexpected mutational tolerance.

We agree with the reviewer that a more detailed analysis of buried residues with exposed-like substitution profiles would be very interesting.

We note that for proteins where the VAMP-seq score distribution is shifted towards high values (as it is the case for PTEN, TPMT and CYP2C9), our identification of such residues may be a result of the score distribution differences between the six datasets. To confidently identify mutationally tolerant core regions, it would be best to (a) correct for the distribution differences prior to the analysis or (b) focus the analysis on residues that fall far below the diagonal in Figure S18.

In additional data (which can be found at https://github.com/KULL-Centre/_2024_Schulze_abundance-analysis)) ,we provide, for each of the proteins, a list of buried residues for which RMSD_exposed <RMSD_buried (for more than 95% of resampled substitution profiles, as described under 1.6). We have not analysed these residues further.

(1.2) A systematic comparison of matrix-based vs. ΔΔG-based predictions could help understand both exposed sites that behave as buried (as analyzed in the paper) and buried sites that behave as exposed (1.1), potentially revealing mechanisms underlying abundance changes.

In our revised manuscript, we have added additional analyses to compare matrixbased and ΔΔG-based predictions, focusing on exposed sites for which one prediction method captures variant effects on abundance considerably better the other prediction method. We have not investigated buried sites with exposed-like behaviour any further in this work.

(1.3) Explore different normalization approaches when pooling data across proteins. In particular, consider using log(abundance score): if the experimental error in abundance measurements is multiplicative (which can be checked from the reported standard errors), then log transformation would convert this into a constant additive error, making the analysis more statistically sound.

As we answer below to point 2.2, the abundance scores are, within each dataset, min-max normalised to nonsense and synonymous variant scores, and the score scale is thus in this way consistent across the six datasets. We have explained above and in the revised manuscript that abundance score distribution differences across datasets are likely partially a result of the FACS binning of assay-specific variant libraries. Using only the VAMP-seq scores (that is, without further information about the individual experiments), we cannot correct for the influence of the sorting strategy on the reported scores. A score normalisation across datasets that places all data points on a single scale would require inter-dataset references variant scores, which we do not have. We note that in a subsequent manuscript (Schulze et al, bioRxiv, 2025) we have attempted to take system- and experimentspecific score distributions into account. We now refer to this work in the revised manuscript.

(1.4) Consider using correlation coefficients between predicted and observed abundance profiles as an alternative to RMSD, which is sensitive to the absolute values of the scores.

We agree with the reviewer that using correlation coefficients to compare substitution profiles might also be useful, in particular for datasets with relatively unique VAMP-seq score distributions, such as the ASPA dataset. To explore this idea, we have repeated the analysis presented in Fig. S18 using the Pearson correlation coefficient r rather than the RMSD.

As in Fig. S18, we derive r_buried and r_exposed for every residue in the six proteins, specifically by calculating r between the abundance score substitution profile of every individual residue and the average abundance score substitution profiles of buried and exposed residues. VAMP-seq data for the protein for which r_buried and r_exposed are evaluated is omitted from the calculation of average abundance score substitution profiles, and we use only monomer structures to determine whether residues are buried or exposed. 

We show the results of this analysis in an Author response image 1 below. In each panel of the figure, r_buried and r_exposed are shown for individual residues of a single protein. Blue datapoints indicate residues that are solvent-exposed in the wild-type protein structures, and yellow datapoints indicate residues that are buried in the wild-type structures. Residues for which it is not the case that r_buried < r_exposed or r_exposed<r_buried in more than 95% of 1000 resampled residue substitution profiles (see explanation of resampling method above) are coloured grey. “Acc.” is the balanced classification accuracy, calculated using all non-grey datapoints, indicating how many buried residues have buried-like substitution profiles (r_exposed<r_buried) and how many solvent-exposed residues have exposed-like substitution profiles (r_buried < r_exposed). The classification accuracy per protein in this figure cannot be compared to the classification accuracy of the same protein in Fig. S18, since the number of datapoints used in the accuracy calculation differ between the r- and RMSD-based analyses. 

Author response image 1.

Comparing the r-based approach to the RMSD-based approach (Fig. S18), it is clear that the r-based method is less robust than the RMSD-based method for noisy and incomplete datasets. For the noisiest and most mutationally incomplete VAMP-seq datasets (i.e., PTEN, TPMT and CYP2C9) (Fig. 1), there are relatively few residues for which we with high confidence can determine if the substitution profile is more buried- or more exposed-like. When the VAMP-seq data is less noisy and has high mutational completeness, the r-based method becomes more robust and may thus be relevant in potential future work on new VAMP-seq data with small error bars.

