Author response:

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

Reviewer #1 (Public review):

Major comments:

(1) The main issue that I have with this study is the lack of exploration of "why" the model produces the results it does. Considering this is a model, it should be possible to find out why the three timescales of half-act/inact parameter modifications lead to different sets of results. Without this, it is simply an exploratory exercise. (The model does this, but we do not know the mechanism.) Perhaps this is enough as an interesting finding, but it remains unconvincing and (clearly) does not have the impact of describing a potential mechanism that could be potentially explored experimentally.

This is now addressed in a new section in Results (“Potential Mechanism”):

“To explore why the properties of the resulting bursters depend on the timescale of half-(in)activation adjustments, we examined what happens when SP1 is assembled under different half-(in)activation timescales: (1) fast, (2) intermediate (matching the timescale of ion channel density changes), and (3) infinitely slow (i.e., effectively turned off). The effects of these timescales can be seen by comparing the zoomed-in views of the SP1 activity profiles under each condition (Figure 4).

When half-(in)activations are fast, the time evolution of — which tracks how far the activity pattern is from its targets (see Methods)—shows an abrupt jump as it searches for a voltage-dependence configuration that meets calcium targets (Figure 4A). As this happens, the channel densities are slightly altered, and this process continues again. Slowing the half-(in)activations alterations reduces these abrupt fluctuations (Figure 4B). Making the alterations infinitely slow effectively removes half-(in)activation changes altogether, leaving the system reliant solely on slower alterations in maximal conductances (Figure 4C). Because each timescale of half-(in)activation produces a different channel repertoire at each time step, different timescales of half-(in)activation alteration led the model through a different path in the space of activity profiles and intrinsic properties. Ultimately, this resulted in distinct final activity patterns – all of which were consistent with the Ca²⁺ targets [22].

(2) A related issue is the use of bootstrapping to do statistics for a family of models, especially when the question is in fact the width of the distribution of output attributes. I don't buy this. One can run enough models to find say N number of models within a tight range (say 2% cycle period) and the same N number within a loose range (say 20%) and compare the statistics within the two groups with the same N.

We appreciate the reviewer’s skepticism regarding our statistical approach with the “Group of 5” and “Group of 20.” These groups arose from historical aspects of our analysis and this analysis does not directly advance the main point—that changes in the timescale of channel voltage-dependence alterations impact the properties of bursters to which the homeostatic mechanism converges. Therefore, we removed the references to the Group of 5 and focus on how the Group of 20 responds to variations in the timescale of voltage-dependent alterations.

(3) The third issue is that many of the results that are presented (but not the main one) are completely expected. If one starts with gmax values that would never work (say all of them 0), then it doesn't matter how much one moves the act/inact curves one probably won't get the desired activity. Alternately, if one starts with gmax values that are known to work and randomizes the act/inact midpoints, then the expectation would be that it converges to something that works. This is Figure 1 B and C, no surprise. But it should work the other way around too. If one starts with random act/inact curves that would never work and fixes those, then why would one expect any set of gmax values would produce the desired response? I can easily imagine setting the half-act/inact values to values that never produce any activity with any gmax.

We appreciate this observation and agree that it highlights a limitation of our initial condition sampling. Our claim that the half-(in)activation mechanism is subordinate to the maximal conductance mechanism is not intended as a general statement. Rather, we make this observation only within the specific range of initial conditions we explored. Within this restricted set, we found that the conductance mechanism was sufficient for successful assembly, while the half-(in)activation mechanism alone was not. We have revised the manuscript to limit the claim.

“The results shown in Figure 1A require activity-dependent regulation of the maximal conductances. When activity-dependent regulation of the maximal conductances is turned off, the model failed to assemble SP1 into a burster (Figure 1B). This was seen in the other 19 Starting Parameters (SP2-SP20), as well [22].

(4) A potential response to my previous criticism would be that you put reasonable constraints on gmax's or half-act/inact values or tie the half-act to half-inact. But that is simply arbitrary ad hoc decisions made to make the model work, much like the L8-norm used to amplify some errors. There is absolutely no reason to believe this is tied to the biology of the system.

Here the reviewer highlights that model choices (e.g., constraints on maximal conductance and half-(in)activation, use of the L8 norm) are not necessarily justified by biology. A discussion of the constraints on maximal conductance and half-(in)activation are in the Model Assumptions section at the end of Methods. The Methods also contains a longer discussion of the use of the L8 norm:

“To compute this match score, we adapted a formulation from Alonso et al (2023),  who originally used a root-mean-square (RMS) or  norm to combine the sensor mismatches. In that approach, each error (, , and ) is divided by its allowable tolerance (, , and ) to produce a normalized error. These normalized errors are then squared, summed, and square-rooted to produce a single scalar score that reflects how well the model matches the target activity pattern.

In our version, we instead used an  norm, which raises each normalized error to the 8th power before summing and taking the 1/8th root. This formulation emphasizes large deviations in any one sensor, making it easier to pinpoint which feature of the activity is limiting convergence. By amplifying outlier mismatches, this approach provided a clearer view of which sensor was driving model mismatch, helping us both interpret failure modes and tune the model’s sensitivity by adjusting the tolerances for individual sensor errors.

Although the  norm emphasizes large deviations more strongly than the  norm, the choice of norm does not fundamentally alter which models can converge—a model that performs well under one norm can also be made to perform well under another by adjusting the allowable tolerances. The biophysical mechanisms by which neurons detect deviations from target activity and convert them into changes in ion channel properties are still not well understood. Given this uncertainty, and the fact that using different norms ultimately shouldn’t affect the convergence of a given model, the use of different norms to combine sensor errors is consistent with the broader basic premise of the model: that intrinsic homeostatic regulation is calcium mediated [22].

(5) The discussion of this manuscript is at once too long and not adequate. It goes into excruciating detail about things that are simply not explored in this study, such as phosphorylation mechanisms, justification of model assumptions of how these alterations occur, or even the biological relevance. (The whole model is an oversimplification - lack of anatomical structure, three calcium sensors, arbitrary assumptions, and how parameter bounds are implemented.) Lengthy justifications for why channel density & half-act/inact of all currents are obeying the same time constant are answering a question that no one asked. It is a simplified model to make an important point. The authors should make these parts concise and to the point. More importantly, the authors should discuss the mechanism through which these differences may arise. Even if it is not clear, they should speculate.

We agree. A long discussion on Model Assumptions and potential biological mechanisms that implement alteration in channel voltage-dependence obscure this. The former is relocated to the Methods section. The latter discussion is shortened. A discussion of a potential mechanism is included in the Results (Figure 4).

(6) There should be some justification or discussion of the arbitrary assumptions made in the model/methods. I understand some of this is to resolve issues that had come up in previous iterations of this approach and in fact the Alonso et al, 2023 paper was mainly to deal with these issues. However, some level of explanation is needed, especially when assumptions are made simply because of the intuition of the modeler rather than the existence of a biological constraint or any other objective measure.

A discussion of Model Assumptions is included in the Methods.

Reviewer #2 (Public review):

Summary:

In this study, Mondal and co-authors present the development of a computational model of homeostatic plasticity incorporating activity-dependent regulation of gating properties (activation, inactivation) of ion channels. The authors show that, similar to what has been observed for activity-dependent regulation of ion channel conductances, implementing activity-dependent regulation of voltage sensitivity participates in the achievement of a target phenotype (bursting or spiking). The results however suggest that activity-dependent regulation of voltage sensitivity is not sufficient to allow this and needs to be associated with the regulation of ion channel conductances in order to reliably reach the target phenotype. Although the implementation of this biologically relevant phenomenon is undeniably relevant, the main conclusions of the paper and the insights brought by this computational work are difficult to grasp.

Strengths:

(1) Implementing activity-dependent regulation of gating properties of ion channels is biologically relevant.

(2) The modeling work appears to be well performed and provides results that are consistent with previous work performed by the same group.

Weaknesses:

(1) The writing is rather confusing, and the state of the art explaining the need for the study is unclear.

We reorganized the manuscript to make its focus clearer.

Introduction: We clarified our explanation of the state-of-the-art. Briefly, prior work on activity-dependent homeostasis has focused on regulating ion channel density. Neurons have also been documented to homeostatically regulate channel voltage-dependence. However, the consequences of channel voltage-dependence alterations on homeostatic regulation remain underexplored. To study this, we extend a computational model of activity-dependent homeostasis — originally developed to only alter channel density— to alter channel voltage-dependence.

Results: We reorganized this section to underscore the main point: that the timescale of half-(in)activation alterations influences the intrinsic properties and activity patterns targeted by a homeostatic mechanism. Figures 1A and 1B were retained to provide context—Figure 1A illustrates how activity can emerge from random initial conditions, while Figure 1B suggests that in these simulations, modulation of half-(in)activation played a specific limited role. Figure 2 builds on Figure 1A by summarizing how intrinsic properties and activity characteristics vary across a population of 20 bursters. Figure 3 then demonstrates that despite playing this specific limited role, altering the timescale of half-(in)activation in these simulations significantly impacted the intrinsic properties and activity characteristics of the bursters targeted by the homeostatic mechanism. Figure 4 supports this by offering a possible mechanistic explanation. Finally, Figure 5 reinforces the central message by showing how the same population responds to perturbation when the timescale of half-(in)activation alterations is varied—essentially extending the analysis of Figure 3 to a perturbed regime.

Discussion: The Discussion concentrates on more specifically on how the timescale of half-(in)activation alterations shape bursters targeted he homeostatic mechanism. Extended content on model assumptions is moved to Methods. The discussion of biological pathways that implement channel voltage-dependence is shortened to avoid distracting from the main message.

Methods: Aside from moving model assumptions here, we removed discussion of the “Group of 5” and explained in more detail why we chose the L8 norm.

(2) The main outcomes and conclusions of the study are difficult to grasp. What is predicted or explained by this new version of homeostatic regulation of neuronal activity?

Our message is general: the timescale of half-(in)activation alterations influences the intrinsic properties and activity characteristics of bursters targeted by a homeostatic mechanism. As such, the implications are general. Their value lies in circumscribing a conceptual framework from which experimentalists may devise and test new hypotheses. We do not aim to predict or explain any specific phenomenon in this work. To address this concern the Discussion highlights two potential implications of our findings—one to neuronal development and another to pathologies that may arise from disruptions to homeostatic processes:

“One application for the simulations involving the self-assembly of activity may be to model the initial phases of neural development, when a neuron transitions from having little or no electrical activity to possessing it (Baccaglini & Spitzer 1977). As shown in Figure 6, the timescale of (in)activation curve alterations define a neuron's activity characteristics and intrinsic properties. As such, neurons may actively adjust these timescales to achieve a specific electrical activity aligned with a developmental phase’s activity targets. Indeed, developmental phases are marked by changes in ion channel density and voltage-dependence, leading to distinct electrical activity at each stage (Baccaglini & Spitzer 1977, Gao & Ziskind-Conhaim 1998, Goldberg et al 2011, Hunsberger & Mynlieff 2020, McCormick & Prince 1987, Moody & Bosma 2005, O'Leary et al 2014, Picken Bahrey & Moody 2003).

Additionally, our results show that activity-dependent regulation of channel voltage-dependence can play a critical role in restoring neuronal activity during perturbations (Figure 5). Specifically, the presence and timing of half-(in)activation modulation influenced whether the model neuron could successfully return to its target activity pattern. Many model neurons only achieved recovery when a half-(in)activation mechanism was present. Moreover, the speed of this modulation shaped recovery outcomes in nuanced ways: some model neurons reached their targets only when voltage-dependence was adjusted rapidly, while others did so only when these changes occurred slowly. These observations all suggest that impairments in a neuron’s ability to modulate the voltage-dependence of its channels may lead to disruptions in activity-dependent homeostasis. This may have implications for conditions such as addiction (Kourrich et al 2015) and Alzheimer’s disease (Styr & Slutsky 2018), where disruptions in homeostatic processes are thought to contribute to pathogenesis.”

Reviewer #3 (Public review):

Mondal et al. use computational modeling to investigate how activity-dependent shifts in voltage-dependent (in)activation curves can complement activity-dependent changes in ion channel conductance to support homeostatic plasticity. While changes in the voltage-dependent properties of ion channels are known to modulate neuronal excitability, their role as a homeostatic plasticity mechanism interacting with channel conductance has been largely unexplored. The results presented here demonstrate that activity-dependent regulation of voltage-dependent properties can interact with plasticity in channel conductance to allow neurons to attain and maintain target activity patterns, in this case, intrinsic bursting. These results also show that the rate of channel voltage-dependent shifts can influence steady-state parameters reached as the model stabilizes into a stable intrinsic bursting state. That is, the rate of these modifications shapes the range of channel conductances and half-(in)activation parameters as well as activity characteristics such as burst period and duration. A major conclusion of the study is that altering the timescale of channel voltage dependence can seamlessly shift a neuron's activity characteristics, a mechanism that the authors argue may be employed by neurons to adapt to perturbations. While the study's conclusions are mostly well-supported, additional analyses, and simulations are needed.

(1) A main conclusion of this study is that the speed at which (in)activation dynamics change determines the range of possible electrical patterns. The authors propose that neurons may dynamically regulate the timescale of these changes (a) to achieve alterations in electrical activity patterns, for example, to preserve the relative phase of neuronal firing in a rhythmic network, and (b) to adapt to perturbations. The results presented in Figure 4 clearly demonstrate that the timescale of (in)activation modifications impacts the range of activity patterns generated by the model as it transitions from an initial state of no activity to a final steady-state intrinsic burster. This may have important implications for neuronal development, as discussed by the authors.

However, the authors also argue that the model neuron's dynamics - such as period, and burst duration, etc - could be dynamically modified by altering the timescale of (in)activation changes (Figure 6 and related text). The simulations presented here, however, do not test whether modifications in this timescale can shift the model's activity features once it reaches steady state. In fact, it is unlikely that this would be the case since, at steady-state, calcium targets are already satisfied. It is likely, however, as the authors suggest, that the rate at which (in)activation dynamics change may be important for neuronal adaptation to perturbations, such as changes in temperature or extracellular potassium. Yet, the results presented here do not examine how modifying this timescale influences the model's response to perturbations. Adding simulations to characterize how alterations in the rate of (in)activation dynamics affect the model's response to perturbations-such as transiently elevated extracellular potassium (Figure 5) - would strengthen this conclusion.

The reviewer suggests that our core message — namely, that the timescale of half-(in)activation alterations influences the intrinsic properties and activity patterns targeted by a homeostatic mechanism — should also hold during perturbations. We agree that this extension strengthens the central message and have incorporated it into the subsection of the Results (“Half-(in)activation Alterations Contribute to Activity Homeostasis”) and Figure 5.

(2) Another key argument in this study is that small, coordinated changes in channel (in)activation contribute to shaping neuronal activity patterns, but that, these subtle effects may be obscured when averaging across a population of neurons. This may be the case; however, the results presented don't clearly demonstrate this point. This point would be strengthened by identifying correlations, if they exist, between (in)activation curves, conductance, and the resulting bursting patterns of the models for the simulations presented in Figure 2 and Figure 4, for example. Alternatively, or additionally, relationships between (in)activation curves could be probed by perturbing individual (in)activation curves and quantifying how the other model parameters compensate, which could clearly illustrate this point.

In part of the Discussion, we noted that small, coordinated shifts in half-(in)activation curves could be obscured when averaging across a population of neurons. Our intention was not to present this as a primary result, but to highlight an emergent consequence of the model: that distinct initial maximal conductances may converge to activity targets via different small shifts in half-(in)activation, making such changes difficult to detect at the population level. However, we did not systematically examine correlations between (in)activation parameters, conductances, and activity features, nor how these correlations might vary with the timescale of (in)activation modulation. While this observation is consistent with model behavior, it does not directly advance the study’s main point — that the timescale of half-(in)activation modulation influences the types of bursting patterns that satisfy the activity target. To keep the focus clear, we have removed this remark from the Discussion, though we agree that a more detailed analysis of these correlations may offer a fruitful direction for future work.

Reviewer #1 (Recommendations for the authors):

Minor comments:

(1) Page 5: remove "an" from "achieve a given an activity..."

The sentence containing this error has been removed.

(2) Page 7, bottom of page. Explain what prespecifying means here. This requires a conceptual explanation, even if the equations are given in the methods. Was one working ad hoc model built from which the three sensor values were chosen? What was this model and how was it benchmarked? The sensors are never shown. In any figure, but presumably they have different kinetics. What is meant by "average value"? What was the window of averaging and why?

The intention of this passage was to provide a broad overview of the homeostatic mechanism, with the rationale for using sensor “averages” as homeostatic targets explained in detail in the Methods. We have replaced the word “average” with “target” to maintain this focus.

(3) Page 9: add "the" in "electrical activity of the neuron as [the] model seeks...".

Done

(4) Page 9: say briefly what alpha is before using it. Also, please be consistent in either using the symbol for alpha or spelling it out across the manuscript and the figures.

Done

(5) Page 10: the paragraph "In general, ..." is confusing although it becomes clear later on what this is all about. Please rewrite and expand this to clarify some points. For instance, the word "degenerate" is first used here and it is unclear in what sense these models are degenerate. Then it is unclear why the first 5 models were chosen and then 15 more added. What was the point of doing this? What is the intent? Set this up properly before saying that you just did it. This also would clarify the weird terminology used later on of Group of 20 vs. Group of 5. The 20 and 5 are arbitrary. Say what the purpose is. Finally, is the "mean" at the very end the same 416 ms? If not, what do you mean by "the mean"? In fact, I find these 2% and 20% to be imprecise substitutes of (say) two distinct values of CV which are an order of magnitude different. Is that the intent?

This comment refers to a passage that was removed during revision.

(6) Page 10: this may be clear to you, but it took me a while to understand that in Figure 1C, you took the working model at the end of 1A, fixed the gmax values and randomized just the half-act/inact values to run it. Perhaps rewrite this to clarify?

This comment refers to a figure that was removed during revision.

(7) Page 13: why do channel densities not change much after the perturbation?

This comment refers to a figure that has since been reworked during revision. In particular, we only study what happens during perturbation. This question is interesting and is the subject of ongoing work.

Reviewer #2 (Recommendations for the authors):

The article should be carefully corrected, because the current quality of writing might obscure the interest of the study. Particular attention should be paid to the state-of-the-art section and to the discussion, but even the writing of the results should be carefully reworked. The current state of the article makes it very difficult to understand the motivation behind the study but also what the main result provided by this work is.

The Introduction, Results, and Discussion have been reworked to build on the central premise of the work: the timescale of half-(in)activation alterations influences the intrinsic properties and activity patterns targeted by the neuron’s homeostatic mechanism. These changes are detailed in Public Comment #1.

Reviewer #3 (Recommendations for the authors):

The manuscript presents an interesting computational study exploring how activity-dependent regulation of (in)activation dynamics interacts with conductance plasticity to shape neuronal activity patterns. While the study provides valuable insights, some aspects would benefit from clarification, further analyses, and/or additional simulations to strengthen the conclusions. Below, I outline concerns and comments related to specific details of the model and results presentation that were not included in the public review.

(1) The results presented in Figure 5 show that adaptation occurs in both channel conductances and (in)activation dynamics; however, the changes in conductance remain relatively permanent after the model recovers from the transient elevation in extracellular potassium. It therefore seems likely that the model would recover bursting more quickly in response to a subsequent exposure to simulated elevated extracellular potassium since large modifications in the slowly changing conductances would not be required. If this is the case, it could provide a plausible mechanism for adaptation to repeated high-potassium exposure, as demonstrated experimentally in Cancer borealis by this group (PMID: 36060056).

This is an astute observation and the subject of our present follow-up investigation.

(2) In the text relating to Figure 5, it is argued that the resulting shifts in (in)activation curves may be conceptualized as alterations in window currents. It would be helpful to illustrate this by plotting and comparing changes in window currents of these channels alongside the changes in their (in)activation curves.

This comment refers to a passage that was removed during revision.

(3) Some discussion of the role these homeostatic mechanisms may play when the neuron is synaptically integrated into a rhythmically active network could be informative. Surely, phasic and tonic inputs to the neuron would alter its conductance and voltage-dependent properties. Therefore, the model's parameters in an intact network could be very different from those in the synaptically isolated case.

This is an excellent point. We agree that synaptic context—particularly tonic and phasic inputs—would likely influence a neuron’s conductances and voltage-dependent properties, potentially leading to different homeostatic outcomes than in the isolated case. While our current study focuses on synaptically isolated neurons, the Marder lab has considered how homeostatically stabilized neurons might interact in network settings. For example, O'Leary et al (2014) presents an example network of three such neurons operating under homeostatic regulation. However, systematically exploring this question remains a challenge. We are currently developing ideas to study this in the context of a simplified half-center oscillator model, where network-level dynamics can be more tractably analyzed.

(4) Why are the transitions of alpha typically so abrupt, essentially either 1 or 0? Similarly, what happens in the model when there are transient transitions from what appears to be a steady-state alpha that abruptly shifts from 0 to 1 or 1 to 0? For example, what is occurring in Figure 1A at ~150s and ~180s when alpha jumps between 1 and 0, or in Figure 1B when the model transiently jumps up from 0 to 1 at ~400s and ~830s? In Figure 1A, does the bursting pattern change at all after ~250s, or is it identical to the pattern at c?

This is addressed in the revision (Lines 141 – 150).

(5) Are the final steady-state parameters of the 25 (sic) models consistent with experimental observations?

It is difficult to assess — it is hard to design an experiment to do what the reviewer is suggesting.

(6) Why isn't gL allowed to change dynamically? This seems like the most straightforward way to allow a neuron to adjust its excitability (aside from tonic synaptic inputs).

Passive currents could, in principle, be subject to homeostatic regulation. However, our study focused on active intrinsic currents. This focus stems from earlier investigations, which showed that active currents are dynamically regulated during homeostasis – for instance Turrigiano et al (1995) and (Desai et al 1999).

Alonso LM, Rue MCP, Marder E. 2023. Gating of homeostatic regulation of intrinsic excitability produces cryptic long-term storage of prior perturbations. Proc Natl Acad Sci U S A 120: e2222016120

Baccaglini PI, Spitzer NC. 1977. Developmental changes in the inward current of the action potential of Rohon-Beard neurones. J Physiol 271: 93-117

Desai NS, Rutherford LC, Turrigiano GG. 1999. Plasticity in the intrinsic excitability of cortical pyramidal neurons. Nature Neuroscience 2: 515-20

Gao BX, Ziskind-Conhaim L. 1998. Development of ionic currents underlying changes in action potential waveforms in rat spinal motoneurons. J Neurophysiol 80: 3047-61

Goldberg EM, Jeong HY, Kruglikov I, Tremblay R, Lazarenko RM, Rudy B. 2011. Rapid developmental maturation of neocortical FS cell intrinsic excitability. Cereb Cortex 21: 666-82

Hunsberger MS, Mynlieff M. 2020. BK potassium currents contribute differently to action potential waveform and firing rate as rat hippocampal neurons mature in the first postnatal week. J Neurophysiol 124: 703-14

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Author response:

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

Reviewer #1 (Public review):

Weaknesses:

(1) The manuscript's logical flow is challenging and hard to follow, and key arguments could be more clearly structured, particularly in transitions between mechanistic components.

We have revised our manuscript so as to make it easy for readers to follow the logical flow in transitions between mechanistic components by adding the descriptions of Figure S1E-J, Figure S2F-K, Figure S3A-H, Figure S4A-F, Figure S5, and Figure S6 in the revised manuscript.

(2) The causality between stress-induced α2A-AR internalization and the enhanced MAO-A remains unclear. Direct experimental evidence is needed to determine whether α2A-AR internalization itself or Ca2+ drives MAO-A activation, and how they activate MAO-A should be considered.

We believe that the causality between stress-induced α2A-AR internalization and the enhancement of MAO-A is clearly demonstrated by our current experiments, while our explanations may be improved by making them easier to understand especially for those who are not expert on electrophysiology.

Firstly, it is well established that autoinhibition in LC neurons is mediated by α2A-AR coupled-GIRK (Arima et al., 1998, J Physiol; Williams et al., 1985, Neuroscience). We found that spike frequency adaptation in LC neurons was also mediated by α2A-AR coupled GIRK-I (Figure 1A-I), and that α2A-AR coupled GIRK-I underwent [Ca²⁺]_i dependent rundown (Figures 2, S1, S2), leading to an abolishment of spike-frequency adaptation (Figures S4). [Ca²⁺]_i dependent rundown of α2A-AR coupled GIRK-I was prevented by barbadin (Figure 2G-J), which prevents the internalization of G-protein coupled receptor (GPCR) channels.

Abolishment of spike frequency adaptation itself, i.e., “increased spike activity” can increase [Ca²⁺]_i because [Ca²⁺]_i is entirely dependent on the spike activity as shown by [Ca²⁺]_i imaging method in Figure S3.

Thus, α2A-AR internalization can increase [Ca²⁺]_i through the abolishment of autoinhibition or spike frequency adaptation, and a [Ca²⁺]_i increase drives MAO-A activation as reported previously (Cao et al., 2007, BMC Neurosci). The mechanism how Ca²⁺ activates MAO-A is beyond the scope of the current study.

Our study just focused on the mechanism how chronic or sever stress can cause persistent overexcitation and how it results in LC degeneration.

(3) The connection between α2A-AR internalization and increased cytosolic NA levels lacks direct quantification, which is necessary to validate the proposed mechanism.

Direct quantification of the relationship between α2A-AR internalization and increased cytosolic NA levels may not be possible, and may not be necessarily needed to be demonstrated as explained below.

The internalization of α2A-AR can increase [Ca²⁺]_i through the abolishment of autoinhibition or spike frequency adaptation, and [Ca²⁺]_i increases can facilitate NA autocrine (Huang et al., 2007), similar to the transmitter release from nerve terminals (Kaeser & Regehr, 2014, Annu Rev Physiol).

Autocrine released NA must be re-uptaken by NAT (NA transporter), which is firmly established (Torres et al., 2003, Nat Rev Neurosci). Re-uptake of NA by NAT is the only source of intracellular NA, and NA re-uptake by NAT should be increased as the internalization of NA biding site (α2A-AR) progresses in association with [Ca²⁺]_i increases (see page 11, lines 334-336).

Thus, the connection between α2A-AR internalization and increased cytosolic NA levels is logically compelling, and the quantification of such connection may not be possible at present (see the response to the comment made by the Reviewer #1 as Recommendations for the authors (2) and beyond the scope of our current study.

(4) The chronic stress model needs further validation, including measurements of stress-induced physiological changes (e.g., corticosterone levels) to rule out systemic effects that may influence LC activity. Additional behavioral assays for spatial memory impairment should also be included, as a single behavioral test is insufficient to confirm memory dysfunction.

It is well established that restraint stress (RS) increases corticosterone levels depending on the period of RS (García-Iglesias et al., 2014, Neuropharmacology), although we are not reluctant to measure the corticosterone levels. In addition, there are numerous reports that showed the increased activity of LC neurons in response to various stresses (Valentino et al., 1983; Valentino and Foote, 1988; Valentino et al., 2001; McCall et al., 2015), as described in the text (page 4, lines 96-98). Measurement of cortisol levels may not be able to rule out systemic effects of CRS on the whole brain.

We had already done another behavioral test using elevated plus maze (EPM) test.By combining the two tests, it may be possible to more accurately evaluate the results of Y-maze test by differentiating the memory impairment from anxiety. However, the results obtained by these behavioral tests are just supplementary to our current aim to elucidate the cellular mechanisms for the accumulation of cytosolic free NA. Therefore, we have softened the implication of anxiety and memory impairment (page 13, lines 397-400 in the revised manuscript).

(5) Beyond b-arrestin binding, the role of alternative internalization pathways (e.g., phosphorylation, ubiquitination) in α2A-AR desensitization should be considered, as current evidence is insufficient to establish a purely Ca²⁺ -dependent mechanism.

We can hardly agree with this comment. 

It was clearly demonstrated that repeated application of NA itself did not cause desensitization of α2A-AR (Figure S1A-D), and that the blockade of b-arrestin binding by barbadin completely suppressed the Ca^2a-dependent downregulation of GIRK (Figure 2G-K). These observations can clearly rule out the possible involvement of phosphorylation or ubiquitination for the desensitization.

Not only the barbadin experiment, but also the immunohistochemistry and western blot method clearly demonstrated the decrease of α2A-AR expression on the cell membrane (Figure 3).

Ca²⁺-dependent mechanism of the rundown of GIRK was convincingly demonstrated by a set of different protocols of voltage-clamp study, in which Ca²⁺ influx was differentially increased. The rundown of GIRK-I was orderly potentiated or accelerated by increasing the number of positive command pulses each of which induces Ca²⁺ influx (compare Figure S1E-J, Figure S2A-E and Figure S2F-K along with Figure 2A-F). The presence or absence of Ca²⁺ currents and the amount of Ca²⁺ currents determined the trend of the rundown of GIRK-I (Figures 2, S1 and S2). Because the same voltage protocol hardly caused the rundown when it did not induce Ca²⁺ currents in the absence of TEA (Figure S1F; compare with Figure 2B), blockade of Ca²⁺ currents by nifedipine would not be so beneficial.

We believe the series of voltage-clamp protocols convincingly demonstrated the orderly involvement of [Ca²⁺]_i in accelerating the rundown of GIRK-I.

(6) NA leakage for free NA accumulation is also influenced by NAT or VMAT2. Please discuss the potential role of VMAT2 in NA accumulation within the LC in AD. 

It has been demonstrated that reduced VMAT2 levels increased susceptibility to neuronal damage: VMAT2 heterozygote mice displayed increased vulnerability to MPTP as evidenced by reductions in nigral dopamine cell counts (Takahashi et al, 1997, PNAS). Thus, when the activity of VMAT2 in LC neurons were impaired by chronic restraint stress, cytosolic NA levels in LC neurons would increase. We have added such discussion in the revised manuscript (page 12, lines 381-384).

(7) Since the LC is a small brain region, proper staining is required to differentiate it from surrounding areas. Please provide a detailed explanation of the methodology used to define LC regions and how LC neurons were selected among different cell types in brain slices for whole-cell recordings.

LC neurons were identified immunohistochemically and electrophysiologically as we previously reported (see Fig. 2 in Front. Cell. Neurosci. 16:841239. doi: 10.3389/fncel.2022.841239). We have added this explanation in the method section of the revised manuscript (page 15, lines 474-475). A delayed spiking pattern in response to depolarizing pulses (Figure S10 in the revised manuscript) applied at a hyperpolarized membrane potential was commonly observed in LC neurons in many studies (Masuko et al., 1986; van den Pol et al., 2002; Wagner-Altendorf et al., 2019).

Reviewer #2 (Public review):

Weaknesses:

(1) The manuscript reports that chronic stress for 5 days increases MAO-A levels in LC neurons, leading to the production of DOPEGAL, activation of AEP, and subsequent tau cleavage into the tau N368 fragment, ultimately contributing to neuronal damage. However, the authors used wild-type C57BL/6 mice, and previous literature has indicated that AEP-mediated tau cleavage in wild-type mice is minimal and generally insufficient to cause significant behavioral alterations. Please clarify and discuss this apparent discrepancy.

In our study, normalized relative value of AEP-mediated tau cleavage (Tau N368) was much higher in CRS mice than non-stress wild-type mice. It is not possible to compare AEP-mediated tau cleavage between our non-stress wild type mice and those observed in previous study (Zhang et al., 2014, Nat Med), because band intensity is largely dependent on the exposure time and its numerical value is the normalized relative value. In view of such differences, our apparent band expression might have been intensified to detect small changes.

(2) It is recommended that the authors include additional experiments to examine the effects of different durations and intensities of stress on MAO-A expression and AEP activity. This would strengthen the understanding of stress-induced biochemical changes and their thresholds.

GIRK rundown was almost saturated after 3-day RS and remained the same in 5-day RS mice (Fig. 4A-G), which is consistent with the downregulation of α2A-AR and GIRK1 expression by 3-day RS (Fig. 3C, F and G; Fig. 4J and K). However, we examined the protein levels of MAO-A, pro/active-AEP and Tau N368 only in 5-day RS mice without examining in 3-day RS mice. This is because we considered the possibility that a high [Ca²⁺]_i condition may have to be sustained for some period of time to induce changes in MAO-A, AEP and Tau N368, and therefore 3-day RS may be insufficient to induce such changes. We have added this in the revised manuscript (page 17, lines 521-525).

(3) Please clarify the rationale for the inconsistent stress durations used across Figures 3, 4, and 5. In some cases, a 3-day stress protocol is used, while in others, a 5-day protocol is applied. This discrepancy should be addressed to ensure clarity and experimental consistency.

Please see our response to the comment (2).

(4) The abbreviation "vMAT2" is incorrectly formatted. It should be "VMAT2," and the full name (vesicular monoamine transporter 2) should be provided at first mention.

Thank you for your suggestion. We have revised accordingly.

Reviewer #3 (Public review):

Weaknesses:

Nevertheless, the manuscript currently reads as a sequence of discrete experiments rather than a single causal chain. Below, I outline the key points that should be addressed to make the model convincing.

Please see the responses to the recommendation for the authors made by reviewer #3.

Reviewer #1 (Recommendations for the authors):

(1) Improve the clarity and organization of the manuscript, ensuring smoother transitions between concepts and mechanisms.

Please see the response to the comment raised by Reviewer #1 as Weakness

(2) Adjust any quantifying method for cytosolic NA levels under different conditions to support the link between receptor internalization and NA accumulation.

If fluorescent indicator of cytosolic free NA is available, it would be possible to measure changes in cytosolic NA levels. However, at present, there appeared to be no fluorescence probe to label cytosolic NA. For example, NS521 labels both dopamine and norepinephrine inside neurosecretory vesicles (Hettie & Glass et al., 2014, Chemistry), and BPS3 fluorescence sensor labels NA around cell membrane by anchoring on the cell membrane (Mao et al., 2023, Nat Comm). Furthermore, the method reported in “A Genetically Encoded Fluorescent Sensor for Rapid and Specific In Vivo Detection of Norepinephrine” is limited to detect NA only when α2AR is expressed. In the present study, increases in cytosolic NA levels are caused by internalization of α2AR. Cytosolic NA measurements with GRAB NE photometry may not be applicable in the present study. However, we have discussed the availability of such fluorescent methods to directly prove the increase in cytosolic NA as a limitation of our study (page 14, lines 429-436 in the revised manuscript).

(3) Include validation of the chronic stress model with physiological and behavioral measures (e.g., corticosterone levels and another behavioral test).

Please see the response to the comment raised by Reviewer #1 as Weakness (4).

(4) All supplemental figures should be explicitly explained in the Results section. Specifically, clarify and describe the details of Figure S1G-K, Figure S2F-K, Figure S3A-H, Figure S4A-F, Figure S5, and Figure S6 to ensure all supplementary data are fully integrated into the main text.

We have more explicitly and clearly described the details of Figure S1E-J, Figure S2F-K, Figure S3A-H, Figure S4A-F, Figure S5, and Figure S6 and fully integrated those explanations into the main text in the revised manuscript.

(5) In Figure 3, the morphology of TH-positive cells differs between panels D and E. Additionally, TH is typically expressed in the cytosol, but in the provided images, it appears to be localized only to the membrane. Please clarify this discrepancy and provide a lower-magnification image to display a larger area, not one cell.

In a confocal image, TH is not necessarily expressed homogenously in the cytosol, but is expressed in a ring-shaped pattern inside the plasma membrane, avoiding the cell nucleus and its surrounding Golgi apparatus and endoplasmic reticulum (ER) (Henrich et al., 2018, Acta Neuropathol Commun; see Fig. 4a and 6e), especially when the number of z-stack of confocal images is small. This is presumably because LC neurons are especially enriched with numerous Golgi apparatus and ER (Groves & Wilson, 1980, J Comp Neurol).

In Figure S7, we showed a lower-magnification image of LC and its adjacent area (mesencephalic trigeminal nucleus). In the LC area, there are a variety of LC neurons, which include oval shaped neurons (open arrowhead; similar to Figure 3D) and also rhombus-like shaped neurons (open double arrowheads, similar to Figure 3E). A much lower-magnification image of LC neurons constituting LC nucleus was shown in Figure 5A.

(6) In Figure 5, the difference in MAO-A expression is not clearly visible in the fluorescence images. Enzymatic assays for AEP and MAO-A should be included to demonstrate the increased activity better.

In the current study, we did not elaborate to detect the changes in TH, MAO-A and AEP in terms of immunohistochemical method. Instead, we elaborated to detect such changes in terms of western blot method. The main conclusions in the current study were drawn primarily by electrophysiological techniques as we have expended much effort on electrophysiological experiments. Because the relative quantification of active AEP and Tau N368 proteins by western blotting analysis may accurately reflect changes in those enzyme activities, enzymatic assay may not be necessarily required but is helpful to better demonstrate AEP and MAO-A activity. We have described the necessity of enzymatic assay to better demonstrate the AEP and MAO-A activities (page 10, lines 314-315).

Reviewer #3 (Recommendations for the authors):

(1) Causality across the pathway

Each step (α2A internalisation, GIRK rundown, Ca²⁺ rise, MAO-A/AEP upregulation) is demonstrated separately, but no experiment links them in a single preparation. Consider in vivo Ca²⁺ or GRAB NE photometry during restraint stress while probing α2A levels with i.p. clonidine injection or optogenetic over excitation coupled to biochemical readouts. Such integrated evidence would help to overcome the correlational nature of the manuscript to a more mechanistic study.

It is not possible to measure free cytosolic NA levels with GRAB NE photometry when α2A AR is internalized as described above (see the response to the comment made by reviewer #1 as the recommendation for the authors).

(2) Pharmacology and NE concentration

The use of 100 µM noradrenaline saturates α and β adrenergic receptors alike. Please provide ramp measurements of GIRK current in dose-response at 1-10 µM NE (blocked by atipamezole) to confirm that the rundown really reflects α2A activity rather than mixed receptor effects.

It is true that 100 µM noradrenaline activates both α and β adrenergic receptors alike. However, it was clearly showed that enhancement of GIRK-I by 100 µM noradrenaline was completely antagonized by 10 µM atipamezole and the Ca²⁺ dependent rundown of NA-induced GIRK-I was prevented by 10 µM atipamezole. Considering the Ki values of atipamezole for α2A AR (=1~3 nM) (Vacher et al., 2010, J Med Chem) and β AR (>10 µM) (Virtanen et al., 1989, Arch Int Pharmacodyn Ther), these results really reflect α2A AR activity but not β AR activity (Figure S5). Furthermore, because it is already well established that NA-induced GIRK-I was mediated by α2A AR activity in LC neurons (Arima et al., 1998, J Physiol; Williams et al., 1985, Neuroscience), it is not necessarily need to re-examine 1-10 µM NA on GIRK-I.

(3) Calcium dependence is not yet definitive

The rundown is induced with a TEA-enhanced pulse protocol. Blocking L-type channels with nifedipine (or using Cd²⁺) during this protocol should show whether Ca²⁺ entry is necessary. Without such a control, the Ca²⁺ link remains inferential.

The Ca²⁺ link was precisely demonstrated by a series of voltage clamp experiment, in which Ca²⁺ influx was orderly potentiated by increasing the number of positive voltage pulses (Figures S1 and S2). As the number of positive voltage pulses was increased, the rundown of GIRK-I was accelerated or enhanced more. The relationship between the number of spikes and the Ca²⁺ influx detected as Ca²⁺ transients was well documented in Ca2+ imaging experiments using fura-2 (Figure S3).

The presence or absence of Ca²⁺ currents and the amount of Ca²⁺ currents determined the trend of the rundown of GIRK-I (Figs. 2, S1 and S2). The same voltage protocol hardly caused the rundown when it did not induce Ca²⁺ currents in the absence of TEA (Fig. S1F; compare with Fig. 2B), and the series of voltage-clamp protocols convincingly demonstrated the orderly involvement of [Ca²⁺]_i in accelerating the rundown of GIRK-I. Therefore, blockade of Ca²⁺ currents by nifedipine may not be so beneficial.

(4) Age mismatch and disease claims

All electrophysiology and biochemical data come from juvenile (< P30) mice, yet the conclusions stress Alzheimer-related degeneration. Key endpoints need to be replicated in adult or aged mice, or the manuscript should soften its neurodegenerative scope.

As described in the section of Conclusion, we never stress Alzheimer-related degeneration, but might give such an impression. To avoid such a misunderstanding, we have added a description “However, the present mechanism must be proven to be valid in adult or old mice, to validate its involvement in the pathogenesis of AD.” (page 14, lines 448-450).

(5) Direct evidence for extracellular/cytosolic NE

The proposed rise in reuptake NA is inferred from electrophysiology. Modern fluorescent sensors (GRAB NE, nLight) or fast scan voltammetry could quantify NE overflow and clearance during stress, directly testing the model.

Please see the response to the comment made by Reviewer #1 as the Recommendations for the authors (2) as described above.

(6) Quantitative histology

Figure 5 presents attractive images but no numerical analysis. Please provide ROI-based fluorescence quantification (with n values) or move the images to the supplement and rely on the Western blots.

We have moved the immunohistochemical results in Fig. 5 to the supplement as we believe the quantification of immunohistochemical staining is not necessarily correct.

Author response:

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

Reviewer #1 (Public review): 

To elucidate the mechanisms and evolution of animal biomineralization, Voigt et al. focused on the sponge phylum - the earliest branching extant metazoan lineages exhibiting biomineralized structures - with a particular emphasis on deciphering the molecular underpinnings of spicule formation. This study centered on calcareous sponges, specifically Sycon ciliatum, as characterized in previous work by Voigt et al. In S. ciliatum, two morphologically distinct spicule types are produced by a set of two different types of cells that secrete extracellular matrix proteins, onto which calcium carbonate is subsequently deposited. Comparative transcriptomic analysis between a region with active spicule formation and other body regions identified 829 candidate genes involved in this process. Among these, the authors focused on the calcarine gene family, which is analogous to the Galaxins, the matrix proteins known to participate in coral calcification. The authors performed three-dimensional structure prediction using AlphaFold, examined mRNA expression of Calcarin genes in spiculeforming cell types via in situ hybridization, conducted proteomic analysis of matrix proteins isolated from purified spicules, and carried out chromosome arrangement analysis of the Calcarin genes.

Based on these analyses, it was revealed that the combination of Calcarin genes expressed during spicule formation differs between the founder cells-responsible for producing diactines and triactinesand the thickener cells that differentiate from them, underscoring the necessity for precise regulation of Calcarin gene expression in proper biomineralization. Furthermore, the observation that 4 Calcarin genes are arranged in tandem arrays on the chromosome suggests that two rounds of gene duplication followed by neofunctionalization have contributed to the intricate formation of S. ciliatum spicules. Additionally, similar subtle spatiotemporal expression patterns and tandem chromosomal arrangements of Galaxins during coral calcification indicate parallel evolution of biomineralization genes between S. ciliatum and aragonitic corals. 

Strengths: 

(1) An integrative research approach, encompassing transcriptomic, genomic, and proteomic analyses as well as detailed FISH. 

(2) High-quality FISH images of Calcarin genes, along with a concise summary clearly illustrating their expression patterns, is appreciated. 

(3) It was suggested that thickener cells originate from founder cells. To the best of my knowledge, this is the first study to demonstrate trans-differentiation of sponge cells based on the cell-typespecific gene expression, as determined by in situ hybridization. 

(4) The comparison between Calcarins of Calcite sponge and Galaxins of aragonitic corals from various perspective-including protein tertiary structure predictions, gene expression profiling during calcification, and chromosomal sequence analysis to reveal significant similarities between them. 

We thank the reviewer for this assessment. 

(1) The conclusions of this paper are generally well supported by the data; however, some FISH images require clearer indication or explanation.

We have modified Fig. 3 by including some insets indicating the depicted part of the sponge body and to change the color-scheme as suggested by reviewer3 for the FISH images. In accordance to the following comment, we decided to remove single-channel views in Fig. 3 A. 

(2) Figure S2 (B, C, D): The fluorescent signals in these images are difficult to discern. If the authors choose to present signals at such low magnification, enhancing the fluorescence signals would improve clarity. Additionally, incorporating Figure S2A as an inset within Figure S2E may be sufficient to convey the necessary information about signal localization. 

We changed the figure according to the suggestions.

(3) Figure S3A: The claim that Cal2-expressing spherical cells are closely associated with the choanoderm at the distal end of the radial tube is difficult to follow. Are these Cal2-expressing spherical cells interspersed among choanoderm cells, or are they positioned along the basal surface of the choanoderm? Clarifying their precise localization and indicating it in the image would strengthen the interpretation. 

In the figure, the view is on the choanoderm that lines the inner surface of the radial tube. Our interpretation is that the spherical cells are positioned at the basal surface of the choanoderm. We updated Fig. S3, which now includes another view to support our interpretation and also indicate some choanocytes.

(4) To further highlight the similarities between S.ciliatum and aragonitic corals in the molecular mechanisms of calcification, consider including a supplementary figure providing a concise depiction of the coral calcification process. This would offer valuable context for readers.

We considered this suggestion, and have included such a supplementary figure (Fig. S9).

Reviewer #2 (Public review): 

Summary: 

This paper reports on the discovery of calcarins, a protein family that seems involved in calcification in the sponge Sycon ciliatum, based on specific expression in sclerocytes and detection by mass spectrometry within spicules. Two aspects stand out: (1) the unexpected similarity between Sycon calcarins and the galaxins of stony corals, which are also involved in mineralization, suggesting a surprising, parallel co-option of similar genes for mineralization in these two groups; (2) the impressively cell-type-specific expression of specific calcarins, many of which are restricted to either founder or thickener cells, and to either diactines, triactines, or tetractines. The finding that calcarins likely diversified at least partly by tandem duplications (giving rise to gene clusters) is a nice bonus. 

Strengths: 

I enjoyed the thoroughness of the paper, with multiple lines of evidence supporting the hypothesized role of calcarins: spatially and temporally resolved RNAseq, mass spectrometry, and whole-mount in situ hybridization using CISH and HCR-FISH (the images are really beautiful and very convincing). The structural predictions and the similarity to galaxins are very surprising and extremely interesting, as they suggest parallel evolution of biomineralization in sponges and cnidarians during the Cambrian explosion by co-option of the same "molecular bricks". 

Weaknesses: 

I did not detect any major weakness, beyond those inherent to working with sponges (lack of direct functional inhibition of these genes) or with fast-evolving gene families with complex evolutionary histories (lack of a phylogenetic tree that would clarify the history of galaxins/calcarins and related proteins). 

We thank the reviewer for this assessment and the detailed comments be addressed below.

Reviewer #3 (Public review):

Summary: 

The study explores the extent to which the biomineralization process in the calcitic sponge Sycon ciliatum resembles aragonitic skeleton formation in stony corals. To investigate this, the authors performed transcriptomic, genomic, and proteomic analyses on S. ciliatum and examined the expression patterns of biomineralization-related genes using in situ hybridization. Among the 829 differentially expressed genes identified in sponge regions associated with spicule formation, the authors focused on calcarin genes, which encode matrix proteins analogous to coral galaxins. The expression patterns of calcarins were found to be diverse but specific to particular spicule types. Notably, these patterns resemble those of galaxins in stony corals. Moreover, the genomic organization of calcarine genes in S. ciliatum closely mirrors that of galaxin genes in corals, suggesting a case of parallel evolution in carbonate biomineralization between calcitic sponges and aragonitic corals. 

Strengths: 

The manuscript is well written, and the figures are of high quality. The study design and methodologies are clearly described and well-suited to addressing the central research question. Particularly noteworthy is the authors´ integration of various omics approaches with molecular and cell biology techniques. Their results support the intriguing conclusion that there is a case of parallel evolution in skeleton-building gene sets between calcitic sponges and aragonitic corals. The conclusions are well supported by the data and analyses presented. 

Weaknesses: 

The manuscript is strong, and I have not identified any significant weaknesses in its current form. 

We thank the reviewer for the insight and addressed the detailed comments below.

Reviewer #1 (Recommendations for the authors): 

The description of the region "radial tube" is unclear. Please define and explain it at its first mention in the manuscript, and, if possible, refer to the appropriate figure(s) (e.g., Figure 1A). 

We now explain radial tubes at the beginning of the results and added a label in figure 1A. “Sycon ciliatum is a tube-shaped sponge with a single apical osculum and a sponge wall of radial tubes around the central atrium (Fig. 1A). The radial tubes are internally lined with choanoderm, which forms elongated chambers in an angle of approximately 90° to the tube axis”. 

Reviewer #2 (Recommendations for the authors): 

Scientific suggestions: 

(1) Page 13: "Despite their presence in the same orthogroups, the octocoral and stony coral proteins were only distantly related to the calcareous sponge calcarins (e.g., 12-24% identity between octocoral and calcareous sequences in orthogroup Cal 2-4-6), resulting in poor alignment. Their homology to calcarins, therefore, remains to be determined." Could 3D structures of these coral proteins be predicted with AlphaFold to substantiate (or nuance) the comparison with calcarins? 

We run additional alphafold predictions for two octocoral and two scleractinian galaxins. A galaxin-like sequence from Pinnigorgia flava was only a short fragment and therefore we did not attempt any structure predictions. The result shows that the octocoral galaxin-like proteins show some structural similarity (12 beta-harpins), while the scleractinian galaxin-like proteins differ from the sponge counterparts of the same orthogroup. We added this information to the results and in the new Fig. S7.

Minor improvements to the text: 

(1)  Page 7 : "The expression of Cal1 to Cal8 was investigated using chromogenic in situ hybridization (CISH) and hairpin-chain reaction fluorescence in situ hybridization (HCR-FISH), confirming their presence in sclerocytes." - Figure 3 should be cited here. 

We refer to the figure now.

(2) Page 8-9: "Cal6 expression mirrors that of Cal2, occurring in rounded cells at the distal tip of radial tubes and in a ring of cells around the oscular ring." - Please cite a figure here. 

We refer now to Fig. 3K

(3) Page 11-12: Please define eigengene, this term is not necessarily common knowledge. 

We provide now a short definition in this sentence: “ The analysis provided eight meta-modules, of which four showed significant changes in expression module eigengenes —summary profiles that capture the overall expression pattern of each module— between samples with high spicule formation context (osculum region and regeneration stages older than four days) and samples with low spicule formation (sponge-wall and early regeneration stages until day 3-4) (Fig. S5).” 

(4) Page 13: "Species without skeletons, such as the cnidarians Hydra, Actinia, Exaiptasia, and Nematostella, also possess galaxin-like proteins." This is too concise - can you explain what evidence was used? PANTHER, AlphaFold, OrthoFinder, Blastp...? 

The evidence used is from PANTHER, and we enhanced clarification of this by modifying the last sentence of the section.

(5) Page 20: "We have identified calcarins, galaxin-like proteins, as crucial components of the biomineralization toolkit in calcareous sponges." I'm not sure you showed they are crucial (this would require functional evidence). Perhaps "novel" components or some other adjective would fit better. 