In conclusion, we find that RMSD-based approach to compare substitution profiles is more robust than an r-based approach for several of the VAMP-seq datasets that are included in our analysis. We do believe than an approach based on the correlation coefficient, or potentially several metrics, could be relevant to use, since abundance score distributions from VAMP-seq datasets can differ significantly across datasets. So as not to increase the length of the main text of our manuscript, we have not added this analysis to the revised manuscript.

(1.5) Consider treating missing abundance scores as zero values, as they might indicate variants with very low abundance, rather than omitting them from the analysis.

This suggestion would be most relevant for the PTEN, TPMT and CYP2C9 datasets, which all have a relatively small average mutational depth and completeness, as shown in Fig. 1B and 1C. To assess if setting missing abundance scores as zero values would be reasonable, we have compared the distributions of predicted ΔΔG values (from RaSP and ThermoMPNN) and of predicted abundance scores (from our exposure-based substitution matrices) for variants with reported and missing VAMP-seq data. We show the result in Author response image 2, with data aggregated across the six protein systems:

Author response image 2.

We find that variants with and without VAMP-seq data have similar ΔΔG score distributions and similar predicted abundance score distributions, and there is thus no clear enrichment of predicted loss of abundance for variants with missing VAMP-seq scores. This suggests that missing abundance scores do not necessarily indicate very low abundance. One cause of missing data might instead be problems with library generation (Matreyek et al, 2018, 2021).

We show in Fig. S9 (Fig. S8 of the revised manuscript) that predicted scores for variants with experimental abundance scores of 0 are often overestimated for NUDT15, ASPA and PRKN, but this is not so much a problem for PTEN, TMPT and CYP2C9, the datasets with most missing scores. The lack of an enrichment of low abundance variants from the various predictors would thus still support that missing scores do not necessarily indicate low abundance.

(1.6) Develop a proper statistical framework for comparing buried vs exposed predictions (whether using RMSD or correlations), including confidence intervals, rather than using arbitrary thresholds.

As explained above and in the methods section of our revised manuscript, we have expanded our approach to compare the substitution profile of a residue to the average profiles of buried and exposed residues, and our method now accounts for the noise in the VAMP-seq data, making the analysis more statistically rigorous. In our expanded approach, we compare the substitution profiles of individual residues to the average profiles for buried and exposed residues 10,000 times per residue to get a residue-specific distribution of RMSD_buried and RMSD_exposed values. Individual RMSD_buried and RMSD_exposed values are calculated by resampling abundance scores from a Gaussian distribution defined by the experimentally reported abundance score and abundance score standard deviation per variant. We now only report a residue to have e.g. a buried-like substitution profile if RMSD_buried < RMSD_exposed in at least 95% of our samples. We do not recalculate average scores in substitution matrices for this analysis. We have updated the plots in our manuscript, e.g. in Fig. S18 and S19 of the revised version, to indicate which residues are confidently classified as buried- or exposed-like.

(2) Presentation improvements:

(2.1) In Figure 4, consider removing the average abundance scores, which are not directly related to the RMSD comparison being shown.

We have decided to keep the average abundance scores in Fig. 4 (now Fig. 5), as we find the average abundance scores useful for guiding interpretation of the RMSD values. For example, an unusually small average abundance score with a relatively small standard deviation may explain a case where RMSD_buried and RMSD_exposed are both large. This is for example the case for residue G185 in ASPA. 

In our preprint, the error bars on the average abundance scores in Fig. 4 (now Fig. 5) indicated the standard deviation across the abundance scores that were used to calculate the average per position. We have removed these error bars in the revised manuscript, as we realised that these were not necessarily helpful to the reader.

(2.2) I am assuming that abundance scores are defined as the ratio abundance_variant/abundance_wt throughout the analysis, but I don't think this has been explicitly defined. If this is correct, please state it explicitly. In such case, log(abundance_score) would have a simple interpretation as the difference in abundance between variant and wild-type.

Abundance scores are defined throughout the manuscript as sequence-based scores that have been min-max normalised to the abundance of nonsense and synonymous variants, i.e. abundance_score = (abundance_variant abundance_nonsense)/(abundance_wt–abundance_nonsense). We have described the normalisation of scores to wild-type and nonsense variant abundance in lines 164-166 of the original manuscript. We have now added additional information about the normalisation scheme in the methods section. We note that we did not ourselves apply this normalisation to the data; the scores were reported in this manner in the original publications that reported the VAMP-seq experiments for the six proteins.

(2.3) Consider renaming "rASA" to the more commonly used "RSA" for relative solvent accessibility.

We have decided to keep using “rASA” throughout the manuscript.

(2.4) The weighted contact number function used differs from the established WCN measure (Σ1/rij²) introduced by Lin et al. (2008, Proteins). This should be acknowledged and the choice of alternative weighting scheme justified.

As we have also responded to the first minor point of reviewer 1, we have previously found WCN, as it is defined in our manuscript, to be a useful input feature for a classifier that determines whether individual residues are important for maintaining protein abundance or function (Cagiada et al, 2023). We have also previously found this type of WCN to correlate well with variant abundance of individual proteins, as measured with VAMP-seq or protein fragment complementation assays (Grønbæk-Thygesen et al., 2024; Clausen et al., 2024; Gersing et al., 2024). We acknowledge that residue contact numbers or weighted contact numbers could also be expressed in other ways and that alternative contact number definitions would likely also produce values that correlate well with VAMP-seq data. Since the WCN, as defined in our manuscript, already correlates relatively well with abundance scores, we have not explored whether alternative definitions produce better correlations.  

(2.5) Replace the phrase "in the above" with specific references to sections or simply "above" where appropriate. Also, consider replacing many instances of "moreover" with simpler alternatives such as "also" or "in addition" to improve readability.

We have changed several sentences according to this suggestion and hope that we have improved the readability of our manuscript.

Reviewer #3 (Recommendations for the authors):

(1) It should be explicitly confirmed earlier that complex structures are used for NUDT15 and ASPA when assessing rASA/WCN. Additionally, it would be interesting to see the effect that deriving the matrices using NUDT15 and ASPA monomers would have.

We have commented on the use of NUDT15 and ASPA homodimer structures earlier in the revised manuscript (specifically already in the subsection Abundance scores correlate with the degree of residue solvent-exposure section).

When residues are classified using monomer rather than dimer structures of NUDT15 and ASPA, there is a small effect on the resulting “buried” and “exposed” substitution matrices. Entries in this set of substitution matrices calculated using either monomer or dimer structures typically differ by less than 0.05, and only a single entry differ by more than 0.1. As expected, the “exposed” matrix tend to contain slightly larger numbers when derived from dimer structures than when derived from monomer structures, meaning that when the interface residues are included in the exposed residue category, the average abundance scores of the “exposed” matrix are lowered. For buried residues, the picture is more mixed, although the overall tendency is that the interface residues make the “buried” matrix contain smaller average abundance scores for dimer compared to monomer structures. These results generally support the use of dimer structures for the residue classification.

We here show the differences between the substitution matrices calculated with dimer or monomer structures of NUDT15 and ASPA and using data for all six proteins in our combined VAMP-seq dataset (average_abundance_score_differece = average_abundance_score_dimers – average_abundance_score _monomers):

Author response image 3.

We have not explored these alternative matrices further.

(2) While the supplemental analyses are rigorous, the abundance of various metrics being presented can be confusing, especially when they seem to differ in their result. For instance, the discussion of Figure S17 (paragraph starting 428) contains mentions of mean differences but then switches to correlations, while both are presented for all panels. The claim "The datasets thus mainly differ due to differences in substitution effects in buried environments. " is well supported by the observed mean differences, but for Pearson's correlations the average panel A ,B values of buried 0.421 vs exposed 0.427 are hardly different. Which of the metrics is more meaningful, and are both needed?

We agree with the reviewer that the claim that “The datasets thus mainly differ due to differences in substitution effects in buried environments” is not well-supported by the r between the substitution matrices, and we have removed this claim from the text.

Since some datasets share VAMP-seq score distribution features, while others do not, the absolute difference between scores or matrices may be relevant to check for some dataset pairs, while the r may be more relevant to check for other dataset pairs. Hence, we have included both metrics in Fig S17 (Fig S11 in the revised manuscript).

(3) Lines 337-340 - does not feel like S7 is the topic, perhaps the authors meant Figure 2A, B? In general, the supplemental figure references are out of order and panel combinations are sometimes confusing.

We have corrected figures references to now be correct and changed the arrangement of supplemental figures so that they now occur in the correct order. We have looked through the panel combinations with clarity in mind, and hope that the current set of main and supplementary figures balances overview and detail.

(4) Line 363 "are also are also".

We have corrected this typo.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This is an excellent study by a superb investigator who discovered and is championing the field of migrasomes. This study contains a hidden "gem" - the induction of migrasomes by hypotonicity and how that happens. In summary, an outstanding fundamental phenomenon (migrasomes) en route to becoming transitionally highly significant.

Strengths:

Innovative approach at several levels. Migrasomes - discovered by Dr Yu's group - are an outstanding biological phenomenon of fundamental interest and now of potentially practical value.

Weaknesses:

I feel that the overemphasis on practical aspects (vaccine), however important, eclipses some of the fundamental aspects that may be just as important and actually more interesting. If this can be expanded, the study would be outstanding.

We sincerely thank the reviewer for the encouraging and insightful comments. We fully agree that the fundamental aspects of migrasome biology are of great importance and deserve deeper exploration.