We changed the adjective to “novel”.

Suggestions for the figures: 

(1) Figure 1A: radial tubes should be labelled. 

A label was added.

(2) Figure 3 is beautiful but hard to parse. The name of all markers should be written on each panel (notably B, C, and D) and ideally placed in a consistent position (top right corner?) so that the reader's eye doesn't have to look for them anew in each panel. Consider depicting the same gene with the same color in all panels if possible (confocal imaging gives virtual colors anyway, there's no reason to be bound to the real-life color of the fluorophores used - if that was the original intent). Finally, the red/green color scheme is not colorblind-readable, so please consider switching to another scheme (white/cyan/magenta, for example).

We have updated the figure according to the suggestions. The names of all markers are now included on each panel. Placing them in the upper right corner was not feasible for all panels, so we adjusted their placement as needed. Reoccurring genes are shown in the same color where possible. To improve accessibility for individuals with red/green color vision deficiency, we adopted a cyan/magenta/yellow color scheme. Each HCR-FISH image was processed in ImageJ by splitting the image into channels, applying cyan, magenta, or yellow lookup tables, converting each channel to RGB, and then stacking and blending them using the Z-Project function with maximum intensity projection. Since the original channel information is not preserved after this processing, we provide the original red/green/blue version of the figure in the supplementary material in Fig S11. Additionally, we added small sketches of Figure 1A to indicate the sponge body regions depicted, where relevant.

(3) Figure S3: the blue staining is not explained. It is also unclear where choanocytes are - could individual choanocytes be indicated with arrows or lines? 

We added the information to the figure legend. The blue channel shows “Autofluorescence detected with the Leica TXR filter (approx. 590–650 nm), included to help distinguish true signal from background autofluorescence observed in the FITC channel (used for Spiculin detection).”

Reviewer #3 (Recommendations for the authors): 

I have no major concerns about the manuscript - only minor edits and comments, which are listed below: 

(1) On page 13, the authors refer to Figure S8; however, I believe this should be Figure S7. 

We now refer to the correct Figure. Because of introducing a new Fig. S7, now the correct reference is Fig. S8.

(2) On page 16, please correct "Spciulin" to "Spiculin". 

Now corrected.

(3) On page 17, there are two commas following "(Sycon)"; please remove one. 

Corrected.

(4) In the Data Accessibility section, none of the provided links appear to work. Please ensure all links are functional. 

We apologize for this oversight and now provide working links. 

(5) In Figure 3, the description of panel L is missing from the figure legend. 

We added the description of this panel.

(6) On page 39, change "Fig. 4" to "Figure 4" to maintain consistency throughout the manuscript. 

Changed.

(7) Figure S7 is not cited in the main text. Please, address this. 

Corrected (see above at point 1)

(8) In the legend for Table S2, the reference to Soubigou et al. (3) is incorrect, as it is not listed in the SI reference section. Please correct this. 

Soubigou et al. (2020) is now included in the SI reference list.

Add IPFS! Desktop

Can do it manually

Author response:

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

Reviewer#1 (Public review):

This work regards the role of Aurora Kinase A (AurA) in trained immunity. The authors claim that AurA is essential to the induction of trained immunity. The paper starts with a series of experiments showing the effects of suppressing AurA on beta-glucan-trained immunity. This is followed by an account of how AurA inhibition changes the epigenetic and metabolic reprogramming that are characteristic of trained immunity. The authors then zoom in on specific metabolic and epigenetic processes (regulation of S-adenosylmethionine metabolism & histone methylation). Finally, an inhibitor of AurA is used to reduce beta-glucan's anti-tumour effects in a subcutaneous MC-38 model.

Strengths:<br /> With the exception of my confusion around the methods used for relative gene expression measurements, the experimental methods are generally well-described. I appreciate the authors' broad approach to studying different key aspects of trained immunity (from comprehensive transcriptome/chromatin accessibility measurements to detailed mechanistic experiments). Approaching the hypothesis from many different angles inspires confidence in the results (although not completely - see weaknesses section). Furthermore, the large drug-screening panel is a valuable tool as these drugs are readily available for translational drug-repurposing research.

We thank the reviewer for the positive and encouraging comments.

Weaknesses:

(1) The manuscript contains factual inaccuracies such as:

(a) Intro: the claim that trained cells display a shift from OXPHOS to glycolysis based on the paper by Cheng et al. in 2014; this was later shown to be dependent on the dose of stimulation and actually both glycolysis and OXPHOS are generally upregulated in trained cells (pmid 32320649).

We appreciate the reviewer for pointing out this inaccuracy, and we have revised our statement to ensure accurate and updated description in manuscript. We are aware that trained immunity involves different metabolic pathways, including both glycolysis and oxidative phosphorylation [1, 2]. We also detected Oxygen Consumption Rate (please see response to comment 8 of reviewer#1) but observed no obvious increase of oxygen consumption in trained BMDMs in our experiment setting. As the reviewer pointed out, it might be dependent on the dose of stimulation.

(b) Discussion: Trained immunity was first described as such in 2011, not decades ago.

We are sorry for the inaccurate description, and we have corrected the statement in our revised manuscript as “Although the concept of ‘trained immunity’ has been proposed since 2011, the detailed mechanisms that regulate trained immunity are still not completely understood.”

(2) The authors approach their hypothesis from different angles, which inspires a degree of confidence in the results. However, the statistical methods and reporting are underwhelming.

(a) Graphs depict mean +/- SEM, whereas mean +/- SD is almost always more informative. (b) The use of 1-tailed tests is dubious in this scenario. Furthermore, in many experiments/figures the case could be made that the comparisons should be considered paired (the responses of cells from the same animal are inherently not independent due to their shared genetic background and, up until cell isolation, the same host factors like serum composition/microbiome/systemic inflammation etc). (c) It could be explained a little more clearly how multiple testing correction was done and why specific tests were chosen in each instance.

We sincerely thank the reviewer for this thoughtful comment. (a) The data from animal experiments in which trained immunity was induced in vivo are presented as mean ± SD, while the statistical results from cell-based experiments are presented as mean ± SEM in the revised manuscript. (b) We have replaced one-tailed test with two-tailed test (see Figure 3J in revised manuscript, with updated P value label). We agree that cells derived from the same animal and subjected to different treatment conditions may be deemed paired data. We reanalyzed our data using paired statistical tests. While this led to a slight reduction in statistical significance for some comparisons, the overall trends remained consistent, and our biological interpretation remains unchanged. For in vitro experiments unpaired statistical tests are commonly used in literature [3, 4]. Thus, we still used unpaired test results here. (c) We have provided a detailed description of how multiple comparisons were performed in revised figure legends.

(d) Most experiments are done with n = 3, some experiments are done with n = 5. This is not a lot. While I don't think power analyses should be required for simple in vitro experiments, I would be wary of drawing conclusions based on n = 3. It is also not indicated if the data points were acquired in independent experiments. ATAC-seq/RNA-seq was, judging by the figures, done on only 2 mice per group. No power calculations were done for the in vivo tumor model.

We are sorry for the confusion in our description in figure legends. For the in vivo experiment, we determined the sample size (n=5, n refers to number of mice used as biological replicates) by referring to the animal numbers used for similar experiments in literatures. And according to a reported resource equation approach for calculating sample size in animal studies [5], n=5-7 is suitable for most of our mouse experiments. The in vitro cell assay was performed at least three independent experiments (BMs isolated from different mice), and each experiment was independently replicated at least three times and points represents biological replicates in our revised manuscript. In Figure 1A, 5 biological replicates of these experiments are presented to carefully determine a working concentration of alisertib that would not significantly affect the viability of trained macrophages, and that was subsequently used in all related cell-based experiments. As for seq data, we acknowledge the reviewer's concern regarding the small sample size (n=2) in our RNA-seq/ATAC-seq experiment. We consider the sequencing experiment mainly as an exploratory/screening approach, and performed rigorous quality control and normalization of the sequencing data to ensure the reliability of our findings. For RNA-seq data analysis, we referred to the DESeq2 manual, which specifies that its statistical framework is based on the Negative Binomial Distribution and is capable of robustly inferring differential gene expression with a minimum of two replicates per group. Therefore, the inclusion of two replicates per group was deemed sufficient for our analysis. Nevertheless, the genomic and transcriptome sequencing data were used primarily for preliminary screening, where the candidates have been extensively validated through additional experiments. For example, we conducted ChIP followed by qPCR for detecting active histone modification enrichment in Il6 and Tnf region to further verify the increased accessibility of trained immunity-induced inflammatory genes.

(e) Furthermore, the data spread in many experiments (particularly BMDM experiments) is extremely small. I wonder if these are true biological replicates, meaning each point represents BMDMs from a different animal? (disclaimer: I work with human materials where the spread is of course always much larger than in animal experiments, so I might be misjudging this.).

Thanks for your comments. In our initially submitted manuscript, some of the statistical results were presented as the representative data (technical replicates) from one of three independent biological replicates (including BMDMs experiments showing the suppression and rescue experiments of trained immunity under different inhibitors or activators, see original Figure 1B-C, Figure 5D, and Figure 5H, also related to Figure 1B-C, Figure 5D, and Figure 5H respectively in our revised manuscript) while other experimental data are biological replicates including CCK8 experiment, metabolic assay and ChIP-qPCR. In response to your valuable suggestion, we have revised the manuscript to present all statistical results as biological replicates from three independent experiments (presented as mean ± SEM), and we have provided all the original data for the statistical analysis results (please see Appendix 2 in resubmit system).

(3) Maybe the authors are reserving this for a separate paper, but it would be fantastic if the authors would report the outcomes of the entire drug screening instead of only a selected few. The field would benefit from this as it would save needless repeat experiments. The list of drugs contains several known inhibitors of training (e.g. mTOR inhibitors) so there must have been more 'hits' than the reported 8 Aurora inhibitors.

Thank you for your suggestion and we have briefly reported the outcomes of the entire drug screening in the revised manuscript. The targets of our epigenetic drug library are primarily categorized into several major classes, including Aurora kinase family, histone methyltransferase and demethylase (HMTs and KDMs), acetyltransferase and deacetylase (HDACs and SIRTs), JAK-STAT kinase family, AKT/mTOR/HIF, PARP family, and BRD family (see New Figure 1, related to Figure 1-figure supplement 1B in revised manuscript). Notably, previous studies have reported that inhibition of mTOR-HIF1α signaling axis suppressed trained immunity[6]. Our screening results also indicated that most inhibitors targeting mTOR-HIF1α signaling exhibit an inhibitory effect on trained immunity. Additionally, cyproheptadine, a specific inhibitor for SETD7, which was required for trained immunity as previously reported [7], was also identified in our screening.

JAK-STAT signaling is closely linked to the interferon signaling pathway, and certain JAK kinase inhibitors also target SYK and TYK kinases. A previous drug library screening study has reported that SYK inhibitors suppressed trained immunity [8]. Consistently, our screening results reveal that most JAK kinase inhibitors exhibit suppressive effects on trained immunity.

BRD (Bromodomain) and Aurora are well-established kinase families in the field of oncology. Compared to BRD, the clinical applications of the Aurora kinase inhibitor are still at early stage. In previous studies using inflammatory arthritis models where trained immunity was established, both adaptive and innate immune cells exhibited upregulated expression of AurA [9, 10]. Our study provides further evidence supporting an essential role of AurA in trained immunity, showing that AurA inhibition leads to the suppression of trained immunity.

(4) Relating to the drug screen and subsequent experiments: it is unclear to me in supplementary figure 1B which concentrations belong to secondary screens #1/#2 - the methods mention 5 µM for the primary screen and "0.2 and 1 µM" for secondary screens, is it in this order or in order of descending concentration?

Thank you for your comments and we are sorry for unclear labelled results in original manuscript (related to Figure 1-supplement 1C). We performed secondary drug screen at two concentrations, and drug concentrations corresponding to secondary screen#1 and #2 are 0.2 and 1 μM respectively. It was just in this order, but not in an order of descending concentration.

(a) It is unclear if the drug screen was performed with technical replicates or not - the supplementary figure 1B suggests no replicates and quite a large spread (in some cases lower concentration works better?)

Thank you for your question. The drug screen was performed without technical replicates for initial screening purpose, and we need to verify any hit in the following experiment individually. Yes, we observed that lower concentration works better in some cases. We speculate that it might be due to the fact that the drug's effect correlates positively with its concentration only within a specific range. But in our primary screening, we simply choose one concentration for all the drugs. This is a limitation for our screening, and we acknowledge this limitation in our discussion part.

(5) The methods for (presumably) qPCR for measuring gene expression in Figure 1C are missing. Which reference gene was used and is this a suitably stable gene?

We are sorry for this omission. The mRNA expression of Il6 and Tnf in trained BMDMs was analyzed by a quantitative real-time PCR via a DDCt method, and the result was normalized to untrained BMDMs with Actb (β-actin) as a reference gene, a well-documented gene with stable expression in macrophages. We have supplemented the description for measuring gene expression in Material and Methods in our revised manuscript.

(6) From the complete unedited blot image of Figure 1D it appears that the p-Aurora and total Aurora are not from the same gel (discordant number of lanes and positioning). This could be alright if there are no/only slight technical errors, but I find it misleading as it is presented as if the actin (loading control to account for aforementioned technical errors!) counts for the entire figure.

We are very sorry for this omission. In the original data, p-Aurora and total Aurora were from different gels. In this experiment the membrane stripping/reprobing after p-Aurora antibody did not work well, so we couldn’t get all results from one gel, and we had to run another gel using the same samples to blot with anti-aurora antibody and used β-tubulin as loading control for total AurA (please see New Figure 2A, also related to original Figure 1D). We have provided the source data for β-tubulin from the same membrane of total AurA (please see Figure 1-source data). To avoid any potential misleading, we have repeated this experiment and updated this Figure (please see New Figure 2B, also related to Figure 1D in revised manuscript) with phospho-AurA, total AurA and β-actin from the same gel. The bands for phospho AurA (T288) were obtained using a new antibody (Invitrogen, 44-1210G) and we have revised this information in Material and Methods. We have provided data of three biological replicates to confirm the experiment result also see New Figure 2B, related to Figure 1D in revised manuscript, and the raw data have been added in source data for Figure 1)

(7) Figure 2: This figure highlights results that are by far not the strongest ones - I think the 'top hits' deserve some more glory. A small explanation on why the highlighted results were selected would have been fitting.

We appreciate the valuable suggestion. Figure 2 (see also Figure 2 in revised manuscript) presented information on the chromatin landscape affected by AurA inhibition to confirm that AurA inhibition impaired key gene activation involved in pro-inflammatory macrophage activation by β-glucan. In Figure 2B we highlighted a few classical GO terms downregulated including “regulation of growth”, “myeloid leukocyte activation” and “MAPK cascade” (see also Figure 2B in revised manuscript), among which “regulation of growth” is known function of Aurora A, just to show that alisertib indeed inhibited Aurora A function in vivo as expected. “Myeloid leukocyte activation” and “MAPK cascade” were to show the impaired pro-inflammatory gene accessibility. We highlighted KEGG terms downregulated like “JAK-STAT signaling pathway”, “TNF signaling pathway” and “NF-kappa B signaling pathway” in Figure 2F (see also Figure 2F in revised manuscript), as these pathways are highly relevant to trained immunity. Meanwhile, KEGG terms “FOXO signaling pathway” (see also Figure 2G in revised manuscript) was highlighted to confirm the anti-inflammation effect of alisertib in trained BMDMs, which was further illustrated in Figure 5 (see also Figure 5 in revised manuscript, illustrating FOXO3 acts downstream of AurA). Some top hits in Figure 2B like “positive regulation of cell adhesion”, and “pathway of neurodegeneration” and "ubiquitin mediated proteolysis" in Figure 2F and 2G, is not directly related to trained immunity, thus we did not highlight them, but may provide some potential information for future investigation on other functions of Aurora A.

(8) Figure 3 incl supplement: the carbon tracing experiments show more glucose-carbon going into TCA cycle (suggesting upregulated oxidative metabolism), but no mito stress test was performed on the seahorse.

We appreciate this question raised by the reviewer. We previously performed seahorse XF analyze to measure oxygen consumption rate (OCR) in β-glucan-trained BMDMs. The results showed no obvious increase in oxidative phosphorylation (OXPHOS) indicated by OCR under β-glucan stimulation (related to Figure 3-figure supplement 1 A) although the carbon tracing experiments showed more glucose-carbon going into TCA cycle. We speculate that the observed discrepancy between increased glucose incorporation into TCA cycle and unchanged OXPHOS may reflect a characteristic metabolic reprogramming induced by trained immunity. The increased incorporation of glucose-derived carbon into the TCA cycle likely serves a biosynthetic purpose—supplying intermediates for anabolic processes—rather than augmenting mitochondrial respiration[6]. Moreover, the unchanged OXPHOS may be attributed to a reduced reliance on fatty acid oxidation- “catabolism”, with glucose-derived acetyl-CoA becoming the predominant substrate. Thus, while overall OXPHOS remains stable, the glucose contribution to the TCA cycle increases. This is in line with reports showing that trained immunity promotes fatty acid synthesis- “anabolism”[11]. Alternatively, the partial decoupling of the TCA cycle from OXPHOS could result from the diversion of intermediates such as fumarate out of the cycle. Oxygen consumption rate (OCR) after a mito stress test upon sequential addition of oligomycin (Oligo, 1 μM), FCCP (1 mM), and Rotenone/antimycin (R/A, 0.5 μM), in BMDMs with different treatment for 24 h. β-glucan, 50 μg/mL; alisertib, 1 μM.

(9) Inconsistent use of an 'alisertib-alone' control in addition to 'medium', 'b-glucan', 'b-glucan + alisertib'. This control would be of great added value in many cases, in my opinion.

Thank you for your comment. We appreciate that including “alisertib-alone” group throughout all the experiments may further solidify the results. We set the aim of the current study to investigate the role of Aurora kinase A in trained immunity. Therefore, in most settings, we did not include the group of alisertib only without β-glucan stimulation.

(10) Figure 4A: looking at the unedited blot images, the blot for H3K36me3 appears in its original orientation, whereas other images appear horizontally mirrored. Please note, I don't think there is any malicious intent but this is quite sloppy and the authors should explain why/how this happened (are they different gels and the loading sequence was reversed?)

Thank you for pointing out this error. After checking the original data, we found that we indeed misassembled the orientation of several blots in original data submitted. We went through the assembling process and figured out that the orientation of blots in original data was assembled according to the loading sequences, but not saved correctly, so that the orientations in Figure 4A were not consistent with the unedited blot image. We are sorry for this careless mistake, and we have double checked to make sure all the blots are correctly assembled in the revised manuscript. We also provided three replicates of for the Western blot results showing the level of H3K36me3 in trained BMDMs was inhibited by alisertib (as seen in New Figure 7 at recommendation 2 of reviewer#2).

(11) For many figures, for example prominently figure 5, the text describes 'beta-glucan training' whereas the figures actually depict acute stimulation with beta-glucan. While this is partially a semantic issue (technically, the stimulation is 'the training-phase' of the experiment), this could confuse the reader.

Thanks for the reviewer’s suggestion and we have reorganized our language to ensure clarity and avoid any inconsistencies that might lead to misunderstanding.

(12) Figure 6: Cytokines, especially IL-6 and IL-1β, can be excreted by tumour cells and have pro-tumoral functions. This is not likely in the context of the other results in this case, but since there is flow cytometry data from the tumour material it would have been nice to see also intracellular cytokine staining to pinpoint the source of these cytokines.

Thanks for the reviewer’s suggestion. In Figure 6, we performed assay in mouse tumor model and found that trained immunity upregulated cytokines level like IL-6 in tumor tissue, which was downregulated by alisertib administration. In order to rule out the possibility that the detected cytokines such as IL-6 was from tumor cells, we performed intracellular cytokine staining of single cells isolated from tumor tissues (please see New Figure 4). The result showed that only a small fraction of non-immune cells (CD45^- population) expressed IL-6 (0.37% ± 0.11%), whereas a significantly higher proportion of IL-6-positive cells was observed among CD45⁺ population (deemed as immune cells, 13.66% ± 1.82%), myeloid cells (CD45⁺CD11b⁺, 15.60% ± 2.19%), and in particular, macrophages (CD45⁺CD11b⁺F4/80⁺37.24% ± 3.04%). These findings strongly suggest that immune cells, especially macrophages, are the predominant source of IL-6 cytokine within the tumor microenvironment. Moreover, we also detected higher IL-6 positive population in myeloid cells and macrophages (please see Figure 6I in revised manuscript).

Reviewer#2 (Public review):

Summary:

This manuscript investigates the inhibition of Aurora A and its impact on β-glucan-induced trained immunity via the FOXO3/GNMT pathway. The study demonstrates that inhibition of Aurora A leads to overconsumption of SAM, which subsequently impairs the epigenetic reprogramming of H3K4me3 and H3K36me3, effectively abolishing the training effect.

Strengths:

The authors identify the role of Aurora A through small molecule screening and validation using a variety of molecular and biochemical approaches. Overall, the findings are interesting and shed light on the previously underexplored role of Aurora A in the induction of β-glucan-driven epigenetic change.

We thank the reviewer for the positive and encouraging comments.

Weaknesses:

Given the established role of histone methylations, such as H3K4me3, in trained immunity, it is not surprising that depletion of the methyl donor SAM impairs the training response. Nonetheless, this study provides solid evidence supporting the role of Aurora A in β-glucan-induced trained immunity in murine macrophages. The part of in vivo trained immunity antitumor effect is insufficient to support the final claim as using Alisertib could inhibits Aurora A other cell types other than myeloid cells.

We appreciate the question raised by the reviewer. Though SAM generally acts as a methyl donor, whether the epigenetic reprogram in trained immunity is directly linked to SAM metabolism was not formally tested previously. In our study, we provided evidence suggesting the necessity of SAM maintenance in supporting trained immunity. As for in vivo tumor model, we agree that alisertib may inhibits Aurora A in many cell types besides myeloid cells. To further address the reviewer’s concern, we have performed the suggested bone marrow transplantation experiment (trained mice as donor and naïve mice as recipient) to verify the contribution of myeloid cell-mediated trained immunity for antitumor effect (please see New Figure 8, also related to Figure 6C, 6D and Figure 6-figure supplement 1B and 1C in revised manuscript).

Reviewer #1 (Recommendations for the authors):

Some examples of spelling errors and other mistakes (by far not a complete list):

(a) Introduction, second sentence: reads as if Candida albicans (which should be italicised and capitalised properly) and BCG are microbial polysaccharide components.

(b) Methods: ECAR is ExtraCellular Acidification Rate, not 'Extracellular Acid Ratio'

(c) Figure 2C: β-glucan is misspelled in the graph title.

(d) TNFα has been renamed to 'TNF' for a long time now.

(e) Inconsistent use of Tnf and Tfnα (the correct gene symbol is Tnf) (NB: this field does not allow me to italicise gene symbols)

(f) Figure supplement 1B: 'secdonary'

(g) Caption of figure 4: "Turkey's multiple-comparison test"

(h) etc

I would ask the authors that they please go over the entire manuscript very carefully to correct such errors.

We apologize for these errors and careless mistakes. We greatly appreciate your suggestions, and have carefully proofread the revised manuscript to make sure no further mistakes.

Please also address the points I raised in the public review about statistical approaches. Even more important than the relatively low 'n' is my question about biological replicates. Please clarify what you mean by 'biological replicate'.If you are able to repeat at least the in vitro experiments (if this is too much work pick the most important ones) a few more times this would really strengthen the results.

Thank you for your comment. Our biological replicates refer to independently repeated experiments using bone marrow cells isolated from different mice, and n represents the number of mice used. We repeated each experiment at least three times using BMDMs isolated from different mice (n =3, biological replicates). Specifically, we repeated several in vitro experiments showing inhibition of AurA upregulated GNMT in trained BMDMs and showing transcription factor FOXO3 acted as a key protein in AurA-mediated GNMT expression to control trained immunity as well as showing mTOR agonist rescued trained immunity inhibited by alisertib (see New Figure 5, related to Figure 5B-C, Figure 5H in revised manuscript). Additionally, we have provided data with three biological replicates to show the β-glucan induced phosphorylation of AurA (see comment 6 of reviewer#1) and changes of histone modification marker under AurA inhibition and GNMT deficiency (see recommendation 2 of reviewer#2). We also repeated in vivo tumor model to analysis intratumor cytokines (see recommendation 12 of reviewer#1).

Finally: the authors report 'no funders' during submission, but the manuscript contains funding details. Please modify this in the eLife submission system if possible.

Thank you for your kind reminder and we have modified funding information in the submission system.

Reviewer #2 (Recommendations for the authors):

(1) I have the following methodological and interpretative comments for consideration:

Aurora A has been previously implicated in M1 macrophage differentiation and NF-κB signaling. What is the effect of Aurora A inhibition on basal LPS stimulation? Considering that β-glucan + Ali also skews macrophage priming towards an M2 phenotype, as shown in Fig. 2E, further clarification on this point would strengthen the study.

Thanks for your suggestion. Previous study showed AurA was upregulated in LPS-stimulated macrophages and the inhibition of AurA downregulated M1 markers of LPS-stimulated macrophages through NF-κB pathway but did not affect IL-4-induced M2 macrophage polarization [12]. Consistently, we also found that AurA inhibition downregulated inflammatory response upon basal LPS stimulation as shown by decreased IL-6 level (see New Figure 6). In original Figure 2E (also related to Figure 2E in revised manuscript), we showed an increased accessibility of Mrc1 and Chil3 under “β-glucan +Ali” before re-challenge, both of which are typical M2 macrophage markers. Motif analysis showed that AurA inhibition would upregulate genes controlled by PPARγ (STAT6 was not predicted). Different from STAT6, a classical transcriptional factor in controlling M2 polarization (M2a) dependent on IL-4 or IL-13, PPARγ mediates M2 polarization toward M2c and mainly controls cellular metabolism on anti-inflammation independent on IL-4 or IL-13. Thus, we speculate that inhibition of AurA might promote non-classical M2 polarization, and the details warrant future investigation.

(2) In Figure 4A, it looks like that H3K27me3 is also significantly upregulated by β-glucan and inhibited by Ali. How many biological replicates were performed for these experiments? It would be beneficial to include densitometric analyses to visualize differences across multiple Western blot experiments for better reproducibility and quantitative assessment. In addition, what is the effect of treatment of Ali alone on the epigenetic profiling of macrophages?

We are sorry for this confusion. Each experiment was performed with at least three independent biological replicates. In original Figure 4-figure supplement 1 (also related to Figure 4-figure supplementary 1 in the revised manuscript), we presented the densitometric analysis results from three independent Western blot experiments, which showed that β-glucan did not affect H3K27me3 levels under our experimental conditions. Three biological replicates data for histone modification were shown as follows (New Figure 7, as related to Figure 4-figure supplement 1 in revised manuscript). We appreciate that assay for “Ali alone” in macrophages may add more value to the findings. We set the aim of the current study to investigate the role of Aurora kinase A in trained immunity, and we know that alisertib itself would not induce or suppress trained immunity. Therefore, in most settings, we did not test the effect of Alisertib alone without β-glucan stimulation.

(3) The IL-6 and TNF concentrations exhibit considerable variability (Fig. 3K and Fig. 5H), ranging from below 10 pg/mL to 500-1000 pg/mL. Please specify the number of replicates for these experiments and provide more detail on how variability was managed. Including this information would enhance the robustness of the conclusions.

Thank you for your comment. These experiments were replicated as least three times using BMDMs isolated from different mice. The observed variations in cytokines concentration may be attributed to factors such as differences in cell density, variability among individual mice, and the passage number of the MC38 cells used for supernatant collection. We have prepared new batch of BMDMs and repeated the experiment and provided consistent results in the revised manuscript (please see Figure 5H in revised manuscript). Data for biological replicates have been provided (please see Appendix 2 in resubmit system).

(4) The impact of Aurora A inhibition on β-glucan-induced anti-tumor responses appears complex. Specifically, GNMT expression is significantly upregulated in F4/80- cells, with stronger effects compared to F4/80+ cells as seen in Fig. 6D. To discern whether this is due to the abolishment of trained immunity in myeloid cells or an effect of Ali on tumor cells which inhibit tumor growth, I suggest performing bone marrow transplantation. Transplant naïve or trained donor BM into naïve recipients, followed by MC38 tumor transplantation, to clarify the mechanistic contribution of trained immunity versus off-target effects.

Thanks for your valuable suggestion. Following your suggestion, we have performed bone marrow transplantation to clarify that alisertib acts on the BM cells to inhibit anti-tumor effect induced by trained immunity (see New Figure 8, related to Figure 6C-D in revised manuscript). As the results shown below, transplantation of trained BM cells conferred antitumor activity in recipient mice, while transplantation of trained BM cells with alisertib treatment lost such activity, further demonstrating that alisertib inhibited AurA in trained BM cells to impair their antitumor activity.

References

(1) Ferreira, A.V., et al., Metabolic Regulation in the Induction of Trained Immunity. Semin Immunopathol, 2024. 46(3-4): p. 7.

(2) Keating, S.T., et al., Rewiring of glucose metabolism defines trained immunity induced by oxidized low-density lipoprotein. J Mol Med (Berl), 2020. 98(6): p. 819-831.

(3) Cui, L., et al., N(6)-methyladenosine modification-tuned lipid metabolism controls skin immune homeostasis via regulating neutrophil chemotaxis. Sci Adv, 2024. 10(40): p. eadp5332.

(4) Yu, W., et al., One-Carbon Metabolism Supports S-Adenosylmethionine and Histone Methylation to Drive Inflammatory Macrophages. Mol Cell, 2019. 75(6): p. 1147-1160 e5.

(5) Arifin, W.N. and W.M. Zahiruddin, Sample Size Calculation in Animal Studies Using Resource Equation Approach. Malays J Med Sci, 2017. 24(5): p. 101-105.

(6) Cheng, S.C., et al., mTOR- and HIF-1α-mediated aerobic glycolysis as metabolic basis for trained immunity. Science, 2014. 345(6204): p. 1250684.

(7) Keating, S.T., et al., The Set7 Lysine Methyltransferase Regulates Plasticity in Oxidative Phosphorylation Necessary for Trained Immunity Induced by β-Glucan. Cell Rep, 2020. 31(3): p. 107548.

(8) John, S.P., et al., Small-molecule screening identifies Syk kinase inhibition and rutaecarpine as modulators of macrophage training and SARS-CoV-2 infection. Cell Rep, 2022. 41(1): p. 111441.

(9) Glant, T.T., et al., Differentially expressed epigenome modifiers, including aurora kinases A and B, in immune cells in rheumatoid arthritis in humans and mouse models. Arthritis Rheum, 2013. 65(7): p. 1725-35.

(10) Jeljeli, M.M. and I.E. Adamopoulos, Innate immune memory in inflammatory arthritis. Nat Rev Rheumatol, 2023. 19(10): p. 627-639

(11) Ferreira, A.V., et al., Fatty acid desaturation and lipoxygenase pathways support trained immunity. Nat Commun, 2023. 14(1): p. 7385.

(12) Ding, L., et al., Aurora kinase a regulates m1 macrophage polarization and plays a role in experimental autoimmune encephalomyelitis. Inflammation, 2015. 38(2): p. 800-11.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

This fundamental study identifies a new mechanism that involves a mycobacterial nucleomodulin manipulation of the host histone methyltransferase COMPASS complex to promote infection. Although other intracellular pathogens are known to manipulate histone methylation, this is the first report demonstrating the specific targeting of the COMPASS complex by a pathogen. The rigorous experimental design using state-of-the art bioinformatic analysis, protein modeling, molecular and cellular interaction, and functional approaches, culminating with in vivo infection modeling, provides convincing, unequivocal evidence that supports the authors' claims. This work will be of particular interest to cellular microbiologists working on microbial virulence mechanisms and effectors, specifically nucleomodulins, and cell/cancer biologists that examine COMPASS dysfunction in cancer biology. 

Strengths: 

(1) The strengths of this study include the rigorous and comprehensive experimental design that involved numerous state-of-the-art approaches to identify potential nucleomodulins, define molecular nucleomodulin-host interactions, cellular nucleomodulin localization, intracellular survival, and inflammatory gene transcriptional responses, and confirmation of the inflammatory and infection phenotype in a small animal model. 

(2) The use of bioinformatic, cellular, and in vivo modeling that are consistent and support the overall conclusions is a strength of the study. In addition, the rigorous experimental design and data analysis, including the supplemental data provided, further strengthen the evidence supporting the conclusions. 

Weaknesses: 

(1) This work could be stronger if the MgdE-COMPASS subunit interactions that negatively impact COMPASS complex function were better defined. Since the COMPASS complex consists of many enzymes, examining the functional impact on each of the components would be interesting. 

We thank the reviewer for this insightful comment. A biochemistry assays could be helpful to interpret the functional impact on each of the components by MgdE interaction. However, the purification of the COMPASS complex could be a hard task itself due to the complexity of the full COMPASS complex along with its dynamic structural properties and limited solubility. 

(2) Examining the impact of WDR5 inhibitors on histone methylation, gene transcription, and mycobacterial infection could provide additional rigor and provide useful information related to the mechanisms and specific role of WDR5 inhibition on mycobacterial infection. 

We thank the reviewer for the comment. A previous study showed that WIN-site inhibitors, such as compound C6, can displace WDR5 from chromatin, leading to a reduction in global H3K4me3 levels and suppression of immune-related gene expression (Hung et al., Nucleic Acids Res, 2018; Bryan et al., Nucleic Acids Res, 2020). These results closely mirror the functional effects we observed for MgdE, suggesting that MgdE may act as a functional mimic of WDR5 inhibition. This supports our proposed model in which MgdE disrupts COMPASS activity by targeting WDR5, thereby dampening host pro-inflammatory responses.

(3) The interaction between MgdE and COMPASS complex subunit ASH2L is relatively undefined, and studies to understand the relationship between WDR5 and ASH2L in COMPASS complex function during infection could provide interesting molecular details that are undefined in this study. 

We thank the reviewer for the comment. In this study, we constructed single and multiple point mutants of MgdE at residues S⁸⁰, D²⁴⁴, and H²⁴⁷ to identify key amino acids involved in its interaction with ASH2L (Figure 5A and B; Figure S5). However these mutations did not interrupt the interaction with MgdE, suggesting that more residues are involved in the interaction.

ASH2L and WDR5 function cooperatively within the WRAD module to stabilize the SET domain and promote H3K4 methyltransferase activity with physiological conditions (Couture and Skiniotis, Epigenetics, 2013; Qu et al., Cell, 2018; Rahman et al., Proc Natl Acad Sci U S A, 2022). ASH2L interacts with RbBP5 via its SPRY domain, whereas WDR5 bridges MLL1 and RbBP5 through the WIN and WBM motifs (Chen at al., Cell Res, 2012; Park et al., Nat Commun, 2019). The interaction status between ASH2L and WDR5 during mycobacterial infection could not be determined in our current study. 

(4) The AlphaFold prediction results for all the nuclear proteins examined could be useful. Since the interaction predictions with COMPASS subunits range from 0.77 for WDR5 and 0.47 for ASH2L, it is not clear how the focus on COMPASS complex over other nuclear proteins was determined.  

We thank the reviewer for the comment. We employed AlphaFold to predict the interactions between MgdE and the major nuclear proteins. This screen identified several subunits of the SET1/COMPASS complex as high-confidence candidates for interaction with MgdE (Supplementary Figure 4A). This result is consistent with a proteomic study by Penn et al. which reported potential interactions between MgdE and components of the human SET1/COMPASS complex based on affinity purification-mass spectrometry analysis (Penn et al., Mol Cell, 2018).

Reviewer #2 (Public review): 

Summary: 

The manuscript by Chen et al addresses an important aspect of pathogenesis for mycobacterial pathogens, seeking to understand how bacterial effector proteins disrupt the host immune response. To address this question, the authors sought to identify bacterial effectors from M. tuberculosis (Mtb) that localize to the host nucleus and disrupt host gene expression as a means of impairing host immune function. 

Strengths: 

The researchers conducted a rigorous bioinformatic analysis to identify secreted effectors containing mammalian nuclear localization signal (NLS) sequences, which formed the basis of quantitative microscopy analysis to identify bacterial proteins that had nuclear targeting within human cells. The study used two complementary methods to detect protein-protein interaction: yeast two-hybrid assays and reciprocal immunoprecipitation (IP). The combined use of these techniques provides strong evidence of interactions between MgdE and SET1 components and suggests that the interactions are, in fact, direct. The authors also carried out a rigorous analysis of changes in gene expression in macrophages infected with the mgdE mutant BCG. They found strong and consistent effects on key cytokines such as IL6 and CSF1/2, suggesting that nuclear-localized MgdE does, in fact, alter gene expression during infection of macrophages. 

Weaknesses: 

There are some drawbacks in this study that limit the application of the findings to M. tuberculosis (Mtb) pathogenesis. The first concern is that much of the study relies on ectopic overexpression of proteins either in transfected non-immune cells (HEK293T) or in yeast, using 2-hybrid approaches. Some of their data in 293T cells is hard to interpret, and it is unclear if the protein-protein interactions they identify occur during natural infection with mycobacteria. The second major concern is that pathogenesis is studied using the BCG vaccine strain rather than virulent Mtb. However, overall, the key findings of the paper - that MgdE interacts with SET1 and alters gene expression are well-supported. 

We thank the reviewer for the comment. We agree that the ectopic overexpression could not completely reflect a natural status, although these approaches were adopted in many similar experiments (Drerup et al., Molecular plant, 2013; Chen et al., Cell host & microbe, 2018; Ge et al., Autophagy, 2021). Further, the MgdE localization experiment using Mtb infected macrophages will be performed to increase the evidence in the natural infection.

We agree with the reviewer that BCG strain could not fully recapitulate the pathogenicity or immunological complexity of M. tuberculosis infection.  We employed BCG as a biosafe surrogate model since it was acceptable in many related studies (Wang et al., Nat Immunol, 2025; Wang et al., Nat Commun, 2017; Péan et al., Nat Commun, 2017; Li et al., J Biol Chem, 2020). 

Reviewer #3 (Public review): 

In this study, Chen L et al. systematically analyzed the mycobacterial nucleomodulins and identified MgdE as a key nucleomodulin in pathogenesis. They found that MgdE enters into host cell nucleus through two nuclear localization signals, KRIR^108-111 and RLRRPR^300-305, and then interacts with COMPASS complex subunits ASH2L and WDR5 to suppress H3K4 methylation-mediated transcription of pro-inflammatory cytokines, thereby promoting mycobacterial survival. This study is potentially interesting, but there are several critical issues that need to be addressed to support the conclusions of the manuscript.

(1) Figure 2: The study identified MgdE as a nucleomodulin in mycobacteria and demonstrated its nuclear translocation via dual NLS motifs. The authors examined MgdE nuclear translocation through ectopic expression in HEK293T cells, which may not reflect physiological conditions. Nuclear-cytoplasmic fractionation experiments under mycobacterial infection should be performed to determine MgdE localization. 

We thank the reviewer for the comment. The MgdE localization experiment using Mtb infected macrophages will be performed.

(2) Figure 2F: The authors detected MgdE-EGFP using an anti-GFP antibody, but EGFP as a control was

We thank the reviewer for pointing this out. The new uncropped blots containing the EGFP band will be provided in Supplementary Information.

(3) Figure 3C-3H: The data showing that the expression of all detected genes in 24 h is comparable to that in 4 h (but not 0 h) during WT BCG infection is beyond comprehension. The issue is also present in Figure 7C, Figure 7D, and Figure S7. Moreover, since Il6, Il1β (proinflammatory), and Il10 (anti-inflammatory) were all upregulated upon MgdE deletion, how do the authors explain the phenomenon that MgdE deletion simultaneously enhanced these gene expressions? 

We thank the reviewer for the comment. A relative quantification method was used in our qPCR experiments to normalize the WT expression levels in Figure 3C–3H, Figure 7C, 7D, and Figure S7. 

The concurrent induction of both types of cytokines likely represents a dynamic host strategy to fine-tune immune responses during infection. This interpretation is supported by previous studies (Podleśny-Drabiniok et al., Cell Rep, 2025; Cicchese et al., Immunological Reviews, 2018).

(4) Figure 5: The authors confirmed the interactions between MgdE and WDR5/ASH2L. How does the interaction between MgdE and WDR5 inhibit COMPASS-dependent methyltransferase activity? Additionally, the precise MgdE-ASH2L binding interface and its functional impact on COMPASS assembly or activity require clarification. 

We thank the reviewer for this insightful comment. We cautiously speculate that the MgdE interaction inhibits COMPASS-dependent methyltransferase activity by interfering with the integrity and stability of the COMPASS complex. Accordingly, we have incorporated the following discussion into the revised manuscript (Lines 298-310):

“The COMPASS complex facilitates H3K4 methylation through a conserved assembly mechanism involving multiple core subunits. WDR5, a central scaffolding component, interacts with RbBP5 and ASH2L to promote complex assembly and enzymatic activity (Qu et al., 2018; Wysocka et al., 2005). It also recognizes the WIN motif of methyltransferases such as MLL1, thereby anchoring them to the complex and stabilizing the ASH2L-RbBP5 dimer (Hsu et al., Cell, 2018). ASH2L further contributes to COMPASS activation by interacting with both RbBP5 and DPY30 and by stabilizing the SET domain, which is essential for efficient substrate recognition and catalysis (Qu et al., Cell, 2018; Park et al., Nat Commun, 2019). Our work shows that MgdE binds both WDR5 and ASH2L and inhibits the methyltransferase activity of the COMPASS complex. Site-directed mutagenesis revealed that residues D²²⁴ and H²⁴⁷ of MgdE are critical for WDR5 binding, as the double mutant MgdE-D²²⁴A/H²⁴⁷ A fails to interact with WDR5 and shows diminished suppression of H3K4me3 levels (Figure 5D).”

Regarding the precise MgdE-ASH2L binding interface, we attempted to identify the key interaction site by introducing point mutations into ASH2L. However, these mutations did not disrupt the interaction (Figure 5A and B; Figure S5), suggesting that more residues are involved in the interaction.

(5) Figure 6: The authors proposed that the MgdE-regulated COMPASS complex-H3K4me3 axis suppresses pro-inflammatory responses, but the presented data do not sufficiently support this claim. H3K4me3 inhibitor should be employed to verify cytokine production during infection. 

We thank the reviewer for the comment. We have now revised the description in lines 824825 “MgdE may suppresses COMPASS complex-mediated inflammatory responses by inhibiting H3K4 methylation” and in lines 219-220 "MgdE suppresses host inflammatory responses probably by inhibition of COMPASS complex-mediated H3K4 methylation." 

(6) There appears to be a discrepancy between the results shown in Figure S7 and its accompanying legend. The data related to inflammatory responses seem to be missing, and the data on bacterial colonization are confusing (bacterial DNA expression or CFU assay?). 

We thank the reviewer for the comment. Figure S7 specifically addresses the effect of MgdE on bacterial colonization in the spleens of infected mice, which was assessed by quantitative PCR rather than by CFU assay. 

We have now revised the legend of Figure S7 as below (Lines 934-938):

“MgdE facilitates bacterial colonization in the spleens of infected mice. Bacterial colonization was assessed in splenic homogenates from infected mice (as described in Figure 7A) by quantifying bacterial DNA using quantitative PCR at 2, 14, 21, 28, and 56 days post-infection.”

(7) Line 112-116: Please provide the original experimental data demonstrating nuclear localization of the 56 proteins harboring putative NLS motifs. 

We thank the reviewer for the comment. We will provide this data in the new Supplementary Table 2.

• lymphocytosis >10^8 to 10^9/L is suggestive in young children.
• gold standard: culture of nasopharyngeal secretions. TAT ~5days
• multi target DNA PCR has primers for Bordetella pertussis, Bordetella parapertussis, Bordetella holmesii.
• best specimen: nasopharyngeal aspirate.

procedure: - fine flexible plastic catheter is used. - it is attached to a 10mL syringe. - passed into the nasopharynx. - withdrawn with gentle suction.

appropriate medium: - bordet-gengou - regan Lowe.

precautions while collection: - B. pertussis is susceptible to drying. - inoculate the sample onto the media immediately or. - flush the catheter with phosphate buffered saline solution. - if rayon or Dacron swabs are used - utilise transport medium - Regan-Lowe charcoal medium.

if untreated nasopharyngeal cultures are positive for ~3 weeks.

if treated - the become negative by day 5

much of the period of organism detection falls into the catarrhal phase.

in patients symptomatic for >4 weeks: - serologic diagnosis is preferred. - EIA detecting IgA IgG to: - pertussis toxin - filamentous haemagglutinin - pertactin - fimbriae.

two or four fold raise in the titres are suggestive of pertussis

cross reactivity can occur between few antigens such as filamentous heamagglutinin an pertectin

particularly in the outbreak setting antibodies to pertussis toxin are being standardised

Author response:

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

Reviewer #1 (Public review):

Summary:

The authors aim to explore the effects of the electrogenic sodium-potassium pump (Na⁺/K⁺-ATPase) on the computational properties of highly active spiking neurons, using the weakly-electric fish electrocyte as a model system. Their work highlights how the pump's electrogenicity, while essential for maintaining ionic gradients, introduces challenges in neuronal firing stability and signal processing, especially in cells that fire at high rates. The study identifies compensatory mechanisms that cells might use to counteract these effects, and speculates on the role of voltage dependence in the pump's behavior, suggesting that Na⁺/K⁺-ATPase could be a factor in neuronal dysfunctions and diseases

Strengths:

(1) The study explores a less-examined aspect of neural dynamics-the effects of (Na⁺/K⁺-ATPase) electrogenicity. It offers a new perspective by highlighting the pump's role not only in ion homeostasis but also in its potential influence on neural computation.

(2) The mathematical modeling used is a significant strength, providing a clear and controlled framework to explore the effects of the Na+/K+-ATPase on spiking cells. This approach allows for the systematic testing of different conditions and behaviors that might be difficult to observe directly in biological experiments.

(3) The study proposes several interesting compensatory mechanisms, such as sodium leak channels and extracellular potassium buffering, which provide useful theoretical frameworks for understanding how neurons maintain firing rate control despite the pump's effects.

Weaknesses:

(1) While the modeling approach provides valuable insights, the lack of experimental data to validate the model's predictions weakens the overall conclusions.

(2) The proposed compensatory mechanisms are discussed primarily in theoretical terms without providing quantitative estimates of their impact on the neuron's metabolic cost or other physiological parameters.

We thank the reviewer for their concise and accurate summary and appreciate the constructive feedback on the article’s strengths and weaknesses. Experimental work is beyond the scope of our modeling-based study. However, we would like our work to serve as a framework for future experimental studies into the role of the electrogenic pump current (and its possible compensatory currents) in disease, and its role in evolution of highly specialized excitable cells (such as electrocytes).

Quantitative estimates of metabolic costs in this study are limited to the ATP that is required to fuel the pump. By integrating the net pump current over time and dividing by one elemental charge, one can find the rate of ATP that is consumed by the Na⁺/K⁺pump for either compensatory mechanism. The difference in net pump current is thus proportional to ATP consumption, which allows for a direct comparison of the cost efficiency of the Na⁺/K⁺ pump for each proposed compensatory mechanism. The Na⁺/K⁺ pump is, however, not the only ATP-consuming element in the electrocyte, and some of the compensatory mechanisms induce other costs related to cell

‘housekeeping’ or presynaptic processes. We now added a section in the appendix titled

‘Considerations on metabolic costs of compensatory mechanisms’ (section 11.4), where we provide ballpark estimates for the influence of the compensatory mechanisms on the total metabolic costs of the cell and membrane space occupation. Although we argue that according these estimates, the impact of discussed compensatory mechanisms could be significant, due to the absence of more detailed experimental quantification, a plausible quantitative cost approximation on the whole cell level remains beyond the scope of this article.

Reviewer #1 (Recommendations for the authors):

(1)  For the f-I curves in Figures 1 and 6, the firing rate increases as the input current increases. I am curious to know: (a) whether the amplitudes of the action potentials (APs) vary with increased input current; (b) whether the waveform of APs (such as in Fig. 1I) transitions into smaller amplitude oscillations at higher input currents; and (c) if the waveform does change at higher input currents, how do the "current contributions," "current," and "ion exchanges per action potential" in Figures 1HJ and 6AB respond?

To fully answer these questions, we added a supplemental figure with accompanied text in section 11.1 (Fig. A1). We also added a reference to this figure in the main text (section 4.1). Here, it is shown that, as previously illustrated in [1], AP amplitude decreases when the input current increases (Fig. A1 A, left). This effect remains upon addition of either a pump with constant pump rate and co-expressed sodium leak channels (Fig. A1 A, center), or a voltage-dependent pump (Fig. A1 A, right). Interestingly, even though the shape of the current contributions (Fig. A1 B) and the APs (Fig. A1 C) look very different for low (Fig. A1 C, top) and high inputs (Fig. A1 C, bottom), the total sodium and potassium displacement per AP, and thus the pump rate, is roughly the same (Fig. A1 D). Under the assumption that voltage-gated sodium channel (NaV) expression is adjusted to facilitate fixed-AP amplitudes, however, (as in [1]) more NaV channels would be expressed in fish with higher synaptic drives. This would then result in an additional sodium influx per AP and result in higher energetic requirements per AP for electrocytes with higher firing rates (also shown in [1]).

(2) Could the authors clarify what the vertical dashed line represents in Figures 1B and 1F? Does it correspond to an input current of 0.63uA?

(Reviewer comment refers to Fig. 1C and 1F in new version): Yes, it corresponds to the input current that is also used in figures 1D and 1G. We clarified this by adding an additional tick label on the x-axis in 1F. The current input of 0.63uA was chosen as a representative input for this cell as follows: we first modeled an electrocyte with a periodic synaptic drive as in [1]. The frequency of this drive was set to 400 Hz, which is an intermediate value in the range of reported EODfs (and thus presumably pacemaker firing rates) of 200-600Hz [2]. Then, acetylcholine receptor currents I_AChRNa and I_AChRNa were summed and averaged to obtain the average input current of 0.63uA. This is now also explained in new Methods section 6.2.1.

(3) What input current was used for Figures 1H, 1I, and 1J?

Response: In a physiological setting, where the electrocyte is electrochemically coupled to the pacemaker nucleus, stimulation of the electrocyte occurs through neurotransmitter release in the synaptic cleft, which then leads to the opening of acetylcholine receptor channels. As figures 1H-J concern different ion fluxes, we aimed to also include currents stemming from acetylcholine receptor channels. We therefore did not stimulate the electrocyte with a constant input current as in Fig. 1C and F, but simulated elevated constant neurotransmitter levels in the synaptic cleft, which then leads to elevated acetylcholine receptor currents. In the model, this neurotransmitter level, or ‘synaptic drive’ is represented by parameter syn_clamp. A physiologically relevant value for syn_clamp was deduced by averaging the synaptic drive during a 400 Hz pacemaker stimulus. This is now also explained in new Methods section 6.2.1.