In line with the reviewer’s suggestion, we have expanded our discussion on the basic biology of engineered migrasomes (eMigs). A recent study by the Okochi group at the Tokyo Institute of Technology demonstrated that hypoosmotic stress induces the formation of migrasome-like vesicles, involving cytoplasmic influx and requiring cholesterol for their formation (DOI: 10.1002/1873-3468.14816, February 2024). Building on this, our study provides a detailed characterization of hypoosmotic stressinduced eMig formation, and further compares the biophysical properties of natural migrasomes and eMigs. Notably, the inherent stability of eMigs makes them particularly promising as a vaccine platform.

Finally, we would like to note that our laboratory continues to investigate multiple aspects of migrasome biology. In collaboration with our colleagues, we recently completed a study elucidating the mechanical forces involved in migrasome formation (DOI: 10.1016/j.bpj.2024.12.029), which further complements the findings presented here.

Reviewer #2 (Public review):

Summary:

The authors' report describes a novel vaccine platform derived from a newly discovered organelle called a migrasome. First, the authors address a technical hurdle in using migrasomes as a vaccine platform. Natural migrasome formation occurs at low levels and is labor intensive, however, by understanding the molecular underpinning of migrasome formation, the authors have designed a method to make engineered migrasomes from cultured, cells at higher yields utilizing a robust process. These engineered migrasomes behave like natural migrasomes. Next, the authors immunized mice with migrasomes that either expressed a model peptide or the SARSCoV-2 spike protein. Antibodies against the spike protein were raised that could be boosted by a 2nd vaccination and these antibodies were functional as assessed by an in vitro pseudoviral assay. This new vaccine platform has the potential to overcome obstacles such as cold chain issues for vaccines like messenger RNA that require very stringent storage conditions.

Strengths:

The authors present very robust studies detailing the biology behind migrasome formation and this fundamental understanding was used to form engineered migrasomes, which makes it possible to utilize migrasomes as a vaccine platform. The characterization of engineered migrasomes is thorough and establishes comparability with naturally occurring migrasomes. The biophysical characterization of the migrasomes is well done including thermal stability and characterization of the particle size (important characterizations for a good vaccine).

Weaknesses:

With a new vaccine platform technology, it would be nice to compare them head-tohead against a proven technology. The authors would improve the manuscript if they made some comparisons to other vaccine platforms such as a SARS-CoV-2 mRNA vaccine or even an adjuvanted recombinant spike protein. This would demonstrate a migrasome-based vaccine could elicit responses comparable to a proven vaccine technology. 

We thank the reviewer for the thoughtful evaluation and constructive suggestions, which have helped us strengthen the manuscript. 

Comparison with proven vaccine technologies:

In response to the reviewer’s comment, we now include a direct comparison of the antibody responses elicited by eMig-Spike and a conventional recombinant S1 protein vaccine formulated with Alum. As shown in the revised manuscript (Author response image 1), the levels of S1-specific IgG induced by the eMig-based platform were comparable to those induced by the S1+Alum formulation. This comparison supports the potential of eMigs as a competitive alternative to established vaccine platforms. 

Author response image 1.

eMigrasome-based vaccination showed similar efficacy compared with adjuvanted recombinant spike protein The amount of S1-specific IgG in mouse serum was quantified by ELISA on day 14 after immunization. Mice were either intraperitoneally (i.p.) immunized with recombinant Alum/S1 or intravenously (i.v.) immunized with eM-NC, eM-S or recombinant S1. The administered doses were 20 µg/mouse for eMigrasomes, 10 µg/mouse (i.v.) or 50 µg/mouse (i.p.) for recombinant S1 and 50 µl/mouse for Aluminium adjuvant.

Assessment of antigen integrity on migrasomes:

To address the reviewer’s suggestion regarding antigen integrity, we performed immunoblotting using antibodies against both S1 and mCherry. Two distinct bands were observed: one at the expected molecular weight of the S-mCherry fusion protein, and a higher molecular weight band that may represent oligomerized or higher-order forms of the Spike protein (Figure 5b in the revised manuscript).

Furthermore, we performed confocal microscopy using a monoclonal antibody against Spike (anti-S). Co-localization analysis revealed strong overlap between the mCherry fluorescence and anti-Spike staining, confirming the proper presentation and surface localization of intact S-mCherry fusion protein on eMigs (Figure 5c in the revised manuscript). These results confirm the structural integrity and antigenic fidelity of the Spike protein expressed on eMigs.

Recommendations for the authors

Reviewer #1 (Recommendations For The Authors):

I feel that the overemphasis on practical aspects (vaccine), however important, eclipses some of the fundamental aspects that may be just as important and actually more interesting. If this can be expanded, the study would be outstanding.

I know that the reviewers always ask for more, and this is not the case here. Can the abstract and title be changed to emphasize the science behind migrasome formation, and possibly add a few more fundamental aspects on how hypotonic shock induces migrasomes?

Alternatively, if the authors desire to maintain the emphasis on vaccines, can immunological mechanisms be somewhat expanded in order to - at least to some extent - explain why migrasomes are a better vaccine vehicle?

One way or another, this reviewer is highly supportive of this study and it is really up to the authors and the editor to decide whether my comments are of use or not.

My recommendation is to go ahead with publishing after some adjustments as per above.

We’d like to thank the reviewer for the suggestion. We have changed the title of the manuscript and modified the abstract, emphasizing the fundamental science behind the development of eMigrasome. To gain some immunological information on eMig illucidated antibody responses, we characterized the type of IgG induced by eM-OVA in mice, and compared it to that induced by Alum/OVA. The IgG response to Alum/OVA was dominated by IgG1. Quite differently, eM-OVA induced an even distribution of IgG subtypes, including IgG1, IgG2b, IgG2c, and IgG3 (Figure 4i in the revised manuscript). The ratio between IgG1 and IgG2a/c indicates a Th1 or Th2 type humoral immune response. Thus, eM-OVA immunization induces a balance of Th1/Th2 immune responses.

Reviewer #2 (Recommendations For The Authors):

The study is a very nice exploration of a new vaccine platform. This reviewer believes that a more head-to-head comparison to the current vaccine SARS-CoV-2 vaccine platform would improve the manuscript. This comparison is done with OVA antigen, but this model antigen is not as exciting as a functional head-to-head with a SARS-CoV-2 vaccine.

I think that two other discussion points should be included in the manuscript. First, was the host-cell protein evaluated? If not, I would include that point on how issues of host cell contamination of the migrasome could play a role in the responses and safety of a vaccine. Second, I would discuss antigen incorporation and localization into the platform. For example, the full-length spike being expressed has a native signal peptide and transmembrane domain. The authors point out that a transmembrane domain can be added to display an antigen that does not have one natively expressed, however, without a signal peptide this would not be secreted and localized properly. I would suggest adding a discussion of how a non-native signal peptide would be necessary in addition to a transmembrane domain.

We thank the reviewer for these thoughtful suggestions and fully agree that the points raised are important for the translational development of eMig-based vaccines.

(1) Host cell proteins and potential immunogenicity:

We appreciate the reviewer’s suggestion to consider host cell protein contamination. Considering potential clinical application of eMigrasomes in the future, we will use human cells with low immunogenicity such as HEK-293 or embryonic stem cells (ESCs) to generate eMigrasomes. Also, we will follow a QC that meets the standard of validated EV-based vaccination techniques. 

(2) Antigen incorporation and localization—signal peptide and transmembrane domain:

We also agree with the reviewer’s point that proper surface display of antigens on eMigs requires both a transmembrane domain and a signal peptide for correct trafficking and membrane anchoring. For instance, in the case of full-length Spike protein, the native signal peptide and transmembrane domain ensure proper localization to the plasma membrane and subsequent incorporation into eMigs. In case of OVA, a secretary protein that contains a native signal peptide yet lacks a transmembrane domain, an engineered transmembrane domain is required. For antigens that do not naturally contain these features, both a non-native signal peptide and an artificial transmembrane domain are necessary. We have clarified this point in the revised discussion and explicitly noted the requirement for a signal peptide when engineering antigens for surface display on migrasomes.

The Atlantic Seaboard Fall Line is a 900-mile escarpment where the Piedmont and Atlantic coastal plain meet in the eastern United States. Before navigation improvements, such as locks, the fall line was generally the head of navigation on rivers due to their rapids or waterfalls, and the necessary portage around them.

Towards the head

Interrupt chaining is a mechanism of better utilizing several interrupt handlers. In this scheme, the interrupt vector will hold pointers to a list containing the interrupt handlers, each handler will accept a certain type of an interrupt. Once an interrupt is received the CPU jumps through the chain of handlers, and they are executed one after another. This enables interrupts to be better organized, and prioritized and all the appropriate handlers are called in the right sequence without any overlapping or missing any valuable service routines.

this is so true because maybe in high school the writing prompts weren't so long and take so much time and effort, college has a different level of showing, I could say I was a better writer in elementary school, I used to write pages and pages teachers would tell me I was going to be a great novel writer, but that wasn't my plan to become one, writing was and is one of the things I like to do. I was reading the chapter 5 of the AI article about how I could even go to bed thinking about one thing I also related to that because sometimes I can't even go to bed because my head is always running and thinking what I could do tomorrow. sounds crazy but when I was reading this article i could just imagine everything, interesting article by the way :)

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

One possible remaining conceptual concern that might require future work is determining whether STN primarily mediates higher-level cognitive avoidance or if its activation primarily modulates motor tone.