(4) In Figure 4A, there is a slight delay between the PN spikes (driver) and the EO (receiver), and no EO spikes occur without PN spikes. However, the firing rate of EO (receiver) appears to decrease before the chirp initiations in Fig 4B; and this delay seems to disappear in Fig 4C. Could the authors explain these observations?

As shown in the bottom right of figure 4A, when plotting the instantaneous firing rate as one over the inter-spike-interval (1/ISI), the firing rate of a cell is only plotted at the end of every ISI. Therefore, even though the PN drives the electrocyte and thus spikes earlier in time than the electrocyte, when it initiates chirps, these will only be plotted as an instantaneous firing rate at the end of the chirp. If the electrocyte fires spontaneously within this chirp, its instantaneous firing rate will appear earlier in time than the initiation of the chirp of the PN. The PN did, however, initiate the chirp before that and causality between the PN and electrocyte is not disturbed.

(5) Regarding Figure 6, could the authors specify the input current used in Figures 6A and 6B?

Figure 6A and 6B have the same synaptic drive as Fig. 1 H, I and J (syn_clamp=0.13).

(6) In Section 6, I would recommend that the authors provide a table of parameters and their corresponding values for clarity.

Thank you for your suggestion. We now reorganized the method section and added two tables with parameters for clarity. Table 1 (see Methods 6.1) includes all parameters that differ from the parameters reported in [1], and parameters that arise from the additionally modeled equations to simulate ion concentration dynamics and pump. We also added the parameters used to simulate the different stimulus protocols (and corresponding tuned parameters) that are presented in the article in Table 2 (see Methods 6.2).

Reviewer #2 (Public review):

Summary:

The paper 'The electrogenicity of the Na⁺/K⁺-ATPase poses challenges for computation in highly active spiking cells' by Weerdmeester, Schleimer, and Schreiber uses computational models to present the biological constraints under which electrocytes-specialized highly active cells that facilitate electro-sensing in weakly electric fish-may operate. The authors suggest potential solutions these cells could employ to circumvent these constraints.

Electrocytes are highly active or spiking (greater than 300Hz) for sustained periods (for minutes to hours), and such activity is possible due to an influx of sodium and efflux of potassium ions into these cells for each spike. This ion imbalance must be restored after each spike, which in electrocytes, as with many other biological cells, is facilitated by the Na-K pumps at the expense of biological energy, i.e., ATP molecules. For each ATP molecule the pump uses, three positively charged sodium ions from the intracellular space are exchanged for two positively charged potassium ions from the extracellular volume. This creates a net efflux of positive ions into the extracellular space, resulting in hyperpolarized potentials for the cell over time. This does not pose an issue in most cells since the firing rate is much slower, and other compensatory mechanisms and other pumps can effectively restore the ion imbalances. In electrocytes of weakly electric fish, however, that operate under very different circumstances, the firing rate is exceptionally high. On top of this, these cells are also involved in critical communication and survival behaviors, emphasizing their reliable functioning.

In a computation model, the authors test four increasingly complex solutions to the problem of counteracting the hyperpolarized states that occur due to continuous NaK pump action to sustain baseline activity. First, they propose a solution for a well-matched Na leak channel that operates in conjunction with the NaK pump, counteracting the hyperpolarizing states naturally. Additionally, their model shows that when such an orchestrated Na leak current is not included, quick changes in the firing rates could have unexpected side effects. Secondly, they study the implication of this cell in the context of chirps - a means of communication between individual fishes. Here, an upstream pacemaking neuron entrains the electrocyte to spike, which ceases to produce a so-called chirp - a brief pause in the sustained activity of the electrocytes. In their model, the authors show that it is necessary to include the extracellular potassium buffer to have a reliable chirp signal. Thirdly, they tested another means of communication in which there was a sudden increase in the firing rate of the electrocyte followed by a decay to the baseline. For reliable occurrence of this, they emphasize that a strong synaptic connection between the pacemaker neuron and the electrocyte is warranted. Finally, since these cells are energy-intensive, they hypothesize that electrocytes may have energyefficient action potentials, for which their NaK pumps may be sensitive to the membrane voltages and perform course correction rapidly.

Strengths:

The authors extend an existing electrocyte model (Joos et al., 2018) based on the classical Hodgkin and Huxley conductance-based models of Na and K currents to include the dynamics of the NaK pump. The authors estimate the pump's properties based on reasonable assumptions related to the leak potential. Their proposed solutions are valid and may be employed by weakly electric fish. The authors explore theoretical solutions that compound and suggest that all these solutions must be simultaneously active for the survival and behavior of the fish. This work provides a good starting point for exploring and testing in in vivo experiments which of these proposed solutions the fish use and their relative importance.

Weaknesses:

The modeling work makes assumptions and simplifications that should be listed explicitly. For example, it assumes only potassium ions constitute the leak current, which may not be true as other ions (chloride and calcium) may also cross the cell membrane. This implies that the leak channels' reversal potential may differ from that of potassium. Additionally, the spikes are composed of sodium and potassium currents only and no other ion type (no calcium). Further, these ion channels are static and do not undergo any post-translational modifications. For instance, a sodium-dependent potassium pump could fine-tune the potassium leak currents and modulate the spike amplitude (Markham et al., 2013).

This model considers only NaK pumps. In many cell types, several other ion pumps/exchangers/symporters are simultaneously present and actively participate in restoring the ion gradients. It may be true that only NaK pumps are expressed in the weakly electric fish Eigenmannia virescens. This limits the generalizability of the results to other cell types. While this does not invalidate the results of the present study, biological processes may find many other solutions to address the non-electroneutral nature of the NaK pump. For example, each spike could include a small calcium ion influx that could be buffered or extracted via a sodium-calcium exchanger.

Finally, including testable hypotheses for these computational models would strengthen this work.

We thank the reviewer for the detailed summary and the identified weaknesses according to which we improved our article. Our model assumptions and simplifications are now mentioned in more detail in the introduction of the article (section 3), and justified in the Methods (section 6.1).

Furthermore, we added a discussion section (section 5.1) where we outline the conditions under which the present study can be extended to other cell types. We now also state more clearly that the pump current will be present for any excitable cell with significant sodium flux (assuming that the NaK pump carries out the majority of its active transport), but that compensatory mechanisms (if employed at all in a particular cell) could also be implemented via other ionic currents and transporters. We furthermore now highlight the testable hypotheses that we put forward with our computational study on the weakly electric fish electrocyte more explicitly in the first paragraph of the discussion.

Reviewer #2 (Recommendations for the authors):

Main text

Please explicitly state this model's assumptions in the introduction and elaborate on them in the discussion if necessary. For example, some assumptions that I find relevant to mention are: - The Na and K channels are classic HH conductance-based channels, with no post-translational modifications or beta subunit modifications as seen in other high-frequency firing cells (10.1523/JNEUROSCI.23-12-04899.2003).

Neither calcium nor chloride ions are considered in the spike generation. Nor are Na-dependent K channels (10.1152/jn.00875.2012).

Only the Na-K pump (and not the Na-Ca exchanger, Ca-pump, or Cl pumps) is modeled,

Calmodulin, which can buffer calcium, is highly expressed in electric eels, but it is not considered. If some of these assumptions have valid justifications in weakly electric fish electrocytes, please state so with the citations. I recognize that including these in your models is beyond the scope of the current paper.

We thank the reviewer for pointing out this issue. We now specified in the introduction that the model only contains sodium and potassium ions and only classic HH conductance-based channels. We there also explicitly specify the details on the Na⁺/K⁺-ATPase: it is the only active transporter in this model, thus solely responsible for maintaining ionic homeostasis; its activity is only modulated by intracellular sodium and extracellular potassium concentrations. In the discussion (6.1), we now elaborate on how ion-channel-related aspects (i.e., the addition of resurgent Na⁺ or Na⁺ -dependent K⁺ channels), additional ion fluxes (including some not relevant for the electrocyte but for other excitable cells), and additional active transporters and pumps would influence the results presented in the article.

In addition, there might be other factors that the authors and the reviewers have yet to consider. The model is a specific case study about the weakly electric fish electrocyte with high-frequency firing. It is almost guaranteed that biology will find other compensatory ways in different cell types, systems, and species (auditory nerve, for example). Given this, it would be prudent to use phrases such as 'this model suggests,' 'perhaps,' 'could,' 'may,' and 'eludes to,' etc., to accommodate other possible solutions to ion homeostasis in rapidly spiking neurons. The solutions the authors are proposing are some of many.

We rephrased some of the statements to highlight more the hypothetical nature of the compensatory mechanisms in specific cells and to draw attention to the fact that there can be many more such factors. This fact is now also explicitly mentioned in discussion section 5.2.

Figures

Some of my comments on the figures are stylistic, others are to improve clarity, and some are critical for accuracy.

The research problem concerns weakly electric fish E. virescens. I suggest introducing a picture of an electric fish in the beginning (such as that in Figure 3, but not exactly; see specific comments on this fish figure) along with a schema of the research question. 

We agree, and added an overview schema in Fig. 1A.

Font sizes change between the panels in all the figures. Please maintain consistency. The figure panel titles and axis labels should start with a capital letter.

Thank you for pointing this out, both issues have been resolved in the new version of the article.

Figure 1:

Please rearrange the figure - BCFG belong together and should appear in the same order. The x-axis labels could be better placed.

Consider using fewer pump current f-I curves (B, D, E, F). Five is sufficient to make the point. Having 10 curves adds to the clutter. The placement of the color bar could be better. Similarly, the placement of the panel titles 'without co-expression' and 'with co-expression' and the panel labeling (BCFG) makes it confusing. The panel labels should be above the panel title.

Response (C, D, F, G in new version): We improved the layout of figure 1. Panels B, C, F, G are now C, D, F, G. We opted to include panel E before panels F and G, because it shows the coexpression mechanism before its effect on the tuning curve. We did move the colorbar, added x-axis labels to B and C, and adjusted the location of the panel labels for clarity. We also plotted fewer pump currents.

B, F: What does the dashed line indicate?

Response (C, F in new version): The dashed line indicates the input current that was used in figures 1D and 1G. We now clarified this by adding this value on the x-axis.

C: Any reason not to show the lower firing rates?

Response (B in new version): In the previous version of the article, pump currents were estimated for electrocytes that were stimulated with the mean synaptic drive that stems from periodic stimulation in the 200-600 Hz regime. We now extended the range of synaptic inputs to obtain lower (and higher) firing rates. The linear relationship between firing rate and pump current also holds for these additional firing rates.

D: There is no difference between the curves at the top and the bottom. One fills the area between the curve and the zero line; the other shows the curve itself. Please use only one of the two representations.

Response (panel I in new version): In the previous version, the difference between the plots was that one showed the absolute values of the currents (the curves), and the other plot showed the contributions of the currents to the total (area between the curves). We now only depict the current contributions.

The I and H orders can be swapped.

Thank you, they are now swapped.

The colors used for Na and K are very dull (light blue and pink).

We now use darker colors in the new version of the article.

Figure 2:

Please verify that without the synaptic input perturbations (i.e., baseline in A, D), the firing rate (B, E) and pump current (C, F) converge to the baseline. There is a noticeable drift (downward for firing rate and upward for pump currents) at the 10-second time point.

Thanks to you noticing, we identified a version mismatch in the code that estimates the pump current required for ionic homeostasis (see Methods 6.1.2). We have now corrected the code and made sure to start the simulation in the steady state so that there is no drift at baseline firing. We also used this corrected code to present tuned parameters for different stimulus protocols in Table 2 (Methods 6.2).

Figure 3:

A. The dipole orientation with respect to the fish in panel B needs to be corrected. Consider removing this as this work is not about the dipole.

This panel has been removed.

B. This figure has already been overused in multiple papers; please redraw it. Localized expressions of different pumps and ion channels are present within each electrocyte, which generates the dipole. Either show this correctly or don't at all (the subfigure pointed out by the red arrow).

This panel has been moved to Fig. 1A. We opted to remove the localized expressions.

C and D belong together; please place them next to each other. Consider introducing panel D first since it follows a similar protocol to the last figure.

Response (A in new version): Panel placement has been adjusted. We opted to maintain the order to maintain the flow of the text, but we do now combine them in one panel.

E and F are very similar in that they are swapped on the x and y axes. Either that or I have severely misunderstood something, in which case it needs to be shown better.

Response (B and C in new version): We adjusted the placement of these panels. They are not the same, panel B shows the mean of physiological periodic inputs, and figure C shows that when this mean is fed to the electrocyte, it also induces tonic firing. The range of mean currents that result from periodic synaptic stimulation in the physiological regime (panel B, y-axis) is now indicated in panel C by a grey box along the x-axis.

G. Why show the lines with double arrow ends? The curves are diverging - that's enough.

Good point, we updated this panel accordingly (now panel D).

Figure 4

Please verify the time units in these plots. Something seems amiss. B and D lower plots-perhaps this is seconds? B could use an inset box/ background gray color (t1, t2) indicating the plots of the C panel (left, right). Likewise, for D (t1, t2), connect to E (left, right).

You are right, the x-axes were supposed to be in seconds, we updated this. We indicated the relations between D-C and D-E by gray backgrounds and by adding the corresponding panel label on the x-axis.

A: Indicate the perturbation in the schematic, i.e., extracellular K buffer.

The perturbation is now indicated.

D: Even with the extracellular K buffer, there is a decay (slower than in B) of the pump current over time. Please verify (you do not have to show in your paper) that this decay saturates.

After the ten chirps are initiated, pacemaker firing goes back to baseline. In both cases (panel B and panel D), the pump current goes back to baseline after some time. With extracellular potassium buffering, this happens more slowly due to a decreased reaction speed of the pump to changes in firing rate (in comparison to the case without extracellular potassium buffer).

The decrease in reaction speed however merely delays the effects of changes in firing rates on the pump current in time. Therefore, even with an extracellular potassium buffer, when more chirps are initiated in a short period of time, the pump current can still decrease to an extent that impairs entrainment. Using the same protocol as in panel B and D, we increased the number of chirps and found that with an extracellular potassium buffer, a maximum of 13 chirps could be encoded without entrainment failure (as opposed to 2 chirps without the buffer as shown in panel B).

Figure 5

Please verify the time units in these plots, as for Figure 4. B and E lower plots-perhaps this is seconds? B could use an inset box/ background gray color (t1, t2) indicating the plots of the panels C and D. Likewise, for E (t1, t2), connect to F and G.

The time axis in this figure was indeed also in seconds, which we corrected here. The relations between plots B-C/D and E-F/G are now indicated through gray backgrounds and corresponding panel references on the x-axis.

A: Indicate the perturbation in the schematic, i.e., the synapse's strength. There is no need to include the arrow or to mention freq. rise. The placement of the time scale can be misinterpreted as a current clamp. Instead, plot it as a zoomed inset.

The arrow is removed and we now also show a zoomed inset. Also, the perturbation is now indicated.

E: Verify that the pump current in the strong synapse case already starts at 1.25

We verified this and noticed that the pump current in the strong synapse case is indeed lower than that in the weak synapse case. This is because to ensure a fair comparison for this stimulation protocol, voltage-gated sodium channel conductance was tuned to maintain a spike amplitude of 13 mV in both cases (see Methods 6.2). In this case, a weak synapse leads to a lower influx of sodium via AChR channels, but a higher influx via voltage-gated sodium channels. The total sodium influx in this case is larger than that for a stronger synapse with relatively less voltage-gated sodium currents, and thus a larger pump current. In the previous version of the article, this was wrongly commented on in the figure captions, and we removed the erroneous statement.

This is not critical, but because the R-value here can be obtained as a continuous value, it would be appropriate to show it for the whole duration of the weak and strong synapses in B and E. Maybe consider including a schema that shows how R is calculated in panel A.The caption has a typo, 'during frequency rises before (D) and after (E)'. It should be before C) and after (D) instead.

The caption typo has been corrected. The R-value for the whole duration of the weak and strong synapses in B and E is 1.000. This is because the R-value is the variance of all phase relations between the PN and the electrocyte, and for the entire duration of the stimulus protocol, there are only a few outliers in phase relations at the maxima of the frequency rises. We decided to include this R-value to show that in general, synchronization between the PN and the electrocyte is very stable. The schema that explains how R is calculated has not been included in favor of not overcrowding the figure. We did add a reference in the figure caption to the methods section in which the calculation of R is explained.

Figure 6:

A: The top and bottom plots are redundant. Use one of the two. They show the same thing. It may be better to plot Na, K, pump, and net currents on the top panels and the Na leak, which is of smaller magnitude, in a different panel.

We now only show current contributions.

B: Please change the color schema. It is barely visible on my prints.

D: Pump current, instantaneous case, is barely visible

Color schemes were adjusted.

Figure A1: It's all good.

Methods:

Please provide some internal citations for where specific equations were used in the results/figures. You do this for sections 6.2.3, referencing Figure 5 (c,d,e,g), and 6.2.4, referencing Fig 5 C-E.

There are now internal references in each methods section to where in the figures they were used. We also included a table with stimulus parameters for each figure with a stimulus protocol (Table 2).

Also, the methods could be ordered in the same order as the results are presented. Please consider if some details in the methods could be moved to the appendix.

The ordering of the methods has now been changed to separately explain the model expansions (6.1) and the stimulus protocols (6.2). Both sections are in corresponding order of the figures presented in the article. We opted to maintain all details in the methods.

6.1.1 Please cite 26 after the first line. Where was this used? In Figure 3C, 4, 5?

We added the citation. The effects of co-expressed leak channels are shown in Fig. 1 EG, and were used to compensate for pump currents at baseline firing in figures 1 D, H-J (left, with pump), 2, 4, 5, and 6 A-B (left), C (top). This is now also added to the text for clarity.

Traditionally (Hodgkin, A. L. and Huxley, A. F. (1952). J. Physiol. (Lond.), 117:500-544. Table 3; & Hodgkin, A. L. and Huxley, A. F. (1952). J. Physiol. (Lond.), 116:473-496 Table 5 and the paragraph around it), leak potential is set such that it accounts for all leak from all ions. While in your work, this potential is equal to the reversal of potassium - it need not be so in the animal. There may be leaks from other ions as well, particularly sodium and chloride. Please verify that assuming the leak reversal is the same as that of potassium (Ek, in Equation 3) does not lead to having to model Na leak currents separately.

In the original model [1], it was assumed that the reversal potential of the leak was the same as that of potassium, which contains the implicit assumption that only potassium ions contribute to the leak. In our article, we also assume that sodium ions contribute to the leak. This can be modeled by adjusting the leak reversal potential accordingly, or by adding an additional leak current that solely models the sodium leak. We opted for the latter in order to track all sodium and potassium ions separately so that ion concentration dynamics could also be modeled properly. Chloride ions were neglected in this study; in our model they do not contribute to the leak. If one were to also model chloride currents and chloride concentration dynamics, it would be beneficial to model these as an additional separate leak current.

The notation of I_pump_0 needs to be more convenient. Please consider another notation instead of the _0 (pump at baseline). Similarly for [Na⁺]_in_0 [Na⁺]_out_0 and [K⁺]_in_0 and [K+]_out_0

We changed the notation for baseline similarly to [3], with ‘0’ as a superscript instead of a subscript.

Equation 11: Please mention why AChRs do not let calcium ions through. Please cite a justification for this. If this is an assumption of the model, please state this explicitly.

The AChR channels that were found in the E. virescence electrocytes are muscle-type acetylcholine nicotinic receptors [4], which are non-selective cation channels that could indeed support calcium flux [5]. No calcium currents were, however, modeled in the original electrocyte model [1], presumably due to the lack of significant contributions of calcium currents or extracellular calcium concentrations to electrocyte action potentials of a similar weakly electric electrogenic wave-type fish Sternopygus macrurus [6].

Due to the lack of calcium currents in the original electrocyte model, and due to the limitation of this study to sodium and potassium ions, we chose not to include calcium currents stemming from AChR channels. This assumption is now explicitly stated in Methods 6.1.

Equation 12, V_in, where the intracellular volume. If possible, avoid the notation of 'V' - you already use a small v for membrane potential.

We changed the notation for volume to ‘ω’ similarly to [3]. As we previously used ω as a notation for the firing rate, we changed the notation for firing rate to ‘r’.

Equation 17: Does this have any assumptions? Would the I_AchRNa, and thus Sum(mean(I_Na))) not change depending on the synaptic drive?

The assumptions of this equations are the following (now also mentioned in Methods 6.1.2):

The sum of all sodium currents also includes sodium currents through acetylcholine channels (I_AChRNa).

All active sodium transport (from intra- to extracellular space) is carried out by the Na⁺/K⁺-ATPase, and active sodium transport through additional transporters and pumps is negligible.

The time-average of sodium currents is either taken in a tonic firing regime where the timeinterval that is averaged over is a multiple of the spiking period, nT, or if it is taken for a more variable firing regime, the size of the averaging window should be sufficiently large to properly sample all firing statistics.

Under these assumptions, Eq. 17 can be used to compute suitable pump currents for different synaptic drives (as Sum(mean(I_Na))) and thus I_pump0 indeed change with the synaptic drive, see Table 2 in Methods 6.2). 

6.2: Please rewrite the first sentence of this paragraph.

The first sentence of this paragraph, which has been moved to section 6.2.2 for improved structuring of the text, has been rewritten.

6.2.1: The text section could use a rewrite.

Please elaborate on what t_p is. If it is not time, please do not use 't.' What is p here? What are the units of the equation (22), t_p < 0.05 (?)

This section has now also been moved to 6.2.2. It has been rewritten to improve clarity and t_p has been renamed to t_pn (as it does reflect time, which is now better explained). The units have now also been added to the equation (which is now Eq. 26).

6.2.4: Please rewrite this.

This section has been rewritten (and has been moved to section 6.1.4).

Bibliography

Some references are omitted (left anonymous) or inconsistent on multiple occasions.

Thank you for pointing this out! It is now rectified.

References used for author response

(1) Joos B, Markham MR, Lewis JE, Morris CE. A model for studying the energetics of sustained high frequency firing. PLOS ONE. 2018 Apr;13:e0196508.

(2) Hopkins CD. Electric communication: Functions in the social behavior of eigenmannia virescens. Behaviour. 1974;50(3-4):270–304.

(3) Hübel N, Dahlem MA. Dynamics from seconds to hours in hodgkin-huxley model with time-dependent ion concentrations and buer reservoirs. PLoS computational biology.ff2014;10(12):e1003941.

(4) BanY, Smith BE, Markham MR. A highly polarized excitable cell separates sodium channels from sodium-activated potassium channels by more than a millimeter. Journal of neurophysiology. 2015; 114(1):520–30.

(5) Vernino S, Rogers M, Radcliffe KA, Dani JA. Quantitative measurement of calcium flux through muscle and neuronal nicotinic acetylcholine receptors. Journal of Neuroscience. 1994;14(9):5514-5524.

(6) Ferrari M, Zakon H. Conductances contributing to the action potential of sternopygus electro-cytes. Journal of Comparative Physiology A. 1993;173:281–92.

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Reply to the reviewers

Below is a point-by-point response to reviewers concerns.

Main changes are colored in red in the revised manuscript.

Reviewer #1 (Significance (Required)):

General assessment:

This study provides a valuable computational framework for investigating the dynamic interplay between DNA replication and 3D genome architecture. While the current implementation focuses on Saccharomyces cerevisiae, whose genome organization differs significantly from mammalian systems.

Advance: providing the first in vivo experimental evidence in investigating the role(s) of Cohesin and Ctf4 in the coupling of sister replication forks.

Audience: broad interests; including DNA replication, 3D genome structure, and basic research

Expertise: DNA replication and DNA damage repair within the chromatin environment.

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

By developing a new genome-wide 3D polymer simulation framework, D'Asaro et al. investigated the spatiotemporal interplay between DNA replication and chromatin organization in budding yeast: (1) The simulations recapitulate fountain-like chromatin patterns around early replication origins, driven by colocalized sister replication forks. These findings align with Repli-HiC observations in human and mouse cells, yet the authors advance the field by demonstrating that these patterns are independent of Cohesin and Ctf4, underscoring replication itself as the primary driver. (2) Simulations reveal a replication "wave" where forks initially cluster near the spindle pole body (SPB) and redistribute during S-phase. While this spatial reorganization mirrors microscopy-derived replication foci (RFis), discrepancies in cluster sizes compared to super-resolution data suggest unresolved mechanistic nuances. (3) Replication transiently reduces chromatin mobility, attributed to sister chromatid intertwining rather than active forks.

This work bridges replication timing, 3D genome architecture, and chromatin dynamics, offering a quantitative framework to dissect replication-driven structural changes. This work provides additional insights into how replication shapes nuclear organization and vice versa, with implications for genome stability and regulation.

We thank Reviewer 1 for her/his enthusiasm and her/his comments that help us to greatly improve the manuscript.

However, the following revisions could strengthen the manuscript:

Major:

Generalizability to Other Species While the model successfully recapitulates yeast replication, its applicability to larger genomes (e.g., mammals) remains unclear. Testing the model against (Repli-HiC/ in situ HiC, and Repli-seq) data from other eukaryotes (particularly in mammalian cells) could enhance its broader relevance.

We agree with the reviewer that testing the model in higher eukaryotes would be highly informative. The availability of Repli-HiC on one hand and higher resolution microscopy on the other could enable insightful quantitative analyses. With our formalism, it is in principle already possible to capture realistic 1D replication dynamics as the integrated mathematical formalism (by Arbona et al. ref. [63]) was already used to model human genome S-phase. In addition, the formalism developed for chain duplication is generic and can be contextualized to any species. However, when addressing the problem in 3D, we would likely require including other crucial structural features such as TADs or compartments. Such a model would require an extensive characterization worthy of its own publication. These considerations are now mentioned in the Discussion as exciting future perspectives (Page 17).

On the other hand, we would like to highlight that, while very minimal in many aspects, our model includes many layers of complexity (explicit replication, different forks interactions, stochastic 1D replication dynamics, physical constraints at the nuclear level). In addition, addressing this problem in budding yeast offers the great advantage of simultaneously capturing at the same time both the local and global spatio-temporal properties of DNA replication and to focus first only on those aspects and not on the interplay with other mechanisms like A/B compartmentalization (absent in yeast) that may add confusions in the data analysis and comparison with experimental data . Studying such an interplay is a very important and challenging question that, we believe, goes beyond the scope of the present work.

Validation with Repli-HiC or Time-Resolved Techniques

The Hi-C data in early S-phase supports the model, but the intensity of replication-specific chromatin interactions is faint, which could be further validated using Repli-HiC, which captures interactions around replication forks. Alternatively, ChIA-PET or HiChIP targeting core component(s) (eg. PCNA or GINS) of replisomes may also solidify the coupling of sister replication forks.

We thank the reviewer for the suggestion. Unfortunately, corroborating our HiC results using Repli-HiC or HiChIP would require developing and adapting the protocols to budding yeast which is well beyond the scope of this work mainly focused on computational modelling. In addition, we believe that the signature found in our Hi-C data is clear and significant enough to demonstrate the effect.

However, we included in the Discussion (Page 15) a more detailed description on how our work compares with the Repli-HiC study in mammals. In particular, we added a new supplementary figure (new Fig. S23) where we discuss our prediction on how Repli-HiC maps would appear in yeast in both scenarios of sister-forks interaction. Interestingly, we find that:

1) Fountain signals are strongly enhanced when sister forks interact.

2) Only mild replication dependent enrichment is detected when diverging forks do not interact.

These two results imply that disrupting putative sister-forks interaction would have a drastic effect on Repli-HiC if compared to HiC.

Interactions Between Convergent Forks

The study focuses on sister-forks but overlooks convergent forks (forks moving toward each other from adjacent origins), whose coupling has been observed in Repli-HiC. Could the simulation detect the coupling of convergent fork dynamics?

We thank the reviewer for this suggestion. We included in our Hi-C analysis aggregate plots around termination sites. Interestingly, no clear signature of coupling between convergent forks was detected (such as type II fountains in mammals) in vivo and in silico. Similarly, from visual inspection of individual termination sites, no fountains were clearly observed. These results can be found in the new Fig. S24 and possible mechanistic explanations are described more in detail in the Discussion (Page 15).

Unexpected Increase in Fountain Intensity in Cohesin/Ctf4 Knockouts.

In Fig.3A, a schematic illustrating the cell treatment would improve clarity. In Sccl- and Ctf4-depleted cells, fountain signals persist or even intensify (Fig. 3A). This counterintuitive result warrants deeper investigation. Could the authors provide any suggestions or discussions? Potential explanations may include:

Compensatory mechanisms (e.g., other replisome proteins stabilizing sister-forks).

Altered chromatin mobility in mutants, enhancing Hi-C signal resolution.

Artifacts from incomplete depletion (western blots for Sccl/Ctf4 levels should be included).

A scheme illustrating the experimental protocol for degron systems (CDC45-miniAID & SCC1-V5-AID) with the corresponding western blots and cell-cycle progression are shown in Fig. S26. Note that for Ctf4, we are using a KO cell line where the gene was deleted.

We do agree with the reviewer that there exist several possible explanations explaining the differences between WT fountains and those observed in mutants. In the revised manuscript, we discussed some of them in Section 2 II B (Page 8):

(1) As already suggested in the paper, asynchronization of cells may impact the intensity of the fountains due a dilution effect mediated by the cells still in G1. Therefore, possible differences in the fractions of replicating/non-relicating cells between the different experiments (new Fig. S7C) would also result in differences in the signal. Moreover, it is important to highlight that aggregate plots are normalized (Observed/Expected) by the average signal (P(s)). Therefore, as Scc1-depleted cells do not exhibit cohesin-mediated loop-extrusion (see aggregate plots around CARs in new Fig. S7B), we may expect an enhancement of signal at origins due to dividing each pixel by a lower contact frequency with respect to the one found in WT.

(2) In the new Fig. S10, we plotted the relative enrichment of Hi-C reads around origins. While we already used the same approach to compare replicon sizes between simulations and experiments (see Fig S7A and response to comment n°9 of Reviewer 3), this analysis is instructive also when comparing different experimental conditions. While we find that the experiment in WT and Scc1-depleted cells show very similar replicon sizes, we do observe a small increase in the peak height for the cohesin mutant. This may also partially motivate differences in the intensity of the fountain. For ctf4Δ, we observe significantly smaller replicons. We speculate that such a mutant might exhibit slower replication and consequently might be enriched in sister-forks contacts.

(3) Compensatory mechanisms: we now briefly discussed this in the Discussion (Page 15).

Inconsistent Figure References

Several figure citations are mismatched. For instance, Fig. S1A has not been cited in the manuscript. Moreover, there is no Fig.1E in figure 1, while it has been cited in the text. All figure/panel references must be cross-checked and corrected.

We thank the reviewer for this observation. We have now corrected the mismatches.

Minor:

Page2: "While G1 chromosomes lack of structural features such as TADs or loops [3]" However, Micro-C captures chromatin loops, although much smaller than those in mammalian cells, within budding yeast.

Loops of approx 20-40 kb are found in interphase in budding yeast but only after the onset of S-phase ( ref. [52-61]). For this reason, our G1 model of yeast without loops well captures the experimental P(s) curves (Fig. S2). See also answer to point 12 of reviewer 2 .

In figure 2E, chromatin fountain signals can be readily observed in the fork coupling situation and movement can also be observed. However, the authors should indicate the location of DNA replication termination sites and show some examples at certain loci but not only the aggregated analysis.

The initial use of aggregate plots was motivated by the fact that fountains are quite difficult to observe at the single origin level in the experimental Hi-C due to the strong intensity of surrounding contacts (along the diagonal). However, when dividing early-S phase maps by the corresponding G1 map, we can now observe clear correlation between origin and fountain positions on such normalized maps. We now added an example for chromosome 7 in Fig.3 indicating early/late origins.

In Fig. S8 and S9 (where we also included termination sites), we show that fountains are prominently found at origins during S-phase and are lost in G2/M.

Reviewer #2 (Significance (Required)):

The topic is relevant and the problem being addressed is very interesting. While there has been some earlier work in this area, the polymer simulation approach used here is novel. The simulation methodology is technically sound and appropriate for the problem. Results are novel. The authors compare their simulations with experimental data and explore both interacting and non-interacting replication forks. Most conclusions are supported by the data presented. Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The manuscript by D'Asaro et al. investigates the relationship between DNA replication and chromatin organization using polymer simulations. While this is primarily a simulation-based study, the authors also present relevant comparisons with experimental data and explore mechanistic aspects of replication fork interactions.

We thank Reviewer 2 for her/his positive evaluation of our work and her/his suggestions that help us to clarify many aspects in our manuscript.

The primary weakness is that many aspects are not clear from the manuscript. Below is a list of questions that the authors must clarify:

In the Model and Methods section, it is written "Arbitrarily, we choose the backbone to be divided into two equally long arms, in random directions." It is unclear what is meant by "backbone to be divided" and "two equally long arms." Does this refer to replication?

We agree with the reviewer that the term backbone may be ambiguous. In the context of the initialization of the polymer, it refers to the L/4 initial bonds used to recursively build an unknotted polymer chain of final size L using the Hedgehog algorithm (see refs [101,109]). As shown in the Fig S1A, these initial L/4 bonds define the initial backbone of each chromosome before they are recursively grown to their final size. We chose to divide them into two branches (called “arms” in the old version of the manuscript) of equal length (L/8) and with random orientations. To avoid any ambiguity between the term arm used in that context and the chromosome arms in a biological sense (sequences on the left and right with respect to centromeres), we changed it to “linear branches” to improve clarity. We highlighted in Fig. S1A two examples of such a “V-shaped” backbone.

As stated in the text, these initial configurations are artificial and just aim to generate unknotted, random structures. After initiating the structures, we then added the geometrical constraints to the centromeric, telomeric and rDNA beads. This, combined with the tendency of the polymer to explore and fill the spherical volume, determine the relaxed G1-like state (see Fig. S2) obtained after an equilibration stage (corresponding to 10^7 MCS). Only after that initialization protocol, DNA replication is activated.

In chromosome 12, since the length inside the nucleolus (rDNA) is finite, the entry and exit points should be constrained. Have the authors applied any relevant constraint in the model?

Indeed, we did not introduce any specific constraint on the relative distance between rDNA boundary monomers in our model. They can therefore freely diffuse, independently from each other, on the nucleolus surface. This point is now clarified in the text. Note that, in this paper, we did not aim to finely describe the rDNA organization and its interactions with the rest of the genome, that is why we did not explicitly model rDNA. Moreover, to the best of our knowledge, there is not available experimental data to potentially tune such additional restraints.

Previous models such as Tjong et al. (ref. [66]) and Di Stefano et al. (ref [67]) have used very similar approximations than us. In the works of Wong et al. (ref.[61]) and Arbona et al. (ref.[63]), rDNA is explicitly modelled via larger/thicker beads/segments, and thus accounts for some generic polymer-based constraints between rDNA boundary elements.

However, note that all these different models, including ours, still correctly predict the strong depletion of contacts between rDNA boundaries, indicating that there exists a spatial separation between the two boundary elements that is qualitatively well captured by our model (See Fig. S1 D and Fig. 1B).

What is the rationale for normalizing the experimental and simulation results by dividing by the respective P_intra(s = 10 kb)?

This normalization was used in Fig. 1 to obtain a rescaling between experiments and simulations. This approach assumes that simulated and experimental Hi-C maps are proportional by a factor that, in Fig 1B, was set to P_exp(s=16kb)/P_sim(s=16kb). Similar strategies are used in a number of modeling studies (for example ref. [103,106]).

We use the average contact frequency (P_intra) at this genomic scale (s in the order of 10s of kb) because our polymer simulations well capture the experimental P(s) decay above this scale. This method allows to plot the two signals with the same color scale and to give a qualitative, visual intuition on the quality of the modeling. Note that normalization has no impact on the Pearson correlation given in text. More generally, it allows to semi-quantitatively compare predicted and experimental Hi-C data.

In Fig 1D, we instead normalize the average signal between pairs of centromeres (inter-chromosomal aggregate plot off-diagonal) by the average P_intra(s=10kb). This method allows estimating how frequently centromeres of different chromosomes are in contact relative to intra-chromosomal contacts at the chosen scale (10 kb). In the new paragraph “Comparison with in vivo HiC maps in G1” (Page 22) , we describe more in detail the quantitative insights that can be recovered from such analysis.

As a comparison, such normalization is not required when computing Observed/Expected maps (Fig. 1C or aggregate plots in Fig. 2 and Fig. 3) as simulation and experimental maps are normalized by their own P(s) curves. We now clarify this aspect in the Materials in Methods under the paragraph “Comparison between on diagonal aggregate plots” (Page 22).

In the sentence "For instance, chromosomes are strictly bound by the strong potential to localize between 250 and 320 nm from the SPB," is it 320 or 325 nm? Is there a typo?

We confirm that the upper bound is indeed 325 nm as stated in Eq.2 and not 320 nm.

Please list the number of beads in each chromosome and the location of the centromere beads.

A new table (Table S2) was included to highlight beads number and centromere positions.

In Eq. 7, when the Euclidean distance between the sister forks d_ij > 50 nm, the energy becomes more and more negative. This implies that the preferred state of sister forks is at distances much greater than 50 nm. Then how is "co-localization of sister forks" maintained?

We corrected the typo sign in Eq.7. The corrected equation without the minus sign - consistently with what simulated - implies that sister forks tend to minimize their 3D distance. The term goes to zero when their distance is within 40 nm (2 nearest-neighbouring sites).

The section on "non-specific fork interactions" is unclear. You state that the interaction is between "all the replication forks in the system," but f_ij is non-zero only for second nearest-neighbors. The whole subsection needs clarification.

We corrected the text, specifying that the energy is non-zero for both first and second neighbours. In practice, two given forks do not experience any attractive energy unless their 3D distance is less than 2 nearest-neighbours. To clarify this aspect, we articulated more in the methods how non-specific fork interactions are implemented in the lattice during the KMC algorithm. We also included a new supplementary image (Fig. S15), where we schematize how forks move in 3D and how changes in their position update the table that tracks the number of forks around each lattice site.

Eq. 6 has no H_{sister-forks}. Is this a typo?

We confirm that it is a typo and the formula was corrected to H_{sister-forks}.

While discussing the published work, the authors may cite the recent paper [https://doi.org/10.1103/PhysRevE.111.054413].

The reference is now included when discussing previous polymer models of DNA replication.

It is not clear how the authors actually increase the length of new DNA in a time-dependent manner. For example, when a new monomer is added near the replication origin (green bead in Fig. 3C), what happens to the red and blue polymer segments? Do they get shifted? How do the authors take into account self-avoidance while adding a new monomer? These details are not clear.

The detailed description of the chain duplication algorithm and its systematic analysis was performed in our previous study (ref. [25]).

However, we agree with the reviewer that to improve self-consistency more details must be included in the present manuscript (see also answer to comment 1 of Reviewer 3). In particular, we now highlight in Materials and Methods that self-avoidance is indeed temporarily broken when we add a newly replicated monomer on top of the site where the fork is. Such double occupancy in the lattice rapidly vanishes due to 3D local moves. We refer to our PRX work (ref [25] and in particular to the following figure (extracted from FIG. S1 in ref.[25]) which illustrates how the bonds/segments of the two sister chromatids are consistently maintained.

How do the authors ensure that monomers get added at a rate corresponding to velocity v? The manuscript mentions "1 MCS = 0.075 msec," but in how many MC steps is a new monomer added? How is it decided?

Similarly to origin firing, replication by fork movement along the genome occurs stochastically, with a rate which we derive by converting the physiological fork speed in yeast 2.2 kb/min (ref. [41]) into a rate in (number of monomer/MCS) units. In practice, we generate a random number that, if smaller than such a rate, leads to forks duplication. We clarify this aspect in the Materials and Methods, also referring to our previous work for a more detailed summary.

The authors stress the relevance of loop extrusion. However, in their polymer simulation, the newly replicated chromatin does not form any loops. Is this consistent with what is known?

Indeed, our simulations do not have any concurrent extrusion mechanism such as cohesin-mediated loops. This choice was purposely made to isolate and characterize replication-dependent effects.

That is why we compare our predictions on chromatin fountain patterns (Fig. 3) with data obtained for the Scc1 mutant strain where cohesin is absent in order to disentangle the possible interference with loop-extruding cohesin. For subsection C where microscopy data are available only in WT condition, we cannot rule out that the observed discrepancies between experiments and predictions cannot be due to missing mechanisms including loop extrusion. It was already mentioned in the Discussion (Page 16). It is however unclear whether sparse and small loops between CARs (see Fig. S7B) in S-phase, could be sufficient to recapitulate the microscopy estimates on the sizes of replication foci and no clear signature of inter-origin loops (possibly mediated by loop extrusion) are observed in Hi-C data in WT and Scc1 deficient conditions.

Moreover, as mentioned in the Discussion, the poorly characterized mechanisms behind forks/extruding-cohesin encounters does not allow for a straightforward modelling of such processes whose accurate description/simulation would require its own study.

Please add a color bar to Fig. 4B.

The color bar was included.

In the MSD plot (Fig. 6), even though it appears to be a log-log plot, the exponents are not computed. Typically, exponents define the dynamics.

We plot the expected 0.5 exponent at smaller time-scales as mentioned in the main text in Fig. 6, previously included only in new Fig. S19A.

The dynamics will depend on the precise nature of interactions, such as the presence or absence of loop extrusion. If the authors present dynamics without extrusion, is it likely to be correct?

The reviewer is correct in highlighting how our model does not capture the potential decrease in dynamics due to cohesin mediated loop extrusion. However, our model does capture the expected Rouse regime (see Fig. 6A, S19A and ref [83]), which justify our timemapping strategy. In comment 16 of reviewer 3, we discuss more in detail the robustness of our results with respect to variation in such a mapping. In the specific context of Fig. 6A, we predict the gradual decrease in dynamics due to sister chromatids intertwining independently of any cohesin-associated activity (both loop-extruding and cohesive). As loop extrusion is also decreasing chromatin mobility overall (ref. [87]), if such a decrease in mobility is observed in WT in vivo, it may be indeed difficult to assign such a decrease to replication rather than loop extrusion. That is why in the Discussion (Page 16), we propose to compare our prediction to experiments in cohesin-depleted cells. In the context of Fig.6B&C, we don’t expect loop extrusion to be a confounding effect as the predicted decrease in dynamics is specific to forks.

Reviewer #3 (Significance (Required)):

The work has been conducted thoroughly, and in general the paper is well written with good attention to detail. As far as I am aware, this is the first study where replication is simulated in a whole nucleus context, and the scale of the simulations is impressive. This allows the authors to address questions on replication foci and the spatiotemporal organisation of replication which would not be possible with more limited simulations, and to compare the model with previous experimental work. This, together with the new HiC data, I think this makes this a strong paper which will be of interest to biophysics and molecular biology researchers; the manuscript is written such that it would suit an interdisciplinary basic research audience.

We thank Reviewer 3 for her/his enthusiasm and her/his comments that help us to greatly improve the manuscript.

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

The paper "Genome-wide modelling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes" by D'Asaro et. al presents new computational and experimental results on the dynamics of genome replication in yeast. The authors present whole-nucleus scale simulations using a kinetic Monte Carlo polymer physics model. New HiC data for synchronised yeast samples with different protein knock-downs are also presented.

The main questions which the paper addresses are whether sister forks remain associated during replication, whether there is more general clustering of replication forks, and whether replication occurs in a 'spatial wave' through the nucleus. While the authors' model data are not able to conclusively show whether sister forks remain co-localised, the work provides some important insights which will be of high interest to the field.

I have no major issues with the paper, only some minor comments and suggestions to improve the readability of the manuscript or provide additional detail which will be of interest to readers. I list these here in the order in which they appear in the paper. There are also a number of typos and grammatical issues through the text, so I recommend thorough proofreading.

The paper seems to be aimed at a broad interdisciplinary audience of biophysicists and molecular biologists. For this reason, the introduction could be expanded slightly to include some more background on DNA replication, the key players and terminology. Also, it seems that this work builds on previous modelling work (Ref. 19), so a bit more detail of what was done there, and what is new here would be helpful. The final paragraph the introduction mentions chromosome features such as TADs and loops, which should be explained in more detail.

We now have expanded the introduction to address some of these aspects. In particular, also as a response to comment 1 of Reviewer 4, we included additional background on the eukaryotic replication time program. We address in more detail its known interplay and correlation with crucial 3D structural features such as compartments and TADs. Finally, we add a sentence to clarify how the current work is distinct from the prior implementation and the novelty introduced here.

In the first results section, end of p2, the "typical brush-like architecture" is mentioned. This is not well explained, some additional detail or a diagram might help.

As very briefly summarized in the mentioned paragraph, the yeast genome is organized in the so-called Rabl organization where chromosome arms are all connected via the centromeres at the Spindle Pole Body (SPB). This is analogous to the definition of a polymer brush where several branches (the arms in this case), are grafted to a surface or to another polymer (see new Inset panel in Fig S1B). We refer in the main text to the scheme in Fig. S1B where we also include the snapshot of a single chromosome and the physical constraints that characterize this large-scale organization and extend the caption to clarify the analogy. A typical emerging feature at the single chromosome level is described in Fig. 1 B and C.

On p3-4, some previous work is described, with Pearson correlations of 0.86 and 0.94 are mentioned. What cases these two different values correspond to is not clear.

These Pearson correlations are obtained for our own modeling. We correct the values in the main text and more clearly indicate the specific correspondence with the maps used. We describe now in the Materials and Methods (new paragraph “Comparison with in vivo HiC maps in G1” and Table S2) how these values were obtained.

In section II-A-2, on the modelling details, it should be made clearer that the nucleus volume is kept constant, and that this is an approximation since typically the nucleus grows during S-phase. This is discussed in the Methods section, but it would be useful to also mention it here (and give some justification why it will not likely change the results).

We now state more clearly in the main text the limitation of our model regarding the doubling of DNA content without any increase of nuclear size. As mentioned in the Discussion, we do not expect this approximation to strongly impact our results, which mainly focus on early S-phase.

We now also included in the Discussion how the detection of the “replication wave” should be qualitatively independent of the density regime. In fact, even in the case of growing nuclei and constant density, the polarity induced by the Rabl organization and replication timing are the main drivers of such fork redistribution.

Regarding the slowdowning in diffusion due to sister chromatids intertwinings (see response to comment 13), we instead verified that the effect is indeed density independent (new Fig S21).

Fig 2. The text in Fig 2B is much smaller than other panels and difficult to read. Also Fig 3B, Fig 6.

This is now corrected.

In 2E, are the times given above each map the range which is averaged over? This could be clearer in the caption. In the caption it stated that these are 'observed over expected'; what the 'expected' is could be clearer.

We reformulate the description in the caption to make clearer that the time indicated above the plots indicate the time window used for the computation. As mentioned more in detail in the response to comment 17 below (and comment 3 of Reviewer 2), we included in the Material and Methods a more precise description on the normalization used in the case of on-diagonal aggregate plots (observed-over-expected).

In section II-B-2, the authors state that the cells are fixed 20 mins after release from S-phase. Can they comment on the rationale behind this choice, since from Fig 2 their simulations predict that the fountain pattern will no-longer be visible by that time.

In the experimental setup, cells are arrested in G1 with alpha-factor and then released in S-phase (see Fig S26 with corresponding scheme). The release from G1 synchronisation is not immediate, and staging of cells by flow-cytometry every 5 minutes for 30 minutes after release (data not shown in the main text but provided below) proved 20 minutes to be an adequate early S-phase timepoint (Page 17 in the Materials and Methods). As a consequence, the times indicated when describing the in vivo experiment, do not correspond to the ones indicated in our in silico system, for which the onset of replication is well defined. For these reasons, we have to determine which time window among the ones used in Fig 2E, is the most appropriate to compare with the experiment (see response to comment 9 for more details).

Fig.R1: Cell cycle progression monitored by flow cytometry after the release. For the first 15 minutes, cells are still mainly in G1 and only start replicating ~20 minutes after the release.

Section II-B-2(b) could be clearer. I don't understand what the conclusion the authors take from the metaphase arrest maps is. I'm not sure why they discuss again the Cdc45-depleted cells here, since this was already covered in the previous section.

Taken together, the G1, Cdc20 (metaphase-arrested cells), and Cdc45-depleted (early S cells but not replicated) conditions suggest that fountains reflect ongoing replication. Namely, G1-arrest shows that fountains require S-phase entry; Cdc45-depletion shows that fountains require origin firing and is not due to another S-phase event; and metaphase-arrested cells show that fountains are not permanent structures established by replication, but a transient replication-dependent structure.

This demonstrates that the emerging signal is not trivially dependent on (1) the presence of the second sister chromatids; or on (2) potential overlaps between origin positions and barriers (CARs) to loop extrusion (see also comment 12 of Reviewer 2). A sentence at the end of II-a was added to clarify the different information gained with the two strains.

We discuss again the cdc20 and cdc45 mutants in II-b to highlight how the results in II-a do not exclude potential interplay between cohesin-mediated loop-extrusion in presence forks progression. These considerations motivated our experiment in Scc1-depleted cells during early S-phase.

At the start of p8 (II-B-3) there is a discussion of the mapping to times to the early-S stage experiments. This could have more explanation. I don't follow what the issue is, or the process which has been used to do the mapping. From Fig 2B, it seems that the simulation time is already mapped well to real time.

As mentioned above in comment 7, we cannot clearly define a “t=0” when replication starts in vivo as the release from the G1-arrest is not immediate and perfectly synchronous. On the other hand, the times indicated within the text are those following the onset of polymer self-duplication in our simulations. Note that the mean replication time (MRT) shown in Fig.2B does not represent an absolute time, but rather an average relative timing along S-phase (signal rescaled between 0 and 1).

For all these considerations, we think that the most reliable strategy to compare fountains in vivo and in silico is to look at the replicon size via the enrichment in raw contacts around early origins, as illustrated in Fig S7A. In practice, looking at the relative counts of contacts around early origins we have a proxy for the average replicon size that we can match by computing the same analysis on simulated signals (Fig S7A). As a result, we find that the best simulated time window is between 5 and 7.5 minutes, compatible with early-S phase and with an approximate duration of G1 after release of 15 minutes as observed in other studies (ref. [61]).

Note that our conclusions are robust with respect to modulating this mapping method. In particular in Fig. S7, we thoroughly investigated how several confounding factors (such as time window used or partial synchronization) may impact the quantitative nature of our prediction without affecting the qualitative insights.

We included a more precise reference to the Supplementary Materials, where the approach is described and clarified.

In Fig 4A above each plot there is a cartoon showing the fork scenario. The left-hand cartoon is rendered properly, but the right-hand one has overlapping black boxes which I don't think should be there. These black boxes are present in many other figures (4B, 3B, 2E etc).

This issue seems to appear using the default PDF viewer on Mac OS. We have corrected the problem and no more black boxes should appear in the main text and in the Supplementary Material.

In II-C-2(b) it is mentioned that the number of forks within RFis is always assumed to be even. This discussion could be clearer. In particular, the authors state that under both fork scenarios, in the simulations they can detect odd numbers of forks within RFis - how can this happen in the case where sister forks are held together?

We included a more accurate description in the main text about why Saner et al. (ref [20]) make these assumptions in their estimates. We highlight possible inconsistencies such as the presence of termination events which, in our formalism, break sister forks interactions and lead to single forks to be detected. We also clarify the latter point when describing Fig 5B and describe in more detail replication bubbles merging events in the Materials and Methods.

Fig 6B and C, it would be useful if the same scale was used on both plots.

We now use the same scale when plotting Fig 6B and C.

Section II-D-1. There is a discussion on the presence of catenated chains; I did not understand how the replicated DNA becomes catenated, and what this actually means in this context. The way the process is described and the snapshots in Fig2C do not suggest that the chains are catenated. Some further discussion or a diagram would be useful here.

We included a small paragraph to better explain how intertwining of sister chromatids occurs, and more clearly refer to a snapshot in supplementary figure S19D (Page 14). As correctly mentioned by the reviewer, replication bubbles by construction are always unknotted during their growth (see example in Fig. 2C). As we thoroughly characterize in our previous work (ref. [25]), when several replication bubbles merge, the random orientation of sister chromatids potentially lead to catenation points and intertwined structures. We show below a scheme from our previous work (ref [25]). While in this past work, we demonstrated that the center of mass of the two sister chromatids show subdiffusive behaviour due to the additional topological constraints of their intertwining, this new analysis in the present work suggests that possible effects may also be observed when tracking the MSD (mean square displacement at the locus level) in a more realistic scenario where we included correct replication timing, chromosome sizes and Rabl-organization.

On p14 (section III) there is a section discussing possible mechanisms for sister fork interactions, and that result that Ctf4 might not play a role in this, as previously suggested. Are there any other candidate proteins which could be tested in the future?

To the best of our knowledge, there is no other candidate protein of the replisome that has been directly associated to sister-fork pairing in previous studies (as Ctf4). However, components of the replisome such as Cdt1, that have the capacity to oligomerize/self-interact, could be good candidates. We now mention this possibility in the Discussion (Page 15).

As on p14, second paragraph: there is a sentence "replication wave [51] cannot be easily visualised at the single cell level.", which seems to contradict the discussion on p9 "such a "wave" can also be observed at the level of an individual trajectory (Video S3,4) even if much more stochastic." I think more explanation is needed here.

We rephrased the mentioned passages to clarify the differences in detecting such “replication wave” at the population vs single cell level. In video S3 and S4, we can still observe an enrichment of forks at the SPB and later in S-phase a shift towards the equatorial plane. However, the stochasticity of polymer dynamics and 1D replication strongly hinder the ability to clearly visualize such redistribution.

In the methods section, p18, it is mentioned that the volume fraction is 3%. I assume this is before replication, and so after replication is complete this will increase to 6%. This should be stated more explicitly, with also a comment on the 5% volume fraction used in the time-scale mapping discussed on p17.

Indeed, we choose to map the experimental MSD measured in ref [83] by simulating a homopolymer 5% volume fraction and in periodic boundary conditions for consistency to previous work in the group (ref. [102-106]) and our previous replication model (ref.[25]). Moreover, this intermediate density regime also lies in between the minimal (3%) and maximal (6%) densities present in our system. When redoing the time mapping with the G1 MSD plotted in Fig 6A and new Fig S19A, we obtain a very similar value of approx. 1MC=0.6ms. Note that the time mapping aims to obtain a rough estimation of real times as several factors, such as active processes, non-constant density, cell-cycle progression may all contribute to chromatin diffusion in vivo (see also comment 15 to Reviewer 2). In the context of our formalism, differences in time mapping do not affect the 1D replication dynamics as all the parameters to model the 1D process are rescaled by the same factor. Moreover, as we characterized in more depth in our previous work (ref [25]), a crucial aspect that defines self-replicating polymers is the relationship between fork progression and the polymer relaxation dynamics. In physiological conditions, we remain in the regime where forks progress almost quasi-statically to allow the bubbles to re-equilibrate. Therefore, small discrepancies in the time mapping will not modify this regime and our results should remain robust.

On p20, processing of simulated HiC using cooltools is discussed. For readers unfamiliar with this software, a bit more detail should be given. Specifically, how does the normalisation account for having some segments which have been replicated and some which have not. Later on the same page (IV-C-2) two different strategies for comparing HiC maps are given; why are two different methods required, and what is the reasoning in each case?

In the raw - unbalanced - data, we observe an artificial increase in contacts around origins in S-phase for both simulation and experiments. This is simply due to the presence of the second Sister chromatids and the fact that contacts between distinct DNA segments are mapped to a single bin.

In the new Fig. S25, we illustrate this effect by computing aggregate plots around early origins using single-chromosome simulations. We demonstrate that the ICE normalization corrects for the variations in copy number due to replication and thus for such artificial increases in contacts during S-phase. We show that such a normalization is equivalent to explicitly divide each bin by the average copy-number of the corresponding segments.

We have now included a sentence in the Materials and Methods to clarify this. Moreover, a detailed description of the other alternative strategies used to compare experiments and simulations were presented in response to comment 3 to Reviewer 2 and two new paragraphs were added in the Materials and Methods.

The references section has an unusual formatting with journal names underlined.

We updated the formatting.

Reviewer #4 (Significance (Required)):

D’Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condensin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

We thank Reviewer 4 for her/his positive evaluation of our work and her/his comments that help us to greatly improve the manuscript.

Reviewer Comments / Significance

In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors D’Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

D’Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condesin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

Major Comments

There is a tremendous amount of work coupling RT domains to 3D genome architecture, especially deriving from the ENCODE and 4D Nucleome consortia. These studies are not adequately highlighted in the introduction and discussion of this manuscript, and this treatment of the literature would ideally be amended in any revised manuscript.

We include new sentences in the introduction to discuss more in detail the correlation between 3D genome architecture and replication timing program, and advancement in this field in the last decades. We also included additional citations to reviews and publications (ref [8-16]). These references were also included at the end of the Discussion where we address the exciting perspective of employing our model in higher eukaryotes and potentially tackle the complex interplay between 3D nuclear compartmentalization and replication dynamics (see also response 1 to Reviewer 1).

S. cerevisiae origins of replication differ from metazoan origins of replication in that they are sequence-defined and are known to fire in a largely deterministic pattern (see classic study PMID11588253). From the methods of the authors it is not clear that the known deterministic firing pattern is being used here, but instead a stochastic sampling method? Please clarify in the manuscript. Specifically, it would be good to understand how the Initiation Probability Landscape Signal correlates with what is already known about origin firing timing.

In our model, the positions of origins are stochastically sampled proportionally to the IPLS which was inferred directly from experimental MRT (ref. [63]) and RFD (ref. [44]). This modeling approach allows reproducing with a very high accuracy the known replication timing data (correlation of 0.96) and Fork directionality data (correlation of 0.91) (see ref. [71]). Origins were defined as the peaks in the IPLS signal. In Fig S3, we extensively compare these origins and the known ARS positions from the Oridb database. For example, most of our early origins (96%) are located close to known, confirmed ARS. Moreover, even if our algorithm is stochastic for origin firing, we remark that each early origin will fire in 90 % of the simulations, coherent with the quasi-deterministic pattern of origin firing and experimental MRT and RFD data. We now have added such statistics of firing in the revised manuscript (Page 4).

It seems possible that experimental sister chromatid Hi-C data (PMID32968250) and nanopore replicon data (PMID35240057) could be used to further ascertain the validity of some of the findings of this paper. Specifically, could the authors demonstrate evidence in sister chromatid Hi-C data that the replisome is in fact extruding sister chromatids? Moreover, are the interactions being measured specifically in cis (as opposed to trans sister contacts)? For the nanopore replicon data, how do replicon length, replication timing, and position along the replication 'wave' correlate?

We thank the reviewer for the suggestions.

Hopelessly there is currently no Sister-C data available during S-phase. In the seminal study (PMID32968250), cells were arrested in G2/M via nocodazole treatment. For a different unpublished work, we already analysed in detail the SisterC dataset and we did not observe clear fountain-like signature, consistent with our own G2/M Hi-C maps (cdc20) where fountains were absent. Note that, in the present work, in order to compare our predictions with standard HiC data, we included all contacts (cis and trans chromatids), mapping pairwise contacts from distinct replicated sequences/monomers to a single bin (see also response to comment 17 to Reviewer 3 and new Fig. S25).

We now mention in the Discussion that Sister-C data during S-phase could help monitoring the role of replisomes on relative sister-chromatids organization (Page 15).

Main results from the nanopore replicon data study include the observed high symmetry between sister forks and their linear progression, as the density of replicons appears to be uniform with respect to their length. Since these two specific constraints are already present in the framework of Arbona et al. (ref. [63]), our model is able to reproduce these features of DNA replication captured by the nanopore data.

Moreover, as we model with very high accuracy replication timing data (see response to comment 2) and forks positioning, we can assume that our formalism well captures replicon positioning and lengths observed in vivo.

As this study does not include any additional exploration or variation of the parameters inferred by Arbona et al. (ref. [63]), we consider a quantitative comparison with the nanopore replicon data to be beyond the scope of this paper.

Minor Comments:

The paper is in most places easy to follow. However, Section C bucked this trend and in general was quite difficult to follow. We would recommend that the authors try to revise this section to make clearer the actual physical parameters that govern a 'replication wave' and the formation of replication foci - how many forks, the extent to which the sisters are coordinated, etc for early vs. late replicating regions.

We now state more clearly with a sentence in the main text the driving forces behind the formation of such a “replication wave”. We believe that the several additions and clarifications following the various comments, improved the clarity of the manuscri

Author response:

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

Reviewer #1 (Public review): 

Summary:  

The Szczupak lab published a very interesting paper in 2012 (Rodriquez et al. J Neurophysiol 107:1917-1924) on the effects of the segmentally-distributed non-spiking (NS) cell on crawl-related motoneurons. As far as I can tell, the working model presented in 2012, for how the non-spiking (NS) cell impacts the crawling motor pattern, is the same functional model presented in this new paper. Unfortunately, the Discussion does not address any of the findings in the previous paper or cite them in the context of NS alterations of fictive crawling. Aside from different-looking figures and some new analyses, the results and conclusions are the same. 

Reviewers #1 and #2 called our attention to our failure to cite the Rodriguez et al. 2012 article in the context of the main goal of the present work. We do now explain how the present study is framed by the published work. See lines 74-79.

In Rodriguez et al. 2012, we hypothesized that the inhibitory signals onto NS were originated in the motoneuron firing. We now cite this reference in line 104. In the current manuscript we further investigated the connection between the inhibitory signals onto NS and the motoneuron activity (Figure 2) and proved that the hypothesis was wrong. Thus, the model presented here differs from the one proposed in Rodriguez et al. 2012.

In Rodriguez et al. 2012, we speculated that the inhibitory signals received by NS were transmitted to the motoneurons, but an important control was missing in that study. In the current study depolarization of NS during crawling is tested against a control series that allows to properly examine the hypothesis (lines 138-147). But, most important, because NS is so widely connected with the layer of motoneurons it was necessary to test the effect on other motoneurons during the fictive crawling cycle. We now explain this rationale in lines 249-257.

Strengths: 

The figures are well illustrated. 

Weaknesses:  

The paper is a mix of what appears to be two different studies and abruptly switches gears to examine how closely the crawl patterning is in the intact animal as compared to the fictive crawl patterning in the intact animal. Unfortunately, previous studies in other labs are not cited even though identical results have been obtained and similar conclusions were made. Thus, the novelty of the results is missing for those who are familiar with the leech preparation. The lack of appropriate citations and discussion of previous studies also deprives the scientific community of fully comprehending the impact of the data presented and the science it was built upon.  

The main aim of the manuscript is to learn the role of premotor NS neurons in the crawling motor pattern studied using spike sorting in extracellular nerve recordings. This readout allows to  simultaneously monitor a larger number of units  than in any previous study. This approach aims to determine whether and how a recurrent inhibitory peripheral circuit is involved in coordinating or modulating the rhythmic motor pattern.

Our rationale was that the known effect of NS on one particular motoneuron (DE-3) may have overlooked a more general effect on crawling (lines 253-257). Moreover, we wanted to investigate whether this effect was due to the recurrent inhibitory circuit or if other elements were involved, and to study whether the modulation was mediated by the recurrent synapse between NS and the motoneurons.

In the context of this aim we studied the rhythmic activity of cell DE-3, together with motoneurons that fire in-phase and anti-phase, in isolated ganglia (Figure 4). To reveal the effect of NS manipulation we applied a quantitative analysis that showed the phase-specific effect of NS (Figure 6). 

Given that this is the first study using a spike sorting algorithm to detect and describe the activity of motoneurons in nerve recordings we found it reasonable to compare these results with an in vivo study; thus, providing information to the general reader, that supports the correspondence between the ex vivo and the in vivo patterns.

(1) Results, Lines 167-170: "While multiple extracellular recordings have been performed previously (Eisenhart et al., 2000), these results present the first quantitative analysis of motor units activated throughout the crawling cycle. The In-Phase units are expected to control the contraction stage by exciting or inhibiting the longitudinal or circular muscles, respectively, and the Anti-Phase units to control the elongation stage by exciting or inhibiting the circular or longitudinal muscles, respectively."  

Reviewer: The first line above is misleading. The study by Puhl and Mesce (2008, J. Neurosci, 28:4192- 420) contains a comprehensive analysis of the motoneurons active during fictive crawling with the aim of characterizing their roles and phase relationships and solidifying the idea that the oscillator for crawling resides in a single ganglion. Intracellular recordings from a number of key crawl-related motoneurons were made in combination with extracellular recordings of motoneuron DE-3, a key monitor of crawling. In their paper, it was shown that motoneurons AE, VE-4, DI-1, VI-2, and CV were all correlated with crawl activity, and fired repeatedly either in phase or out-of-phase with DE-3. They were shown to be either excitatory or inhibitory. At a minimum, the above paper should be cited. 

The sentence in the submitted manuscript explicitly refers to the quantitative analysis of extracellular recordings, but we recognize that it may lead to confusion. We have now added a clarification (lines 197-199). 

The article by Puhl and Mesce 2008 shows very nice intracellular recordings of the AE, CV, VE-4, DE-3, DI-1, and Vi-2, accompanied by extracellular recordings of DE-3 in the DP nerve. In all cases, there is only one intracellular recording paired with the DP nerve recording.

While it is possible to perform up to 3-4 simultaneous intracellular recordings, these are technically challenging, and more so when the recordings have to last 10-20 minutes. Due to this difficulty, and because our objective was to record multiple units simultaneously in order to comprehensively describe the different crawling stages, we implemented the spike sorting analysis on multiple extracellular recordings. This approach enabled us to reliably obtain multiple units per experiment and thus execute a quantitative analysis of the activity of each identified unit.

The article by Puhl and Mesce 2008 mentions several quantitative aspects of the neurons that fire in-phase or out-of-phase with DE-3, but, as far as we understand, there is no figure that summarizes activity levels and span in the way Figures 4 and 6 do in the current manuscript. To the best of our knowledge, no previous work renders this information.

It is very important for us to emphasize that the work by Puhl and Mesce was seminal for our research. We cited it four times in the original manuscript and 10 times in the present version. But, like any important discovery, it sets the ground for further work that can refine certain measurements that in the original discovery were not central.

This is why we believe that the cited sentence in our manuscript is not misleading.  However, to comply with the requirement of Reviewer #1, we added a sentence preceding the mentioned paragraph (lines 185-187) that acknowledges the description made using intracellular recordings, and explains the need for implementing the approach we chose.

The submitted paper would be strengthened if some of these previously identified motoneurons were again recorded with intracellular electrodes and concomitant NS cell stimulation. The power of the leech preparation is that cells can be identified as individuals with dual somatic (intracellular) and axonal recordings (extracellular). 

Most of the motoneurons mentioned by Reviewer #1 are located on the opposite side (dorsal) of the ganglion to NS (ventral), and therefore, simultaneous intracellular recordings in the context of fictive crawling are challenging.

In the publication of Rodriguez et al. 2009, Mariano Rodriguez did manage to record NS from the dorsal side together with DE-3 and MN-L (!) and this led to the discovery that these motoneurons are electrically coupled, but the recurrent inhibitory circuit masks this interaction. Repeating this type of experiments during crawling, which requires stable recordings for around 15 minutes, is not a reasonable experimental setting.

Rodriguez et al. 2012 shows intracellular recordings of motoneurons AE and CV during crawling in conjunction with NS, and their activity presented the expected correlation. 

The shortfall of this aspect of the study (Figure 5) is that the extracellular units have not been identified here. 

The Reviewer is right in that the extracellular units have not been identified in terms of cell identity. As we explained earlier, most motoneurons are on the opposite side (ventral/dorsal) of the ganglion relative to NS. 

However, we do characterize the units in terms of the nerve through which they project to the periphery and their activity phase. In lines 345-349 we use this information and, based on published work, we propose possible cellular identities of the different units.

In xfact, these units might not even be motoneurons. 

We are surprised by this comment. The classical work of Ort and collaborators (1974) showed that spikes detected in extracellular nerve recordings were emitted by specific motoneurons, and several previous publications have validated extracellular nerve recordings as a means to study fictive motor patterns (Wittenberg & Kristan 1992, Shaw & Kristan 1997, Eisenhart et al. 2000).

For further reassurance, we only took in consideration units whose activity was locked to DE3; any non-rhythmical activity was filtered out (see lines 433-435). 

They could represent activity from the centrally located sensory neurons, dopamine-modulated afferent neurons or peripherally projecting modulatory neurons. 

Peripheral nerves also contain axons from sensory neurons. However, in a previous article, we studied the activity of mechanosensory neurons (Alonso et al. 2020) and showed that they remain silent during crawling. Moreover, the low-threshold T sensory neurons are inhibited in phase with DE-3 bursts and NS IPSPs (Kearney et al. 2022). Alonso et al. 2000 showed that spiking activity of T cells affects the crawling motor pattern, revealing the relevance of keeping them silent.

What does the Reviewer mean by “dopamine-modulated afferents”? We are not aware of this category of leech neurons.

The neuromodulatory Rz neurons project peripherally through the recorded nerves, but intracellular recordings of these neurons from our lab show no rhythmic activity in those cells during dopamine-induced crawling.

Essentially, they may not have much to do with the crawl motor pattern at all.

Does the Reviewer consider that neurons engaged in a coherent rhythmic firing could be unrelated to the pattern? As indicated above, the units reported in our manuscript were selected because dopamine evoked their rhythmic activity, locked to DE-3. 

Does the Reviewer consider that dopamine could evoke spurious neuronal activity?

(2) Results Lines 206-210: "with the elongation and contraction stages of in vivo behavior. However the isometric stages displayed in vivo have no obvious counterpart in the electrophysiological recordings. It is important to consider that the rhythmic movement of successive segments along the antero-posterior axis of the animal requires a delay signal that allows the appropriate propagation of the metachronal wave, and this signal is probably absent in the isolated ganglion." 

Reviewer: The so-called isometric stages, indeed, have an electrophysiological counterpart due in part to the overlapping activities across segments. This submitted paper would be considerably strengthened if it referred to the body of work that has examined how the individual crawl oscillators operate in a fully intact nerve cord, excised from the body but with all the ganglia (and cephalic ganglion) attached. Puhl and Mesce 2010 (J. Neurosci 30: 2373-2383) and Puhl et al. 2012 (J. Neurosci, 32:17646 -17657) have shown that "appropriate propagation of the metachronal wave" requires the brain, especially cell R3b-1. They also show that the long-distance projecting cell R3b-1 synapses with the CV motoneuron, providing rhythmic excitatory input to it.  

We would like to draw the Reviewer’s attention to the fact that Puhl and Mesce 2008, 2010 and Puhl et al. 2012 characterized crawling in intact (or nearly intact) animals considering the whole body. In our in vivo analysis, we studied the changes in length of the whole animal and of sections demarcated by the drawn points, as described in the Materials and Methods/Behavioral

Experiments. Because of this different analysis, we defined “isometric” stages as those in which a given section of the animal does not change its length. We now clarify this (line 230).

In the paragraph cited by the Reviewer, we intended to state that, in the context of our study, the intersegmental lag caused by the coordinating mechanisms has no counterpart “in the electrophysiological recordings of motoneurons in the isolated ganglia”. We have now completed this idea with the expression underlined in the previous sentence (line 231).

As the Reviewer indicates, in the intact nerve cord the behavioral isometric stages correspond to the “waiting time” between segments. We did refer to the metachronal order but did not cite the articles by Puhl and Mesce 2010 and Puhl et al. 2012; we now do so (lines 234).

For this and other reasons, the paper would be much more informative and exciting if the impacts of the NS cell were studied in a fully intact nerve cord. Those studies have never been done, and it would be exciting to see how and if the effects of NS cell manipulation deviated from those in the single ganglion.  

The Reviewer may consider that a systematic analysis of multiple nerves in several ganglia along the whole nerve cord would have been a different enterprise than the one we carried out. The Reviewer is right in recognizing the interest of such study, but in our opinion, the value of the present work lies in presenting a thorough quantitative analysis of multiple nerves to demonstrate its usefulness for the study of the network underlying leech crawling. In this manuscript, we used it to analyze the role of the premotor NS neuron. Without the recording of units firing in-phase and out-ofphase with DE-3, we would have been unable to assess the span of NS effects.

(3) Discussion Lines 322-324. "The absence of descending brain signals and/or peripheral signals are assumed as important factors in determining the cycle period and the sequence at which the different behavioral stages take place." 

Reviewer: The authors could strengthen their paper by including a more complete picture of what is known about the control of crawling. For example, Puhl et al. 2012 (J Neurosci, 32:17646-17657) demonstrated that the descending brain neuron R3b-1 plays a major role in establishing the crawlcycle frequency. With increased R3b-1 cell stimulation, DE-3 periods substantially shortened throughout the entire nerve cord. Thus, the importance of descending brain inputs should not be merely assumed; empirical evidence exists.  

We now strengthen the concept using “known descending brain signals” (line 358) and cite Puhl et al. 2012. We believe that extending the discussion to cell R3b-1 does not contribute meaningfully to the focus of this manuscript.

(4) Discussion Lines 325-327: "the sequence of events, and the proportion of the active cycle dedicated to elongation and contraction were remarkably similar in both experimental settings. This suggests that the network activated in the isolated ganglion is the one underlying the motor behavior." 

Reviewer: The results and conclusions drawn in the current manuscript mirror those previously reported by Puhl and Mesce (2008, J. Neurosci, 28:4192- 420) who first demonstrated that the essential pattern-generating elements for leech crawling were contained in each of the segmental ganglia comprising the nerve cord. Furthermore, the authors showed that the duty cycle of DE-3, in a single ganglion treated with dopamine, was statistically indistinguishable from the DE-3 duty cycle measured in an intact nerve cord showing spontaneous fictive crawling, in an intact nerve cord induced to crawl via dopamine, and in the intact behaving animal. What was statistically significant, however, was that the DE-3 burst period was greatly reduced in the intact animal (i.e., a higher crawl frequency), which was replicated in the submitted paper.  

There is no doubt that the article by Puhl and Mesce 2008 is seminal to the work we present here. The Reviewer seems to suggest that we do not recognize the value of this work. The contrary is true, all our related papers cite this important breakthrough. We cite the paper very early in the article in the Introduction (see lines 51 and 52-53). Likely, we would like the Reviewer to recognize the novelty of the current report. To clarify what has been shown and what is new in our manuscript, considerer the following:

i. Figures 1-6 in Puhl and Mesce 2008 provide representative intracellular recordings that describe neurons that fire in phase and out of phase relative to DE-3. Some general measurements are given in the text, but none of these figures quantify the relative activity of neurons that fire in different stages; only DE-3 activity was quantified. A quantitative description of multiple units active in phase and out of phase with DE-3 is presented here for the first time, are we wrong? This quantification is particularly relevant when assessing how a treatment affects the function of the circuit.

ii. Regarding the cycle period, we referred to the work from the Kristan lab, which reported this value long before the requested reference. We now cite Puhl and Mesce 2008 in lines 222 regarding in vivo measurements, and in line 221 regarding isolated ganglia.

iii. Regarding the duty cycle: 

Puhl and Mesce 2008 measured the duty cycle of DE-3 in three configurations: a. spontaneous whole cord, b. DA-mediated whole cord and c. DA mediated single ganglion crawling. However, it does not report the duty cycle of neurons out-of-phase with DE-3. Our current manuscript carried out this analysis. One could argue that the silence between DE-3 bursts captures that value, but this is a speculation that needed a proper measure.

Puhl and Mesce 2008 does not indicate the duty cycle of the contraction and elongation stages in vivo. Our current manuscript does. 

Therefore, the sentence cited by the Reviewer refers to data presented in this manuscript, and not in any prior manuscript. It is true that Puhl and Mesce 2008 inspire the intuition that the sentence is true, but does not present the data that the current manuscript does.

Finally, our study focused only on the body sections corresponding to the same segmental range used in the ex vivo experiments, rather than the whole animal. The comparison was made only to validate that the duty cycles of neurons firing in phase and out of phase with DE-3 matched the dynamic stages in the studied sections of the leech (line 364).

In my opinion, the novelty of the results reported in the submitted manuscript is diminished in the light of previously published studies. At a minimum, the previous studies should be cited, and the authors should provide additional rationale for conducting their studies. They need to explain in the discussion how their approach provided additional insights into what has already been reported.  

Throughout our reply, we have provided a detailed explanation of the rationale and necessity behind each experiment. Following the Reviewer’s suggestion, we have rephrased the research objectives, included what is known from our previously published work, and highlighted the substantial new data contributed by the present study. See lines 80-85. 

Additionally, we further cite our published article in lines 93, 104, 138, 146 and 250. 

Reviewer #2 (Public review):  

The paper is well-written overall. The findings are clearly presented, and the data seems solid overall. I do have, however, a few major and some minor comments representing some concerns.

My major comments are below. 

(1) This may seem somewhat semantic, yet, it has implications on the way the data is presented and moreover on the conclusions drawn - a single ganglion cannot show fictive crawling. It can demonstrate rhythmic patterns of activity that may serve in the (fictive) crawling motor pattern. The latter is a result of the intrinsic within single-ganglion connectivity AND the inter-ganglia connections and interactions (coupling) among the sequential ganglia. It may be affected by both short-range and long-range connections (e.g., descending inputs) along the ganglia chain. 

Semantics is not a trivial issue in science communication. It entails metaphors that enter the bibliography as commonly used “shortcuts” to a complex concept that are adopted by a community of researchers. And yes, indeed, they can be misleading.

However, if recording the activity in an isolated ganglion shows that a wide group of motoneurons, that control known muscle movements, presents a rhythmic output that maintains the appropriate cycle period and phase relationships, the “shortcut” is incomplete but could be valid (Puhl and Mesce 2008). If we were to include the phase lag component, a single ganglion cannot generate the fictive motor output.

Because any new study builds knowledge on the basis of the cited bibliography, the way we name concepts is a sensitive point. Adopting the terminology used by previous publications (Puhl and Mesce 2008) seems important to allow readers to follow the development of knowledge. However, attending the observation made by Reviewer #2, we included a sentence clarifying that the concept “fictive crawling” does not include intersegmental connectivity (lines 54-57)

(2) The point above is even more critical where the authors set to compare the motor pattern in single ganglia with the intact animals. It would have made much more sense to add a description of the motor pattern of a chain of interconnected ganglia. The latter would be expected to better resemble the intact animal. Furthermore, this project would have benefitted from a three-way comparison (isolated ganglion-interconnected ganglia-intact animal.  

As we answered to Reviewer #1, the present manuscript does not intend to present a thorough study on how the activity in the isolated nervous system compares with the animal behavior. To do so we would have needed to perform a completely different set of experiments. To better define the relevance of our comparison with the in vivo experiments we rephrased the objective of the behavioral analysis (lines 197-199).

The main aim of the manuscript is to learn the role of premotor NS neurons in the crawling motor pattern studied using a readout (spike sorting in extracellular nerve recordings) that allows simultaneous screening of a larger number of units than in any previous study, in order to determine whether and how a recurrent inhibitory peripheral circuit is involved in coordinating or modulating the rhythmic motor pattern.

Our rationale was that the known effect of NS on one particular motoneuron (DE-3) may have overlooked a more general effect on crawling (lines 253-257). Moreover, we wanted to investigate whether this effect was due to the recurrent inhibitory circuit or if other elements were involved, and to study whether the modulation was mediated by the recurrent synapse between NS and the motoneurons.

In the context of this aim we studied the rhythmic activity of cell DE-3, together with motoneurons that fire in-phase and anti-phase, in isolated ganglia (Figure 4). To reveal the effect of NS manipulation we applied a quantitative analysis that showed the phase-specific effect of NS (Figure 6). 

Given that this is the first study using a spike sorting algorithm to detect and describe the activity of motoneurons in nerve recordings we found it reasonable to compare these results with an in vivo study; thus, providing information to the general reader, that supports the correspondence between the ex vivo and the in vivo patterns.

(3) Two previous studies by the same group are repeatedly mentioned (Rela and Szczupak, 2003; Rodriguez et al., 2009) and serve as a basis for the current work. The aim of one of these previous studies was to assess the role of the NS neurons in regulating the function of motor networks. The other (Rodriguez et al., 2009) reported on a neuron (the NS) that can regulate the crawling motor pattern. LL 71-74 of the current report presents the aim of this study as evaluating the role of the known connectivity of the premotor NS neuron in shaping the crawling motor pattern. The authors should make it very clear what indeed served as background knowledge, what exactly was known about the circuitry beforehand, and what is different and new in the current study. 

Rela and Szczupak 2003 and Rodriguez et al. 2009 analyze the interactions of motoneurons with NS. We believe that Reviewer #2 refers here to Rodriguez et al. 2012. A similar observation was made by Reviewer #1. Below, we copy the answer previously stated:

Following the Reviewer’s suggestion, we have rephrased the research objectives, included what is known from our previously published work, and highlighted the substantial new data contributed by the present study. See lines 80-85. 

Additionally, we further cite our published article in lines 93, 104, 138, 146 and 250. 

Reviewer #1 (Recommendations for the authors):  

Please edit for correct word usage. 

Reviewer #2 (Recommendations for the authors):  

Minor Concerns 

(1) LL33-36: These lines are somewhat vague and non-informative. Why is the functional organization of motor systems an open question? What are the mechanisms at the level of the nerve cord that are an open question? Maybe be more explicit? 

We did as suggested (lines 30-32).

(2) L62: The homology between the NS neurons and the vertebrate Renshaw cells is mentioned already in the Abstract and here again. While a reference is provided (citing the lead author of this current work), the reader would benefit from some further short words of explanation regarding the alleged homology. 

We included a description of Renshaw cell connectivity (lines 64-65).

(3) LL90-92: The NS recording in Figure 1 (similar to Figure 3 in Rodriguez et al.) demonstrates clear distinct IPSPs. Could these be correlated with DE-3 spikes? 

We investigated this correlation in detail and the answer is that there is no strictly a 1:1 DE-3 spike to IPSP correlation. NS receives inputs from other dorsal and ventral excitors of longitudinal muscles, and the NS trace is too “noisy” to reflect any short-term correlation. Originally we proposed that the NS IPSPs were due to the polysynaptic interaction between the MN and NS (Rodríguez et al. 2012). However, the present work demonstrates that the IPSPs in NS are caused by a source upstream from the MNs. 

(4) LL145-145: Do you mean - inhibitory signals FROM NS premotor neurons? Not clear. 

We see the confusion, and we rewrote the sentence (lines 164). We hope it is clearer now: “…inhibitory signals onto NS premotor neurons were transmitted to DE-3 motoneurons via rectifying electrical synapses and counteracted their excitatory drive during crawling, limiting their firing frequency.”

(5) LL153-154: Why isn't AA included in Figure 4A? 

Reading our original text, the Reviewer #1 is right in expecting to see the AA recording. We changed the sentence: “we performed extracellular recordings of DP along with AA and/or PP root nerves” (lines 171-172).

We dissected the three nerves but, unfortunately, we did not always obtain good recordings from the three of them.

(6) LL237-238: The statistical significance (B- antiphase) is not clear. Furthermore, with N of 7-8, I'm not sure the parametric tests utilized are appropriate. 

Regarding the Reviewer's concern about the tests, please note that all the assumptions made for each model were tested (see now Materials and Methods lines 466-467).The information on each model is provided in Supplementary Table 2 under the column 'Model, random effect,' which specifies whether a Linear Mixed Model (LMM) or a Generalized Linear Mixed Model (GLMM) was implemented. For GLMMs, the corresponding distribution and link function are also specified. For the analysis of Max bFF of Anti-Phase motor units, we found a significant interaction between epoch and treatment, indicating a difference between treatments. This is indicated on the left of the y-axis (##). In control experiments, all three comparisons (pre-test, pre-post, test-post) show significant differences in Max bFF: this variable decreased (slightly but significantly) along the subsequent epochs, suggesting a change over time. We now corrected the text to indicate that these changes were small (line 268). In contrast, Max bFF in depo experiments remained stable between pre-test and pre-post, but significantly decreased between the depo and post epochs. Thus, in our view the comparison between control and the test supports the conclusion that NS depolarization was limited to counteracting this decrease (lines 270-273). Supplementary Table 2 provides the significance and modeled estimated ratio for each comparison in the column for pairwise simple contrasts.

Thanks to this question, we realized that the nomenclature used in the table for the epochs (pre - depo - post) needed to be changed to pre - test - post, and we have now corrected it.

(7) LL240-241: I fail to see a difference from Control. 

For the Relative HW of In-Phase units, we also found a significant interaction between epoch and treatment, indicating a difference between treatments, as denoted to the left of the y-axis (#). Then, the significance of the comparisons across epochs within each treatment are shown in the figure (*). What is important to notice is that obtaining the same significance for each treatment does not imply identical results, but we failed to describe this in our original text and we do now in lines 275-279.

(8) LL244-245: I must admit that Table 2 is beyond me. Maybe add some detail or point out to the reader what is important (if at all). 

We have now clarified what each column of the tables indicates in the corresponding legends. 

Here, we also share an insight into how the experiments were designed and analyzed:

To account for possible temporal drifts of the variables during the recordings that could mask or confuse the results, we compared two experimental series: one in which NS was subjected to depolarizing current pulses (depo), and another series (ctrl) in which the neurons were not depolarized.

The statistical analysis was made using Linear Mixed Models (LMMs) or Generalized Linear Mixed Models (GLMMs). In these analyses treatments and epochs are used as explanatory variables to evaluate the interaction between these factors. These models allow us to determine whether changes in each variable across epochs differ depending on the treatment. For example, whether the variation in firing frequency from pre to test to post differs between control experiments and those in which NS was depolarized.

A significant interaction between treatment and epoch indicates that NS depolarization affected the variable. In such cases, we performed pairwise comparisons between epochs (pre-test, test-post, pre-post) within each treatment. In contrast, the absence of a significant interaction can result from two possibilities: either the variable did not change across epoch in either treatment, or a similar temporal drift occurred in both cases.

(9) LL245-256: Move this paragraph to the discussion. 

Because we introduced a rationale for the experiments described in Figure 6 (lines 282-284) the paragraph was mostly removed, but the part that supports the methodological approach was left.

(10)  LL259-260: see my second minor point above. This is explained in LL270-272 for the first time. 

We amended according to comment (2).

(11) Figures: The quantitative analysis shown in Figure 3B is very useful. Why isn't this type of analysis utilized for the comparisons shown in Figures 4 and 6? 

We chose different ways of plotting the data based on their nature. In Figure 3B, we present data from an identified neuron (DE-3) recorded in different experiments. In contrast, in Figure 6 we analyze data from neurons classified into the same group based on their activity during the fictive crawling cycle, but their individual identity was not ascertained. Therefore, we consider it important to plot the results for each unit individually, to assess the effect of temporal drift and NS depolarization.

(12) Figures: Figure 7 is meant to be compared to Figure 1C; the point being the addition of an inhibitory connection onto the NS neuron. Why are other details of the figure also different (different colored M)? 

While Figure 1C illustrates the known connection between NS and both DE-3 and CV motoneurons, Figure 7 shows the connections between NS and the different groups of motor units described in this study. The units are represented in the circuit using the same colors that identify them in Figures 4 and 6. Since the CV motoneuron was not recorded in this study, the circuit represents the AntiPhase neurons but does not identify them with CV. Figure 7 legend now clarifies what the colors represent, and Figure 1C has been updated to match the same color scheme.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

The authors investigated sleep and circadian rhythm disturbances in Fmr1 KO mice. Initially, they monitored daily home cage behaviors to assess sleep and circadian disruptions. Next, they examined the adaptability of circadian rhythms in response to photic suppression and skeleton photic periods. To explore the underlying mechanisms, they traced retino-suprachiasmatic connectivity. The authors further analyzed the social behaviors of Fmr1 KO mice and tested whether a scheduled feeding strategy could mitigate sleep, circadian, and social behavior deficits. Finally, they demonstrated that scheduled feeding corrected cytokine levels in the plasma of mutant mice. 

Strengths: 

(1) The manuscript addresses an important topic-investigating sleep deficits in an FXS mouse model and proposing a potential therapeutic strategy. 

(2) The study includes a comprehensive experimental design with multiple methodologies, which adds depth to the investigation. 

We thank the reviewer for the positive comments.

Weaknesses: 

(1) The first serious issue in the manuscript is the lack of a clear description of how they performed the experiments and the missing definitions of various parameters in the results.  

We thank the reviewer for pointing out lapses in the editing of the manuscript. We were trying to keep the descriptions of previously published methods brief but must have gone too far, the manuscript has been carefully checked for grammar and readability. Description of the experimental design has been refined and a graphical presentation has been added as Suppl Fig 3. The sleep and circadian parameters have been thoroughly explained in the methods and briefly in the figure legnds.

(2) Although the manuscript has a relatively long Methods section, some essential information is missing. For instance, the definition of sleep bout, as described above, is unclear. Additional missing information includes

Figure 2: "Rhythmic strength (%)" and "Cycle-to-cycle variability (min)." 

Figure 3: "Activity suppression." 

Figure 4: "Rhythmic power (V%)" (is this different from rhythmic strength (%)?) and "Subjective day activity (%)." 

We have provided definitions for the general audience of the terms used in the field of circadian rhythms, such as sleep bout, rhythm power, cycle-to-cycle, masking, and % of activity during the day in the methods and Fig legends. Most of the techniques used in this study, for example, the behavioral measurement of sleep or locomotor activity, are well established and have been used in multiple published works, including our own. We have made sure to include citations for interested readers.

Figure 5: Clear labeling of the SCN's anatomical features and an explanation for quantifying only the ventral part instead of the entire SCN. 

We have added more landmarks (position of the third ventricle and optic chiasm) to Fig 5, and have outlined the shell and core of the SCN in two additional images of the ventral hypothalamus in Suppl fig 4.

We had actually quantified the fluorescence in the whole SCN as well as in the ventral part.This was/is described in the methods as well as reported in the results section and Table 4 “Likewise, a subtle decrease in the intensity of the labelled fibers was found in the whole SCN (Table 4) of the Fmr1 KO mice as compared to WT.“ 

Methods: ” Two methods of analyses were carried out on the images of 5 consecutive sections per animal containing the middle SCN. First, the relative intensity of the Cholera Toxin fluorescent processes was quantified in the whole SCN, both left and right separately, by scanning densitometry using the Fiji image processing package of the NIH ImageJ software (https://imagej.net). A single ROI of fixed size (575.99 μm x 399.9 μm, width x height) was used to measure the relative integrated density (mean gray values x area of the ROI) in all the images. The values from the left and right SCN were averaged per section and 5 sections per animal were averaged to obtain one value per animal………..”

Since the retinal innervation of the SCN is strongest in the ventral aspect, where the retino-hypothalamic fibers reach the SCN and our goal was to identify differences in the input to the SCN, e.g. defects in the retino-SCN connectivity as suggested by some deficits in circadian behaviour; we also looked at intensity of Cholera Toxin in the fibers arriving to the ventral SCN from the retina.

We have added a sentence in the methods about the rationale for measuring the intensity of the cholera toxin labelled fiber in the whole SCN and also just in the ventral part: “Second, the retinal innervation of the SCN is strongest in the ventral aspect, where the retino-hypothalamic fibers reach the SCN, hence, the distribution….”

Figure 6: Inconsistencies in terms like "Sleep frag. (bout #)" and "Sleep bouts (#)." Consistent terminology throughout the manuscript is essential.

We have now clearly explained that sleep bouts are a measure of sleep fragmentation throughout the manuscript and in the fig legends; in addition, we have corrected the figures, reconciled the terminology, which is now consistent throughout the results and methods.

Methods: “Sleep fragmentation was determined by the number of sleep bouts, which were operationally defined as episodes of continuous immobility with a sleep count greater than 3 per minute, persisting for at least 60 secs.”

(3) Figure 1A shows higher mouse activity during ZT13-16. It is unclear why the authors scheduled feeding during ZT15- 21, as this seems to disturb the rhythm. Consistent with this, the body weights of WT and Fmr1 KO mice decreased after scheduled feeding. The authors should explain the rationale for this design clearly.

We have added to the rationale for the feeding schedule. This protocol was initially used by the Panda group to counter metabolic dysfunction (Hatori et al., 2012). We have used it for many years now (see citations below) in various mouse models presenting with circadian disruption to reset the clock and improve sleep. This study represents our first application/intervention in a mouse model of a neurodevelopmental disease.

Hatori M, Vollmers C, Zarrinpar A, DiTacchio L, Bushong EA, Gill S, Leblanc M, Chaix A, Joens M, Fitzpatrick JA, Ellisman MH, Panda S. Time-restricted feeding without reducing caloric intake prevents metabolic diseases in mice fed a high-fat diet. Cell Metab. 2012 Jun 6;15(6):848-60. doi: 10.1016/j.cmet.2012.04.019. Epub 2012 May 17. PMID: 22608008; PMCID: PMC3491655.

Chiem E, Zhao K, Dell'Angelica D, Ghiani CA, Paul KN, Colwell CS. Scheduled feeding improves sleep in a mouse model of Huntington's disease. Front Neurosci. 2024 18:1427125. doi: 10.3389/fnins.2024.1427125. PMID: 39161652.

Whittaker DS, Akhmetova L, Carlin D, Romero H, Welsh DK, Colwell CS, Desplats P. Circadian modulation by time-restricted feeding rescues brain pathology and improves memory in mouse models of Alzheimer's disease. Cell Metab. 2023 35(10):1704- 1721.e6. doi: 10.1016/j.cmet.2023.07.014. PMID: 37607543

Brown MR, Sen SK, Mazzone A, Her TK, Xiong Y, Lee JH, Javeed N, Colwell CS, Rakshit K, LeBrasseur NK, Gaspar-Maia A, Ordog T, Matveyenko AV. Time-restricted feeding prevents deleterious metabolic effects of circadian disruption through epigenetic control of β cell function. Sci Adv. 2021 7(51):eabg6856. doi: 10.1126/sciadv.abg6856. PMID: 34910509

Whittaker DS, Loh DH, Wang HB, Tahara Y, Kuljis D, Cutler T, Ghiani CA, Shibata S, Block GD, Colwell CS. Circadian-based Treatment Strategy Effective in the BACHD Mouse Model of Huntington's Disease. J Biol Rhythms. 2018 33(5):535-554. doi: 10.1177/0748730418790401. PMID: 30084274.

Wang HB, Loh DH, Whittaker DS, Cutler T, Howland D, Colwell CS. Time-Restricted Feeding Improves Circadian Dysfunction as well as Motor Symptoms in the Q175 Mouse Model of Huntington's Disease. eNeuro. 2018 Jan 3;5(1):ENEURO.0431-17.2017. doi: 10.1523/ENEURO.0431-17.2017.

Loh DH, Jami SA, Flores RE, Truong D, Ghiani CA, O'Dell TJ, Colwell CS. Misaligned feeding impairs memories. Elife. 2015 4:e09460. doi: 10.7554/eLife.09460.

(4) The interpretation of social behavior results in Figure 6 is questionable. The authors claim that Fmr1 KO mice cannot remember the first stranger in a three-chamber test, writing, "The reduced time in exploring and staying in the novelmouse chamber suggested that the Fmr1 KO mutants were not able to distinguish the second novel mouse from the first now-familiar mouse." However, an alternative explanation is that Fmr1 KO mice do remember the first stranger but prefer to interact with it due to autistic-like tendencies. Data in Table 5 show that Fmr1 KO mice spent more time interacting with the first stranger in the 3-chamber social recognition test, which support this possibility. Similarly, in the five-trial social test, Fmr1 KO mice's preference for familiar mice might explain the reduced interaction with the second stranger.

Thank you for this interesting interpretation of the social behavior experiments. We used the common interpretations for both the three-chamber test and the 5-trial social interaction test, but have now modified the text leaving space for alternative interpretations, have soften the language, and mentioned decreased sociability in the Fmr1 KO mice. “The reduced time spent exploring the novel-mouse chamber suggest that the mutants were, perhaps, unable to distinguish the second novel mouse from the first, now familiar, mouse, along with decreased sociability.”

In Figure 6C (five-trial social test results), only the fifth trial results are shown. Data for trials 1-4 should be provided and compared with the fifth trial. The behavioral features of mice in the 5-trial test can then be shown completely. In addition, the total interaction times for trials 1-4 (154 {plus minus} 15.3 for WT and 150 {plus minus} 20.9 for Fmr1 KO) suggest normal sociability in Fmr1 KO mice (it is different from the results of 3-chamber). Thus, individual data for trials 1-4 are required to draw reliable conclusions.  

We have added a suppl figure showing the individual trial results for both WT and Fmr1 KO mice as requested (Suppl. Fig. 2).  

In Table 6 and Figure 6G-6J, the authors claim that "Sleep duration (Figures 6G, H) and fragmentation (Figures 6I, J) exhibited a moderate-strong correlation with both social recognition and grooming." However, Figure 6I shows a p-value of 0.077, which is not significant. Moreover, Table 6 shows no significant correlation between SNPI of the three-chamber social test and any sleep parameters. These data do not support the authors' conclusions. 

Thanks for pointing out the error with statement about Fig. 6I.

“…. Sleep duration (Fig. 6G, H; Table 6) exhibited a moderate to strong correlation with both social recognition and grooming time, while sleep fragmentation (measured by sleep bouts number) only correlated with the latter (Fig. 6J); the length of sleep bouts (Table 6) showed moderate correlation with both social recognition and repetitive behavior. In addition, a moderate correlation was seen between grooming time and the circadian parameters, rhythmic power and activity onset variability (Table 6). In short, our work suggests that even when tested during their circadian active phase, the Fmr1 KO mice exhibit robust repetitive and social behavioral deficits. Moreover, the shorter and more fragmented the daytime sleep, the more severe the behavioral impairment in the mutants.”

(5) Figure 7 demonstrates the effect of scheduled feeding on circadian activity and sleep behaviors, representing another critical set of results in the manuscript. Notably, the WT+ALF and Fmr1 KO+ALF groups in Figure 7 underwent the same handling as the WT and Fmr1 KO groups in Figures 1 and 2, as no special treatments were applied to these mice. However, the daily patterns observed in Figures 7A, 7B, 7F, and 7G differ substantially from those shown in Figures 2B and 1A, respectively. Additionally, it is unclear why the WT+ALF and Fmr1 KO+ALF groups did not exhibit differences in Figures 7I and 7J, especially considering that Fmr1 KO mice displayed more sleep bouts but shorter bout lengths in Figures 1C and 1D. 

We appreciate the reviewer’s attention to the subtle details of the behavioral measurement of sleep and believe the reviewer to be referring to differences in the behavioral measurements of sleep with data shown in Table 1 and Table 7. The first set of experiments described in this study was carried out between 2016 and 2017 and involves the comparison between WT and Fmr1 KO mice. The WT and mutants were obtained from JAX. In this initial set of experiments (Table 1), the total amount of sleep in 24 hrs was reduced in the KO, albeit not significantly, and these also exhibited sleep bouts of significantly reduced duration. The pandemic forced us to greatly slow down the research and reduce our mouse colonies. Post-pandemic, we used new cohorts of Fmr1 KO ordered again from JAX for the TRF experiment presented in this study. In these cohorts, the KO mice exhibited a significant reduction in total sleep (Table 7) and the sleep bouts were still shorter but not significantly. We have added to our text to explain that the description of the mutants and TRF interventions were carried out at different times (2017 vs 2022). We would like to emphasize that we always run contemporaneously controls and experimental groups to be used for the statistical analyses. We believe that the data are remarkably consistent over these years, even with different students doing the measurements. 

Furthermore, it is not specified whether the results in Figure 7 were collected after two weeks of scheduled feeding (for how many days?) or if they represent the average data from the two-week treatment period.

This is another good point raised by the reviewer. The activity measurements are collected during the 2 weeks (14 days) then the TRF was extended for a 3 more days to allow the behavioral sleep measurements.

We have added a supplementary figure (Supp Fig 3) depicting the different experimental designs.

The rationale behind analyzing "ZT 0-3 activity" in Figure 7D instead of the parameters shown in Figures 2C and 2D is also unclear. 

We have added to our explanation. In prior work, we found that the TRF protocol has a big impact on the beginning of the sleep time, hence, we specifically targeted this 3-hours interval in the analysis.

In Figure 7F, some data points appear to be incorrectly plotted. For instance, the dark blue circle at ZT13 connects to the light blue circle at ZT14 and the dark blue circle at ZT17. This is inconsistent, as the dark blue circle at ZT13 should link to the dark blue circle at ZT14. Similarly, it is perplexing that the dark blue circle at ZT16 connects to both the light blue and dark blue circles at ZT17. Such errors undermine confidence in the data. The authors need to provide a clear explanation of how these data were processed. 

Thank you for bringing this to our attention. The data were plotted correctly, however, those data points completely overlapped with those behind, masking them. We have now offset a bit them for clarity.

Lastly, in the Figure 7 legend, Table 6 is cited; however, this appears to be incorrect. It seems the authors intended to refer to Table 7. 

We have corrected this error, thank you.  

(6) Similar to the issue in Figure 7F, the data for day 12 in Supplemental Figure 2 includes two yellow triangles but lacks a green triangle. It is unclear how the authors constructed this chart, and clarification is needed. 

We have corrected this error. As the reviewer pointed out, we filled the triangle on day 12 with yellow instead of green.  

(7) In Figure 8, a 5-trial test was used to assess the effect of scheduled feeding on social behaviors. It is essential to present the results for all trials (1 to 4). Additionally, it is unclear whether the results for familial mice in Figure 8A correspond to trials 1, 2, 3, or 4. 