Our results using viral and electrolytic lesions (Fig. 11) and optogenetic inhibition of STN neurons (Fig. 10) show that signaled active avoidance is virtually abolished, and this effect is reproduced when we selectively inhibit STN fibers in the midbrain (Fig. 12). Inhibition of STN projections in either the substantia nigra pars reticulata (SNr) or the midbrain reticular tegmentum (mRt) eliminates cued avoidance responses while leaving escape responses intact. Importantly, mice continue to escape during US presentation after lesions or during photoinhibition, demonstrating that basic motor capabilities and the ability to generate rapid defensive actions are preserved.

These findings argue against the idea that STN’s role in avoidance reflects a nonspecific suppression or facilitation of motor tone, even if the STN also contributes to general movement control. Instead, they show that STN output is required for generating “cognitively” guided cued actions that depend on interpreting sensory information and applying learned contingencies to decide when to act. Thus, while STN activity can modulate movement parameters, the loss-of-function results point to a more selective role in supporting cued, goal-directed avoidance behavior rather than a general adjustment of motor tone.

Reviewer #2 (Public review):

All previous weaknesses have been addressed. The authors should explain how inhibition of the STN impairing active avoidance is consistent with the STN encoding cautious action. If 'caution' is related to avoid latency, why does STN lesion or inhibition increase avoid latency, and therefore increase caution? Wouldn't the opposite be more consistent with the statement that the STN 'encodes cautious action'?

The reviewer’s interpretation treats any increase in avoidance latency as evidence of “more caution,” but this holds only when animals are performing the avoidance behavior normally. In our intact animals, avoidance rates remain high across AA1 → AA2 → AA3, and the active avoidance trials (CS1) used to measure latency are identical across tasks (e.g., in AA2 the only change is that intertrial crossings are punished). Under these conditions, changes in latency genuinely reflect adjustments in caution, because the behavior itself is intact, actions remain tightly coupled to the cue, and the trials are identical.

This logic does not apply when STN function is disrupted. STN inhibition or lesions reduce avoidance to near chance levels; the few crossings that do occur are poorly aligned to the CS and many likely reflect random movement rather than a cued avoidance response. Once performance collapses, latency can no longer be assumed to reflect the same cognitive process. Thus, interpreting longer latencies during STN inactivation as “more caution” would be erroneous, and we never make that claim.

A simple analogy may help clarify this distinction. Consider a pedestrian deciding when to cross the street after a green light. If the road is deserted (like AA1), the person may step off the curb quickly. If the road is busy with many cars that could cause harm (like AA2), they may wait longer to ensure that all cars have stopped. This extra hesitation reflects caution, not an inability to cross. However, if the pedestrian is impaired (e.g., cannot clearly see the light, struggles to coordinate movements, or cannot reliably make decisions), a delayed crossing would not indicate greater caution—it would reflect a breakdown in the ability to perform the behavior itself. The same principle applies to our data: we interpret latency as “caution” only when animals are performing the active avoidance behavior normally, success rates remain high, and the trial rules are identical. Under STN inhibition or lesion, when active avoidance collapses, the latency of the few crossings that still occur can no longer be interpreted as reflecting caution. We have added these points to the Discussion.

Reviewer #3 (Public review):

Original Weaknesses:

I found the experimental design and presentation convoluted and some of the results over-interpreted.

We appreciate the reviewer’s comment, but the concern as stated is too general for us to address in a concrete way. The revised manuscript has been substantially reorganized, with simplified terminology, streamlined figures, and removal of an entire set of experiments to avoid over-interpretation. We are confident that the experimental design and results are now presented clearly and without extrapolation beyond the data. If there are specific points the reviewer finds convoluted or over-interpreted, we would be happy to address them directly.

As presented, I don't understand this idea that delayed movement is necessarily indicative of cautious movements. Is the distribution of responses multi-modal in a way that might support this idea; or do the authors simply take a normal distribution and assert that the slower responses represent 'caution'? Even if responses are multi-modal and clearly distinguished by 'type', why should readers think this that delayed responses imply cautious responding instead of say: habituation or sensitization to cue/shock, variability in attention, motivation, or stress; or merely uncertainty which seems plausible given what I understand of the task design where the same mice are repeatedly tested in changing conditions. This relates to a major claim (i.e., in the title).

We appreciate the reviewer’s question and address each component directly.

(1) What we mean by “caution” and how it is operationalized

In our study, caution is defined operationally as a systematic increase in avoidance latency when the behavioral demand becomes higher, while the trial structure and required response remain unchanged. Specifically, CS1 trials are identical in AA1, AA2, and AA3. Thus, when mice take longer to initiate the same action under more demanding contexts, the added time reflects additional evaluation before acting—consistent with longestablished interpretations of latency shifts in cognitive psychology (see papers by Donders, Sternberg, Posner) and interpretations of deliberation time in speed-accuracy tradeoff literature.