The legend for Figure 8 also appears to be incorrect: "The left panels show the time spent in social interactions when the second novel stranger mouse was introduced to the testing mouse in the 5-trial social interaction test. The significant differences were analyzed by two-way ANOVA followed by Holm-Sidak's multiple comparisons test with feeding treatment and genotype as factors." This description does not align with the content of the left panels. Moreover, two-way ANOVA is not the appropriate statistical analysis for Figure 8A. The authors need to provide accurate details about the analysis and revise the figure legend accordingly. 

We apologies for the confusing Figure legend which has been revised: 

“Fig. 8: TRF improved social memory and stereotypic grooming behavior in the Fmr1 KO mice. (A) Social memory was evaluated with the 5-trial social interaction test as described above. The social memory recognition was significantly augmented in the Fmr1 KO by the intervention, suggesting that the treated mutants were able to distinguish the novel mouse from the familiar mouse. The time spent in social interactions with the novel mouse in the 5^th-trial was increased to WT-like levels in the mutants on TRF. Paired t-tests were used to evaluate significant differences in the time spent interacting with the test mouse in the 4^th (familiar mouse) and 5^th (novel mouse) trials.  *P < 0.05 indicates the significant time spent with the novel mouse compared to the familiar mouse. (B) Grooming was assessed in a novel arena in mice of each genotype (WT, Fmr1 KO) under each feeding condition and the resulting data analyzed by two-way ANOVA followed by the Holm-Sidak’s multiple comparisons test with feeding regimen and genotype as factors. *P < 0.05 indicates the significant difference within genotype - between diet regimens , and #P < 0.05 those between genotypes - same feeding regimen. (C) TRF did not alter the overall locomotion in the treated mice. See Table 8.”

To assess social recognition memory, mice underwent a five-trial social interaction paradigm in a neutral open-field arena. Each trial lasted 5 minutes and was separated by a 1-minute inter-trial interval. During trials 1–4, the test mouse was exposed to the same conspecific (Stimulus A) enclosed within a wire cup to permit olfactory and limited tactile interaction. In trial 5, a novel conspecific (Stimulus B) was introduced. Time spent investigating the stimulus B mouse (defined as sniffing or directing the nose toward the enclosure within close proximity) was scored using AnyMaze software. A progressive decrease in investigation time across trials 1–4 reflects habituation, while a significant increase in trial 5 indicates dishabituation and intact social recognition memory. In our data, there was not a lot of habituation in both genotypes, but clear differences can be appreciated between trial 4 with the now familiar mouse and trial 5 with novel mouse. Fig. 8A plots the results from individual animals in Trial 4 with a familiar mouse and in Trial 5 with a novel mouse, we have well specified this in the legends. As such, these data were analyzed with a pair t-test. 

We used Tow-Way ANOVA to analyse the data reported in Panel 8B and as well as the results in Table 8.  This has been clarified in the legend.

(8) The circadian activity and sleep behaviors of Fmr1 KO mice have been reported previously, with some findings consistent with the current manuscript, while others contradict it. Although the authors acknowledge this discrepancy, it seems insufficiently thorough to simply state that the reasons for the conflicts are unknown. Did the studies use the same equipment for behavior recording? Were the same parameters used to define locomotor activity and sleep behaviors? The authors are encouraged to investigate these details further, as doing so may uncover something interesting or significant. 

We agree with the reviewers, and believe that the main differences were likely in the experimental design and possibly interpretation.

(9) Some subtitles in the Results section and the figure legends do not align well with the presented data. For example, in the section titled "Reduced rhythmic strength and nocturnality in the Fmr1 KOs," it is unclear how the authors justify the claim of altered nocturnality in Fmr1 KO mice. How do the authors define changes in nocturnality? Additionally, the tense used in the subtitles and figure legends is incorrect. The authors are encouraged to carefully review all subtitles and figure legends to correct these errors and enhance readability. 

Nocturnality is defined as the % of total activity within a 24-h cycle that occurred in the night, since this can be confusing and we agree that it was not well explained we have removed it from the subtitle/figure legends. 

We have adjusted the subtitles as recommended; however, the tense of the verbs might be a matter of writing style.

Reviewer #2 (Public review): 

Summary: 

In the present study, the authors, using a mouse model of Fragile X syndrome, explore the very interesting hypothesis that restricting food access over a daily schedule will improve sleep patterns and, subsequently, behavioral capacities. By restricting food access from 12h to 6h over the nocturnal period (active period for mice), they show, in these KO mice, an improvement of the sleep pattern accompanied by reduced systemic levels of inflammatory markers and improved behavior. Using a classical mouse model of neurodevelopmental disorder (NDD), these data suggest that eating patterns might improve sleep quality, reduce inflammation and improve cognitive/behavioral capacities in children with NDD. 

Strengths: 

Overall, the paper is very well-written and easy to follow. The rationale of the study is generally well-introduced. The data are globally sound. The provided data support the interpretation overall. 

Thank you for the positive comments.  

Weaknesses:  

(1) The introduction part is quite long in the Abstract, leaving limited space for the data provided by the present study.

We have revised the Abstract to better focus on the most impactful findings as suggested. 

(2) A couple of points are not totally clear for a non-expert reader:  - The Fmr1/Fxr2 double KO mice are not well described. What is the rationale for performing both LD and DD measures? 

We did not use the Fmr1/Fxr2 double KO mice in this study.  

While measurement of day/night differences in activity rhythms are standardly done in a light/dark (LD) cycle, the organisms must be under constant conditions (DD) to measure their endogenous circadian rhythms (free running activity); this is often needed to uncover a compromised clock as entrainment to the LD cycle can mask deficits in the endogenous circadian rhythms.

(3) The data on cytokines and chemokines are interesting. However, the rationale for the selection of these molecules is not given. In addition, these measures have been performed in the systemic blood. Measures in the brain could be very informative. 

The panel that we used had 16 cytokines/chemokines which are reported in Table 9. The experiment included WT and mutants held under 2 different feeding conditions with an n=8 per group. If we are able to obtain more resources, we would like to also carry out a comprehensive investigation of immunomediator levels as well as RNA-seq or Nanostring in selected brain regions associated with ASD aberrant behavioural phenotypes, for instance the prefrontal cortex.

(4) An important question is the potential impact of fasting vs the impact of the food availability restriction. Indeed, fasting has several effects on brain functioning including cognitive functions. 

We did not address this issue in the present study. Briefly, the distinction between caloric restriction (CR) and TRF, in which no calories are restricted, has important mechanistic implications in mouse models. While both interventions can impact metabolism, circadian rhythms, and aging, they operate via overlapping but distinct molecular pathways. These have been the topic of recent reviews and investigations. Importantly, the fast-feed cycle can also act as a circadian entrainer (Zeitgeber)

Ribas-Latre A, Fernández-Veledo S, Vendrell J. Time-restricted eating, the clock ticking behind the scenes. Front Pharmacol. 2024 Aug 8;15:1428601. doi: 10.3389/fphar.2024.1428601. PMID: 39175542; PMCID: PMC11338815.

Wang R, Liao Y, Deng Y, Shuang R. Unraveling the Health Benefits and Mechanisms of Time-Restricted Feeding: Beyond Caloric Restriction. Nutr Rev. 2025 Mar 1;83(3):e1209-e1224. doi: 10.1093/nutrit/nuae074.

(5) How do the authors envision the potential translation of the present study to human patients? How to translate the 12 to 6 hours of food access in mice to children with Fragile X syndrome? 

Time-restricted feeding (TRF) is a type of intermittent fasting that limits food intake to a specific window of time each day (usually 8–12 hours in humans), is being actively studied in adults for benefits on metabolic health, sleep, and circadian rhythms. However, applying TRF to children is not currently recommended as a general intervention, and there are important developmental, medical, and ethical considerations to take into account.  

On the other hand, we believe that the Fmr1 KO mouse is a good preclinical model for FXS because it closely recapitulates key molecular, cellular, and behavioral phenotypes observed in humans with the disorder. A number of the behavioral phenotypes seen in the mouse mirror those seen in patients including increased anxiety-like behavior, sensory hypersensitivity, social interaction deficits and repetitive behaviors so there is strong face validity.  

As we show in this study, Fmr1 KO mice present with disrupted sleep/wake cycles and reduced amplitude of circadian rhythms, consistent with findings in individuals with FXS. This makes the Fmr1 KO an excellent model to test out circadian based interventions such as scheduled feeding.

We believe that pre-clinical research in Fmr1 KO mice bridges the gap between basic discovery and human clinical application. It provides a controlled, cost-effective, and biologically relevant platform for understanding disease mechanisms and testing interventions. These types of experiments need to be done before jumping to humans to ensure that the human trials are scientifically justified and ethically sound.

Reviewer #1 (Recommendations for the authors): 

The authors should: 

(1) Revise the Methods section for clarity and completeness.  

We have re-worked the methods for clarity and completeness. 

(2) Provide consistent and precise definitions for all parameters and terms.  

We believe that we have provided definitions for all terms.  

(3) Clarify the rationale for experimental designs, such as the feeding schedule.  

We have added to the rationale for the feeding schedule.  This feeding schedule has been used in a number of prior studies including our own.  All this work is cited in the manuscript.   

(4) Reanalyze and transparently present data, including individual trial results.  

We have added to the figure showing the individual trail results for the 5-trial tests as requested (Supplementary Fig. 2).  

(5) Conduct appropriate statistical tests and correct figure legends.  

We believe that we have carried out appropriate statistical tests and have carefully rechecked the figure legends.  

(6) Investigate discrepancies with prior studies to enhance the discussion. 

We have added to our discussion of prior work. 

(7) Improve language quality and ensure consistency in terminology and grammar.  

We have edited the manuscript to improve language quality.  

Reviewer #2 (Recommendations for the authors): 

(1) The Abstract should be rewritten to provide more room for the obtained data.  

We have re-written the Abstract to focus on the most impactful findings. 

(2) An additional sentence describing the double KO mice should be added.  

We did not use double KO mice in this study.  

(3) The rationale for studying LD and DD should be provided. 

Measurement of day/night differences are standardly done in a light/dark cycle.  To measure the endogenous circadian rhythms, the organisms must be under constant conditions (Dark/Dark).

(4) The data on cytokines/chemokines should be strengthened by performing a larger panel of measures both in blood and the brain.  

The panel that we used had 16 cytokines/chemokines which we report in Table 9.  This was a large experiment with 2 genotypes being held under 2 feeding conditions with n=8 mice per group. If we are able to obtain more resources, we would like to also carry out RNA-seq in different brain regions.  

(5) The authors should discuss in more detail the potential role of fastening vs restriction of food access.  

We did not address this issue in the present study.  Briefly, the distinction between caloric restriction (CR) and TRF when no calories are restricted has important mechanistic implications in mouse models. While both interventions can impact metabolism, circadian rhythms, and aging, they operate via overlapping but distinct molecular pathways. 

(6) The authors should also provide some insight into their view on the potential translation of their experimental studies.  

We believe that the Fmr1 KO mouse is considered a good preclinical model for FXS because it closely recapitulates key molecular, cellular, and behavioral phenotypes observed in humans with the disorder. A number of the behavioral phenotypes seen in the mouse mirror those seen in patients including increased anxiety-like behavior, sensory hypersensitivity, social interaction deficits and repetitive behaviors so there is strong face validity.   As we  demonstrate in this study, Fmr1 KO mice exibit disrupted sleep/wake cycles and reduced amplitude of circadian rhythms, consistent with findings in individuals with FXS.  This makes the Fmr1 KO an excellent model to test out circadian based interventions such as scheduled feeding.  

Still we are mindful that the translation of therapeutic findings from mouse to human has proven challenging e.g., mGluR5 antagonists failed in clinical trials despite strong preclinical data (Berry-Kravis et al., 2016).  Therefore, we are cautious in overreaching in our translational interpretations. 

Berry-Kravis, E., Des Portes, V., Hagerman, R., Jacquemont, S., Charles, P., Visootsak, J., Brinkman, M., Rerat, K., Koumaras, B., Zhu, L., Barth, G. M., Jaecklin, T., Apostol, G., & von Raison, F. (2016). Mavoglurant in fragile X syndrome: Results of two randomized, double-blind, placebo-controlled trials. Science translational medicine, 8(321), 321ra5. https://doi.org/10.1126/scitranslmed.aab4109).

Reviewer #3 (Public review):

Summary

This study investigates how task components can be learned and transferred across different task contexts. The authors designed two consecutive sequence learning tasks, in which complex image sequences were generated from the combination of two graph-based structural "building blocks". One of these components was shared between the prior and transfer task environments, allowing the authors to test compositional transfer. Behavioral analyses using generalized linear models (GLMs) assessed participants' sensitivity to the underlying structure. MEG data were recorded and analyzed using classifications and feature representational similarity analysis (RSA) to examine whether neural similarity increased for stimuli sharing the same relational structure. The paper aims to uncover the neural dynamics that support compositional transfer during learning.

Strengths and weaknesses

I found the methods and task design of this paper difficult to follow, particularly the way stimuli were constructed and how the experimental sequences were generated from the graph structures. These aspects would be hard to replicate without some clarification. I appreciate the integration of behavioral and neuroimaging data. The overall approach, especially the use of compositional graph structures in sequence learning, is interesting and could be used and revised in further studies in compositionality and transfer learning. I appreciated the authors' careful interpretation of their findings in the discussion. However, I would have liked a similar level of caution in the abstract, which currently overstates some claims.

Major Comments:

(1) While the introduction mentions brain areas implicated in the low-dimensional representation of task knowledge, the current study uses M/EEG and does not include source reconstruction. As a result, the focus is primarily on the temporal dynamics of the signal rather than its spatial origins. Although I am not suggesting that the authors should perform source reconstruction in this study, it would strengthen the paper to introduce the broader M/EEG literature on task-relevant representations and transfer. The same applies to behavioral studies looking at structural similarities and transfer learning. I encourage the authors to integrate relevant literature to better contextualize their results.

Duan, Y., Zhan, J., Gross, J., Ince, R. A. & Schyns, P. G. Pre-frontal cortex guides dimension-reducing transformations in the occipito-ventral pathway for categorization behaviors. Current Biology 34, 3392-3404 (2024).

Luyckx, F., Nili, H., Spitzer, B. & Summerfield, C. Neural structure mapping in human probabilistic reward learning. eLife 8, e42816 (2019). (This is in the references but not in the text).

Zhang, M. & Yu, Q. The representation of abstract goals in working memory is supported by task-congruent neural geometry. PLoS biology 22, e3002461 (2024).

L. Teichmann, T. Grootswagers, T. Carlson, A.N. Rich Decoding digits and dice with magnetoencephalography: evidence for a shared representation of magnitude Journal of cognitive neuroscience, 30 (7) (2018), pp. 999-1010

Garner, K., Lynch, C. R. & Dux, P. E. Transfer of training benefits requires rules we cannot see (or hear). Journal of Experimental Psychology: Human Perception and Performance 42, 1148 (2016).

Holton, E., Braun, L., Thompson, J., Grohn, J. & Summerfield, C. Humans and neural networks show similar patterns of transfer and interference during continual learning (2025).

(2) I found it interesting that the authors chose to perform PCA for dimensionality reduction prior to conducting RSA; however, I haven't seen such an approach in the literature before. It would be helpful to either cite prior studies that have employed a similar method or to include a comparison with more standard approaches, such as sensor-level RSA or sensor-searchlight analysis.

(3) Connected to the previous point, the choice to use absolute distance as a dissimilarity measure is not justified. How does it compare to standard metrics such as correlation distance or Mahalanobis distance? The same applies to the use of Kendall's tau.

(4) The analysis described in the "Abstract representation of dynamical roles in subprocesses" does not appear to convincingly test the stated prediction of a structural scaffolding account. The authors hypothesize that if structure and dynamics from prior experiences are repurposed, then stimuli occupying the same "dynamical roles" across different sequences should exhibit enhanced neural similarity. However, the analysis seems to focus on decoding transitions rather than directly assessing representational similarity. Rather, this approach may reflect shared temporal representation in the sequences without necessarily indicating that the neural system generalizes the abstract function or position of a stimulus within the graph. To truly demonstrate that the brain captures the dynamical role across different stimuli, it would be more appropriate to directly assess whether neural patterns evoked by stimuli, in the same temporal part of the sequence, with shared roles (but different visual identities) are more similar to each other than to those from different roles.

(5) In the following section, the authors correlate decoding accuracy with participants' behavioral performance across different conditions. However, out of the four reported correlations and the additional comparison of differences between conditions, only one correlation and one correlation difference reach significance, and only marginally so. The interpretation of this finding should therefore be more cautious, especially if it is used to support a link between neural representations and behavior. Additionally, it is possible that correlation with a more clearly defined or targeted neural signature, more directly tied to the hypothesized representational content, could yield stronger or more interpretable correlations.

Minor Comments:

During preprocessing, sensors were excluded based on an identified noise level. However, the authors do not specify the threshold used to define this noise level, nor do they report how many sensors were excluded per participant. It would be helpful to have these details. Additionally, it is unclear why the authors opted to exclude sensors rather than removing noise with MaxFiltering or interpolating bad sensors. Finally, the authors should report how many trials were discarded on average (and standard deviation) per participant.

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Referee #2

Evidence, reproducibility and clarity

SUMMARY OF THE PRESENTED FINDINGS

Abstract

1. LCOR (Ligand-dependent corepressor), which suppresses tumor growth by inducing the antigen presentation machinery (APM) of the tumor cells and constrains cellular plasticity.
2. poly β-(amino esters) (pBAE) nanoparticles (NPs).. Our results show optimal endosomal escape, which results in high transfection efficiency in vitro and in vivo
3. the combination of Lcor mRNA-loaded NPs with anti-PDL1 or anti-CTLA4 immunotherapies eradicated most of the tumors in our preclinical TNBC model.

Introduction

a. These structures facilitate endosomal escape due to protonation of tertiary amines at lower pH7.

Results

b. In human models MDAMB-231 and MCF7 cells, the NPs also showed high eGFP mRNA transfection efficiency

c. The efficiency of eGFP mRNA-loaded pBAE-NPs to transfect mRNA into different mouse breast cancer cells (AT3, 4T07, EO771, EMT6, 66cl4, EpRAS, and 4T1) was tested using NPs encapsulating eGFP mRNA,

d. Synthetic Lcor mRNA contained a Cap1, 5' and 3' untranslated regions (UTR) and a standard polyA tail (Fig. S2A), and all uracil were replaced for 5-methoxyuracil (5-moU) to avoid immunogenic reactions27,28. First, we measured and detected high levels of Lcor mRNA by qRT-PCR

e. NPs were stable at 25ºC for 24 h (Fig. S2C). In contrast, under conditions simulating the physiological environment (37ºC), a decrease in FRET signaling was detected ... indicating disassembly of the NPs after 2 h (Fig. S2C).

f. Lcor mRNA NPs, induces the expression of APM genes in AT3 and 4T07 cell lines

g. AT3 cells that constitutively overexpress ovalbumin (OVA). In these cells, OVA is cleaved, generating the SIINFEKL antigen peptide presented in the H-2Kb context. This can be used to measure APM activity using the anti-SIINFEKL antibody via flow cytometry.

h. We also observed a time- and dose-dependent effect regarding APM induction.

i. When tumors reached 0.5 x 0.5 cm2, we treated them intratumorally with pBAE-NPs loaded with 5 ug of synthetic FLuc or eGFP mRNA. We detected BLI at 3 h, meaning that tumor cells had taken up the mRNA-loaded NPs and translated a luciferase active protein within 3 h. In both models, expression peaked around 6 to 10 hours after administration

j. After local administration of 5 μg of Lcor mRNA-loaded NPs, we observed a rapid increase in Lcor mRNA in the tumor tissue, followed by a decrease, reaching baseline levels after 24 h (Fig. 3C). ..To unravel the protein dynamics, we used ... LCOR-HA protein and uniquely detect the ectopic protein using anti-HA by IF. As expected, LCOR-HA protein expression was delayed, peaking 3 h after administration (Fig. 3D). Linked to protein expression, at 3 h and 6 h after administration, we detected an increase in APM genes by RT-qPCR (Fig. 3E and S3D).

k. the combination of Lcor mRNA-loaded NPs with anti-PDL1 therapy not only reduced tumor growth but also led to tumor eradication in 5 out of 7 mice.

l. The combination of Lcor mRNA-loaded NPs with different ICIs showed high efficiency in preclinical models, thus supporting the feasibility of starting clinical studies and thus bringing the treatment closer to patients.

Major points

L. 277: "NPs were stable at 25ºC for 24 h (Fig. S2C). In contrast, under conditions simulating the physiological environment (37ºC), a decrease in FRET signaling was detected ... indicating disassembly of the NPs after 2 h (Fig. S2C)." - The disassembly of the NPs after 2 h is key to the performance of the chosen approach.

L. 296: "The results showed an increased number of cells with higher OVA-SIINFEKL presentation, indicating the enhanced activity of the APM induced by the Lcor mRNA-loaded pBAE-NPs... demonstrate the efficiency of this mRNA nanotechnology to rescue the function of the LCOR TF in inducing tumor cell immunogenicity and thus modulating tumor phenotypes." - There is a key difference between activating antigen-presenting machinary and inducing immunogenicity, i.e. recognition by the immune system and activation of effector cells. There is no indication on how effective endogenous immune responses (e.g. antibody titers, TIL infiltration, cytokine release) are to the administration of Lcor mRNA-loaded NPs.

L. 325: "Based on these results, we estimated an optimal therapeutic regimen of Lcor-mRNA-loaded pBAE-NPs administration in our preclinical experimental models would be every 3 days." - It is highly unclear how the authors came to this conclusion, as it should be based on the time frame of optimal immune responses.

L. 332: "Lcor mRNA-loaded NPs were administered at a dose of 250 μg/kg by intratumoral (i.t.) injection twice a week" - This possibly is the strongest limitation of this study. Intratumor injections of largely unfeasible/unrealistic in clinical setting. Even more, the management of metastatic disease appears out of question.

L. 337: "the results revealed that Lcor mRNA monotherapy was enough to reduce 4T07 tumor 338 growth." - These effects appear rather limited (Fig. 4A,B) and are not statistically significant in Fig. S4B and Fig. S5A.

L. 338: "the combination of Lcor mRNA-loaded NPs with anti-PDL1 therapy not only reduced tumor growth but also led to tumor eradication in 5 out of 7 mice" - Fig. 4A bottom left panel. Three of the tumor growth curves abruptly stop at below 200 mm3. Typically, this is mouse death. This reduces the tumor pool to four xenografts. Among these, we notice two complete responses and two tumor progressions. Two tumor progressions are seen also in the combination Lcor mRNA+ α-PD-L1 group. We are unsure about the statistics of this experiment.

L. 350: "The combination of Lcor mRNA-loaded NPs with different ICIs showed high efficiency in preclinical models, thus supporting the feasibility of starting clinical studies and thus bringing the treatment closer to patients."

• Please see comment on L. 332. It appears unrealistic to consider clinical studies in patients unless a systemic administration of Lcor mRNA-loaded NPs is tackled and corresponding therapeutic efficacy is shown.

Significance

General assessment:strengths and limitations.

The identification of a candidate therapeutic means, by supplying Lcor mRNA for induction of antigen-presenting molecules is of potential interest. As this is not a basic science study, but aims at developing feasible therapeutics, it falls short in this respect, as most likely unfeasible in patients. The combined effect with anti-immune blockade agents is of interest. However, if one assumes that effective immunostimulation was indeed induced by Lcor mRNA, its overall impact on tumor growth is per se weak, if any. Maybe only antigen presentation is induced, but this is in the absence of costimulatory signals? This needs to be investigated.

Advance

This article is based on good papers that were published years ago. The science novelty is limited. As the idea is to develop a novel therapeutic approach, the lack of realistic feasibility severely limits merits.

Audience

Scientists involved in preclinical studies.

Reviewer expertise

This reviewer and his research group have cloned the genes and biochemically characterized novel tumor drivers. He identified their function as stimulators of tumor cell growth and of metastatic spreading, together with roles in cell-cell adhesion, signal transduction and local cancer invasion. This led to the discovery of their prognostic / predictive relevance in human cancer. Two murine models of rare genetic diseases were generated by ablating the corresponding murine genes. He then pioneered the development of software for the identification of fusion oncogenes and of transcription factor-DNA binding sites. This reviewer fostered novel anti-cancer immunotherapies. He generated anti-cancer cytotoxic T lymphocytes, by the use of in vitro engineered antigen presenting cells. Using proprietary discovery platforms, this reviewer developed novel anti-cancer monoclonal antibodies, that selectively target cancer cells. This led to the engineering of humanized antibody-drug conjugates, bispecific anti-CD3/activated Trop-2 antibodies and innovative CAR-T designs. ADCs are now being tested in clinical trials in cancer patients.

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Reply to the reviewers

Manuscript number: RC-2023-02191

Corresponding author: Jan Rehwinkel

1. General Statements

The authors wish to thank all three reviewers and the Review Commons team for carefully evaluating our study. We have addressed all points raised as detailed below.

We have thoroughly revised our bulk RNAseq analysis, which is now performed at the transcript level using the latest GENCODE release. We have updated Figure 3 and associated supplementary figures and tables. This change from gene to transcript level was important for accurate motif analysis as requested by reviewer 2: matching promoters to individual IFN-regulated transcripts – rather than aggregating all promoters per gene – avoids significant signal dilution. This strategy yields higher-resolution expression data and is biologically preferable. Indeed, several well characterised IFN-regulated RNAs (e.g., the ADAR1-202 transcript encoding the p150 isoform) originate from promoters located far from the constitutive promoters of their host genes. In our revised manuscript, we now provide in the new supplementary figure 13 the requested promoter motif analysis. Using two computational approaches – de novo motif search and analysis of a curated motif database – we find strong enrichment of interferon-stimulated response elements (ISREs) in promoters of type I IFN regulated transcripts. No other motifs reached similarly high levels of enrichment, and our analysis did not reveal differences between different type I IFNs. These new data show that all type I IFNs engage a common regulatory pathway, supporting our overall conclusion that different type I IFNs do not induce qualitatively different responses in PBMCs.

Regrettably, in the process of analysing the bulk RNAseq data at transcript level, we noticed that our original lncRNA analysis contained numerous false positives. Closer inspection showed that many “differentially expressed” LNCipedia models were likely not full-length transcripts and commonly shared a single IFN-induced set of exons that artificially inflated expression estimates for every overlapping model. To correct this issue, we replaced LNCipedia with the latest high-quality non-coding RNA catalogue from GENCODE, most entries of which were defined by full-length RNA sequencing [1]. We also tightened our filtering criteria and now report only transcripts that are robustly expressed in our dataset and are either classified as high-confidence by GENCODE or robustly supported at every splice junction by our RNAseq.

We hope our manuscript is sufficiently improved and suitable for publication in PLoS Biology. New or revised text is highlighted in green in our revised manuscript.

2. Point-by-point description of the revisions

Reviewer #1

Evidence, reproducibility and clarity:

The study can be directly connected to a landmark paper in the field (Mostafavi et al. , Cell 2016). By comparison with this study, the authors use improved technologies to address the question if and how responses to type I IFN differ between human peripheral blood-derived cells types. In line with Mostafavi et al. the authors conclude that only a comparably low number of interferon-stimulated genes (ISG) is induced in all cell types and that considerable differences exist between cell types in the IFN-induced transcriptome. The authors address a second relevant aspect, whether and how the many different subtypes of type I IFN differ in the way they engage IFN signals to produce transcriptome changes. The data lead the authors to conclude that any differences are of quantitative rather than qualitative nature.

The authors' conclusions are based on a mass cytometry approach to phenotype STAT activation in different cell types, bulk RNA sequencing to study ISG expression in PBMC, and single cell sequencing to study ISG responses in individual cell types. The data are solid, clear and reproducible in biological replicates (eg different blood donors).

Significance: While some of the data can be considered confirmatory, the comprehensive analysis of cell-type specificity and IFN-I subtype specificity advances the field and provides a reference for future analyses. The study is complete and there is no obvious lack of a critical experiment. The number of scientists interested in the multitude of open questions around type I IFN is large, thus the study is likely to attract a broad readership.

We thank the reviewer for her/his positive assessment of our study.

The biggest limitation is to my opinion the low sequencing depth of scRNAseq which is clearly the downside of this technology. Using 11 hematopoietic cell types and bulk RNA sequencing the total number of ISG was determined to be 975 by Mostafavi et al. and the core ISG numbered 166. This is in stark contrast to this studies' 10 core ISG. The authors limitations paragraph should discuss the fact that scRNAseq reduces the overall ISG number that can be analyzed.

Thank you for this valid comment. We amended the limitations paragraph as requested. We agree that the Mostafavi et al. 2016 Cell paper [2] is important but note that there are many differences to our study: Mostafavi et al. use mice, a seemingly very high IFN dose (10,000 Units) and microarrays (not RNAseq).

A minor point concerns the 25 supplementary figures of the study. There must be a better way to support the conclusions with the necessary data.

We agree that our supplementary materials are extensive. However, this is not unusual for studies reporting multiple large datasets. We would be delighted to organise our supplementary information differently in due course according to journal guidelines.

Reviewer #2

Evidence, reproducibility and clarity:

The manuscript entitled “Single-cell analysis of signalling and transcriptional responses to type I interferon" by Rigby et al. examines the response to type I IFN subtypes in PBMCs using an integrative proteomics and transcriptomics approach. Some of the analysis could be deepened to provide better insights into what governs the magnitude of change in gene expression as well as the cell type-specific response to expression and generate more excitement for the study.

We thank the reviewer for evaluating our study and the suggestions made.

*Major Comments: *

• Although the authors appropriately conclude that type I IFNs induce qualitatively similar, the response is not quantitatively similar. What elements in the promoters of ISGs make them more responsive to IFN subtypes? (PMID: 32847859) We thank the reviewer for the suggestion to study the promoters of genes regulated by type I IFNs. The analyses outlined below were performed by A. Fedorov, who is now a new co-author of our study. To investigate promoter features that might underlie the observed transcriptional responses across type I IFNs, we first performed a de novo*motif search using STREME [3] on our bulk RNAseq dataset (Figure 3). Specifically, we compared the promoters of transcripts that were up- or down-regulated by each IFN subtype (e.g., IFN-β-induced) either with one another or with promoters of robustly expressed RNAs that remained unresponsive to any treatment. No significant motifs emerged from these comparisons, except when we compared promoters of IFN-induced transcripts to the background set of unresponsive RNAs. This comparison consistently yielded strong enrichment of interferon-stimulated response element (ISRE)-like motifs in the promoters of up-regulated RNAs (new Figure S13a).

Next, we conducted a complementary analysis using known transcription factor (TF) motifs from the JASPAR database [4]. We screened all promoters of annotated RNAs using clustered JASPAR motifs and Z-standardised motif scores relative to all high-confidence GENCODE RNAs, including those not expressed in PBMCs. We reasoned that TFs actively mediating IFN responses would likely bind promoters with high motif scores (Z ≥ 2), while promoters with low scores (Z ≤ -1) would represent an unregulated background. This approach produced two sets of RNAs per TF cluster: putatively regulated and unregulated. We then restricted each set to RNAs expressed in our dataset and associated each transcript with its estimated fold change in response to each type I IFN, regardless of statistical significance. Next, we compared median fold changes between the likely regulated and unregulated sets across all TF clusters and IFN subtypes (Figure S13b). Among all tested TF motifs, only the ISRE-like cluster showed strong and consistent associations with transcriptional changes across all IFN subtypes. We also observed statistically significant but much weaker associations for other TFs, including a known negative regulator of innate antiviral signaling, NRF1 [5]. However, effect sizes for these motifs were dwarfed by those of ISRE-like motifs, suggesting that no JASPAR TFs other than those within the ISRE-like cluster play a major role in PBMCs under our conditions. Overall, these findings support the idea that all type I IFNs engage a common regulatory pathway, differing primarily in the magnitude rather than the nature of their transcriptional effects.

How do they relate to the activation of kinases by IFN subtypes?

We did not analyse the activation of the canonical kinases (i.e., TYK2 and JAK1) downstream of IFNAR. This would be interesting and may be possible using phospho-specific antibodies to these kinases in our CyTOF setup. However, this would require a very large investment of time and resources to identify specific antibodies, optimise a new CyTOF staining panel and to acquire and analyse new datasets. We therefore believe this should be pursued as a separate future study.

*Are there distinct features that dictate differential responses in monocytes and lymphocytes? *

Following the computational approach described above, we applied STREME to identify DNA motifs that could distinguish promoters associated with monocyte- and lymphocyte-specific ISGs. Regrettably, this analysis did not yield any significant motifs, likely due in part to the limited number of genes in each category.

• Figure 2a, d-h - Consider using the same scale for all heatmaps. This will allow for comparison of pSTATs median expression. Consider increasing the range in the color scale as some of the subtle changes in STAT phosphorylation across subtypes are not well appreciated. This also applies to Supplementary figures related to Figure 2.*

Thank you for this suggestion. We tried using the same scale for all heatmaps. However, given that the values for pSTAT1 are higher than those for other pSTATs, the resulting heatmaps did not show differences for the other pSTATs well. We therefore decided to leave these panels unchanged. Please also note that Figures 2b and S3b provide comparison between pSTATs (and other markers) using the same scale.

Minor Comments:

• The title of subsections are a bit generic (e.g "Analysis of the signalling response to type I IFNs using mass cytometry". Consider updating them to reflect some of the findings from each analysis.* Thank you for this suggestion. We have amended sub-headers accordingly.

• Figure 3 and S3 - Increase the heatmap scale to better appreciate changes in gene expression.*

The scales have been enlarged for better visibility as requested.

• Consider combining panel a and b in figure S7 for better contrasts of the response to IFNa1 or IFNb. *

Thank you for the suggestion. We combined these panels.

• Figure 4 - The authors could visualize ISGs that are unique across IFN types or cell types. *

Figure 5 and several accompanying supplementary figures already depict ISGs unique to IFN subtypes or cell types. Whilst we appreciate the suggestion, we prefer not to add additional figures to avoid redundancies.

• The gene ontology analysis should be performed with higher statistical stringency to capture the most significant IFN responsive processes. *

Thank you for this comment. We changed the presentation of the GO analysis in Fig S11 by sorting on p-value (instead of % of hits in category). We hope this shows more clearly that GO category enrichment amongst genes encoding IFN-induced transcripts had high statistical significance (log10 p-values of about -5 or lower for many categories).

Significance:* ** The authors provide an extensive compendium of cell type specific changes in response to type I IFN stimulation. They have created a public repository which extends the value of this dataset. *

Audience: *** This is a valuable resource for immunologists, virologists, and bioinformaticians.*

Thank you for these encouraging comments.

Reviewer #3

Evidence, reproducibility and clarity:

*Summary *

Rigby and collaborators analyzed the signaling responses and changes in gene expression of human PBMCs stimulated with different IFN type I subtypes, using mass cytometry, bulk and single-cell RNA sequencing. Their study represents the first single-cell atlas of human PBMCs stimulated with five type I IFN subtypes. The generated datasets are useful resources for anyone interested in innate immunity. The data and the methods are well presented. We thus recommend publication.

Thank you for your positive assessment of our work and for recommending publication.

*Major comments: *

• *

*Two of the key conclusions are not very convincing. *

• *

First, the authors claim that the magnitude of the responses varied between the 5 types of IFNs, however, as they point out in the 'limitation' paragraph, doses of the different IFNs were normalized using bioactivity. Knowing that this bioactivity is based on assays performed on A549 lung cells, this normalization likely induces a bias. How do the authors explain similar antiviral bioactivity but differing magnitudes of modulation of ISG expression? Would the authors expect the same differences of expression between the several IFNs tested in A549 cells? We thus recommend being very cautious when comparing magnitude of the response between the 5 types of IFNs.

We thank the reviewer for this important point and included the following reasoning in our discussion:

“An important technical consideration for our study was the normalisation of type I IFN doses used to treat cells (see also ‘Limitations of the study’ below). We relied on bioactivity (U/ml) that is measured by the manufacturer of recombinant type I IFNs using a cytopathic effect (CPE) inhibition assay. In brief, the lung cancer cell line A549 is treated with type I IFN and is infected with the cytopathic encephalomyocarditis virus (EMCV). Control cells not treated with IFN are killed by EMCV, whereas cells treated with sufficient IFN survive. How, then, is it possible that different type I IFNs induce differing magnitudes of STAT phosphorylation and ISG expression despite being used at the same bioactivity? Cell survival in the CPE inhibition assay may be due to one or a few ISGs. Indeed, single ISGs can mediate powerful antiviral defence. For example, MX1 is crucial for host defence against influenza A virus [6]. Thus, similar bioactivity of different IFNs in A549 cells against EMCV-triggered cell death may not reflect the breadth of effects on many ISGs. Moreover, IFN-induced survival of A549 cells following EMCV infection is a binary readout. Induction of the relevant ISG(s) mediating protection beyond a threshold required for cell survival is unlikely to register in this assay. Thus, similar antiviral bioactivity (in the CPE inhibition assay) and differing magnitudes of modulation of ISG expression (at transcriptome level) are compatible.”

We believe inclusion of this paragraph demonstrates an appropriate level of caution in our data interpretation. Further, we would expect to make similar observations if we were to apply transcriptomic analysis to A549 cells treated with different type I IFNs. However, given our focus in this study on primary, normal cells, we decided not to pursue work with the transformed and lab adapted A549 cell line.

Second, the qualitatively different responses to type I IFN subtypes claimed by the authors were not apparent. This seems true at the level of the bulk population (Fig. S10) but not at cell-type level (Fig. S15/S16).

We believe there may be a misunderstanding here. In relation to Figure S10, we do not claim “qualitatively different responses to type I IFN subtypes”. Instead, we conclude that “differences in expression between the different type I IFNs were quantitative” (page 8; lines 229-230, now: 238-239). Moreover, Figures S15/S16 (now: S16/S17) do not refer to analyses of responses to different type I IFN subtypes.

The authors state (line 311-312) that 'Consistent with our bulk RNAseq data, differences were again quantitative rather than qualitative' at the cell-type level. The response between cell types seems very different to us since a core set of only 10 ISGs are shared by all cell types and all 5 type I IFNs. Knowing that the expression of hundreds, sometimes thousands of genes, are induced by IFN, this seems like a rather small overlap (and thus qualitatively different responses). Fig S15 and S16 nicely illustrate that the responses are qualitatively different between cell-type. Please modify this conclusion accordingly.

Thank you for highlighting this. The statement in lines 311-312 does not refer to differences between cell types but to differences between type I IFN subtypes. We are sorry this was not clear and changed this sentence (now lines 357-358). Furthermore, we have made it clearer in the revised text that qualitative differences were observed between cell types (e.g. lines 329 and 350-352).

*No additional experiments are needed to support the claims. However, we believe that two additional analyses could provide useful information. *

• *

The levels of IFNAR1 and IFNAR2 expressed at the plasma membrane probably vary between cell types and may thus influence the magnitude of the IFN response. While it would be difficult to measure these levels by flow cytometric analysis on the different cell types, could the authors extract information from their scRNAseq analysis on the expression level of IFNAR1/2 in all cell types? This would give a hint about potential differences in expression (and thus in magnitude).

We analysed IFNAR1/2 transcript levels in our scRNAseq dataset (Figure R1 below). Unfortunately, for many cells, IFNAR1 and IFNAR2 transcripts were not detected (see width of violin plots at zero), probably due to low sequencing depth inherent to scRNAseq analysis. We therefore prefer not to draw conclusions from these data.

Could the authors investigate further the expression of lncRNAs at the single-cell levels? It would be useful to also define a core set of lncRNAs that are shared between cell types and IFN subtypes. If such a core set does not exist (since lncRNAs are less conserved than coding genes), it would be nice to mention it.

Thank you for this suggestion. The expression of lncRNAs is generally lower than protein-coding genes, resulting in high drop-out rates in 10X datasets. Indeed, Zhao et al. comment that “current development of single-cell technologies may not yet be optimized for lncRNA detection and quantification” [7]. We only detected a small number of lncRNAs in our scRNAseq analysis, and only four lncRNAs were significantly differentially expressed between cell types. We thus could not perform a meaningful analysis of lncRNAs in our scRNAseq dataset. This is now mentioned in the limitations paragraph at the end of the manuscript.

Minor comments:

There is a typo in line 355 Fig.4C =>6C.

Thank you for spotting this.

***Referees cross-commenting** *

We agree with Reviewer 1 that the low sequencing depth of scRNAseq restricts the analysis and must be discussed in the 'limitation' paragraph. This would explain why the authors identified only 10 ISGs that are common to all cell types and all 5 IFN subtypes. Of note, as a comparison, Shaw et al (10.1371/journal.pbio.2004086) identified a core set of 90 ISGs that are upregulated upon IFN treatment in cells isolated mainly from kidney and skin of nine mammalian species ("core mammalian ISGs"). It is thus expected that stimulated blood cells isolated from a single mammalian species share more than 10 ISGs.

We amended the limitations section as requested. Shaw et al. [8] used a single type I IFN (universal or IFNα, depending on species) at a very high dose (1000 U/ml). Taken together with the use of bulk RNAseq in this study, it is unsurprising that our work identified fewer core ISGs. We believe our small list of core ISGs is nonetheless both a high confidence and a high utility set of ISGs: these genes are induced by multiple type I IFNs, in all major cell types in blood and their regulation can be measured even when sequencing depth is low.

Significance (Required)

*Multiple single-cell RNAseq analysis of PBMCs, stimulated or not, have been previously performed in multiple contexts (for instance with PBMCs isolated from the blood of patients infected with influenza virus or SARS-CoV-2). The technical advance is thus limited. *

• *

*However, the work represents a conceptual advance for the field since it provides the first single-cell atlas of PBMCs stimulated with five type-I IFN subtypes. The generated datasets represent a great resource for anyone interested in innate immunity (virologists, immunologists and cancerologists). *

• *

Of note, we are studying innate immunity in the context of RNA virus infection but we have no expertise on scRNA sequencing. We may thus have missed a flaw in the analyses.

We thank the reviewer for their positive assessment of the advances of our study and the value of our IFN resource.

A

B

C

D

Figure R1. IFNAR1/2 expression in scRNAseq data.

Violin plots showing expression of IFNAR1 (A,C) or IFNAR2 (B,D) in different cell types. In (A,B), data were pooled across conditions. In (C,D), data are shown separately for unstimulated control cells and cells stimulated with different type I IFNs.

References

Kaur G, Perteghella T, Carbonell-Sala S, Gonzalez-Martinez J, Hunt T, Madry T, et al. GENCODE: massively expanding the lncRNA catalog through capture long-read RNA sequencing. bioRxiv. 2024. Epub 20241031. doi: 10.1101/2024.10.29.620654. PubMed PMID: 39554180; PubMed Central PMCID: PMCPMC11565817. Mostafavi S, Yoshida H, Moodley D, LeBoite H, Rothamel K, Raj T, et al. Parsing the Interferon Transcriptional Network and Its Disease Associations. Cell. 2016;164(3):564-78. Epub 2016/01/30. doi: 10.1016/j.cell.2015.12.032. PubMed PMID: 26824662; PubMed Central PMCID: PMCPMC4743492. Bailey TL. STREME: accurate and versatile sequence motif discovery. Bioinformatics. 2021;37(18):2834-40. doi: 10.1093/bioinformatics/btab203. PubMed PMID: 33760053; PubMed Central PMCID: PMCPMC8479671. Rauluseviciute I, Riudavets-Puig R, Blanc-Mathieu R, Castro-Mondragon JA, Ferenc K, Kumar V, et al. JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles. Nucleic acids research. 2024;52(D1):D174-D82. doi: 10.1093/nar/gkad1059. PubMed PMID: 37962376; PubMed Central PMCID: PMCPMC10767809. Zhao T, Zhang J, Lei H, Meng Y, Cheng H, Zhao Y, et al. NRF1-mediated mitochondrial biogenesis antagonizes innate antiviral immunity. The EMBO journal. 2023;42(16):e113258. Epub 20230706. doi: 10.15252/embj.2022113258. PubMed PMID: 37409632; PubMed Central PMCID: PMCPMC10425878. Grimm D, Staeheli P, Hufbauer M, Koerner I, Martinez-Sobrido L, Solorzano A, et al. Replication fitness determines high virulence of influenza A virus in mice carrying functional Mx1 resistance gene. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(16):6806-11. Epub 20070410. doi: 10.1073/pnas.0701849104. PubMed PMID: 17426143; PubMed Central PMCID: PMCPMC1871866. Zhao X, Lan Y, Chen D. Exploring long non-coding RNA networks from single cell omics data. Comput Struct Biotechnol J. 2022;20:4381-9. Epub 20220804. doi: 10.1016/j.csbj.2022.08.003. PubMed PMID: 36051880; PubMed Central PMCID: PMCPMC9403499. Shaw AE, Hughes J, Gu Q, Behdenna A, Singer JB, Dennis T, et al. Fundamental properties of the mammalian innate immune system revealed by multispecies comparison of type I interferon responses. PLoS Biol. 2017;15(12):e2004086. Epub 2017/12/19. doi: 10.1371/journal.pbio.2004086. PubMed PMID: 29253856.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This fundamental work employed multidisciplinary approaches and conducted rigorous experiments to study how a specific subset of neurons in the dorsal striatum (i.e., "patchy" striatal neurons) modulates locomotion speed depending on the valence of the naturalistic context.

Strengths:

The scientific findings are novel and original and significantly advance our understanding of how the striatal circuit regulates spontaneous movement in various contexts.

We appreciate the reviewer’s positive evaluation.

Weaknesses:

This is extensive research involving various circuit manipulation approaches. Some of these circuit manipulations are not physiological. A balanced discussion of the technical strengths and limitations of the present work would be helpful and beneficial to the field. Minor issues in data presentation were also noted.

We have incorporated the recommended discussion of technical limitations and addressed the physiological plausibility of our manipulations on Page 33 of the revised Discussion section. Specifically, we wrote:

“Judicious interpretation of the present data must consider the technical limitations of the various methods and circuit-level manipulations applied. Patchy neurons are distributed unevenly across the extensive structure of the striatum, and their targeted manipulation is constrained by viral spread in the dorsal striatum. Somatic calcium imaging using single-photon microscopy captures activity from only a subset of patchy neurons within a narrow focal plane beneath each implanted GRIN lens. Similarly, limitations in light diffusion from optical fibers may reduce the effective population of targeted fibers in both photometry and optogenetic experiments. For example, the more modest locomotor slowing observed with optogenetic activation of striatonigral fibers in the SNr compared to the stronger effects seen with Gq-DREADD activation across the dorsal striatum could reflect limited fiber optic coverage in the SNr. Alternatively, it may suggest that non-striatonigral mechanisms also contribute to generalized slowing. Our photometry data does not support a role for striatopallidal projections from patchy neurons in movement suppression. The potential contribution of intrastriatal mechanisms, discussed earlier, remains to be empirically tested. Although the behavioral assays used were naturalistic, many of the circuit-level interventions were not. Broad ablation or widespread activation of patchy neurons and their efferent projections represent non-physiological manipulations. Nonetheless, these perturbation results are interpreted alongside more naturalistic observations, such as in vivo imaging of patchy neuron somata and axon terminals, to form a coherent understanding of their functional role”.

Reviewer #2 (Public review):

Hawes et al. investigated the role of striatal neurons in the patch compartment of the dorsal striatum. Using Sepw1-Cre line, the authors combined a modified version of the light/dark transition box test that allows them to examine locomotor activity in different environmental valence with a variety of approaches, including cell-type-specific ablation, miniscope calcium imaging, fiber photometry, and opto-/chemogenetics. First, they found ablation of patchy striatal neurons resulted in an increase in movement vigor when mice stayed in a safe area or when they moved back from more anxiogenic to safe environments. The following miniscope imaging experiment revealed that a larger fraction of striatal patchy neurons was negatively correlated with movement speed, particularly in an anxiogenic area. Next, the authors investigated differential activity patterns of patchy neurons' axon terminals, focusing on those in GPe, GPi, and SNr, showing that the patchy axons in SNr reflect movement speed/vigor. Chemogenetic and optogenetic activation of these patchy striatal neurons suppressed the locomotor vigor, thus demonstrating their causal role in the modulation of locomotor vigor when exposed to valence differentials. Unlike the activation of striatal patches, such a suppressive effect on locomotion was absent when optogenetically activating matrix neurons by using the Calb1-Cre line, indicating distinctive roles in the control of locomotor vigor by striatal patch and matrix neurons. Together, they have concluded that nigrostriatal neurons within striatal patches negatively regulate movement vigor, dependent on behavioral contexts where motivational valence differs.

We are grateful for the reviewer’s thorough summary of our main findings.

In my view, this study will add to the important literature by demonstrating how patch (striosomal) neurons in the striatum control movement vigor. This study has applied multiple approaches to investigate their functionality in locomotor behavior, and the obtained data largely support their conclusions. Nevertheless, I have some suggestions for improvements in the manuscript and figures regarding their data interpretation, accuracy, and efficacy of data presentation.

We appreciate the reviewer’s overall positive assessment and have made substantial improvements to the revised manuscript in response to reviewers’ constructive suggestions. 

(1) The authors found that the activation of the striatonigral pathway in the patch compartment suppresses locomotor speed, which contradicts with canonical roles of the direct pathway. It would be great if the authors could provide mechanistic explanations in the Discussion section. One possibility is that striatal D1R patch neurons directly inhibit dopaminergic cells that regulate movement vigor (Nadal et al., Sci. Rep., 2021; Okunomiya et al., J Neurosci., 2025). Providing plausible explanations will help readers infer possible physiological processes and give them ideas for future follow-up studies.

We have added the recommended data interpretation and future perspectives on Page 30 of the revised Discussion section. Specifically, we wrote:

“Potential mechanisms by which striatal patchy neurons reduce locomotion involve the suppression of dopamine availability within the striatum. Dopamine, primarily supplied by neurons in the SNc and VTA, broadly facilitates locomotion (Gerfen and Surmeier 2011, Dudman and Krakauer 2016). Recent studies have shown that direct activation of patchy neurons leads to a reduction in striatal dopamine levels, accompanied by decreased walking speed (Nadel, Pawelko et al. 2021, Dong, Wang et al. 2025, Okunomiya, Watanabe et al. 2025). Patchy neuron projections terminate in structures known as “dendron bouquets”, which enwrap SNc dendrites within the SNr and can pause tonic dopamine neuron firing (Crittenden, Tillberg et al. 2016, Evans, Twedell et al. 2020). The present work highlights a role for patchy striatonigral inputs within the SN in decelerating movement, potentially through GABAergic dendron bouquets that limit dopamine release back to the striatum (Dong, Wang et al. 2025). Additionally, intrastriatal collaterals of patch spiny projection neurons (SPNs) have been shown to suppress dopamine release and associated synaptic plasticity via dynorphin-mediated activation of kappa opioid receptors on dopamine terminals (Hawes, Salinas et al. 2017). This intrastriatal mechanism may further contribute to the reduction in striatal dopamine levels and the observed decrease in locomotor speed, representing a compelling avenue for future investigation.”