(2) Why this interpretation does not rely on multi-modal response distributions We do not claim that “cautious” responses form a separate mode in the latency distribution. The distributions are unimodal, and caution is inferred from conditiondependent shifts in these distributions across identical trials, not from the existence of multiple peaks (see Zhou et al, 2022). Latency shifts across conditions with identical trial structure are widely used as behavioral indices of deliberation or caution.

(3) Why alternative explanations (habituation/sensitization, motivation, attention, stress, uncertainty) do not account for these latency changes

Importantly, nothing changes in CS1 trials between AA1 and AA2 with respect to the cue, shock, or required response. Therefore:

- Habituation/sensitization to the cue or shock cannot explain the latency shift (the stimuli and trial type are unchanged). We have previously examined cue-evoked orienting responses and their habituation in detail (Zhou et al., 2023), and those measurements are dissociable from the latency effects described here.

- Motivation or attention are unlikely to change selectively for identical CS1 trials when the task manipulation only adds a contingency to intertrial crossings.

- Uncertainty also does not increase for CS1 trials, they remain fully predictable and unchanged between conditions.

- Stress is too broad a construct to be meaningful unless clearly operationalized; moreover, any stress differences that arise from task structure would covary with caution rather than replace the interpretation.

(4) Clarifying “types” of responses

The reviewer’s question about “response types” appears to conflate behavioral latencies with the neuronal response “types” defined in the manuscript. The term “type” in this paper refers to neuronal activation derived from movement-based clustering, not to distinct behavioral categories of avoidance, which we term modes.

In sum, we interpret increased CS1 latency as “caution” only when performance remains intact and trial structure is identical between conditions; under those criteria, latency reliably reflects additional cognitive evaluation before acting, rather than nonspecific changes in sensory processing, motivation, etc.

Related to the last, I'm struggling to understand the rationale for dividing cells into 'types' based their physiological responses in some experiments.

There is longstanding precedent in systems neuroscience for classifying neurons by their physiological response patterns, because neurons that respond similarly often play similar functional roles. For example, place cells, grid cells, direction cells, in vivo, and regular spiking, burst firing, and tonic firing in vitro are all defined by characteristic activity patterns in response to stimuli rather than anatomy or genetics alone. In the same spirit, our classifications simply reflect clusters of neurons that exhibit similar ΔF/F dynamics around behaviorally relevant events, such as movement sensitivity or avoidance modes. This is a standard analytic approach used in many studies. Thus, our rationale is not arbitrary: the “classes” and “types” arise from data-driven clustering of physiological responses, consistent with widespread practice, and they help reveal functional distinctions within the STN that would otherwise remain obscured.

In several figures the number of subjects used was not described. This is necessary. Also necessary is some assessment of the variability across subjects.

All the results described include the number of animals. To eliminate uncertainty, we now also include this information in figure legends.

The only measure of error shown in many figures relates trial-to-trial or event variability, which is minimal because in many cases it appears that hundreds of trials may have been averaged per animal, but this doesn't provide a strong view of biological variability (i.e., are results consistent across animals?).

The concern appears to stem from a misunderstanding of what the mixed-effects models quantify. The figure panels often show session-averaged traces for clarity, all statistical inferences in the paper are made at the level of animals, not trials. Mixed-effects modeling is explicitly designed for hierarchical datasets such as ours, where many trials are nested within sessions, which are themselves nested within animals.

In our models, animal is the clustering (random) factor, and sessions are nested within animals, so variability across animals is directly estimated and used to compute the population-level effects. This approach is not only appropriate but is the most stringent and widely recommended method for analyzing behavioral and neural data with repeated measures. In other words, the significance tests and confidence intervals already fully incorporate biological variability across animals.

Thus, although hundreds of trials per animal may be illustrated for visualization, the inferences reflect between-animal consistency, not within-animal trial repetition. The fact that the mixed-effects results are robust across animals supports the biological reliability of the findings.

It is not clear if or how spread of expression outside of target STN was evaluated, and if or how or how many mice were excluded due to spread or fiber placements. Inadequate histological validation is presented and neighboring regions that would be difficult to completely avoid, such as paraSTN may be contributing to some of the effects.

The STN is a compact structure with clear anatomical boundaries, and our injections were rigorously validated to ensure targeting specificity. As detailed in the Methods, every mouse underwent histological verification, and injections were quantified using the Brain Atlas Analyzer app (available on OriginLab), which we developed to align serial sections to the Allen Brain Atlas. This approach provides precise, slice-by-slice confirmation of viral spread. We have performed thousands of AAV injections and probe implants in our lab, incorporating over the years highly reliable stereotaxic procedures with multiple depth and angle checks and tools. For this study specifically, fewer than 10% of mice were excluded due to off-target expression or fiber/lesion placement. None of the included cases showed spread into adjacent structures.

Regarding paraSTN: anatomically, paraSTN is a very small extension contiguous with STN. Our study did not attempt to dissociate subregions within STN, and the viral expression patterns we report fall within the accepted boundaries of STN. Importantly, none of our photometry probes or miniscope lenses sampled paraSTN, so contributions from that region are extremely unlikely to account for any of our neural activity results.

Finally, our paper employs five independent loss-of-function approaches—optogenetic inhibition of STN neurons, selective inhibition of STN projections to the midbrain (in two sites: SNr and mRt), and STN lesions (electrolytic and viral). All methods converge on the same conclusion, providing strong evidence that the effects we report arise from manipulation of STN itself rather than from neighboring regions.

Raw example traces are not provided.

We do not think raw traces are useful here. All figures contain average traces to reflect the average activity of the estimated populations, which are already clustered per classes and types.

The timeline of the spontaneous movement and avoidance sessions were not clear, nor the number of events or sessions per animal and how this was set. It is not clear if there was pre-training or habituation, if many or variable sessions were combined per animal, or what the time gaps between sessions was, or if or how any of these parameters might influence interpretation of the results.

As noted, we have enhanced the description of the sessions, including the number of animals and sessions, which are daily and always equal per animals in each group of experiments. The sessions are part of the random effects in the model. In addition, we now include schematics to facilitate understanding of the procedures.  

Comments on revised version:

The authors removed the optogenetic stimulation experiments, but then also added a lot of new analyses. Overall the scope of their conclusions are essentially unchanged. Part of the eLife model is to leave it to the authors discretion how they choose to present their work. But my overall view of it is unchanged. There are elements that I found clear, well executed, and compelling. But other elements that I found difficult to understand and where I could not follow or concur with their conclusions.

We respectfully disagree with the assertion that the scope of our conclusions remains unchanged. The revised manuscript differs in several fundamental ways:

(1) Removal of all optogenetic excitation experiments

These experiments were a substantial portion of the original manuscript, and their removal eliminated an entire set of claims regarding the causal control of cautious responding by STN excitation. The revised manuscript no longer makes these claims.

(2) Addition of analyses that directly address the reviewers’ central concerns The new analyses using mixed-effects modeling, window-specific covariates, and movement/baseline controls were added precisely because reviewers requested clearer dissociation of sensory, motor, and task-related contributions. These additions changed not only the presentation but the interpretation of the neural signals. We now conclude that STN encodes movement, caution, and aversive signals in separable ways—not that it exclusively or causally regulates caution.

(3) Clear narrowing of conclusions

Our current conclusions are more circumscribed and data-driven than in the original submission. For example, we removed all claims that STN activation “controls caution,” relying instead on loss-of-function data showing that STN is necessary for performing cued avoidance—not for generating cautious latency shifts. This is a substantial conceptual refinement resulting directly from the review process.

(4) Reorganization to improve clarity

Nearly every section has been restructured, including terminology (mode/type/class), figure organization, and explanations of behavioral windows. These revisions were implemented to ensure that readers can follow the logic of the analyses.

We appreciate the reviewer’s recognition that several elements were clear and compelling. For the remaining points they found difficult to understand, we have addressed each one in detail in the response and revised the manuscript accordingly. If there are still aspects that remain unclear, we would welcome explicit identification of those points so that we can clarify them further.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Show individual data points on bar plots

- partially addressed. Individual data points are still not shown.

Wherever feasible, we display individual data points (e.g., Figures 1 and 2) to convey variability directly. However, in cases where figures depict hundreds of paired (repeatedmeasures) data points, showing all points without connecting them would not be appropriate, while linking them would make the figures visually cluttered and uninterpretable. All plots and traces include measures of variability (SEM), and the raw data will be shared on Dryad. When error bars are not visible, they are smaller than the trace thickness or bar line—for example, in Figure 5B, the black circles and orange triangles include error bars, but they are smaller than the symbol size.

Also, to minimize visual clutter, only a subset of relevant comparisons is highlighted with asterisks, whereas all relevant statistical results, comparisons, and mouse/session numbers are fully reported in the Results section, with statistical analyses accounting for the clustering of data within subjects and sessions.

(2) The active avoidance experiments are confusing when they are introduced in the results section. More explanation of what paradigms were used and what each CS means at the time these are introduced would add clarity. For example AA1, AA2 etc are explained only with references to other papers, but a brief description of each protocol and a schematic figure would really help.

- partially addressed. A schematic figure showing the timeline would still be helpful.

As suggested, we have added an additional panel to Fig. 5A with a schematic describing