(2) On page 14, Line 301, the authors stated that "Cre-dependent mCheery signals were colocalized with the patch marker (MOR1) in the dorsal striatum (Fig. 1B)". But I could not find any mCherry on that panel, so please modify it.

We have included representative images of mCherry and MOR1 staining in Supplementary Fig. S1 of the revised manuscript.

(3) From data shown in Figure 1, I've got the impression that mice ablated with striatal patch neurons were generally hyperactive, but this is probably not the case, as two separate experiments using LLbox and DDbox showed no difference in locomotor vigor between control and ablated mice. For the sake of better interpretation, it may be good to add a statement in Lines 365-366 that these experiments suggest the absence of hyperactive locomotion in general by ablating these specific neurons.

As suggested by the reviewer, we have added the following statement on Page 17 of the revised manuscript: “These data also indicate that PA elevates valence-specific speed without inducing general hyperactivity”.

(4) In Line 536, where Figure 5A was cited, the author mentioned that they used inhibitory DREADDs (AAV-DIO-hM4Di-mCherrry), but I could not find associated data on Figure 5. Please cite Figure S3, accordingly.

We have added the citation for the now Fig. S4 on Page 25 of the revised manuscript.

(5) Personally, the Figure panel labels of "Hi" and "ii" were confusing at first glance. It would be better to have alternatives.

As suggested by the reviewer, we have now labeled each figure panel with a distinct single alphabetical letter.

(6) There is a typo on Figure 4A: tdTomata → tdTomato

We have made the correction on the figure.

Reviewer #3 (Public review):

Hawes et al. combined behavioral, optical imaging, and activity manipulation techniques to investigate the role of striatal patch SPNs in locomotion regulation. Using Sepw1-Cre transgenic mice, they found that patch SPNs encode locomotion deceleration in a light-dark box procedure through optical imaging techniques. Moreover, genetic ablation of patch SPNs increased locomotion speed, while chemogenetic activation of these neurons decreased it. The authors concluded that a subtype of patch striatonigral neurons modulates locomotion speed based on external environmental cues. Below are some major concerns:

The study concludes that patch striatonigral neurons regulate locomotion speed. However, unless I missed something, very little evidence is presented to support the idea that it is specifically striatonigral neurons, rather than striatopallidal neurons, that mediate these effects. In fact, the optogenetic experiments shown in Fig. 6 suggest otherwise. What about the behavioral effects of optogenetic stimulation of striatonigral versus striatopallidal neuron somas in Sepw1-Cre mice?

Our photometry data implicate striatonigral neurons in locomotor slowing, as evidenced by a negative cross-correlation with acceleration and a negative lag, indicating that their activity reliably precedes—and may therefore contribute to—deceleration. In contrast, photometry results from striatopallidal neurons showed no clear correlation with speed or acceleration.

Figure 6 demonstrates that optogenetic manipulation within the SNr of Sepw1-Cre⁺ striatonigral axons recapitulated context-dependent locomotor changes seen with Gq-DREADD activation of both striatonigral and striatopallidal Sepw1-Cre⁺ cells in the dorsal striatum but failed to produce the broader locomotor speed change observed when targeting all Sepw1-Cre⁺ cells in the dorsal striatum using either ablation or Gq-DREADD activation. The more subtle speed-restrictive phenotype resulting from ChR activation in the SNr could, as the reviewer suggests, implicate striatopallidal neurons in broad locomotor speed regulation. However, our photometry data indicate that this scenario is unlikely, as activity of striatopallidal Sepw1-Cre⁺ fibers is not correlated with locomotor speed. Another plausible explanation is that the optogenetic approach may have affected fewer striatonigral fibers, potentially due to the limited spatial spread of light from the optical fiber within the SNr. Broad locomotor speed change in LDbox might require the recruitment of a larger number of striatonigral fibers than we were able to manipulate with optogenetics. We have added discussion of these technical limitations to the revised manuscript. Additionally, we now discuss the possibility that intrastriatal collaterals may contribute to reduced local dopamine levels by releasing dynorphin, which acts on kappa opioid receptors located on dopamine fibers (Hawes, Salinas et al. 2017), thereby suppressing dopamine release.

The reviewer also suggests an interesting experiment involving optogenetic stimulation of striatonigral versus striatopallidal somata in Sepw1-Cre mice. While we agree that this approach would yield valuable insights, we have thus far been unable to achieve reliable results using retroviral vectors. Moreover, selectively targeting striatopallidal terminals optogenetically remains technically challenging, as striatonigral fibers also traverse the pallidum, and the broad anatomical distribution of the pallidum complicates precise targeting. This proposed work will need to be pursued in a future study, either with improved retrograde viral tools or the development of additional mouse lines that offer more selective access to these neuronal populations as we documented recently (Dong, Wang et al. 2025).

In the abstract, the authors state that patch SPNs control speed without affecting valence. This claim seems to lack sufficient data to support it. Additionally, speed, velocity, and acceleration are very distinct qualities. It is necessary to clarify precisely what patch neurons encode and control in the current study.

We believe the reviewer’s interpretation pertains to a statement in the Introduction rather than the Abstract: “Our findings reveal that patchy SPNs control the speed at which mice navigate the valence differential between high- and low-anxiety zones, without affecting valence perception itself.” Throughout our study, mice consistently preferred the dark zone in the Light/Dark box, indicating intact perception of the valence differential between illuminated areas. While our manipulations altered locomotor speed, they did not affect time spent in the dark zone, supporting the conclusion that valence perception remained unaltered. We appreciate the reviewer’s insight and agree it is an intriguing possibility that locomotor responses could, over time, influence internal states such as anxiety. We addressed this in the Discussion, noting that while dark preference was robust to our manipulations, future studies are warranted to explore the relationship between anxious locomotor vigor and anxiety itself.

We report changes in scalar measures of animal speed across Light/Dark box conditions and under various experimental manipulations. Separately, we show that activity in both patchy neuron somata and striatonigral fibers is negatively correlated with acceleration—indicating a positive correlation with deceleration. Notably, the direction of the cross-correlational lag between striatonigral fiber activity and acceleration suggests that this activity precedes and may causally contribute to mouse deceleration, thereby influencing reductions in speed. To clarify this, we revised a sentence in the Results section: “Moreover, patchy neuron efferent activity at the SNr may causally contribute to deceleration, as indicated by the negative cross-correlational lag, thereby reducing animal speed.”. We also updated the Discussion to read: “Together, these data specifically implicate patchy striatonigral neurons in slowing locomotion by acting within the SNr to drive deceleration.”

One of the major results relies on chemogenetic manipulation (Figure 5). It would be helpful to demonstrate through slice electrophysiology that hM3Dq and hM4Di indeed cause changes in the activity of dorsal striatal SPNs, as intended by the DREADD system. This would support both the positive (Gq) and negative (Gi) findings, where no effects on behavior were observed.

We were unable to perform this experiment; however, hM3Dq has previously been shown to be effective in striatal neurons (Alcacer, Andreoli et al. 2017). The lack of effect observed in Gi-DREADD mice serves as an unintended but valuable control, helping to rule out off-target effects of the DREADD agonist JHU37160 and thereby reinforcing the specificity of hM3Dq-mediated activation in our study. We have now included an important caveat regarding the Gi-DREADD results, acknowledging the possibility that they may not have worked effectively in our target cells: “Potential explanations for the negative results in Gi-DREADD mice include inherently low basal activity among patchy neurons or insufficient expression of GIRK channels in striatal neurons, which may limit the effectiveness of Gi-coupling in suppressing neuronal activity (Shan, Fang et al. 2022).

Finally, could the behavioral effects observed in the current study, resulting from various manipulations of patch SPNs, be due to alterations in nigrostriatal dopamine release within the dorsal striatum?

We agree that this is an important potential implication of our work, especially given that we and others have shown that patchy striatonigral neurons provide strong inhibitory input to dopaminergic neurons involved in locomotor control (Nadel, Pawelko et al. 2021, Lazaridis, Crittenden et al. 2024, Dong, Wang et al. 2025, Okunomiya, Watanabe et al. 2025). Accordingly, we have expanded the discussion section to include potential mechanistic explanations that support and contextualize our main findings.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Here are some minor issues for the authors' reference:

(1) This work supports the motor-suppressing effect of patchy SPNs, and >80% of them are direct pathway SPNs. This conclusion is not expected from the traditional basal ganglia direct/indirect pathway model. Most experiments were performed using nonphysiological approaches to suppress (i.e., ablation) or activate (i.e., continuous chemo-optogenetic stimulation). It remains uncertain if the reported observations are relevant to the normal biological function of patchy SPNs under physiological conditions. Particularly, under what circumstances an imbalanced patch/matrix activity may be induced, as proposed in the sections related to the data presented in Figure 6. A thorough discussion and clarification remain needed. Or it should be discussed as a limitation of the present work.

We have added discussion and clarification of physiological limitations in response to reviewer feedback. Additionally, we revised the opening sentence of an original paragraph in the discussion section to emphasize that it interprets our findings in the context of more physiological studies reporting natural shifts in patchy SPN activity due to cognitive conflict, stress, or training. The revised opening sentence now reads: “Together with previous studies of naturally occurring shifts in patchy neuron activation, these data illustrate ethologically relevant roles for a subgroup of genetically defined patchy neurons in behavior.”

(2) Lines 499-500: How striato-nigral cells encode speed and deceleration deserves a thorough discussion and clarification. These striatonigral cells can target both SNr GABAergic neurons and dendrites of the dopaminergic neurons. A discussion of microcircuits formed by the patchy SPNs axons in the SNr GABAergic and SNC DAergic neurons should be presented.

We have added this point at lines 499–500, including a reference to a relevant review of microcircuitry. Additionally, we expanded the discussion section to address microcircuit mechanisms that may underlie our main findings.

(3) Line 70: "BNST" should be spelled out at the first time it is mentioned.

This has been done.

(4) Line 133: only GCaMP6 was listed in the method, but GCaMP8 was also used (Figure 4). Clarification or details are needed.

Thank you for your careful attention to detail. We have corrected the typographical errors in the Methods section. Specifically, in the Stereotaxic Injections section, we corrected “GCaMP83” to “GCaMP8s.” In the Fiber Implant section, we removed the incorrect reference to “GCaMP6s” and clarified that GCaMP8s was used for photometry, and hChR2 was used for optogenetics.

(5) Line 183: Can the authors describe more precisely what "a moment" means in terms of seconds or minutes?

This has been done.

(6) Line 288: typo: missing / in ΔF.

Thank you this has been fixed.

(7) Line 301-302: the statement of "mCherry and MOR1 colocalization" does not match the images in Figure 1B.

This has been corrected by proving a new Supplementary Figure S1.

(8) Related to the statement between Lines 303-304: Figure 1c data may reflect changes in MOR1 protein or cell loss. Quantification of NeuN+ neurons within the MOR1 area would strengthen the conclusion of 60% of patchy cell loss in Figure 1C.

Since the efficacy of AAV-FLEX-taCasp3 in cell ablation has been well established in our previous publications and those of others (Yang, Chiang et al. 2013, Wu, Kung et al. 2019), we do not believe the observed loss of MOR1 staining in Fig. 1C merely reflects reduced MOR1 expression. Moreover, a general neuronal marker such as NeuN may not reliably detect the specific loss of patchy neurons in our ablation model, given the technical limitations of conventional cell-counting methods like MBF’s StereoInvestigator, which typically exhibit a variability margin of 15–20%.

(9) Lines 313-314: "Similarly, PA mice demonstrated greater stay-time in the dark zone (Figure 1E)." Revision is needed to better reflect what is shown in Figure 1E and avoid misunderstandings.

Thank you this has been addressed.

(10) The color code in Figure 2Gi seems inconsistent with the others? Clarifications are needed.

Color coding in Figure 2Gi differs from that in 2Eii out of necessity. For example, the "Light" cells depicted in light blue in 2Eii are represented by both light gray and light red dots in 2Gi. Importantly, Figure 2G does not encode specific speed relationships; instead, any association with speed is indicated by a red hue.

(11) Lines 538-539: the statement of "Over half of the patch was covered" was not supported by Figure 5C. Clarification is needed.

Thank you. For clarity, we updated the x-axis labels in Figures 1C and 5C from “% area covered” to “% DS area covered,” and defined “DS” as “dorsal striatal” in the corresponding figure legends. Additionally, we revised the sentence in question to read: “As with ablation, histological examination indicated that a substantial fraction of dorsal patch territories, identified through MOR1 staining, were impacted (Fig. 5C).”

(12) Figure 3: statistical significance in Figure 3 should be labeled in various panels.

We believe the reviewer's concern pertains to the scatter plot in panel F—specifically, whether the data points are significantly different from zero. In panel 3F, the 95% confidence interval clearly overlaps with zero, indicating that the results are not statistically significant.

(13) Figures 6D-E: no difference in the speed of control mice and ChR2 mice under continuous optical stimulation was not expected. It was different from Gq-DRADDS study in Figure 5E-F. Clarifications are needed.

For mice undergoing constant ChR2 activation of Sepw1-Cre⁺ SNr efferents, overall locomotor speed does not differ from controls. However, the BIL (bright-to-illuminated) effect on zone transitions is disrupted: activating Sepw1-Cre⁺ fibers in the SNr blunts the typical increase in speed observed when mice flee from the light zone toward the dark zone. This impaired BIL-related speed increase upon exiting the light was similarly observed in the Gq-DREADD cohort. The reviewer is correct that this optogenetic manipulation within the SNr did not produce the more generalized speed reductions seen with broader Gq-DREADD activation of all Sepw1-Cre⁺ cells in the dorsal striatum. A likely explanation is the difference in targeting—ChR2 specifically activates SNr-bound terminals, whereas Gq-DREADD broadly activates entire Sepw1-Cre⁺ cells. Notably, many of the generalized speed profile changes observed with chemogenetic activation are opposite to those resulting from broad ablation of Sepw1-Cre⁺ cells.

The more subtle speed-restrictive phenotype observed with ChR2 activation targeted to the SNr may suggest that fewer striatonigral fibers were affected by this technique, possibly due to the limited spread of light from the fiber optic. Broad locomotor speed change in LDbox might require the recruitment of a larger number of striatonigral fibers than we were able to manipulate with an optogenetic approach. Alternatively, it could indicate that non-striatonigral Sepw1-Cre+ projections—such as striatopallidal or intrastriatal pathways—play a role in more generalized slowing. If striatopallidal fibers contributed to locomotor slowing, we would expect to see non-zero cross-correlations between neural activity and speed or acceleration, along with negative lag indicating that neural activity precedes the behavioral change. However, our fiber photometry data do not support such a role for Sepw1-Cre+ striatopallidal fibers.

We have also referenced the possibility that intrastriatal collaterals could suppress striatal dopamine levels, potentially explaining the stronger slowing phenotype observed when the entire striatal population is affected, as opposed to selectively targeting striatonigral terminals.

These technical considerations and interpretive nuances have been incorporated and clarified in the revised discussion section.

(14) Lines 632: "compliment": a typo?

Yes, it should be “complement”.

(15) Figure 4 legend: descriptions of panels A and B were swapped.

Thank you. This has been corrected.

6) Friedman (2020) was listed twice in the bibliography (Lines 920-929).

Thank you. This has been corrected.

Reviewer #3 (Recommendations for the authors):

It will be helpful to label and add figure legends below each figure.

Thank you for the suggestion.

Editor's note:

Should you choose to revise your manuscript, if you have not already done so, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and, where appropriate, 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript. We noted some instances where only p values are reported.

Readers would also benefit from coding individual data points by sex and noting N/sex.

We have included detailed statistical information in the revised manuscript. Both male and female mice were used in all experiments in approximately equal numbers. Since no sex-related differences were observed, we did not report the number of animals by sex.

References

Alcacer, C., L. Andreoli, I. Sebastianutto, J. Jakobsson, T. Fieblinger and M. A. Cenci (2017). "Chemogenetic stimulation of striatal projection neurons modulates responses to Parkinson's disease therapy." J Clin Invest 127(2): 720-734.

Crittenden, J. R., P. W. Tillberg, M. H. Riad, Y. Shima, C. R. Gerfen, J. Curry, D. E. Housman, S. B. Nelson, E. S. Boyden and A. M. Graybiel (2016). "Striosome-dendron bouquets highlight a unique striatonigral circuit targeting dopamine-containing neurons." Proc Natl Acad Sci U S A 113(40): 11318-11323.

Dong, J., L. Wang, B. T. Sullivan, L. Sun, V. M. Martinez Smith, L. Chang, J. Ding, W. Le, C. R. Gerfen and H. Cai (2025). "Molecularly distinct striatonigral neuron subtypes differentially regulate locomotion." Nat Commun 16(1): 2710.

Dudman, J. T. and J. W. Krakauer (2016). "The basal ganglia: from motor commands to the control of vigor." Curr Opin Neurobiol 37: 158-166.

Evans, R. C., E. L. Twedell, M. Zhu, J. Ascencio, R. Zhang and Z. M. Khaliq (2020). "Functional Dissection of Basal Ganglia Inhibitory Inputs onto Substantia Nigra Dopaminergic Neurons." Cell Rep 32(11): 108156.

Gerfen, C. R. and D. J. Surmeier (2011). "Modulation of striatal projection systems by dopamine." Annual review of neuroscience 34: 441-466.

Hawes, S. L., A. G. Salinas, D. M. Lovinger and K. T. Blackwell (2017). "Long-term plasticity of corticostriatal synapses is modulated by pathway-specific co-release of opioids through kappa-opioid receptors." J Physiol 595(16): 5637-5652.

Lazaridis, I., J. R. Crittenden, G. Ahn, K. Hirokane, T. Yoshida, A. Mahar, V. Skara, K. Meletis, K. Parvataneni, J. T. Ting, E. Hueske, A. Matsushima and A. M. Graybiel (2024). "Striosomes Target Nigral Dopamine-Containing Neurons via Direct-D1 and Indirect-D2 Pathways Paralleling Classic Direct-Indirect Basal Ganglia Systems." bioRxiv.

Nadel, J. A., S. S. Pawelko, J. R. Scott, R. McLaughlin, M. Fox, M. Ghanem, R. van der Merwe, N. G. Hollon, E. S. Ramsson and C. D. Howard (2021). "Optogenetic stimulation of striatal patches modifies habit formation and inhibits dopamine release." Sci Rep 11(1): 19847.

Okunomiya, T., D. Watanabe, H. Banno, T. Kondo, K. Imamura, R. Takahashi and H. Inoue (2025). "Striosome Circuitry Stimulation Inhibits Striatal Dopamine Release and Locomotion." J Neurosci 45(4).

Shan, Q., Q. Fang and Y. Tian (2022). "Evidence that GIRK Channels Mediate the DREADD-hM4Di Receptor Activation-Induced Reduction in Membrane Excitability of Striatal Medium Spiny Neurons." ACS Chem Neurosci 13(14): 2084-2091.

Wu, J., J. Kung, J. Dong, L. Chang, C. Xie, A. Habib, S. Hawes, N. Yang, V. Chen, Z. Liu, R. Evans, B. Liang, L. Sun, J. Ding, J. Yu, S. Saez-Atienzar, B. Tang, Z. Khaliq, D. T. Lin, W. Le and H. Cai (2019). "Distinct Connectivity and Functionality of Aldehyde Dehydrogenase 1a1-Positive Nigrostriatal Dopaminergic Neurons in Motor Learning." Cell Rep 28(5): 1167-1181 e1167.

Yang, C. F., M. C. Chiang, D. C. Gray, M. Prabhakaran, M. Alvarado, S. A. Juntti, E. K. Unger, J. A. Wells and N. M. Shah (2013). "Sexually dimorphic neurons in the ventromedial hypothalamus govern mating in both sexes and aggression in males." Cell 153(4): 896-909.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The objective of this research is to understand how the expression of key selector transcription factors, Tal1, Gata2, Gata3, involved in GABAergic vs glutamatergic neuron fate from a single anterior hindbrain progenitor domain is transcriptionally controlled. With suitable scRNAseq, scATAC-seq, CUT&TAG, and footprinting datasets, the authors use an extensive set of computational approaches to identify putative regulatory elements and upstream transcription factors that may control selector TF expression. This data-rich study will be a valuable resource for future hypothesis testing, through perturbation approaches, of the many putative regulators identified in the study. The data are displayed in some of the main and supplemental figures in a way that makes it difficult to appreciate and understand the authors' presentation and interpretation of the data in the Results narrative. Primary images used for studying the timing and coexpression of putative upstream regulators, Insm1, E2f1, Ebf1, and Tead2 with Tal1 are difficult to interpret and do not convincingly support the authors' conclusions. There appears to be little overlap in the fluorescent labeling, and it is not clear whether the signals are located in the cell soma nucleus.

Strengths:

The main strength is that it is a data-rich compilation of putative upstream regulators of selector TFs that control GABAergic vs glutamatergic neuron fates in the brainstem. This resource now enables future perturbation-based hypothesis testing of the gene regulatory networks that help to build brain circuitry.

We thank Reviewer #1 for the thoughtful assessment and recognition of the extensive datasets and computational approaches employed in our study. We appreciate the acknowledgment that our efforts in compiling data-rich resources for identifying putative regulators of key selector transcription factors (TFs)—Tal1, Gata2, and Gata3—are valuable for future hypothesis-driven research.

Weaknesses:

Some of the findings could be better displayed and discussed.

We acknowledge the concerns raised regarding the clarity and interpretability of certain figures, particularly those related to expression analyses of candidate upstream regulators such as Insm1, E2f1, Ebf1, and Tead2 in relation to Tal1. We agree that clearer visualization and improved annotation of fluorescence signals are crucial to accurately support our conclusions. In our revised manuscript, we will enhance image clarity and clearly indicate sites of co-expression for Tal1 and its putative regulators, ensuring the results are more readily interpretable. Additionally, we will expand explanatory narratives within the figure legends to better align the figures with the results section.

Reviewer #2 (Public review):

Summary:

In the manuscript, the authors seek to discover putative gene regulatory interactions underlying the lineage bifurcation process of neural progenitor cells in the embryonic mouse anterior brainstem into GABAergic and glutamatergic neuronal subtypes. The authors analyze single-cell RNA-seq and single-cell ATAC-seq datasets derived from the ventral rhombomere 1 of embryonic mouse brainstems to annotate cell types and make predictions or where TFs bind upstream and downstream of the effector TFs using computational methods. They add data on the genomic distributions of some of the key transcription factors and layer these onto the single-cell data to get a sense of the transcriptional dynamics.

Strengths:

The authors use a well-defined fate decision point from brainstem progenitors that can make two very different kinds of neurons. They already know the key TFs for selecting the neuronal type from genetic studies, so they focus their gene regulatory analysis squarely on the mechanisms that are immediately upstream and downstream of these key factors. The authors use a combination of single-cell and bulk sequencing data, prediction and validation, and computation.

We also appreciate the thoughtful comments from Reviewer #2, highlighting the strengths of our approach in elucidating gene regulatory interactions that govern neuronal fate decisions in the embryonic mouse brainstem. We are pleased that our focus on a critical cell-fate decision point and the integration of diverse data modalities, combined with computational analyses, has been recognized as a key strength.

Weaknesses:

The study generates a lot of data about transcription factor binding sites, both predicted and validated, but the data are substantially descriptive. It remains challenging to understand how the integration of all these different TFs works together to switch terminal programs on and off.

Reviewer #2 correctly points out that while our study provides extensive data on predicted and validated transcription factor binding sites, clearly illustrating how these factors collectively interact to regulate terminal neuronal differentiation programs remains challenging. We acknowledge the inherently descriptive nature of the current interpretation of our combined datasets.

In our revision, we will clarify how the different data types support and corroborate one another, highlighting what we consider the most reliable observations of TF activity. Additionally, we will revise the discussion to address the challenges associated with interpreting the highly complex networks of interactions within the gene regulatory landscape.

We sincerely thank both reviewers for their constructive feedback, which we believe will significantly enhance the quality and accessibility of our manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The results in Figure 3 and several associated supplements are mainly a description/inventory of putative CREs some of which are backed to some extent by previous transgenic studies. But given the way the authors chose to display the transgenic data in the Supplements, it is difficult to fully appreciate how well the transgenic data provide functional support. Take, for example, the Tal +40kb feature that maps to a midbrain enhancer: where exactly does +40kb map to the enhancer region? Is Tal +40kb really about 1kb long? The legend in Supplemental Figure 6 makes it difficult to interpret the bar charts; what is the meaning of: features not linked to gene -Enh? Some of the authors' claims are not readily evident or are inscrutable. For example, Tal locus features accessible in all cell groups are not evident (Fig 2A,B). Other cCREs are said to closely correlate with selector expression for example, Tal +.7kb and +40kb. However, inspection of the data seems to indicate that the two cCREs have very different dynamics and only +40kb seems to correlate with the expression track above it. Some features are described redundantly such as the Gata2 +22 kb, +25.3 kb, and +32.8 kb cCREs above and below the Gata3 cCRE. What is meant by: The feature is accessible at 3' position early, and gains accessibility at 5' positions ... Detailed feature analysis later indicated the binding of Nkx6-1 and Ascl1 that are expressed in the rV2 neuronal progenitors, at 3' positions, and binding of Insm1 and Tal1 TFs that are activated in early precursors, at 5' positions (Figure 3C).

To allow easier assessment of the overlap of the features described in this study in reference to the transgenic studies, we have added further information about the scATAC features, cCREs and previously published enhancers, as well as visual schematics of the feature-enhancer overlaps in the Supplementary table 4. The Supplementary Table 4 column contents are also now explained in detail in the table legend (under the table). We hope those changes make the feature descriptions clearer. To answer the reviewer's question about the Tal1+40kb enhancer, the length of the published enhancer element is 685 bp and the overlapping scATAC feature length is 2067 bp (Supplementary Table 3, sheet Tal1, row 103).

The legend and the chart labelling in the Supplementary Figure 5 (formerly Supplementary figure 6) have been elaborated, and the shown categories explained more clearly.

Regarding the features at the Tal1 locus, the text has been revised and the references to the features accessible in all cell groups were removed. These features showed differences in the intensity of signal but were accessible in all cell groups. As the accessibility of these features does not correlate with Tal1 expression, they are of less interest in the context of this paper.

The gain in accessibility of the +0.7kb and +40 kb features correlates with the onset of Tal1 RNA expression. This is now more clearly stated in the text, as " For example, the gain in the accessibility of Tal1 cCREs at +0.7 and +40 kb correlated temporally with the expression of Tal1 mRNA (Figure 2B), strongly increasing in the earliest GABAergic precursors (GA1) and maintained at a lower level in the more mature GABAergic precursor groups (GA2-GA6), " (Results, page 4). The reviewer is right that the later dynamics of the +0.7 and +40 cCREs differ and this is now stated more clearly in the text (Results, page 5, last chapter).

The repetition in the description of the Gata2 +22 kb, +25.3 kb, and +32.8 kb cCREs has been removed.

The Tal1 +23 kb cCRE showed within-feature differences in accessibility signal. This is explained in the text on page 5, referring to the relevant figure 2A, showing the accessibility or scATAC signal in cell groups and the features labelled below, and 3C, showing the location of the Nkx6-1 and Ascl1 binding sites in this feature: "The Tal1 +23 kb cCRE contained two scATAC-seq peaks, having temporally different patterns of accessibility. The feature is accessible at 3' position early, and gains accessibility at 5' positions concomitant with GABAergic differentiation (Figure 2A, accessibility). Detailed feature analysis later indicated that the 3' end of this feature contains binding sites of Nkx6-1 and Ascl1 that are expressed in the rV2 neuronal progenitors, while the 5' end contains TF binding sites of Insm1 and Tal1 TFs that are activated in early precursors (described below, see Figure 3C)."

(2) Supplementary Figure 3 is not presented in the Results.

Essential parts of previous Supplementary Figure 3 have been incorporated into the Figure 4 and the previous Supplementary Figure omitted.

(3) The significance of Figure 3 and the many related supplements is difficult to understand. A large number of footprints with wide-ranging scores, many very weak or unbound, are displayed in the various temporal cell groups in different epigenomic regions of Tal1 and Vsx2. The footprints for GA1 and Ga2 are combined despite Tal1 showing stronger expression in GA1 and stronger accessibility (Figure 2). Many possibilities are outlined in the Results for how the many different kinds of motifs in the cCREs might bind particular TFs to control downstream TF expression, but no experiments are performed to test any of the possibilities. How well do the TOBIAS footprints align with C&T peaks? How was C&T used to validate footprints? Are Gata2, 3, and Vsx2 known to control Tal1 expression from perturbation experiments?

Figure 3 and related supplements present examples of the primary data and summarise the results of comprehensive analysis. The methods of identifying the selector TF regulatory features and the regulators are described in the Methods (Materials and Methods page 16). Briefly, the correlation between feature accessibility and selector TF RNA expression (assessed by the LinkPeaks score and p-value) were used to select features shown in the Figure 3.

We are aware of differences in Tal1 expression and accessibility between GA1 and GA2. However, number of cells in GA2 was not high enough for reliable footprint calculations and therefore we opted for combining related groups throughout the rV2 lineage for footprinting.

As suggested, CUT&Tag could be used to validate the footprinting results with some restrictions. In the revised manuscript, we included analysis of CUT&Tag peak location and footprints similarly to an earlier study (Eastman et al. 2025). In summary, we analysed whether CUT&Tag peaks overlap locations in which footprinting was also recognized and vice versa. Per each TF with CUT&Tag data we calculated a) Total number of CUT&Tag consensus peaks b) Total number of bound TFBS (footprints) c) Percentage of CUT&Tag overlapping bound TFBS d) Percentage of bound TFBS overlapping CUT&Tag. These results are shown in Supplementary Table 6 and in Supplementary figure 11 with analysis described in Methods (Materials and Methods, page 19). There is considerable overlap between CUT&Tag peaks and bound footprints, comparable to one shown in Eastman et al. 2025. However, these two methods are not assumed to be completely matching for several reasons: binding by related/redundant TFs, antigen masking in the TF complex, chromatin association without DNA binding, etc. In addition, some CUT&Tag peaks with unbound footprints could arise from non-rV2 cells that were part of the bulk CUT&Tag analysis but not of the scATAC footprint analysis.

The evidence for cross-regulation of selector genes and the regulation of Tal1 by Gata2, Gata3 and Vsx2 is now discussed (Discussion, chapter Selector TFs directly autoregulate themselves and cross-regulate each other, page 12-13). The regulation of Tal1 expression by Vsx2 has, to our knowledge, not been earlier studied.

(4) Figure 4 findings are problematic as the primary images seem uninterpretable and unconvincing in supporting the authors' claims. There is a lack of clear evidence in support of TF coexpression and that their expression precedes Tal1.

Figure 4 has been entirely redrawn with higher resolution images and a more logical layout. In the revised Figure 4, only the most relevant ISH images are shown and arrowheads are added showing the colocalization of the mRNA in the cell cytoplasm. Next to the plots of RNA expression along the apical-basal axis of r1, an explanatory image of the quantification process is added (Figure 4D).

(5) What was gained from also performing ChromVAR other than finding more potential regulators and do the results of the two kinds of analyses corroborate one another? What is a dual GATA:TAL BS?

Our motivation for ChromVAR analysis is now more clearly stated in the text (Results, page 9): “In addition to the regulatory elements of GABAergic fate selectors, we wanted to understand the genome-wide TF activity during rV2 neuron differentiation. To this aim we applied ChromVAR (Schep et al., 2017)" Also, further explanation about the Tal1and Gata binding sites has been added in this chapter (Results, page 9).

The dual GATA:Tal BS (TAL1.H12CORE.0.P.B) is a 19-bp motif that consists of an E-box and GATA sequence, and is likely bound by heteromeric Gata2-Tal1 TF complex, but may also be bound by Gata2, Gata3 or Tal1 TFs separately. The other TFBSs of Tal1 contain a strong E-box motif and showed either a lower activity (TAL1.H12CORE.1.P.B) or an earlier peak of activity in common precursors with a decline after differentiation (TAL1.H12CORE.2.P.B) (Results, page 9).

(6) The way the data are displayed it is difficult to see how the C&T confirmed the binding of Ebf1 and Insm1, Tal1, Gata2, and Gata3 (Supplementary Figures 9-11). Are there strong footprints (scores) centered at these peaks? One can't assess this with the way the displays are organized in Figure 3. What is the importance of the H3K4me3 C&T? Replicate consistency, while very strong for some TFs, seems low for other TFs, e.g. Vsx2 C&T on Tal1 and Gata2. The overlaps do not appear very strong in Supplementary Figure 10. Panels are not letter labeled.

We have added an analysis of footprint locations within the CUT&Tag peaks (Supplementary Figure 11). The Figure shows that the footprints are enriched at the middle regions of the CUT&Tag peaks, which is expected if TF binding at the footprinted TFBS site was causative for the CUT&Tag peaks.

The aim of the Supplementary Figures 9-11 (Supplementary Figures 8-10 in the revised manuscript) was to show the quality and replicability of the CUT&Tag.

The anti-H3K4me3 antibody, as well as the anti-IgG antibody, was used in CUT&Tag as part of experiment technical controls. A strong CUT&Tag signal was detected in all our CUT&Tag experiments with H3K4me3. The H3K4me3 signal was not used in downstream analyses.

We have now labelled the H3K4me3 data more clearly as "positive controls" in the Supplementary Figure 8. The control samples are shown only on Supplementary Figure 8 and not in the revised Supplementary Figure 10, to avoid repetition. The corresponding figure legends have been modified accordingly.

To show replicate consistency, the genome view showing the Vsx2 CUT&Tag signal at Gata2 gene has been replaced by a more representative region (Supplementary Figure 8, Vsx2). The Vsx2 CUT&Tag signal at the Gata2 locus is weak, explaining why the replicability may have seemed low based on that example.

Panel labelling is added on Supplementary Figures S8, S9, S10.  

(7) It would be illuminating to present 1-2 detailed examples of specific target genes fulfilling the multiple criteria outlined in Methods and Figure 6A.

We now present examples of the supporting evidence used in the definition of selector gene target features and target genes. The new Supplementary Figure 12 shows an example gene Lmo1 that was identified as a target gene of Tal1, Gata2 and Gata3.

Reviewer #2 (Recommendations for the authors):

(1) The authors perform CUT&Tag to ask whether Tal1 and other TFs indeed bind putative CREs computed. However, it is unclear whether some of the antibodies (such as Gata3, Vsx2, Insm1, Tead2, Ebf1) used are knock-out validated for CUT&Tag or a similar type of assay such as ChIP-seq and therefore whether the peaks called are specific. The authors should either provide specificity data for these or a reference that has these data. The Vsx2 signal in Figure S9 looks particularly unconvincing.

Information about the target specificity of the antibodies can be found in previous studies or in the product information. The references to the studies have been now added in the Methods (Materials and Methods, CUT&Tag, pages 18-19). Some of the antibodies are indeed not yet validated for ChIP-seq, Cut-and-run or CUT&Tag. This is now clearly stated in the Materials and Methods (page 19): "The anti-Ebf1, anti-Tal1, anti-IgG and anti-H3K4me3 antibodies were tested on Cut-and-Run or ChIP-seq previously (Boller et al., 2016b; Courtial et al., 2012) and Cell Signalling product information). The anti-Gata2 and anti-Gata3 antibodies are ChIP-validated ((Ahluwalia et al., 2020a) and Abcam product information). There are no previous results on ChIP, ChIP-seq or CUT&Tag with the anti-Insm1, anti-Tead2 and anti-Vsx2 antibodies used here. The specificity and nuclear localization have been demonstrated in immunohistochemistry with anti-Vsx2 (Ahluwalia et al., 2020b) and anti-Tead2 (Biorbyt product information). We observed good correlation between replicates with anti-Insm1, similar to all antibodies used here, but its specificity to target was not specifically tested". We admit that specificity testing with knockout samples would increase confidence in our data. However, we have observed robust signals and good replicability in the CUT&Tag for the antibodies shown here.

Vsx2 CUT&Tag signal at the loci previously shown in Supplementary Figure S9 (now Supplementary Figure 8) is weak, explaining why the replicability may seem low based on those examples. The genome view showing the Vsx2 CUT&Tag signal at Gata2 gene locus in Supplementary Figure 8 (previously Supplementary figure 9) has now been replaced by a view of Vsx2 locus that is more representative of the signal.

(2) It is unclear why the authors chose to focus on the transcription factor genes described in line 626 as opposed to the many other putative TFs described in Figure 3/Supplementary Figure 8. This is the major challenge of the paper - the authors are trying to tell a very targeted story but they show a lot of different names of TFs and it is hard to follow which are most important.

We agree with the reviewer that the process of selection of the genes of interest is not always transparent. We are aware that interpretations of a paper are based on the known functions of the putative regulatory TFs, however additional aspects of regulation could be revealed even if the biological functions of all the TFs were known. This is now stated in the Discussion “Caveats of the study” chapter. It would be relevant to study all identified candidate genes, but as often is the case, our possibilities were limited by the availability of materials (probes, antibodies), time, and financial resources. In the revised manuscript, we now briefly describe the biological processes related to the selected candidate regulatory TFs of the Tal1 gene (Results, page 8, "Pattern of expression of the putative regulators of Tal1 in the r1"). We hope this justifies the focus on them in our RNA co-expression analysis. The TFs analysed by RNAscope ISH are examples, which demonstrate alignment of the tissue expression patterns with the scRNA-seq data, suggesting that the dynamics of gene expression detected by scRNA-seq generally reflects the pattern of expression in the developing brainstem.

(3) How is the RNA expression level in Figure 5B and 4D-L computed? These are the clusters defined by scATAC-seq. Is this an inferred RNA expression? This should be made more clear in the text.

The charts in Figures 5B and 4G,H,I show inferred RNA expression. The Y-axis labels have now been corrected and include the term inferred’. RNA expression in the scATAC-seq cell clusters is inferred from the scRNA-seq cells after the integration of the datasets.

(4) The convergence of the GABA TFs on a common set of target genes reminds me of a nice study from the Rubenstein lab PMID: 34921112 that looked at a set of TFs in cortical progenitors. This might be a good comparison study for the authors to use as a model to discuss the convergence data.

We thank the reviewer for bringing this article to our attention. The article is now discussed in the manuscript (Discussion, page 11).

(5) The data in Figure 4, the in-situ figure, needs significant work. First, the images especially B, F, and J appear to be of quite low resolution, so they are hard to see. It is unclear exactly what is being graphed in C, G, and K and it does not seem to match the text of the results section. Perhaps better labeling of the figure and a more thorough description will make it clear. It is not clear how D, H, and L were supposed to relate to the images - presumably, this is a case where cell type is spatially organized, but this was unclear in the text if this is known and it needs to be more clearly described. Overall, as currently presented this figure does not support the descriptions and conclusions in the text.

Figure 4 has been entirely redrawn with higher resolution images and more logical layout. In the revised Figure 4, the ISH data and the quantification plots are better presented; arrows showing the colocalization of the mRNA in the cell cytoplasm were added; and an explanatory image of the quantification process is added on (D).

Minor points

(1) Helpful if the authors include scATAC-seq coverage plots for neuronal subtype markers in Figure 1/S1.

We are unfortunately uncertain what is meant with this request. Subtype markers in Figure 1/S1 scATAC-seq based clusters are shown from inferred RNA expression, and therefore these marker expression plots do not have any coverage information available.

(2) The authors in line 429 mention the testing of features within TADs. They should make it clear in the main text (although tadmap is mentioned in the methods) that this is a prediction made by aggregating HiC datasets.

Good point and that this detail has been added to both page 3 and 16.

(3) The authors should include a table with the phastcons output described between lines 511 and 521 in the main or supplementary figures.

We have now clarified int the text that we did not recalculate any phastcons results, we merely used already published and available conservation score per nucleotide as provided by the original authors (Siepel et al. 2005). (Results, page 5: revised text is " To that aim, we used nucleotide conservation scores from UCSC (Siepel et al., 2005). We overlaid conservation information and scATAC-seq features to both validate feature definition as well as to provide corroborating evidence to recognize cCRE elements.")

(4) It is very difficult to read the names of the transcription factor genes described in Figure 3B-D and Supplementary Figure 8 - it would be helpful to resize the text.

The Figures 3B-D and Supplementary Figure 7 (former Supplementary figure 8) have been modified, removing unnecessary elements and increasing the size of text.

(5) It is unclear what strain of mouse is used in the study - this should be mentioned in the methods.

Outbred NMRI mouse strain was used in this study. Information about the mouse strain is added in Materials and Methods: scRNA-seq samples (page 14), scATAC-seq samples (page 15), RNAscope in situ hybridization (page 17) and CUT&Tag (page 18).

(6) Text size in Figure 6 should be larger. R-T could be moved to a Supplementary Figure.

The Figure 6 has been revised, making the charts clearer and the labels of charts larger. The Figure 6R-S have been replaced by Supplementary table 8 and the Figure 6T is now shown as a new Figure (Figure 7).

Additional corrections in figures

Figure 6 D,I,N had wrong y-axis scale. It has been corrected, though it does not have an effect on the interpretation of the data as Pos.link and Neg.link counts were compared to each other’s (ratio).

On Figure 2B, the heatmap labels were shifted making it difficult to identify the feature name per row. This is now corrected.

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

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Reply to the reviewers

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

As stated by the authors in the introduction, the RNA-binding protein Sxl is foundational to understanding sex determination in Drosophila. Sxl has been extensively studied as the master regulator of female sex determination in the soma, where it is known to initiate an alternative splicing cascade leading to the expression of DsxF. Additionally, Sxl has been shown to be responsible for keeping X chromosome dosage compensation off in females, while males hyperactivate their X chromosome. While these roles have been well defined, the authors explore an aspect of Sxl that is quite separate from its role as master regulator of female fate. They describe Sxl-RAC, a Sxl isoform that is expressed in the male and female nervous system. Using several genomic techniques, the authors conclude that the Sxl-RAC isoform associates with chromatin in a similar pattern to the RNA polymerase II/III subunit, Polr3E, and Sxl depends on Polr3E for chromatin-association. Further, neuronal loss of Sxl causes changes in lifetime and geotaxis in a similar manner as loss of Polr3E. The work is thorough and significant and should be appropriate for publication if a few issues can be addressed.

Major Concerns:*

* 1) How physiological is the Sxl chromatin-association assay? As binding interactions are concentration-dependent, how similar is Sxl-DAM expression to wt Sxl expression in neurons? In addition, does the Sxl-DAM protein function as a wt Sxl protein? Does UAS-Sxl-DAM rescue any Sxl loss phenotypes?*

Author response:

As Reviewer 3 correctly notes, Targeted DamID relies on ribosomal re-initiation (codon slippage) to produce only trace amounts of the Dam-fusion protein. By design, this results in expression levels that are significantly lower than those of the endogenous protein. As such, the experiment can be interpreted within a near–wild-type context, rather than as an overexpression model. The primary aim of this experiment was to determine whether Sxl associates with chromatin, and our dataset provides clear evidence supporting such binding.

2) Is Polr3E chromatin-association also dependent on Sxl? They should do the reciprocal experiment to their examination of Sxl chromatin-association in Polr3E knockdown. This might also help address point 1-if wt Sxl is normally required for aspects of Polr3E chromatin binding, then concerns about whether the Sxl-DAM chromatin-association is real or artifactual would be assuaged.

Author response:

This is an interesting thought, however, if Sxl were required for Polr3E recruitment to RNA Pol III, then, in most male Drosophila melanogaster cells, Polr3E would not be incorporated, and males would not be viable (as it is essential for Pol III activity). While it is possible that there could be a subtle effect on Polr3E recruitment, such an experiment, would not alter the central conclusion of our study - that Sxl is recruited to chromatin (accessory to the Pol III complex) via Polr3E.

Minor concerns:

* The observed Sxl loss of function phenotypes are somewhat subtle (although perhaps any behavior phenotype at all is a plus). Did they try any other behaviour assays-courtship, learning/memory, anything else at all to test nervous system function?*

Author response:

Given the exploratory nature of this study, we focused on broader behavioural and transcriptional assays.

While well written, it is sometimes difficult to understand how the experiment was performed or what genotypes were used without looking into the methods sections. One example is they should describe the nature of the Sxl-DAM fusion protein clearly in the results.

Author response:

We will revise these sections to improve clarity and ensure there is no confusion.

* Reviewer #1 (Significance (Required)):

This manuscript represents a dramatic change in our thinking about the action of the Sex-lethal protein. Previously, Sxl was known as the master regulator of both sex determination and dosage compensation, and performed these roles as an RNA-binding protein affecting RNA splicing and translational regulation. Here, the authors describe a sex-non-specific role of Sxl in the male and female nervous system. Further, this activity appears independent of Sxl's RNA binding activity and instead Sxl functions as a chromatin-associating protein working with the RNA pol2/3 factor Polr3E to regulate gene expression. Thus, this represents a highly significant finding. *

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

Summary: In this paper, the authors report on an unexpected activity for Sex lethal (Sxl) (a known splicing regulator that functions in sex determination and dosage compensation) in binding to chromatin. They show, using DamID, that Sxl binds to approximately the same chromatin regions as Polr3E (a subunit of RNA Pol III). They show that this binding to chromatin is unaffected by mutations in the RNA binding domains or by deletions of either N or C terminal regions of the Sxl protein. This leads the authors to conclude that Sxl must bind to chromatin through some interacting protein working through the central region of the Sxl protein. They show that Sxl binding is dependent on Polr3E function. They show that male-specific neuronal knockdown of Sxl gives similar phenotypes to knockdown of Polr3E in terms of lethality and improved negative geotaxis. They show gene expression changes with knockdown of Sxl in male adult neurons - mainly that metabolic and pigmentation genes go down in expression. They also show that expression of a previously discovered male adult specific form of Sxl (that does not have splicing activity) in the same neurons also leads to changes in gene expression, including more upregulated than downregulated tRNAs. But they don't see (or don't show) that the same tRNA genes are down with knockdown of Sxl. Nonetheless, based on these findings, they suggest that Sxl plays an important role in regulating Pol III activity through the Polr3E subunit.

Major comments:

*

*To be honest, I'm not convinced that the conclusions drawn from this study are correct. The fact that every mutant form of Sxl shows the same result from the DamID labelling is a little concerning. I would like to see independent evidence of the SxlRac protein binding chromatin. *

Do antibodies against this form (or any form) of Sxl bind chromatin in salivary gland polytene chromosomes, for example? Does Sxl from other insects where Sxl has no role in sex determination bind chromatin?

__Author Response: __

Regarding the reviewer’s overall concerns about the legitimacy of the Sxl binding data:

1. i) The fold differences between Dam-Sxl-mutants and the Dam-only control are very robust (up to 9 log2 fold change (500-fold change)), which is higher than what we observe with most transcription factors using Targeted DamID.

2. ii) We observed that Sxl binding was significantly reduced upon knockdown of Polr3E, confirming that the signal we observe is biologically specific and not due to technical noise or background. iii) If the concern relates to potential Sxl binding in non-neuronal tissues such as salivary glands, we would like to clarify that all DamID constructs were expressed under elav-GAL4, a pan-neuronal driver. Furthermore, dissections were performed to isolate larval brains, with salivary glands carefully removed. This ensures that chromatin profiles were derived from neuronal tissue exclusively.

3. iv) Salivary gland polytene chromosome staining with a Sxl antibody in a closely related species (Drosophila virilis) show __binding of Sxl to chromatin __in both sexes (Bopp et al., 1996). We will include more text in the revised manuscript to emphasise these points.

Do antibodies against this form (or any form) of Sxl bind chromatin in salivary gland polytene chromosomes, for example? Does Sxl from other insects where Sxl has no role in sex determination bind chromatin?

Author Response:

Prior work in Drosophila virilis (where Sxl is also required for sex determination and Sxl-RAC is conserved) has already demonstrated Sxl-chromatin association (using a full-length Sxl antibody) in salivary glands using polytene chromosome spreads (Bopp et al., 1996). Binding is observed in both sexes and across the genome, reflecting our observations. We will incorporate this into the revised discussion to support the chromatin-binding role of Sxl across species.

There is a clear and long-overlooked precedent for Sxl's alternative, sex-independent roles, findings that have been largely overshadowed by the gene’s canonical function. Our study not only validates and extends these observations but also brings much-needed attention to this understudied aspect of fundamental biology.

Bopp D, Calhoun G, Horabin JI, Samuels M, Schedl P. Sex-specific control of Sex-lethal is a conserved mechanism for sex determination in the genus Drosophila. Development. 1996 Mar;122(3):971-82. doi: 10.1242/dev.122.3.971. PMID: 8631274.

I would like to see independent evidence of the SxlRac protein binding chromatin.

* *__Author Response: __

We do not believe this is necessary:

1. i) Our data demonstrated that a large N-terminal truncation of Sxl (removing far more of the N-terminal region than is absent in Sxl-RAC) does not impair chromatin binding.
2. ii) Our deletion experiments show that it is the central domain __of Sxl that is required for chromatin association (as removal of the N-or C-terminal domain has no effect). This central domain is __unaffected in Sxl-RAC. iii) Independent Y2H experiments have shown that it is exclusively the__ RBD-1 __(RNA binding domain 1) of the central domain of Sxl that interacts with Polr3E (Dong et al., 1999). Sxl-RAC contains this region, therefore will be recruited by Polr3E.

iv) Review 3 also believes that this is not necessary (see cross-review below) and highlights the robustness of the Y2H experiments performed by Dong et al., 1999.

• *

Also, given that their DamID experiments reveal that Sxl binds half of the genes encoded in the Drosophila genome, finding that it binds around half of the tRNA genes is perhaps not surprising.

__Author Response: __

Our data show that Sxl binds to a range of Pol III-transcribed loci, and this binding pattern supports the proposed model that Sxl plays a broader regulatory role in Pol III activity. Within these Pol III targets, tRNA genes represent a specific and biologically relevant subset. The emphasis on tRNAs is not to suggest they are the exclusive or primary targets of Sxl, but rather to__ highlight a functionally important class of Pol III-transcribed elements__ that align with the model we are proposing. We will revise the text to better reflect this framing and avoid any confusion regarding the scope of Sxl’s binding profile.

*I would like to see evidence beyond citing a 1999 yeast two-hybrid study that Sxl and Polr3E directly interact with one another. *

Author response:

We do not believe this is necessary (these points were also mentioned above):

1. i) The Dong et al., 1999 study was highly comprehensive in its characterisation of Sxl binding to Polr3E.
2. ii) Our DamID data provide strong complementary evidence for this interaction: knockdown of Polr3E robustly reduces Sxl’s recruitment to chromatin, strongly supporting the relevance of the interaction in vivo. iii) Review 3 highlights the robustness of the Y2H experiments performed by Dong et al., 1999.

In my opinion, the differences in lethality observed with loss of Sxl versus control are unlikely to be meaningful given the different genetic backgrounds. The similar defects in negative geotaxis could be meaningful, but I'm unsure how often this phenotype is observed. What other class of genes affect negative geotaxis? It's a little unclear why having reduced expression of metabolic and pigment genes or of tRNAs would improve neuronal function.

Author response:

While the differences in survival were indeed subtle, they were statistically significant and thus warranted inclusion. Our primary aim in this section was to demonstrate that knockdown of Sxl or Polr3E results in comparable behavioural and transcriptional phenotypes, suggesting overlapping functional roles. In this context, we believe the data were presented transparently and effectively support our interpretation.

Regarding the negative geotaxis phenotype, we appreciate the reviewer’s interest and agree that it is both intriguing and atypical. For this reason, we performed the assay multiple times, particularly in Polr3e knockdowns, to confirm the robustness of the result. To address potential confounding variables, we carefully selected control lines that account for genetic background and transgene insertion site, including KK controls and attP40-matched lines. We also employed multiple independent RNAi lines targeting Sxl to validate the phenotype across different genetic backgrounds.

Although the observed improvement in climbing is unexpected, it is not without precedent in the RNA polymerase III field. Notably, Malik et al. (2024) demonstrated that heterozygous Polr3DEY/+ mutants exhibit a significantly delayed decline in climbing ability with age. We allude to this in the discussion and will revise the text to emphasise this connection more explicitly.

Finally, while we recognise that negative geotaxis is a relatively broad assay and thus does not pinpoint the precise cellular mechanisms involved, we interpret the phenotype as suggesting a neural basis and a functional role for Sxl in the nervous system.

One would expect that not just the same classes of genes would be affected by loss and overexpression of Sxl, but the same genes would be affected - are the same genes changing in opposite directions in the two experiments or just the same classes of genes. Likewise, are the same genes changing expression in the same direction with both Sxl and the Polr3E loss? Also, why are tRNA genes not also affected with Sxl loss. Finally, they describe the changes in gene expression as being in male adult neurons, but the sequencing was done of entire heads - so no way of knowing which cell type is showing differential gene expression.

Author response:

While we do examine gene classes, our approach also includes pairwise correlation analyses of gene expression changes between specific genotypes. Notably, we observed a significant positive correlation between Polr3e knockdowns and Sxl knockdowns, and a significant negative correlation between Sxl-RAC–expressing flies and Sxl knockdowns. Furthermore, we examined Sxl-DamID target genes within our RNA-seq datasets and found a consistent relationship between Sxl targets and genes differentially expressed in Polr3e knockdowns.

Regarding the Pol III qPCR results, we note that tRNA expression changes may require a longer duration of RNAi induction (e.g., beyond 4 days) to become apparent, especially given that phenotypic effects such as changes in lifespan and negative geotaxis only emerge after 20 days or more. It is also plausible that Sxl knockdown leads to a partial reduction in Pol III efficiency, which may not be readily detectable through bulk Pol III qPCRs. We are willing to repeat Pol III qPCRs at later timepoints to further investigate this trend.

Finally, we infer that gene expression changes observed in our RNA-seq data are of neuronal origin, as all knockdown and overexpression constructs used in this study were driven pan-neuronally using elav-/nSyb-GAL4. While we acknowledge that bulk RNA-seq does not provide cell-type resolution, tissue-specific assumptions are widely used in the field when driven by a relevant promoter.

I'm also not sure what I'm supposed to be seeing in panel 5F (or in the related supplemental figure) and if it has any meaning - If they are using the Sxl-T2A-Gal4 to drive mCherry, I think one would expect to see expression since Sxl transcripts are made in both males and in females. Also, one would expect to see active protein expression (OPP staining) in most cells of the adult male brain and I think that is what is observed, but again, I'm not sure what I'm supposed to be looking at given the absence of any arrows or brackets in the figures.

Author Response:

Due to the presence of the T2A tag and the premature stop codon in exon 3 of early male Sxl transcripts, GAL4 expression is not expected in males unless the head-specific SxlRAC isoform is produced. The aim of panel 5F is to demonstrate the spatial overlap between SxlRAC expression (as we are examining male brains) and regions of elevated protein synthesis, as detected by OPP staining.

To quantitatively assess this relationship, we performed colocalisation analysis using ImageJ, which showed a positive correlation between Sxl and OPP signal intensity, supporting this interpretation. It is also evident from our images that regions with lower levels of protein synthesis (such as the neuropil - as shown in independent studies Villalobos-Cantor et al., 2023) concurrently lack Sxl-related signal. We have highlighted regions in Fig. 5 exhibiting higher/lower levels of Sxl/OPP signal to better illustrate this relationship. We can also test the effects of knockdown/overexpression on general protein synthesis if required.

Villalobos-Cantor S, Barrett RM, Condon AF, Arreola-Bustos A, Rodriguez KM, Cohen MS, Martin I. Rapid cell type-specific nascent proteome labeling in Drosophila. Elife. 2023 Apr 24;12:e83545. doi: 10.7554/eLife.83545. PMID: 37092974; PMCID: PMC10125018.

Minor comments:

* Line 223 - 225 - I believe that it is expected that Sxl transcripts would be broadly expressed in the male and female adult, given that it is only the spliced form of the transcript that is female specific in expression. *

As explained above, the only isoform that will be ‘trapped’ by the T2A-GAL4 in males is the Sxl-RAC isoform (as the other isoforms contain premature stop codons). Our immunohistochemistry data indicate that Sxl-RAC is expressed in the male brain, specifically in neurons. Therefore, knockdown experiments in males will reduce all mRNA isoforms, of which, Sxl-RAC is the only one producing a protein.

Line 236 - 238 - Sentence doesn't make sense.

We have addressed and clarified this.

Reviewer #2 (Significance (Required)):

It would be significant to discover that a gene previously thought to function in only sex determination and dosage compensation also moonlights as a regulator of RNA polymerase III activity. Unfortunately, I am not convinced by the work presented in this study that this is the case.

My expertise is in Drosophila biology, including development, transcription, sex determination, morphogenesis, genomics, transcriptomics, DNA binding

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

Storer, McClure and colleagues use genome-wide DNA-protein binding assays, transcriptomics, and genetics to work out that Drosophila Sxl, widely known as an RNA-binding protein which functions as a splicing factor to determine sex identity in Drosophila and related species, is also a chromatin factor that can stimulate transcription by Pol III and Pol II of genes involved with metabolism and protein homeostasis, specifically some encoding tRNAs.

The evidence for the tenet of the paper -- that Sxl acts as a chromatin regulator with Polr3E, activating at least some of its targets with either Pol III or Pol II -- is logical and compelling, the paper is well written and the figures well presented. Of course, more experiments could always be wished for and proposed, but I think this manuscript could be published in many journals with just a minor revision not involving additional experiments. I have a few specific comments below, all minor.*

Scientific points: - The approach taken for the evaluation of Sxl DNA-binding activity in Fig2 is not entirely clear. I assume these are crosses of elav-Gal4 x different UAS- lines, then using males or females for UAS-Sxl-Full-Length. But what about the others? Were the experiments done in males only? This is hinted at in the main text but not explicitly indicated in the figure or the methods (at least, that I could easily find). And is this approach extended to all other experiments? Longevity? Climbing assays? Considering the role of Sxl, it may be helpful to be fastidiously systematic with this.

Author Response:

We have revised the wording to ensure greater clarity. Males were used for all survival and behavioural experiments (as only males can be leveraged for knocking down Sxl-RAC without affecting the canonical Sxl-F isoform).

- In the discussion, lines 360-61, the authors say: Indeed, knockdown of Polr3E leads to a loss of Sxl binding to chromatin, suggesting a cooperative mechanism. Maybe I am misunderstanding the authors, but when I read "cooperation" in this context I think of biochemical cooperative binding. This is possible, but I do not think a simple 'requirement' test can suggest specifically that this mechanistic feature of biochemical binding is at play. I would expect, for starters, a reciprocal requirement for binding (which is not tested), and some quantitative features that would be difficult to evaluate in vivo. I do not think cooperative binding needs to be invoked anyway, as the authors do not make any specific point or prediction about it. But if they do think this is going on, I think it would need to be referred to as a speculation.

Author Response:

We appreciate that the original wording may have been unclear and will revise the text to more accurately reflect a functional relationship, rather than implying direct cooperation.

- In lines 428-432, the authors discuss the ancestral role of Sxl and make a comparison with ELAV, in the context of an RNA-binding protein that has molecular functions beyond those of a splicing factor, considering the functions of ELAV in RNA stability and translation, and finishing with "suggesting that similar regulatory mechanisms may be at play". I do not understand this latter sentence. Which mechanisms are these? Are the authors referring to the molecular activities of ELAV and SXL? But what would be the similarity? SXL seems to have a dual capacity to bind RNA and protein interactors, which allows it to work both in chromatin-level regulation as well as post-transcriptionally in splicing; but ELAV seems rather to take advantage of its RNA binding function to make it work in multiple RNA-related contexts, all post-transcriptional. I do not see an obvious parallel beyond the fact that RNA binding proteins can function at different levels of gene expression regulation -- but I would not say this parallel are "similar regulatory mechanisms", so I find the whole comparison a bit confusing.

Author Response:

We have reduced this section, as it is largely speculative and intended to highlight potential, though indirect, links in higher organisms. Our goal was primarily to illustrate the possibility that Sxl may have an ancestral role distinct from its well-characterised function, and to suggest a potential avenue for future research into ELAV2’s involvement in chromatin or Pol III regulation.

- One aspect of the work that I find is missing in the discussion is the possibility that the simultaneous capacity of Sxl for RNA binding and Polr3E binding: are these mutually exclusive? if so, are they competitive or hierarchical? how would they be coordinated anyway?

Author Response:

This is an interesting point, and we have expanded on it further in the Discussion section.

- The only aspect of the paper where I found that one could make an experimental improvement is the claim that Sxl induces the expression of genes that have the overall effect of stimulating protein synthesis. The OPP experiment shows a correlation between the expression of Sxl and the rate of protein synthesis initiation. However, a more powerful experiment would be, rather obviously, to introduce Sxl knock-down in the same experiment, and observe whether in Sxl-expressing neurons the incorporation of OPP is reduced. I put this forth as a minor point because the tenet of the paper would not be affected by the results (though the perception of importance of the newly described function could be reinforced).

• *

Author Response:

This could be a valid experiment and we are prepared to perform it if required.

- In a similar way, it would be interesting to know whether the recruitment of Polr3E and Sxl to chromatin is co-dependent or Sxl follows Polr3E. This is also a minor point because this would possibly refine the mechanism of recruitment but does not alter the main discovery.

Author Response:

We have addressed a similar point for Reviewer 2 (see below) and will include a Discussion point for this:

If Sxl were required for Polr3E recruitment to RNA Pol III, then, in most male Drosophila melanogaster cells, Polr3E would not be incorporated, and males would not be viable (as it is essential for Pol III activity). While it is possible that there could be a subtle effect on Polr3E recruitment, such an experiment, would not alter the central conclusion of our study - that Sxl is recruited to chromatin (accessory to the Pol III complex) via Polr3E.

* Figures and reporting:

• In Figure 2, it would be helpful to see the truncation coordinate for the N and C truncations.

• In Figure 3D, genomic coordinates are missing.

• In Figure 3E, the magnitude in the Y axis is not entirely clear (at least not to me). How is the amount of binding across the genome quantified? Is this the average amplitude of normalised TaDa signal across the genome? Or only within binding intervals?

• Figure S3E-F: it would be interesting to show the degree of overlap between the downregulated genes that are also binding targets (regardless of the outcome).

• Figure 5C-E: similarly to Figure S3, it would be interesting to know how the transcriptional effects compare with the binding targets.

• Authors use Gehan-Breslow-Wilcoxon to test survival, which is a bit unusual, as it gives more weight to the early deaths (which are rare in most Drosophila longevity experiments). Is there any rationale behind this? It may be even favour their null hypothesis.*

Author response:

Thank you for the detailed feedback on our figures. We have__ incorporated__ the suggested changes.

We agree that examining the overlap between Sxl binding sites and transcriptional changes is valuable, and we aimed to highlight this in the pie charts shown in Figures S3 and S5. If the reviewer is suggesting a more explicit quantification of the proportion of Sxl-Dam targets with significant transcriptomic changes, we are happy to include this analysis in the final version of the manuscript.

As noted in the Methods, both Gehan–Breslow–Wilcoxon (GBW) and Kaplan–Meier tests were used. The significance in Figure 4a is specific to the GBW test, which we indicated by describing the effect as mild. Our focus here is not on the magnitude of survival differences, but on the consistent trends observed in both Polr3e and Sxl knockdowns.

Writing and language:*

• Introduction finishes without providing an outline of the findings (which is fine by me if that is what the authors wanted).

• In lines 361-5, the authors say "We speculate that this interaction not only facilitates Pol III transcription but may also influence chromatin architecture and RNA Pol II-driven transcription as observed with Pol III regulation in other organisms". "This interaction" refers to Polr3E-Sxl-DNA interaction and with "Pol III transcription" I presume the authors refer to transcription executed by Pol III. I am not clear about the meaning of the end of the sentence "as observed with Pol III regulation in other organisms". What is the observation, exactly? That Pol III modifies chromatin in Pol II regulated loci, or that Pol III interactors change chromatin architecture?

• DPE abbreviation is not introduced (and only used once).

• A few typos: Line 41 ...splicing of the Sxl[late] transcripts, which is [ARE?] constitutively transcribed (Keyes et al.,... Line 76 ...sexes but appears restricted to the nervous system [OF] male pupae and adults (Cline et Line 289 ...and S41). To assess any effect [ON]translational output, O-propargyl-puromycin (OPP)o Line 323 ...illustrating that the majority (72%) changes in tRNA levels [ARE] due to upregulation...hi Line 402 ...it was discovered [WE DISCOVERED] Line 792 ...Sxl across chromosomes X, 2 L/R, 3 L/R and 4. The y-axis represents the log[SYMBOL] ratio... This happens in other figure legends as well.*

Author response:

Thank you for the detailed feedback, we have clarified and incorporated the suggested changes.

**Referee Cross-commenting***

Reviewer 1 asks how physiological is the Sxl chromatin-association assay. I think the loss of association in Polr3E knock-down and the lack of association of other splicing factors goes a long way into answering this question. It is true that having positive binding data specifically for Sxl-RAC and negative binding data for a deletion mutant of the RMM domain would provide more robust conclusions (see below), but I am not sure it is completely necessary -- though this will depend on which journal the authors want to send the paper to.

I think that the comment of reviewer 1 about the levels of expression of Sxl-DAM does not apply here because of the way TaDa works - it relies on codon slippage to produce minimal amounts of the DAM fusion protein, so by construction it will be expressed at much lower levels than the endogenous protein.

Reviewer 1 also asks whether Polr3E chromatin-association is also dependent on Sxl, to round up the model and also as a way to address whether Sxl association to chromatin is real. While I agree with this on the former aim (this would be a nice-to-have), I think I disagree on the latter; there is no need for Polr3E recruitment to depend on Sxl for Sxl association to chromatin to be physiologically relevant. Polr3E is a peripheral component of Pol III and unlikely to depend on a factor of restricted expression like Sxl to interact with chromatin. The recruitment of Sxl could well be entirely 'hierarchical' and subject to Polr3E.

Revewer 2 is concerned with the fact that every mutant form of Sxl shows the same result from the DamID labelling. I have to agree with this to a point. A deletion mutant of RMM domains would address this. Microscopy evidence in salivary glands would be nice, certainly, but the system may not lend itself to this particular interaction, which might be short-lived and/or weak. I do not immediately see the relevance of the chromatin binding capacity of non-Drosophilidae Sxl -- though it might indicate that the impact of the discovery is less likely to go beyond this group.

Reviewer 2 does not find surprising that some tRNA genes (less than half) are regulated by Sxl. I think the value of that observation is just qualitative, as tRNAs are Pol III-produced transcripts, but their point is correct. A hypergeometric test could settle this.

Reviewer 2 is concerned that the evidence of direct interaction between Sxl and Polr3E is a single 1999 two-hybrid study. But that paper contains also GST pull-downs that narrow down the specific domains that mediate binding, and perform the binding in competitive salt conditions. I think it is enough. The author team, I think, are not biochemists, so finding the right collaborators and performing these experiments would take time that I am not sure is warranted.

Reviewer 2 is also concerned that the longevity assays may not be meaningful due to the difference in genetic backgrounds. This is a very reasonable concern (which I would extend to the climbing assays - any quantitative phenotype is sensitive to genetic background). However, I think the authors here may have already designed the experiment with this in mind - the controls express untargeted RNAi constructs, but I lose track of which one is control of which. This should be clarified in Methods.

Other comments are in line, I think, with what I have pointed out and I generally agree with everything else that has been said.

Reviewer #3 (Significance (Required)):

Drosophila Sxl is widely known as an RNA-binding protein which functions as a splicing factor to determine sex identity in Drosophila and related species. It is a favourite example of how splicing factors and alternative can have profound influence in biology and used cleverly in the molecular circuitry of the cell to enact elegant regulatory decisions.

In this work, Storer, McClure and colleagues use genome-wide DNA-protein binding assays, transcriptomics, and genetics to work out that Sxl is also a chromatin factor with an sex-independent, neuron-specific role in stimulating transcription by Pol III and Pol II, of genes involved with metabolism and protein homeostasis, including some encoding tRNAs.

This opens a large number of interesting biological questions that range from biochemistry, gene regulation or neurobiology to evolution. How is the simultaneous capacity of binding RNA and chromatin (with the same protein domain, RRM) regulated/coordinated? How did this dual activity evolve and which one is the ancestral one? How many other RRM-containin RNA-binding proteins can also bind chromatin? How is Sxl recruited to chromatin to both Pol II and Pol III targets and are they functionally related? If so, how is the coordination of cellular functions activated through different RNA polymerases taking place and what is the role of Sxl in this? What are the functional consequences to neuronal biology? Does this affect similarly all Sxl-expressing neurons?

The evidence for the central tenet of the paper -- that Sxl acts as a chromatin regulator with Polr3E, activating at least some of its targets with either Pol III or Pol II -- is logical and compelling, the paper is well written and the figures well presented. Of course, more experiments could always be wished for and proposed, but I think this manuscript could be published in many journals with just a minor revision not involving additional experiments.*

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

*The convincing analysis demonstrates a role for the Drosophila Sex determining gene sex lethal in controlling aspects of transcription in the nervous system independent of its role in splicing. Interaction with an RNA Pol III subunit mediating Sxl association with chromatin and similar knockdown phenotypes strongly support the role of Sxl in the regulation of neuronal metabolism. Given that Sxl is an evolutionary recent acquisition for sex determination, the study may reveal an ancestral role for Sxl.

The conclusions are well justified by the datasets presented and I have no issues with the study or the interpretation. Throughout the work is well referenced, though perhaps the authors might take a look at Zhang et al (2014) (PMID: 24271947) for an interesting evolutionary perspective for the discussion.*

Author Response:

Thank you for the thoughtful suggestion. We will be sure to incorporate the findings from Zhang et al. regarding the evolution of the sex determination pathway.

*I have some minor comments for clarification:

There is no Figure 2b, should be labelled 2 or label TaDa plots as 2b

Clarify if Fig 2 data are larval or adult *

*Larval

Fig 3d - are these replicates or female and male?

Please elaborate on tub-GAL80[ts] developmental defects

Fig 4e, are transcriptomics done with the VDRC RNAi line? The VDRC and BDSC RNAi lines exhibit different behaviours - former has "better" survival and Better negative geotaxis, the latter seems to have poorer survival but little geotaxis effect?*

*Fig S3 - volcano plot for Polr3E?

Fig S4a - legend says downregulated genes?

The discussion should at least touch on the fact that Sxl amorphs (i.e. Sxl[fP7B0] are male viable and fertile, emphasising that the newly uncovered role is not essential.*

Author Response:

We agree with the suggestions outlined in the comments and have made the appropriate revisions.

Reviewer #4 (Significance (Required)):*

A nonessential role for Sxl in the nervous system independent of sex-determination contributes to better understanding a) the evolution of sex determining mechanisms, b) the role of RNA PolIII in neuronal homeostasis and c) more widely to the neuronal aging field. I think this well-focused study reveals a hitherto unsuspected role for Sxl.*

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

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Reply to the reviewers

1. General Statements

• This manuscript represents a full revision incorporating all reviewer recommendations; the additional follow-up experiments and expanded analyses will be presented in dedicated subsequent manuscripts.
• Congenital dyserythropoietic anemia type I (CDA-I) is a rare hereditary disease characterized by ineffective erythropoiesis and mutations in Codanin1 and CDIN1.
• Our study reveals the structural and functional dynamics of the CDIN1-Codanin1 complex, shedding light on the molecular mechanisms of protein-protein interactions implicated in CDA-I pathology.
• The main goal of our study was to examine the interaction between CDIN1 and the C‑terminal binding domain of Codanin1 using complementary biophysical approaches.
• We quantified binding and identified interacting regions of Codanin1 and CDIN1.
• We found that CDA-I-associated mutations in interacting regions disturb CDIN1‑Codanin1 complex.
• We proposed a hypothetical molecular model of CDIN1-Codanin1 role in CDA-I hallmarks development.
• Our initial studies on BioRxiv (2023) have been cited by leading publications in the field (Jeong, Frater et al. 2025, Sedor and Shao 2025, Nature Communications) and prompted further research on this topic.

2. Point-by-point description of the revisions

*Here we provide a point-by-point reply describing the revisions already carried out and included in the transferred manuscript. *

Reply to the reviewers

Reviewer #1 – Evidence, reproducibility and clarity

This is a rigorous biophysical characterization of a protein-protein interaction relevant to CDA-1 disease. The two proteins were purified in an E. coli host but CD and DLS was performed to ensure that the purified protein is well folded. An impressive native protein EMSA was used to show a 1:1 complex. While common for protein-nucleic acid complexes, EMSAs are much more challenging with protein complexes. A higher-running complex, likely a heterotetramer was implied at higher protein concentrations. These results were supported with SEC-MALS analysis and analytic ultracentrifugation analysis. Thermophoresis and ITC were used to report a nanomolar affinity of the proteins for each other. SEC-SAXS supported the conclusions about stoichiometry and composition inferred from the earlier methods and suggested that the dimerization interface comes from CDIN1. Next HDX-MS was used to identify putative interface residues, which were then mutated in each of the proteins and assessed for binding using coimmunoprecipitation. This study uses at least 10 orthogonal biophysical and/or biochemical methodologies to characterize an important protein-protein interaction and the analysis is clear and so is the writing. I couldn't (reading it once) find any grammatical or other errors in the text or figures. This manuscript is top-quality and suitable for publication.

__Reviewer #1 – Significance __

Such detailed structural and mechanistic studies are greatly lacking in many clinical conditions for which mutations are known (unless they cause cancer, neurodegenerative disease, and so on). We need more such studies on disease topics! This study will be of interest to the hematologic diseases community.

1. Response – ____Significance

We thank Reviewer #1 for the thoughtful and encouraging evaluation of our work. We are particularly grateful for recognizing the significance of studying protein-protein interaction in the context of CDA-I disease, as well as the rigor and clarity of our biophysical and biochemical characterization.

We appreciate the reviewer's acknowledgment of the challenges associated with native protein EMSAs. We are pleased that our use of multiple orthogonal techniques was recognized as a strength of the study. We are gratified that the comprehensiveness and coherence of our data and the manuscript's clarity were well received.

We thank the reviewer for noting the broader impact of our findings on the hematologic disease community. As highlighted, there is a pressing need for a mechanistic understanding of non-oncologic, non-neurodegenerative diseases, and our studies address this gap.

We are honored by the reviewer's endorsement of our manuscript as "top-quality and suitable for publication". We value the reviewer's highly supportive and motivating feedback.

__Reviewer #2 – 1. Evidence, reproducibility and clarity __

This manuscript presents structural and biochemical characterization of the interaction between CDIN1 and the C-terminal domain of Codanin1, shedding light on a complex implicated in Congenital Dyserythropoietic Anemia Type I (CDA-I). While the authors provide valuable structural insights and identify disease-associated mutations that impair CDIN1-Codanin1 binding, I think several important concerns should be addressed to strengthen both the mechanistic claims and their functional relevance.

Contradiction Between Stoichiometry Models:

The authors propose that CDIN1 and Codanin1Cterm primarily form a heterodimer in vitro. However, this appears to contradict previous reports indicating a tetra-heteromeric arrangement. Additionally, while CDIN1 homodimerize seems confusing to me, do the authors suggest it is stable without Codanin1? This seems contrary to findings that CDIN1 is unstable in the absence of Codanin1 (Sedor, S.F., Shao, S. nature comm 2025, Swickley, G., Bloch, Y., Malka, L. et al 2020 BMC Mol and Cell Biol). These inconsistencies raise concerns about whether the observed stoichiometries are physiologically relevant or artifacts of in vitro reconstitution, especially since full-length Codanin1 was not studied.

2.1 Response ____– Consistent stoichiometry of Codanin1Cterm

We thank Reviewer #2 for raising critical points regarding the stoichiometry and physiological relevance of the CDIN1-Codanin1 interaction. The following response clarifies the rationale and interpretation in relation to previous findings.

Stoichiometry of CDIN1-Codanin1Cterm complex:

Recent Cryo-EM studies of full-length Codanin1 (Jeong, Frater et al. 2025, Sedor and Shao 2025) suggest independent internal dimerization domains (452-798 and 841-1000 amino acid residue) driving homodimer formation, with each Codanin1 monomer binding one CDIN1 via the C-terminal region (1005-1227 amino acid residue), resulting in a tetra-heteromeric complex. Therefore, the complete assembly appears as a dimer of heterodimers in the full-length context.

In our study, Codanin1 was truncated to retain only the CDIN1-binding C-terminus (1005-1227 amino acid residues), eliminating the homodimerization ability of Codanin1. Hence, in the case of truncated Codanin1Cterm, the minimal complex we observe is a 1:1 heterodimer of CDIN1-Codanin1Cterm, which is fully consistent with the equimolar stoichiometry of CDIN1-Codanin1 complex seen in the full-length structure.

Stability and oligomeric state of CDIN1 in the absence of Codanin1:

We concur with the reviewer that Sedor et al. (2025) and Swickley et al. (2020) reported decreased CDIN1 levels in cells lacking Codanin1, implying in vivo dependence of CDIN1 on Codanin1 partner for stability (Swickley, Bloch et al. 2020, Sedor and Shao 2025). The purified CDIN1 is monodisperse (Supplementary Figure 2D), exhibits thermal stability with a melting temperature of 48 °C (Supplementary Figure 2E), and displays proper folding as indicated by CD measurements (Supplementary Figure 2B). Additionally, SAXS profiles of CDIN1 correspond to AlphaFold predictions (Fig. 2B). Together, our findings indicate that the recombinant CDIN1 forms a stable conformation in vitro without Codanin1. To the best of our knowledge, no previous research has directly identified the endogenous oligomeric states of CDIN1 within cellular content.

We fully acknowledge that future analysis of the full-length Codanin1-CDIN1 assembly in a cellular context will be necessary for understanding physiological stoichiometries. As outlined in the General statements, our study focuses on the C-terminus of Codanin1 to describe the binding interface and complex biophysical properties of the CDIN-Codanin1Cterm complex.

__Reviewer #2 – ____2. Unvalidated Functional Claims: __

The manuscript identifies several CDA-I-associated mutations that disrupt CDIN1-Codanin1 interaction. However, the authors do not test how these mutations affect the biological function of the complex, particularly its role in ASF1 sequestration or histone trafficking. Given the central importance of this axis in their disease model, functional validation (e.g., ASF1 localization, histone deposition assays) is necessary to support these mechanistic conclusions.

2.2 Response – ____Hypothetical model as discussion merit

We thank the reviewer for the comment regarding the functional implications of CDA-I-associated mutations and their potential impact on ASF1 sequestration and histone trafficking hypothesized within the Discussion. We fully agree that understanding the downstream biological consequences of disrupted CDIN1-Codanin1 interaction is critical for elucidating the full molecular basis of CDA-I pathogenesis.

In the Future research directions of the Discussion, we have acknowledged and emphasized the need for follow-up studies using erythroblast cell lines to determine whether specific disease-associated mutations disrupt CDIN1-Codanin1 binding, leading to functional defects relevant to erythropoiesis and nuclear architecture typical for CDA-I disease.

However, as we respectfully note in General Statements, the main aim of the present study was to provide a rigorous biophysical characterization of the CDIN1-Codanin1Cterm interaction. Proposed cellular experiments, though relevant, are beyond the conceptual scope of the presented studies.

Reviewer #2 – ____3. Speculative and Potentially Contradictory Model:

The proposed model suggests that CDIN1 competes with ASF1 for Codanin1 binding, thereby indirectly promoting histone delivery to the nucleus. However, emerging data indicate that Codanin1, CDIN1, and ASF1 can form a stable ternary complex, calling into question this competitive binding hypothesis (Sedor, S.F., Shao, S. nature comm 2025). The authors do not acknowledge or discuss these findings, and the model in its current form may therefore be oversimplified or inaccurate.

2.3 Response – ____Hypothetical model fully aligned with current knowledge

We fully acknowledged and discussed in the current manuscript the recent findings demonstrating that Codanin1, CDIN1, and ASF1 can form a ternary complex (Sedor, S.F., Shao, S. Nature Comm. 2025; Jeong, T. K. et al. Nature Comm. 2025). Our revised model was updated accordingly to reflect the collaborative binding of Codanin1, CDIN1, and ASF1, and is presented in alignment with published data.

While earlier versions of our work published on the BioRxiv server (May 26, 2023) proposed a competitive hypothesis, the current manuscript incorporates recent literature and prior reviewer feedback to offer a refined model. We believe that the updated hypothesis suggests a plausible mechanism for how CDIN1 modulates Codanin1 function, which will be further tested in future cellular studies.

Reviewer #2 – 4. Significance:

Overall, the study adds to our structural understanding of CDIN1 and Codanin1 interactions, but the functional interpretations are currently speculative, and in some cases in conflict with existing literature. The manuscript would benefit significantly from addressing these discrepancies, incorporating relevant data on ASF1, and clarifying whether the observed assemblies reflect physiological complexes.

__2.4 Response – Significance __

We thank Reviewer #2 for the constructive feedback. As noted in General Statements, our current manuscript is primarily dedicated to defining the molecular architecture and interactions of the CDIN1–Codanin1Cterm core interface. We agree that follow-up ASF1‑dependent functional assays will be critical to fully validate observed assemblies, but these experiments lie outside the scope of the present study and are ongoing in our laboratory.

To address the reviewer's concern about possible speculative interpretation, we have:

• Used cautious language in Results and Discussion to prevent overstatement (e.g., page 31, line 754, “leads” exchanged to “may contribute” in legend of Fig. 4).
• Described in the Discussion how our results enhance and add understanding to the body of published structural data of CDIN1–Codanin1Cterm.
• Updated our hypothetical model in Fig. 4 to be fully in line with published data.
• Clearly stated that the working hypothesis is connected with a subset of CDA-I mutations (p. 31, l. 758-759, “The proposed model represents a working hypothesis relating to a subset of CDA-I mutations and is not currently substantiated by experimental evidence at the cellular level.”)
• Stated in Future research directions of Discussion that functional validation, including ASF1, will motivate future critical studies, p. 32, l. 771-773: “The ability of Codanin1 to interact with both CDIN1 and ASF1 motivates further investigation of how CDIN1 and ASF1 affect the function of full-length Codanin1, which even recent cryo-EM data has not addressed yet.”
• Highlighted the necessity of complementary in vivo studies in erythroblast cell lines to determine if CDA-I-related mutations in CDIN1-Codanin1 interaction region cause typical CDA-I phenotypes, aiming to clarify the molecular mechanisms of inherited CDA-I anemia. We state in Future research directions in Discussion, p. 32, l. 774-780: “…follow-up research utilizing erythroblast model cell lines must be conducted to determine if specific mutations that disrupt CDIN1-Codanin1 binding also affect ASF1 localization and cause a phenotype typical of CDA-I. In future work, additional Codanin1 mutations, including those outside the C-terminal region, should be evaluated to determine how the mutations affect ASF1’s nuclear concentration and subcellular localization. The proposed research directions will provide additional deeper insights into the underlying mechanisms of the molecular origin of inherited anemia CDA-I.” We believe that the revisions objectively clarify the significance and the limits of the current work and set the stage for the detailed functional studies to follow.

__Reviewer #3 – Evidence, reproducibility and clarity: __

Congenital Dyserythropoietic Anemia Type I (CDA I) is an autosomal recessive disorder characterized by ineffective erythropoiesis and distinctive nuclear morphology ("Swiss cheese" heterochromatin) in erythroblasts. CDA I is caused by mutations in CDAN1 and CDIN1. Codanin1, encoded by CDAN1, is part of the cytosolic ASF1-H3.1-H4-Importin-4 complex, which regulates histone trafficking to the nucleus. CDIN1 has been shown to bind the C-terminal domain of Codanin-1, but until now, pathogenic mutations had not been directly linked to the disruption of this interaction.

In this study, the authors used biophysical techniques to characterize the interaction between Codanin-1's C-terminal region (residues 1005-1227) and CDIN1, demonstrating high-affinity, equimolar binding. HDX-MS identified interaction hotspots, and disease-associated mutations in these regions disrupted complex formation. The authors propose that such disruption prevents ASF1 sequestration in the cytoplasm, thereby reducing nuclear histone levels and contributing to the chromatin abnormalities seen in CDA I.

Major Comments:

1. Use of Codanin-1 Fragment:

Most experiments were conducted using only the C-terminal 223 amino acids of Codanin-1. While this region is known to bind CDIN1, it is unclear whether its conformation is maintained in the context of the full-length protein. This could affect binding properties and structural interpretations. The authors should discuss how structural differences between the isolated C-terminus and the full-length Codanin-1 may influence the conclusions.

Response of authors ____#3

3.1 Response: Use of Codanin-1 Fragment as biding part to CDIN1

We thank the reviewer for the important observation regarding the use of the C-terminal fragment of Codanin1. As noted in the manuscript (e.g., p. 30, line 721 and p. 32, line 761), we fully acknowledge that the truncation of Codanin1 may influence its conformational dynamics or contextual folding relative to the full-length protein.

However, several lines of evidence suggest that the C-terminal 223 amino acid residues—responsible for CDIN1 binding—are structurally autonomous and have minimal intramolecular contacts with upstream regions. Published cryo-EM and biochemical data (Jeong, Frater et al. 2025, Sedor and Shao 2025), in conjunction with AlphaFold structural predictions (Fig. 2D) and our co-immunoprecipitation assays (Fig. 3F), consistently support a model wherein the CDIN1-binding region is flexible and spatially isolated from the core structural domains of Codanin1. Additionally, results from our co-immunoprecipitation assay (Fig. 3F) indicate that full-length Codanin1 and truncated Codanin1Cterm interact with CDIN1 similarly, further supporting the isolated manner of the C-terminal fragment. The available data together imply that the C-terminal fragment used in our study retains its native conformation and binding properties when expressed independently.

While our findings are confined to the interaction domain and do not reflect full-length Codanin1’s architecture, we believe the use of the C-terminal minimal fragment of Codanin1 enables precise dissection of the CDIN1-binding interface and yields mechanistic insights without introducing significant structural artifacts.

We agree with the reviewer that future work incorporating full-length Codanin1, especially in a cellular context, will be instrumental to fully characterize higher-order assembly and regulatory functions.

__Reviewer #3 – 2. ____Graphical Abstract and Domain Independence: __

The graphical abstract presents the Codanin-1 C-terminus as an independent domain, but no direct evidence is provided to support its structural autonomy in vivo.

The authors should clarify whether the C-terminal region functions as a distinct domain in the context of the full-length protein.

__3.2 Response –____ Independent C-terminal domain __

We thank the reviewer for bringing up the question of the independence of the C-terminal domain. Although direct in vivo proof of C-terminal autonomy is not yet available, published cryo-EM structures of full-length Codanin1, our biophysical characterization, and AlphaFold models all consistently indicate that the C-terminal 223 amino acid residues of Codanin1 form a structurally independent binding module. In the graphical abstract, we illustrated the C‑terminal domain as a loosely connected part of Codanin1 to highlight its independence and to emphasize the specific focus of our studies.

To articulate limitations of our studies focused on the C-terminal part of Codanin1, we stated in the Functional implications of CDA-I-related mutations in the Discussion, p. 30, l. 721-724: “However, our measurements do not exclude the possible role of the disordered regions in full-length Codanin1. For example, CDIN1 could potentially stabilize full-length Codanin1 by rearranging the disordered regions into a more condensed structure, thereby augmenting the structural stability of Codanin1.”

Reviewer #3 – 3.____Pathogenic Mutations Beyond the Binding Site:

The study highlights a triplet mutation that impairs CDIN1 binding. However, most CDA I‑associated mutations in CDAN1 are dispersed across the entire protein and may not affect CDIN1 interaction directly.

The authors should discuss alternative mechanisms by which mutations in other regions of Codanin-1 might cause disease.

3.3 Response – Pathogenic mutations outside the binding site – alternative mechanisms

We appreciate the reviewer noting that most CDA-I-associated CDAN1 mutations are outside the CDIN1-Codanin1 binding site and suggesting alternative mechanisms. In the revised Discussion, we added a paragraph on alternative pathogenic models, p. 29, l. 702-713:

"Our study centers on the CDIN1-binding C-terminus, however, most CDA-I-associated CDAN1 mutations lie elsewhere and probably act through alternative mechanisms. Mutations such as P672L and F868I in the LOBE2 (452-798 amino acid residue) and F868I in the coiled-coil (841-1000 amino acid residue) domains may disturb Codanin1 homodimerization and higher-order complex assembly, directly affecting ASF1 sequestration (Jeong, T. K. et al. Nature Comm. 2025). Other mutant variants may also interfere with ASF1 sequestration, nuclear targeting, or chromatin-remodeling functions, while destabilizing mutations may induce misfolding and proteasomal degradation. Moreover, CDA-I-associated mutations, such as R714W and R1042W, might compromise the interaction between Codanin1 and ASF1 (Ask, Jasencakova et al. 2012). Collectively, the complementary alternative pathogenic mechanisms associated with Codanin1 mutations in distal regions and mutations in CDIN1‑binding C-terminus of Codanin1 may contribute to erythroid dysfunction in CDA-I."

Reviewer #3 – 4. ____Contradictory Functional Models:

Ask et al. (EMBO J, 2012) reported that Codanin-1 depletion increases nuclear ASF1 and accelerates DNA replication. This contrasts with the current hypothesis that disruption of the Codanin-1/CDIN1 complex reduces nuclear ASF1.

The authors should attempt to reconcile this apparent contradiction, possibly by proposing a context-specific or dual-function model for Codanin-1 in histone trafficking.

3.4 Response – ____Clarified explanation of hypothetical functional model

We thank the reviewer for raising this point, which improved the clarity of our work. There is no real discrepancy between Ask et al. and our findings; both agree that Codanin1 restrains ASF1 in the cytoplasm. Ask et al. examined the complete loss of Codanin1, which abolishes cytoplasmic ASF1 sequestration and thus leads to maximal nuclear accumulation. We suggest the CDA-I-associated mutations selectively disrupt the CDIN1-Codanin1 interface, releasing ASF1 from the cytoplasm into the nucleus.

To enhance clarity, we now state in the legend of Figure 4 describing the hypothesis (p. 31, l. 752-753): "…CDA-I-associated mutations prevent CDIN1-Codanin1 complex formation, thus prevent ASF1 sequestration to cytoplasm; ASF1 remains accumulated in nucleus."

Reviewer #3 – 5. ____Conclusions and Claims:

The proposed model of CDA I pathogenesis (Fig. 4) is plausible but not yet fully supported by the available data. The authors suggest that disruption of the Codanin-1/CDIN1 interaction leads to nuclear histone depletion, but this has not been experimentally confirmed.

Claims about the general pathogenesis of CDA I should be clearly qualified as hypothetical and applicable to a subset of mutations. The presence and localization of ASF1 in the nucleus following disruption of the Codanin-1/CDIN1 complex should be tested experimentally.

3.5 Response – __Tempered ____conclusions and claims: __

We thank the reviewer for underscoring the need to temper our conclusions and to distinguish hypotheses from available results. We fully agree that our Fig. 4 model—linking disruption of the Codanin1-CDIN1 interface to nuclear histone imbalance—remains a working hypothesis, currently supported by indirect biochemical and structural data.

Accordingly, we have:

• Revised the text to explicitly state that this model is hypothetical and pertains to a subset of CDA-I-associated CDAN1 mutations. Specifically, we

• Added to the last paragraph of the section Functional implications of CDA-I-related mutations in Discussion (p. 31, l. 744-749): “In considering functional implications of our findings within available data, it is essential to qualify that mechanistic claims regarding the general pathogenesis of CDA-I remain hypothetical and are restricted to a specific subset of mutations. Furthermore, direct experimental validation, such as immunolocalization or live-cell imaging, to assess ASF1’s nuclear presence and distribution following disruption of the CDIN1-Codanin1 complex is required to substantiate the proposed model.”

• Included in the legend of Fig. 4: ”The proposed model represents a working hypothesis relating to a subset of CDA-I mutations and is not currently substantiated by experimental evidence at the cellular level.”
• Replaced any associated definitive language (e.g., “leads to”) with qualified phrasing (e.g., “may contribute to”) in the legend of Fig. 4.
• Clarified in the Discussion that direct measurement of nuclear ASF1 redistribution and histone levels following interface disruption has not yet been performed. Specifically, we added to the section Functional implications of CDA-I-related mutations in Discussion (p. 30, l. 734-735): “It should be noted, however, that direct quantification of nuclear ASF1 redistribution and histone levels after CDIN1-Codanin1 disruption has not yet been conducted.” Although experimental verification of nuclear ASF1 localization upon CDIN1-Codanin1 complex disruption falls beyond the current manuscript’s scope, we acknowledge its importance and have emphasized the need for such studies in future work within the Future research directions of the Discussion. Specifically, we concluded by stating (p. 32, l. 774-776): “Finally, follow‑up research utilizing erythroblast model cell lines must be conducted to determine if specific mutations that disrupt CDIN1-Codanin1 binding, also affect ASF1 localization and cause a phenotype typical of CDA-I.”

__Reviewer #3 – 6.____Broader Mutation Analysis and ASF1 Localization: __

To strengthen the link between Codanin-1/CDIN1 disruption and disease pathogenesis, it would be important to test the effects of additional CDAN1 mutations, including those outside the C-terminal region. Similarly, the impact on ASF1 nuclear concentration and localization should be directly assessed. These experiments would significantly bolster the central hypothesis. If feasible, they should be pursued or at least acknowledged as important future directions.

3.6 Response – Broader mutation analysis and ASF1 localization in future directions

We thank Reviewer #3 for emphasizing the value of a broader mutation survey and direct ASF1 localization studies. As noted above, our current manuscript is centered on delineating the molecular architecture of the CDIN1-Codanin1Cterm core interface; comprehensive mutational analyses outside the C-terminal binding region and ASF1-dependent functional assays will be critical to extend these findings but fall beyond the scope of the present work and will be the objective of our following studies. To address the reviewer’s concern, we have:

• Expanded the Future Directions section to specify that additional CDA-I-linked CDAN1 variants, including non-C-terminal mutations, and quantitative assessments of ASF1 nuclear localization will be the subject of ongoing and planned investigations. Specifically, we added (p. 32, l. 776-778):” In future work, additional Codanin1 mutations, including those outside the C-terminal region, should be evaluated to determine how the mutations affect ASF1’s nuclear concentration and subcellular localization.”

• Emphasized the need for complementary in vivo validation in erythroblast models to confirm whether the disturbance of CDIN1-Codanin1 binding recapitulates CDA-I phenotypes. We acknowledged the need for cell-line studies in future work within the Future research directions of Discussion (p. 32, l. 774-776): “Finally, follow-up research utilizing erythroblast model cell lines must be conducted to determine if specific mutations that disrupt CDIN1-Codanin1 binding, also affect ASF1 localization and cause a phenotype typical of CDA-I.” We believe these changes more precisely delimit the scope and significance of the current study while laying out a clear roadmap for the essential follow-up experiments.

Reviewer #3 – 7. ____Rigor and Presentation and Cross-commenting

__Minor Comments: __

• Methods and Reproducibility:

The experimental methods are well described, and the results appear reproducible.

• Presentation:

The text and figures are clear and well organized.

Referee Cross-commenting

I agree with reviewer 1 that the paper presents detailed structure study of Codanin-1 and CDIN1 protein. However, as reviewer 2 claims functional studies are missing and therefore the hypothesis regarding the pahtogenesis of CDAI is speculaltive especially with no studies regarding ASF1.

3____.7 Response ____–____ Rigor and Presentation and Cross-commenting:

We thank the reviewers for their positive appraisal of our results' reproducibility, presentation, and method descriptions. We also appreciate the cross-comment that, while our structural analysis of the CDIN1-Codanin1 complex is thorough, functional validation, particularly regarding ASF1, remains to be addressed.

As outlined above, we have revised the manuscript to:

• Emphasize that pathogenic hypotheses drawn from structural data are provisional (refer to Responses 2.2, 2.3, and 3.5).
• Include follow-up studies for ASF1 localization assays and broader mutation profiling in our Future Directions (refer to Responses 2.4, 3.5, 3.6).
• Integrate cautious language throughout to clearly delineate verified findings from model-based speculation (refer to Responses 2.4, 3.5, 3.6). The implemented adjustments ensure that the current work is positioned as a detailed structural and interaction foundation, upon which the essential functional studies will build. We believe that all extensions and clarifications fully satisfy the reviewers’ collective recommendations.

__Reviewer #3 –____ Significance: __

Nature and Significance of the Advance:

This study extends prior work (e.g., Swickley et al., BMC Mol Cell Biol 2020; Shroff et al., Biochem J 2020) on Codanin-1/CDIN1 interaction by applying high-resolution biophysical techniques to identify mutations that disrupt this complex. It provides a plausible cellular mechanism by which specific mutations may lead to CDA I through impaired histone trafficking.

Nevertheless, key question remains: How do mutations outside the Codanin-1 C-terminus contribute to the pathology?

3.8 Response – Significance:

• We thank Reviewer #3 for this important point. Although our work specifically dissects the C-terminal CDIN1-binding domain of Codanin1, we fully acknowledge that CDA-I-associated mutations throughout Codanin1 may operate via additional mechanisms. To address the additional mechanisms, we have added a new paragraph describing other possible pathogenic models to the Discussion (please refer to Response 3.3).
• We also fully acknowledged the need for systematic functional assays of non-C-terminal mutations and their impact on ASF1 localization (please refer to Response 3.6).

• We revised the text to clarify how mutations beyond the C-terminus may contribute to CDA-I pathogenesis and present the significance of our current structural analyses, biophysical characterizations, and molecular insights as a foundation for future research (please refer to Response 3.6). __Audience: __

• Molecular and cellular biologists investigating nuclear-cytoplasmic trafficking mechanisms

• Hematologists and geneticists studying rare red cell disorders
• Clinicians managing CDA I patients and researchers exploring targeted therapies __Reviewer Expertise: __

Pediatric hematologist with over 20 years of research experience in CDA I, including the initial identification of CDAN1 and the elucidation of Codanin-1's role in embryonic erythropoiesis. Not a specialist in the biophysical techniques used in this study.

References

Ask, K., Z. Jasencakova, P. Menard, Y. Feng, G. Almouzni and A. Groth (2012). "Codanin-1, mutated in the anaemic disease CDAI, regulates Asf1 function in S-phase histone supply." The EMBO Journal 31(8): 2013–2023.

Jeong, T.-K., R. C. M. Frater, J. Yoon, A. Groth and J.-J. Song (2025). "CODANIN-1 sequesters ASF1 by using a histone H3 mimic helix to regulate the histone supply." Nature Communications 16(1): 2181.

Sedor, S. F. and S. Shao (2025). "Mechanism of ASF1 engagement by CDAN1." Nature Communications 16(1): 2599.

Swickley, G., Y. Bloch, L. Malka, A. Meiri, S. Noy-Lotan, A. Yanai, H. Tamary and B. Motro (2020). "Characterization of the interactions between Codanin-1 and C15Orf41, two proteins implicated in congenital dyserythropoietic anemia type I disease." Molecular and Cell Biology 21(1).

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

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Reply to the reviewers

Reply to the Reviewers

I would like to thank the reviewers for their comments and interest in the manuscript and the study.

Reviewer #1

1. I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

The directional positioning of CTCF-binding sites at chromatin interaction sites was analyzed by CRISPR experiment (Guo Y et al. Cell 2015). We found that the machine learning and statistical analysis showed the same directional bias of CTCF-binding motif sequence and RAD21-binding motif sequence at chromatin interaction sites as the experimental analysis of Guo Y et al. (lines 229-253, Figure 3b, c, d and Table 1). Since CTCF is involved in different biological functions (Braccioli L et al. Essays Biochem. 2019 ResearchGate webpage), the directional bias of binding sites may be reduced in all binding sites including those at chromatin interaction sites (lines 68-73). In our study, we investigated the DNA-binding sites of proteins using the ChIP-seq data of DNA-binding proteins and DNase-seq data. We also confirmed that the DNA-binding sites of SMC3 and RAD21, which tend to be found in chromatin loops with CTCF, also showed the same directional bias as CTCF by the computational analysis.

__2. Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure. __

Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 435 and 829: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

3. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). We found that the DNA-binding sites of the insulator-associated DBPs were statistically overrepresented in the 5 kb boundary sites more than other DBPs (Fig. 4d). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality of insulator-associated DNA-binding sites is their overall tendency, and it may be difficult to notice the directionality from each binding site because the directionality may be weaker than that of CTCF, RAD21, and SMC3 as shown in Table 1 and Supplementary Table 2. We also observed the directional biases of CTCF, RAD21, and SMC3 by using Micro-C chromatin interaction data as we estimated, but the directionality was more apparent to distinguish the differences between the four directions of FR, RF, FF, and RR using CTCF-mediated ChIA-pet chromatin interaction data (lines 287 and 288).

 I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. *Cell* 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay, and include less long-range interactions due to distance bias. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study.

 I discussed other causes in lines 614-622: Another reason for the difference may be that boundary sites are more closely associated with topologically associated domains (TADs) of chromosome than are insulator sites. Boundary sites are regions identified based on the separation of numerous chromatin interactions. On the other hand, we found that the multiple DNA-binding sites of insulator-associated DNA-binding proteins were located close to each other at insulator sites and were associated with distinct nested and focal chromatin interactions, as reported by Micro-C assay. These interactions may be transient and relatively weak, such as tissue/cell type, conditional or lineage-specific interactions.

 Furthermore, I have added the statistical summary of the analysis in lines 372-395 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.

4. The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 405 - 412: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value 5. Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

6. Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2's comments.

Reviewer #2

1. Introduction, line 95: CTCF appears two times, it seems redundant.

On lines 91-93, I deleted the latter CTCF from the sentence "We examine the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

2. Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

 I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.

3. Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

4. Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines". On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

5. Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

6. Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 501: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

 In Aljahani A et al. *Nature Communications* 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. *Nature Genetics* 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.

I added the following sentence on lines 569-577: The depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. Furthermore, the loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.

 FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. *Molecular Cell* 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. *Nucleic acids research* 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 556: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.

7. In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

 The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. *Nature Genetics* 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. *Proc Natl Acad Sci USA* 2021 ; Ortabozkoyun H et al. *Nature genetics* 2022 ; Wang R et al. *Nature communications* 2022). I have added the following sentences on lines 571-575: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 582-584: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.

 As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. *Nature* 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. *Nature Reviews Molecular Cell Biology* 2021). Regarding loop extrusion, the 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. *EMBO Journal* 2024). I have added the following sentences on lines 543-547: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 577-582: The 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.

 Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. *Elife* 2024) (Ke W et al. *Elife* 2024) (Fujioka M et al. *PLoS Genetics* 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 559-567: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.

8. Do the authors think that the identified DBPs could work in that way as well?

The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

 Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. *Nucleic Acids Research* 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. *Cell Reports* 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 554: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.

9. Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 584-590: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

10. Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 539 - 543 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops.

 To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the *Drosophila even skipped *locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. *PLoS Genetics* 2016).

Reviewer #3

Major Comments:

1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 257 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 20 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 929 - 931 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 974 - 976 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 356 - 360: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 593-597: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

Minor Comments:

1. PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., ____https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2____or ____https://pubmed.ncbi.nlm.nih.gov/37486787____/). The authors should discuss how that would impact their results.

The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, although the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

 As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.

2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

 Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 623-628: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.

 Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1:

(1) Developmental time series:

It was not entirely clear how this experiment relates to the rest of the manuscript, as it does not compare any effects of transport within or across species.

Implemented Changes:  

The importance of species arrival timing for community assembly is addressed in both the introduction and discussion. To accommodate the reviewer’s concerns and further emphasize this point, we have added a clarifying sentence to the results section and included an illustrative example with supporting literature in the discussion.

Results: Clarifying the timing of initial microbial colonization is essential for determining whether and how priority effects mediate community assembly of vertically transmitted microbes in early life, or whether these microbes arrive into an already established microbial landscape. We used non-sterile frogs of our captive laboratory colony (…)

Discussion: For example, early microbial inoculation has been shown to increase the relative abundance of beneficial taxa such as Janthinobacterium lividum (Jones et al., 2024), whereas efforts to introduce the same probiotic into established adult communities have not led to long-term persistence (Bletz, 2013; Woodhams et al., 2016).  

(2) Cross-foster experiment:

The "heterospecific transport" tadpoles were manually brushed onto the back of the surrogate frog, while the "biological transport" tadpoles were picked up naturally by the parent. It is a little challenging to interpret the effect of caregiver species since it is conflated with the method of attachment to the parent. I noticed that the uptake of Os-associated microbes by Os-transported tadpoles seemed to be higher than the uptake of Rv-associated microbes by Rv-associated tadpoles (comparing the second box from the left to the rightmost boxplot in panel S2C). Perhaps this could be a technical artifact if manual attachment to Os frogs was more efficient than natural attachment to Rv frogs.

I was also surprised to see so much of the tadpole microbiome attributed to Os in tadpoles that were not transported by Os frogs (25-50% in many cases). It suggests that SourceTracker may not be effectively classifying the taxa.

Implemented Changes:  

Methods (Study species, reproductive strategies and life history): Oophaga sylvatica (Os) (Funkhouser, 1956; CITES Appendix II, IUCN Conservation status: Near Threatened) is a large, diurnal poison frog (family Dendrobatidae) inhabiting lowland and submontane rainforests in Colombia and Ecuador. While male Os care for the clutch of up to seven eggs, females transport 1-2 tadpoles at a time to water-filled leaf axils where tadpoles complete their development (Pašukonis et al., 2022; Silverstone, 1973; Summers, 1992). Notably, females return regularly to these deposition sites to provision their offspring with unfertilized eggs.

Discussion: Most poison frogs transport tadpoles on their backs, but the mechanism of adherence remains unclear. Similar to natural conditions, tadpoles that are experimentally placed onto a caregiver’s back also gradually adhere to the dorsal skin, where they remain firmly attached for several hours as the adult navigates dense terrain. Although transport durations were standardized, species-specific factors- such as microbial density at the contact site, microbial taxa identity, and skin physiology such as moisture -could influence microbial transmission between the transporting frog and the tadpole. While these differences may have contributed to varying transmission efficacies observed between the two frog species in our experiment, none of these factors should compromise the correct microbial source assignment. We thus conclude that transporting frogs serve as a source of microbiota for transported tadpoles. However, further studies on species-specific physiological traits and adherence mechanisms are needed to clarify what modulates the efficacy of microbial transmission during transport, both under experimental and natural conditions.  

Methods (Vertical transmission): Cross-fostering tadpoles onto non-parental frogs has been used previously to study navigation in poison frogs (Pašukonis et al., 2017). According to our experience, successful adherence to both parent and heterospecific frogs depends on the developmental readiness of tadpoles, which must have retracted their gills and be capable of hatching from the vitelline envelope through vigorous movement. Another factor influencing cross-fostering success is the docility of the frog during initial attachment, as erratic movements easily dislodge tadpoles before adherence is established. Rv are small, jumpy frogs that are easily stressed by handling, making experimental fostering of tadpoles—even their own— impractical. Therefore, we favored an experimental design where tadpoles initiate natural transport and parental frogs pick them up with a 100% success rate. We chose the poison frog Os as foster frogs because adults are docile, parental care in this species involves transporting tadpoles, and skin microbial communities differ from Rv- a critical prerequisite for our SourceTracker analysis. The use of the docile Os as the foster species enabled a 100% cross-fostering success rate, with no notable differences in adherence strength after six hours.

Methods (Sourcetracker Analysis): To assess training quality, we evaluated model selfassignment using source samples. We selected the model trained on a dataset rarefied to the read depth of the adult frog sample with the lowest read count (48162 reads), as it showed the best overall self-assignment performance, whereas models trained on datasets rarefied to the lowest overall read depth performed worse. Unlike studies using technical replicates, our source samples represent distinct biological individuals and sampling timepoints, where natural microbiome variability is expected within each source category. Consequently, we considered self-assignment rates above 70% acceptable. All source samples were correctly assigned to their respective categories (Rv, Os, or control), but with varying proportions of reads assigned as 'Unknown'. Adult frog sources were reliably selfidentified with high confidence (Os: 97.2% median, IQR = 1.4; Rv: 76.3% median, IQR = 38.1). Adult R. variabilis frogs displayed a higher proportion of 'Unknown' assignments compared to O. sylvatica, likely reflecting greater biological variability among individuals and/or a higher proportion of rare taxa not well captured in the training set. The control tadpole source showed lower self-assignment accuracy (median = 30.5%, IQR = 17.1), as expected given the low microbial biomass of these samples, which resulted in low read depth. Low readdepth limits the information available to inform the iterative updating steps in Gibbs sampling and reduces confidence in source assignments. We therefore verified the robustness of our results by performing the second Sourcetracker analysis as described above, training the model only on adult sources and assigning all tadpoles, including lowbiomass controls, as sinks (as described above). Self-assignment rates for the second training set varied (O. sylvatica: 79.2% median, IQR = 29; R. variabilis: 96.6% median, IQR = 3.7), while results remained consistent across analyses, supporting the reliability of our findings.

(3) Cross-species analysis:

Like the developmental time series, this analysis doesn't really address the central question of the manuscript. I don't think it is fair for the authors to attribute the difference in diversity to parental care behavior, since the comparison only includes n=2 transporting species and n=1 non-transporting species that differ in many other ways. I would also add that increased diversity is not necessarily an expectation of vertical transmission. The similarity between adults and tadpoles is likely a more relevant outcome for vertical transmission, but the authors did not find any evidence that tadpole-adult similarity was any higher in species with tadpole transport. In fact, tadpoles and adults were more similar in the non-transporting species than in one of the transporting species (lines 296-298), which seems to directly contradict the authors' hypothesis. I don't see this result explained or addressed in the Discussion.

To address the reviewer’s concerns, we implemented the following changes:  

Results:

We rephrased the following sentence from the results part:  

“These variations may therefore be linked to differing reproductive traits: Af and Rv lay terrestrial egg clutches and transport hatchlings to water, whereas Ll, a non-transporting species, lays eggs directly in water.”

To read

“These variations may therefore reflect differences in life history traits among the three species.”

We moved the information on differing reproductive strategies into the Discussion, where it contributes to a broader context alongside other life history traits that may influence community diversity.

Discussion (1): We added to our discussion that increased microbial diversity was not an expected outcome of vertical transmission.

“However, increased microbial diversity is not a known outcome of vertical transmission, and further studies across a broader range of transporting and non-transporting species are needed to assess the role of transport in shaping diversity of tadpole-associated microbial communities.”

Discussion (2): Likewise, communities associated with adults and tadpoles of transporting species were no more similar than those of non-transporting species. While poison frog tadpoles do acquire caregiver-specific microbes during transport, most of these microbes do not persist on the tadpoles' skin long-term. This pattern can likely be attributed to the capacity of tadpole skin- and gut microbiota to flexibly adapt to environmental changes (Emerson & Woodley, 2024; Santos et al., 2023; Scarberry et al., 2024). It may also reflect the limited compatibility of skin microbiota from terrestrial adults with aquatic habitats or tadpole skin, which differs structurally from that of adults (Faszewski et al., 2008). As a result, many transmitted microbes are probably outcompeted by microbial taxa continuously supplied by the aquatic environment. Interestingly, microbial communities of the non-transporting Ll were more similar to their adult counterparts than those of poison frogs. This pattern might reflect differences in life history among the species. While adult Ll commonly inhabit the rock pools where their tadpoles develop, adults of the two poison frog species visit tadpole nurseries only sporadically for deposition. These differences in habitat use may result in adult Ll hosting skin microbiota that are better adapted to aquatic environments as compared to Rv and Af. Additionally, their presence in the tadpoles’ habitat could make Ll a more consistent source of microbiota for developing tadpoles.

(4) Field experiment: The rationale and interpretation of the genus-level network are not clear, and the figure is not legible. What does it mean to "visualize the microbial interconnectedness" or to be a "central part of the community"? The previous sentences in this paragraph (lines 337-343) seem to imply that transfer is parent-specific, but the genuslevel network is based on the current adult frogs, not the previous generation of parents that transported them. So it is not clear that the distribution or co-distribution of these taxa provides any insight into vertical transmission dynamics.

Implemented Changes:  

We appreciate the reviewer’s close reading and understand how the inclusion of the network visualization without further clarification may have led to confusion. To clarify, the network was constructed from all adult frogs in the population, including—but not limited to—the parental frogs examined in the field experiment. We do not make any claims about the origin of the microbial taxa found on parental frogs. Rather, our aim was to illustrate how genera retained on tadpoles (following potential vertical transmission) contribute to the skin microbial communities of adult frogs of this population beyond just the parental individuals. This finding supports the observation that these retained taxa are generally among the most abundant in adult frogs. However, since this information is already presented in Table S8 and the figure is not essential to the main conclusions, we have removed Supplementary Figure S5 and the accompanying sentence: “A genus-level network constructed from 44 adult frogs shows that the retained genera make up a central part of the community of adult Rv in wild populations (Fig. S5).” We have adjusted the Methods section accordingly.

Reviewer #2:

I did not find any major weaknesses in my review of this paper. The work here could potentially benefit from absolute abundance levels for shared ASVs between adults and tadpoles to more thoroughly understand the influences of vertical transmission that might be masked by relative abundance counts. This would only be a minor improvement as I think the conclusions from this work would likely remain the same, however.

In response to the reviewer’s suggestion, we estimated the absolute abundance of specific ASVs for all samples of tadpoles in which Sourcetracker identified shared ASVs between adults and tadpoles. The resulting scaled absolute abundance values (in copies/μL and copies per tadpole) are provided in Table S10, and a description of the method has been incorporated into the revised Methods section of the manuscript. To support the robustness of this approach in our dataset, we additionally designed an ASV-specific system for ASV24902-Methylocella. Candidate primers were assessed for specificity by performing local BLASTn alignments against the full set of ASV sequences identified in the respective microbial communities of tadpoles. We optimized the annealing temperature via gradient PCR and confirmed primer specificity through Sanger sequencing of the PCR product (Forward: 5′–GAGCACGTAGGCGGATCT–3′ Reverse: 5′–GGACTACNVGGGTWTCTAAT–3′). Using this approach, we confirmed that the relative abundance of ASV24902 (18.05% in the amplicon sequencing data) closely matched its proportion of the absolute 16S rRNA copy number in transported tadpole 6 (18.01%). While we intended to quantify all shared ASVs, we were limited to this single target due to insufficient material for optimizing the assays. As this particular ASV was also detected in the water associated with the same tadpole, we chose not to include this confirmation in the manuscript. Nevertheless, the close match supports the reliability of our approach for scaling absolute abundances in this dataset.

Results: Absolute abundances of shared ASVs likely originating from the parental source pool (as identified by Sourcetracker) after one month of growth ranged from 7804 to 172326 copies per tadpole (Table S10).

Methods: Quantitative analysis of 16S rRNA copy numbers with digital PCR (dPCR)

Absolute abundances were estimated for ASVs that were shared between tadpoles after a one-month growth period and their respective caregivers, and for which Sourcetracker analysis identified the caregiver as a likely source of microbiota. We followed the quantitative sequencing framework described by Barlow et al. (2020), measuring total microbial load via digital PCR (dPCR) with the same universal 16S rRNA primers used to amplify the v4 region in our sequencing dataset. Absolute 16S rRNA copy numbers obtained from dPCR were then multiplied by the relative abundances from our amplicon sequencing dataset to calculate ASV-specific scaled absolute abundances. All dPCR reactions were carried out on a QIAcuity Digital PCR System (Qiagen) using Nanoplates with a 8.5K partition configuration, using the following cycling program: 95°C for 2 minutes, 40 cycles of 95°C for 30 seconds and 52°C for 30 seconds and 72°C for 1 minute, followed by 1 cycle of 40°C for 5 minutes. Reactions were prepared using the QIAcuity EvaGreen PCR Kit (Qiagen, Cat. No. 250111) with 2 µL of DNA template per reaction, following the manufacturer's protocol, and included a negative no-template control and a cleaned and sequenced PCR product as positive control. Samples were measured in triplicates and serial dilutions were performed to ensure accurate quantification. Data were processed with the QIAcuity Software Suite (v3.1.0.0). The threshold was set based on the negative and positive controls in 1D scatterplots. We report mean copy numbers per microliter with standard deviations, correcting for template input, dPCR reaction volume, and dilution factor. Mean copy numbers per tadpole were additionally calculated by accounting for the DNA extraction (elution) volume.  

Recommendations for the authors:

Reviewer #1:

(1) Figure 1b summarizes the ddPCR data as a binary (detected/not detected), but this contradicts the main text associated with this figure, which describes bacteria as present, albeit in low abundances, in unhatched embryos (lines 145-147). Could the authors keep the diagram of tadpole development, which I find very useful, but add the ddPCR data from Figure S1c instead of simply binarizing it as present/absent?

We appreciate the reviewer’s positive feedback on the clarity of the figure. We agree that presenting the ddPCR data in a more quantitative manner provides a more accurate representation of bacterial abundance across developmental stages. In response, we have retained the developmental diagram, as suggested, and replaced the binary (detected/not detected) information in Figure 1B with rounded mean values for each stage. To complement this, we have included mean values and standard deviations in Table S1. The corresponding text in the main manuscript and legends has been revised accordingly to reflect these changes.  

(2) More information about the foster species, Oophaga sylvatica, would be helpful. Are they sympatric with Rv? Is their transporting behavior similar to that of Rv?

We thank the reviewer for this helpful comment. In response, we have added further details on the biology and parental care behavior of Oophaga sylvatica, including information on its distribution range. The species does not overlap with Ranitomeya variabilis at the specific study site where the field work was conducted, although the species are sympatric in other countries. These additions have been incorporated into the Methods section under "Study species, reproductive strategies, and life history."  

(3) Plotting the proportion of each tadpole microbiome attributed to R. variabilis and the proportion attributed to O. sylvatica on the same plot is confusing, as these points are nonindependent and there is no way for the reader to figure out which points originated from the same tadpole. I would suggest replacing Figure 1D with Figure S2C, which (if I understand correctly) displays the same data, but is separated according to source.

We agree with the reviewer that Figure S2C allows for clearer interpretation of our results. In response, we implemented the suggested change and replaced Figure 1D with the alternative visualization previously shown in Figure S2C, which displays the same data separated by source. To provide readers with a complementary overview of the full dataset, we have retained the original combined plot in the supplementary material as Figure S2D.

(4) On the first read, I found the use of "transport" in the cross-fostering experiment confusing until I understood that they weren't being transported "to" anywhere in particular, just carried for 6 hours. A change of phrasing might help readers here.

We acknowledge the reviewer’s concern and have replaced “transported” with “carried” to avoid confusion for readers who may be unfamiliar with the behavioral terminology. However, because “transport” is the term widely used by specialists to describe this behavior, we now introduce it in the context of the experimental design with the following phrasing:

“For this design, sequence-based surveys of amplified 16S rRNA genes were used to assess the composition of skin-associated microbial communities on tadpoles and their adult caregivers (i.e., the frogs carrying the tadpoles, typically referred to as ‘transporting’ frogs).”

(5) "Horizontal transfer" typically refers to bacteria acquired from other hosts, not environmental source pools (line 394).

We addressed this concern by rephrasing the sentence in the Discussion to avoid potential confusion. The revised text now reads:

“Across species, newborns might acquire bacteria not only through transfer from environmental source pools and other hosts (…)”  

(6) The authors suggest that tadpole transport may have evolved in Rv and Af to promote microbial diversity because "increased microbial diversity is linked to better health outcomes" (lines 477-479). It is often tempting to assume that more diversity is always better/more adaptive, but this is not universally true. The fact that the Ll frogs seem to be doing fine in the same environment despite their lower microbiome diversity suggests that this interpretation might be too far of a reach based on the data here.

We appreciate the reviewer’s concern, agree that increased microbial diversity is not inherently advantageous and have revised the paragraph to make this clearer.  

“While increased microbial diversity is not inherently advantageous, it has been associated with beneficial outcomes such as improved immune function, lower disease risk, and enhanced fitness in multiple other vertebrate systems.”

However, rather than claiming that greater diversity is always advantageous, we suggest that this possibility should not be excluded and consider it a relevant aspect of a comprehensive discussion. We also note that whether poison frog tadpoles perform equally well with lower microbial diversity remains an open question. Drawing such conclusions would require experimental validation and cannot be inferred from comparisons with an evolutionarily distant species that differs in life history.

Reviewer #2:

(1) Figure 2: Are the data points in C a subset (just the tadpoles for each species) of B? The numbers look a little different between them. The number of observed ASVs in panel B for Rv look a bit higher than the observed ASVs in panel C.

The data shown in panel C are indeed a subset of the samples presented in panel B, focusing specifically on tadpoles of each species. The slight differences in the number of observed ASVs between panels result from differences in rarefaction depth between comparisons: due to variation in sequencing depth across species and life stages, we performed rarefaction separately for each comparison in order to retain the highest number of taxa while ensuring comparability within each group. Although we acknowledge that this is not a standard approach, we found that results were consistent when rarefying across the full dataset, but chose the presented approach to better accommodate variation in our sample structure. This methodological detail is described in the Methods section:

“All alpha diversity analyses were conducted with datasets rarefied to 90% of the read number of the sample with the fewest reads in each comparison and visualized with boxplots.”

It is also noted in the figure legend: “The dataset was separately rarefied to the lowest read depth f each comparison.” We hope this clarification adequately addresses the reviewer’s concern and therefore have not made additional changes.

(2) Lines 304-305: in the Figure 4B plot, there appear to be 12 transported tadpoles and 8 non-transported tadpoles.

Thank you for catching this. We have corrected the plot and the associated statistics (alpha and beta diversity) in the results section as well as in the figure. Importantly, the correction did not affect any other results, and the overall findings and interpretations remain unchanged.  

(3) Line 311: I think this should be Figure 4B.

(4) Line 430: tadpole transport.

(5) Line 431: I believe commas need to surround this phrase "which range from a few hours to several days depending on the species (Lötters et al., 2007; McDiarmid & Altig, 1999; Pašukonis et al., 2019)".

We thank the reviewer for the thorough review and have corrected all typographical and formatting errors noted in comments (3) – (5).

Author Response:

The following is the authors response to the original reviews.

Reviewer #1 (Public review): 

Summary: 

The authors report four cryoEM structures (2.99 to 3.65 Å resolution) of the 180 kDa, full-length, glycosylated, soluble Angiotensin-I converting enzyme (sACE) dimer, with two homologous catalytic domains at the N- and C-terminal ends (ACE-N and ACE-C). ACE is a protease capable of effectively degrading Aβ. The four structures are C2 pseudo-symmetric homodimers and provide insight into sACE dimerization. These structures were obtained using discrete classification in cryoSPARC and show different combinations of open, intermediate, and closed states of the catalytic domains, resulting in varying degrees of solvent accessibility to the active sites. 

To deepen the understanding of the gradient of heterogeneity (from closed to open states) observed with discrete classification, the authors performed all-atom MD simulations and continuous conformational analysis of cryo-EM data using cryoSPARC 3DVA, cryoDRGN, and RECOVAR. cryoDRGN and cryoSPARC 3DVA revealed coordinated open-closed transitions across four catalytic domains, whereas RECOVAR revealed independent motion of two ACE-N domains, also observed with cryoSPARC-focused classification. The authors suggest that the discrepancy in the results of the different methods for continuous conformational analysis in cryo-EM could result from different approaches used for dimensionality reduction and trajectory generation in these methods. 

Strengths: 

This is an important study that shows, for the first time, the structure and the snapshots of the dynamics of the full-length sACE dimer. Moreover, the study highlights the importance of combining insights from different cryo-EM methods that address questions difficult or impossible to tackle experimentally while lacking ground truth for validation. 

Weaknesses: 

The open, closed, and intermediate states of ACE-N and ACE-C in the four cryo-EM structures from discrete classification were designated quantitatively (based on measured atomic distances on the models fitted into cryo-EM maps, Figure 2D). Unfortunately, atomic models were not fitted into cryo-EM maps obtained with cryoSPARC 3DVA, cryoDRGN, and RECOVAR, and the open/closed states in these cases were designated based on qualitative analysis. As the authors clearly pointed out, there are many other methods for continuous conformational heterogeneity analysis in cryo-EM. Among these methods, some allow analyzing particle images in terms of atomic models, like MDSPACE (Vuillemot et al., J. Mol. Biol. 2023, 435:167951), which result in one atomic model per particle image and can help in analyzing cooperativity of domain motions through measuring atomic distances or angular differences between different domains (Valimehr et al., Int. J. Mol. Sci. 2024, 25: 3371). This could be discussed in the article. 

Reviewer #2 (Public review): 

Summary: 

The manuscript presents a valuable contribution to the field of ACE structural biology and dynamics by providing the first complete full-length dimeric ACE structure in four distinct states. The study integrates cryo-EM and molecular dynamics simulations to offer important insights into ACE dynamics. The depth of analysis is commendable, and the combination of structural and computational approaches enhances our understanding of the protein's conformational landscape. However, the strength of evidence supporting the conclusions needs refinement, particularly in defining key terms, improving structural validation, and ensuring consistency in data analysis. Addressing these points through major revisions will significantly improve the clarity, rigor, and accessibility of the study to a broader audience, allowing it to make a stronger impact in the field. 

Strengths: 

The integration of cryo-EM and MD simulations provides valuable insights into ACE dynamics, showcasing the authors' commitment to exploring complex aspects of protein structure and function. This is a commendable effort, and the depth of analysis is appreciated. 

Weaknesses: 

Several aspects of the manuscript require further refinement to improve clarity and scientific rigor as detailed in my recommendations for the authors. 

Reviewer #3 (Public review): 

Summary: 

Mancl et al. report four Cryo-EM structures of glycosylated and soluble Angiotensin-I converting enzyme (sACE) dimer. This moves forward the structural understanding of ACE, as previous analysis yielded partially denatured or individual ACE domains. By performing a heterogeneity analysis, the authors identify three structural conformations (open, intermediate open, and closed) that define the openness of the catalytic chamber and structural features governing the dimerization interface. They show that the dimer interface of soluble ACE consists of an N-terminal glycan and protein-protein interaction region, as well as C-terminal protein-protein interactions. Further heterogeneity mining and all-atom molecular dynamic simulations show structural rearrangements that lead to the opening and closing of the catalytic pocket, which could explain how ACE binds its substrate. These studies could contribute to future drug design targeting the active site or dimerization interface of ACE. 

Strengths: 

The authors make significant efforts to address ACE denaturation on cryo-EM grids, testing various buffers and grid preparation techniques. These strategies successfully reduce denaturation and greatly enhance the quality of the structural analysis. The integration of cryoDRGN, 3DVA, RECOVAR, and all-atom simulations for heterogeneity analysis proves to be a powerful approach, further strengthening the overall experimental methodology. 

Weaknesses: 

In general, the findings are supported by experimental data, but some experimental details and approaches could be improved. For example, CryoDRGN analysis is limited to the top 5 PCA components for ease of comparison with cryoSPARC 3DVA, but wouldn't an expansion to more components with CryoDRGN potentially identify further conformational states? The authors also say that they performed heterogeneity analysis on both datasets but only show data for one. The results for the first dataset should be shown and can be included in supplementary figures. In addition, the authors mention that they were not successful in performing cryoSPARC 3DFLex analysis, but they do not show their data or describe the conditions they used in the methods section. These data should be added and clearly described in the experimental section. 

Some cryo-EM data processing details are missing. Please add local resolution maps, box sizes, and Euler angle distributions and reference the initial PDB model used for model building. 

Reviewer #1 (Recommendations for the authors): <br /> Major point: 

The authors could discuss the use of continuous conformational heterogeneity analysis methods that analyze particle images in terms of atomic models, based on MD simulations, like MDSPACE (Vuillemot et al., J. Mol. Biol. 2023, 435:167951). MDSPACE can be used on a dataset preprocessed with cryoSPARC or Relion by discrete classification to reduce compositional heterogeneity and obtain initial particle poses. It results in one atomic model per particle image and can help in analyzing the cooperativity of domain motions by measuring atomic distances or angular differences between different domains (Valimehr et al., Int. J. Mol. Sci. 2024, 25: 3371). 

We agree that MDSPACE is a promising and useful tool for analysis, and are excited to implement such a method. Prior to manuscript submission, we have had discussions with the primary author, Slavica Jonic, about how we may employ her software in our analysis. Unfortunately, we were unable to overcome significant computational issues, notably MDSPACE’s lack of GPU functionality, which prevent us from employing MDSPACE in a reasonable manner for our dataset. We hope to employ MDSPACE in future work, once the computational issues have been addressed, and have added a section on MDSPACE to the discussion in an effort to increase the visibility of MDSPACE, as we feel it is an exciting approach that deserves more visibility. We have added a substantial discussion on this point, specifically on MDspace as follows:

line 565-574

Similarly, MDSPACE holds tremendous promise as a method for investigating conformational dynamics from cryo-EM data (61). MDSPACE integrates cryo-EM particle data with short MD simulations to fit atomic models into each particle image through an iterative process which extracts dynamic information. However, the lack of GPU-enabled processing for MDSPACE requires either a dedicated a computational setup that diverges from most other cryo-EM software, or access to a CPU-based supercomputer, which severely limits the accessibility of such software. Despite these challenges, both 3DFlex and MDSPACE use promising approaches to study protein conformational dynamics. We look forward to exploring effective methods to incorporate these strategies into our future research.

Minor points: 

(1) Lines 348-350: "The discrepancy in population size between these clusters is likely due to bias in the initial particle poses, rather than a subunit-specific preference for the open state." Which bias? The cluster size is related to conformations, not to poses. 

We hope to emphasize that the assignment of particles to either the OC or CO cluster is likely due to the particle orientation within the complete dimer refinement, and the discrepancy in size between OC and CO clusters does not necessarily indicate a domain specific preference for one state or another, which would carry allosteric implications. This remains a possibility, but we hope to avoid over-interpretation of our results with the statement above.

The statement was altered to now read:

Line 418-423

“The discrepancy in population size between these clusters is likely due to bias in the initial particle orientation, rather than a subunit-specific preference for the open state. As the O/C state and the C/O state are 180 degree rotations of each other, particle assignment to either cluster is likely influenced by the initial particle orientation of the complete dimer, and we currently lack the data to discern any allosteric implication to the orientation assignment.”

(2) Line 519: "Micrographs with a max CTF value worse than 4Å were removed from the dataset,..." (also, lines 822-823 in supplementary material). <br /> Do you want to say that micrographs with a resolution worse than 4 A were removed? 

Max CTF value was replaced with CTF fit resolution to properly match the parameter used in Cryosparc.

(3) Figure 2C: The black lines are barely visible. Can you make them thicker and in red color? 

The figure has been amended.

(4) Figure 2D: The values for Chain A and Chain B in the second row (ACE-C) of sACE-3.05 columns are 17.9 (I) (Chain A) and 13.9 (C) (Chain B). Shouldn't they be reversed (13.9 (C) (Chain A) and 17.9 (I) (Chain B))? 

The values are now correct. sACE-3.65 chains were flipped in the table, and the updated color scheme should make it easier to map the values from the table to their corresponding structure.

Reviewer #2 (Recommendations for the authors): 

The manuscript presents the first complete full-length dimeric ACE structure. The integration of cryo-EM and MD simulations provides valuable insights into ACE dynamics, showcasing the authors' commitment to exploring complex aspects of protein structure and function. This is a commendable effort, and the depth of analysis is appreciated. However, several aspects of the manuscript require further refinement to improve clarity and scientific rigor. In the view of this reviewer, a major revision is necessary. Please see the detailed comments below: 

(1) Definition of "Conformational Heterogeneity": The term "conformational heterogeneity" should be clearly defined when citing references 27-29. <br /> References 27 and 29 use MD simulations, which reveal "conformational flexibility" rather than "conformational heterogeneity" as observed in cryo-EM data. A more precise distinction should be made. 

We have changed the term “conformational heterogeneity” to the broader “conformational dynamics

(2) Figure Adjustments for Clarity: <br /> Figure 1B: A scale bar is needed for accurate representation. 

A 100 Angstrom scale bar was added to figure 1B.

Figure 2A, B: Using a Cα trace representation would improve clarity and make structural differences more apparent. 

We found using a Cα trace representation makes the figure too confusing and impossible to determine individual structural elements. Everything just becomes a jumble of lines.

Additionally, a Cα displacement vs. residue index plot (with Figure 1A placed along the x-axis) should be included alongside Figures 2A and B to provide quantitative insight into structural variations. 

This analysis has been combined with several other suggestions and now comprises a new figure 4.

(3) Structural Resolution and Validation: <br /> Euler angle distribution and 3D-FSC analysis should be provided to help the audience assess how these factors influence the resolution of each structure. <br /> Local resolution analysis in Relion should be included to determine if there are dynamic differences among the four structures. <br /> To enhance structural interpretation, the manuscript would benefit from showcasing examples of bulky side-chain densities (e.g., Trp, Phe, Tyr) for each of the four structures. 

Information is included in Figure S3 and S5.

(4) Glycan Modeling Considerations: <br /> Since the resolution of cryo-EM does not allow for precise glycan composition determination, additional experimental validation (e.g., Glyco-MS) would strengthen the modeling. If experimental support is unavailable, appropriate references should be cited to justify the modeled glycans. 

Minimal glycan modeling was performed with the goal of demonstrating that the protein is glycosylated. We have highlighted that we chose 12 N-linked glycosylation sites that have the observed extra density, an indication that glycan should be present and modeled them with complex glycans in the manuscript.  

(5) Advanced Cryo-EM and MD Analyses: 3DFlex Analysis: <br /> It is recommended that the authors explore 3DFlex to better capture conformational variability. CryoSPARC's community support can assist in proper implementation. 

We have incorporated our 3Dflex analysis in our discussion as follows:

Line 553-565

Surprisingly, we did not observe such motion using cryoSPARC 3DFlex, a neural network-based method analyzing our cryo-EM data of sACE (54). Central to the working of cryoSPARC 3DFlex is the generation of a tetrahedral mesh used to calculate deformations within the particle population. Proper generation of the mesh is critical for obtaining useful results and must often be determined empirically. Despite several attempts, we were unable to obtain results from 3DFlex comparable to what we observed with our other methods. Even using the results from our 3DVA as prior input to 3DFlex, the largest conformational change we observed was a slight wiggling at the bottom of the D3a subdomain (Movie S12). The authors of 3DFlex note that 3DFlex struggles to model intricate motions, and the implementation of custom tetrahedral meshes currently requires a non-cyclical fusion strategy between mesh segments. Given these limitations, and the complexity of sACE conformational dynamics, it appears that sACE, as a system, is not well-suited to analysis via 3DFlex in its current implementation.

(6) Movie Consistency: <br /> The MD simulation movies should use the same color coding as the first four movies for consistency. Similarly, the 3DVar analysis map should be color-coded to enhance interpretability. 

MD simulation movies are re-colored.

(7) MD Simulations - Data Extraction and Validation: <br /> The manuscript includes several long-timescale MD simulations, but further analysis is needed to extract meaningful dynamic information. Suggested analyses include: <br /> a. RMSF (Root Mean Square Fluctuation) Analysis: Calculate RMSF from MD trajectories and compare it with local resolution variations in cryo-EM maps. 

RMSF values were included in the new figure 4 along with structural depictions colored by RMSF value to localize variation to the structure.

b. Assess whether regions exhibiting lower dynamics correspond to higher resolution in cryo-EM. 

Information is added to Figure 4, Figure S3, S5, S6.

c. Compare RMSF between simulations with and without glycans to identify potential effects. 

This has been done in Figure 4.

d. Clustering Analysis: Use the four solved structures as reference states to cluster MD simulation trajectories. Determine if the population states observed in MD simulations align with cryo-EM findings. 

This has been done in supplementary figure S10.

e. Principal Component Analysis (PCA): Perform PCA on MD trajectories and compare with dynamics inferred from cryo-EM analyses (3DVar, cryoDRGN, and RECOVAR) to ensure consistency. 

This has been done in supplementary figure S11.

f. Correction of RMSF Analysis or the y-axis label in Figure S9: The RMSF values cannot be negative by definition. The authors should carefully review the code used for this calculation or explicitly define the metric being measured. 

The Y-axis label has been corrected to clarify that the plot depicts the change in RMSF values when comparing the glycosylated and non-glycosylated MD simulations.

(8) Discussion on Coordinated Motion and Allostery: <br /> The discussion of coordinated motion and allosteric regulation between sACE-N domains should be explicitly connected to experimental evidence mentioned in the introduction: <br /> "Enzyme kinetics analysis suggests negative cooperativity between two catalytic domains (31-33). However, ACE also exhibits positive synergy toward Ab cleavage and allostery to enhance the activity of its binding partner, the bradykinin receptor (11, 34)." 

(9) The authors should elaborate on how their new insights provide a mechanistic explanation for these experimental observations. 

(10) Connection to Therapeutic Implications: <br /> The discussion section should more explicitly connect the structural findings to potential therapeutic applications, which would significantly enhance the impact of the study. 

These three points (8-10) were addressed in a significant overhaul to the discussion section.

In summary, this study makes a valuable contribution to the field of ACE structural biology and dynamics. The combination of cryo-EM and MD simulations is particularly powerful, and with major revisions, this manuscript has the potential to make a strong impact. Addressing the points outlined above will significantly improve clarity, strengthen the scientific claims, and enhance the manuscript's accessibility to a broader audience. I appreciate the authors' rigorous approach to this complex topic and encourage them to refine their work to fully highlight the significance of their findings. 

Reviewer #3 (Recommendations for the authors): 

(1) The authors incorrectly refer to their ACE construct as full-length throughout the manuscript. Given that they are purifying the soluble region (aa 1-1231), saying full-length ACE is not the correct nomenclature. I suggest removing full-length and using soluble ACE (sACE) throughout the text. 

We utilize the term full-length to highlight the fact that our structures contain both the N and C domains for both subunits in the dimer, in contrast to the previously published ACE cryo-EM structure. We have clarified in the text that we refer to the full-length soluble region of ACE (sACE), and sACE is used to specifically refer to our construct throughout the text, except when referring to ACE in a more generalized biological context in the introduction and discussion.

(2) The authors could show differences between the different structural states by measuring and displaying the alpha carbon distances. For example, in Figures 2A, B, 3A, and 4B and C. 

Alpha carbon displacements for each residue have been added to the new figure 4.

(3) Most figures, with a few exceptions (Figures 2 and S11), are of low quality. Perhaps they are not saved in the same format. In addition, the color schemes used throughout the figures and movies are not consistent. For example, in Figure 1 D2 domains are in green, while they appear yellow in Figure 2 and later. Please double-check all coloring schemes and keep them consistent throughout the manuscript. In addition, it would be good to keep the labeling of the domains in the subsequent figures, as it is difficult to remember which domain is which throughout the manuscript. 

We are unsure of how to address the low quality issue, our files and the online versions appear to be of suitable high quality. We will work with editorial staff to ensure all files are of suitable quality. The color scheme has been revised throughout the manuscript to ensure consistency and better differentiate between domains and chains.

(4) Figure 1. Indicate exactly where in panel A ACE-N ends and ACE-C starts. Also, the pink and magenta, as well as aqua vs. light blue, are hard to distinguish. 

We have updated coloring scheme.

(5) Figure 2. In the figure legend, the use of brackets for defining closed, intermediate, and open states is confusing, given that the panels are also described with brackets, and some letters match between them. Using a hyphen or bolding the abbreviations could help. Also, define chains A and B, make the black lines that I assume indicate distances in C bold or thicker as they are very hard to see in the figure, and add to the legend what those lines mean. 

The abbreviations have been changed from parentheses to quotes, and suggestions have been implemented.

(6) Figure 4 is confusing as shown. Since the authors mention the general range of motion in sACE-N first in the text, wouldn't it make more sense to show panel B first and then panel A? Also, can you point and label the "tip connecting the two long helices of the D1a subdomain" in the figure? It is not clear to me where this region is in B. In addition, add a description of the arrows in B and C to the figure legend. 

Most changes incorporated. The order should make more sense now in light of other changes.

(7) Figure 5. Can the authors add a description to the legend as to what the arrows indicate and their thickness? 

Done

(8) Add a scale bar to the micrograph images in the supplementary figures. 

Figure S2 and S4 need the scale bar.

(9) Provide a more comprehensive description of buffers used in the DF analysis, as this information could be useful to others. 

We have included the data in Table S1.<br /> (10) Line 51: Reference format not consistent with other references: (Wu et al., 2023). 

Fixed

(11) Line 66: Define "ADAM". 

The definition has been added.

(12) Line 90: The authors say: Recent open state structures of sACE-N, sACE monomer, and a sACE-N dimer, along with molecular dynamics (MD) simulations of sACE-C, have begun to reveal the conformational heterogeneity, though it remains under-studied (27-29)." Can the authors clarify what "it" refers to? The full-length ACE, sACE, or its specific domains? 

The sentence now reads: Recent open state structures of sACE-N, sACE monomer, and a sACE-N dimer, along with molecular dynamics (MD) simulations of sACE-C, have begun to reveal ACE conformational dynamics, though they remain under-studied (29-31).

(13) Line 204: "The comparison of our dimeric sACE cryoEM structures of reveals the conformational dynamics of sACE catalytic domains." The second "of" should be removed. 

Fixed<br /> (14) Line 268: "From room mean square fluctuation (RMSF) analysis..." "room" should be replaced with "root."

Fixed

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors have developed self-amplifying RNAs (saRNAs) encoding additional genes to suppress dsRNA-related inflammatory responses and cytokine release. Their results demonstrate that saRNA constructs encoding anti-inflammatory genes effectively reduce cytotoxicity and cytokine production, enhancing the potential of saRNAs. This work is significant for advancing saRNA therapeutics by mitigating unintended immune activation.

Strengths:

This study successfully demonstrates the concept of enhancing saRNA applications by encoding immune-suppressive genes. A key challenge for saRNA-based therapeutics, particularly for non-vaccine applications, is the innate immune response triggered by dsRNA recognition. By leveraging viral protein properties to suppress immunity, the authors provide a novel strategy to overcome this limitation. The study presents a well-designed approach with potential implications for improving saRNA stability and minimizing inflammatory side effects.

We thank Reviewer #1 for their thorough review and for recognizing both the significance of our work and the potential of our strategy to expand saRNA applications beyond vaccines.

Weaknesses:

(1) Impact on Cellular Translation:

The authors demonstrate that modified saRNAs with additional components enhance transgene expression by inhibiting dsRNA-sensing pathways. However, it is unclear whether these modifications influence global cellular translation beyond the expression of GFP and mScarlet-3 (which are encoded by the saRNA itself). Conducting a polysome profiling analysis or a puromycin labeling assay would clarify whether the modified saRNAs alter overall translation efficiency. This additional data would strengthen the conclusions regarding the specificity of dsRNA-sensing inhibition.

We thank the Reviewer for this insightful suggestion. We performed a puromycin labeling assay to assess global translation rates (Figure 3—figure supplement 1c). This experiment revealed that the E3 construct significantly reduces global protein synthesis, despite driving high levels of saRNAencoded transgene expression (Figure 1d, e). In contrast, the E3-NSs-L* construct mitigated this reduction in global translation while maintaining moderate transgene expression. These findings support our hypothesis that E3 enhances transgene output in part by activating RNase L, which degrades host mRNAs and thereby reduces ribosomal competition. We appreciate the Reviewer’s recommendation of this experiment, which has strengthened the manuscript.

(2) Stability and Replication Efficiency of Long saRNA Constructs:

The saRNA constructs used in this study exceed 16 kb, making them more fragile and challenging to handle. Assessing their mRNA integrity and quality would be crucial to ensure their robustness.

Furthermore, the replicative capacity of the designed saRNAs should be confirmed. Since Figure 4 shows lower inflammatory cytokine production when encoding srIkBα and srIkBαSmad7-SOCS1, it is important to determine whether this effect is due to reduced immune activation or impaired replication. Providing data on replication efficiency and expression levels of the encoded anti-inflammatory proteins would help rule out the possibility that reduced cytokine production is a consequence of lower replication.

We thank the Reviewer for these valuable suggestions.

To assess the integrity of the saRNA constructs, we performed denaturing gel electrophoresis (Supplemental Figure 6c). The native saRNA, E3, and E3-NSs-L* constructs each migrated as a single band. The moxBFP, srIκBα, and srIκBα-Smad7-SOCS1 constructs showed both a full-length transcript and a lower-abundance truncated band (Supplemental Figure 6d), suggestive of a cryptic terminator sequence introduced in a region common to these three constructs.

To evaluate replicative capacity, we performed qPCR targeting EGFP, which is encoded by all constructs. This analysis revealed that the srIκBα-Smad7-SOCS1 construct exhibited lower replication efficiency than both native saRNA and E3. Several factors may contribute to this difference, including the longer transcript length, reduced molar input when equal mass was used for transfection, prevention of host mRNA degradation due to RNase L inhibition, or the presence of truncated transcripts.

Given these confounding variables, we revised our approach to analyzing cytokine production. Rather than comparing all six constructs together, we split the analysis into two parts: (1) the effects of dsRNA-sensing pathway inhibition (Figure 4a), and (2) the effects of inflammatory signalling inhibition (Figure 4c). For the latter, we compared srIκBα and srIκBα-Smad7-SOCS1 to moxBFP, as these three constructs are more comparable in size, share the same truncated transcript, and all encode L* to inhibit RNase L. This strategy minimizes the likelihood that differences in the cytokine responses are due to variation in replication efficiency.

(3) Comparative Data with Native saRNA:

Including native saRNA controls in Figures 5-7 would allow for a clearer assessment of the impact of additional genes on cytokine production. This comparison would help distinguish the effect of the encoded suppressor proteins from other potential factors.

We thank the Reviewer for this helpful suggestion. We have added the native saRNA condition to Figure 5 as a visual reference. However, due to the presence of truncated transcripts in the constructs designed to inhibit inflammatory signalling pathways, the actual amount of full-length saRNA delivered in these conditions is likely lower than expected, despite using equal total RNA mass for transfection. This complicates direct comparisons with constructs targeting dsRNAsensing pathways, which do not show transcript truncation. For this reason, native saRNA was included only as a visual reference and was not used in statistical comparisons with the inflammatory signalling inhibitor constructs.

(4) In vivo Validation and Safety Considerations:

Have the authors considered evaluating the in vivo potential of these saRNA constructs? Conducting animal studies would provide stronger evidence for their therapeutic applicability. If in vivo experiments have not been performed, discussing potential challenges - such as saRNA persistence, biodistribution, and possible secondary effectswould be valuable.

(5) Immune Response to Viral Proteins:

Since the inhibitors of dsRNA-sensing proteins (E3, NSs, and L*) are viral proteins, they would be expected to induce an immune response. Analyzing these effects in vivo would add insight into the applicability of this approach.

We appreciate the Reviewer’s points regarding in vivo validation and safety considerations. While in vivo studies are beyond the scope of the present investigation, we agree that evaluating therapeutic potential, biodistribution, persistence, and secondary effects will be essential for future translation. We have now included a brief discussion of these considerations at the end of the revised discussion. In ongoing work, we are planning follow-up studies incorporating in vivo imaging and functional assessments of saRNA-driven cargo delivery in preclinical models of inflammatory joint pain.

Regarding the immune response to viral proteins, we agree that this is an important consideration and have now included a clearer discussion of this limitation in the revised manuscript. Specifically, we highlight that encoding multiple viral inhibitors (E3, NSs, and L*), in combination with the VEEV replicase, may increase the likelihood of adaptive immune recognition via MHC class I presentation. This could lead to cytotoxic T cell–mediated clearance of saRNA-transfected cells, thereby limiting therapeutic durability. We emphasize that addressing both intrinsic cytotoxicity and immune-mediated clearance will be essential for advancing the clinical potential of this platform.

(6) Streamlining the Discussion Section:

The discussion is quite lengthy. To improve readability, some content - such as the rationale for gene selection-could be moved to the Results section. Additionally, the descriptions of Figure 3 should be consolidated into a single section under a broader heading for improved coherence.

Thank you for these helpful suggestions. We have streamlined the Discussion to improve readability and have moved the rationale for gene selection to the results section, as recommended. In addition, we have consolidated the Figure 3 descriptions to improve coherence and to simplify the presentation.

Reviewer #2 (Public review):

Summary:

Lim et al. have developed a self-amplifying RNA (saRNA) design that incorporates immunomodulatory viral proteins, and show that the novel design results in enhanced protein expression in vitro in mouse primary fibroblast-like synoviocytes. They test constructs including saRNA with the vaccinia virus E3 protein and another with E3, Toscana virus NS protein and Theiler's virus L protein (E3 + NS + L), and another with srIκBα-Smad7SOCS1. They have also tested whether ML336, an antiviral, enables control of transgene expression.

Strengths:

The experiments are generally well-designed and offer mechanistic insight into the RNAsensing pathways that confer enhanced saRNA expression. The experiments are carried out over a long timescale, which shows the enhance effect of the saRNA E3 design compared to the control. Furthermore, the inhibitors are shown to maintain the cell number, and reduce basal activation factor-⍺ levels.

We thank Reviewer #2 for their thoughtful and detailed assessment of our manuscript, and for recognizing the mechanistic insights provided by our study. We also appreciate their positive comments on the experimental design, the extended timescale, and the observed effects on transgene expression, cell viability, and basal fibroblast activation factor-α levels.

Weaknesses:

One limitation of this manuscript is that the RNA is not well characterized; some of the constructs are quite long and the RNA integrity has not been analyzed. Furthermore, for constructs with multiple proteins, it's imperative to confirm the expression of each protein to confirm that any therapeutic effect is from the effector protein (e.g. E3, NS, L). The ML336 was only tested at one concentration; it is standard in the field to do a dose-response curve. These experiments were all done in vitro in mouse cells, thus limiting the conclusion we can make about mechanisms in a human system.

Thank you for your detailed feedback. We have added new experiments and clarified limitations in the revised manuscript to address these concerns:

RNA integrity: We performed denaturing gel electrophoresis on the in vitro transcribed saRNA constructs (Supplemental Figure 7c). Constructs targeting dsRNA-sensing pathways migrated as a single band, while those targeting inflammatory signalling pathways showed both a full-length product and a common, lower-abundance truncated transcript. This suggests that the actual amount of full-length RNA delivered for the constructs inhibiting inflammatory signalling was overestimated. To account for this, we avoided direct comparisons between the two types of constructs and instead focused on comparisons within each type to ensure more meaningful interpretation.

Confirmation of protein expression: While we acknowledge that direct measurement of each protein would provide additional insight, we believe the functional assays presented offer strong evidence that the encoded proteins are expressed and exert their intended biological effects. Additionally, IRES functionality was confirmed visually using fluorescent protein reporters, supporting the successful expression of downstream genes.

ML336 concentration–response: We have now performed a concentration–response analysis for ML336 (Figure 8a and b), which demonstrates its ability to modulate transgene expression in a concentration-dependent manner.

Use of human cells: We agree that testing these constructs in human cells is essential for future translational applications and are actively exploring opportunities to evaluate them in patientderived FLS. However, previous studies have shown that Theiler’s virus L* does not inhibit human RNase L (Sorgeloos et al., PLoS Pathog 2013). As a result, it is highly likely that the E3-NSs-L* construct will not function as intended in human systems. Addressing this limitation will be a priority in our future work, where we aim to develop constructs incorporating inhibitors specific to human RNase L to ensure efficacy in human cells.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Figure 2c is not indicated.

Thank you for pointing out this error. It has now been corrected in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

(1) The Graphical Abstract is a bit confusing; suggest modifying it to represent the study and findings more accurately.

We have revised the graphical abstract to improve clarity and better reflect the study’s design and main findings. Thank you for the suggestion.

(2) The impact of this paper would be greatly improved if these experiments were repeated, at least partially, in human cells. The rationale for mouse cells in vitro is unclear.

The rationale for developing constructs targeting mouse cells is based on our intention to utilize these constructs in mouse models of inflammatory joint pain in future studies.

We recognize that incorporating data from human cells would significantly enhance the translational relevance of our work, and we are actively pursuing collaborations to test these constructs in patient-derived FLS. However, a key component of our saRNA constructs—Theiler’s virus L*—has been shown to inhibit mouse, but not human, RNase L (Sorgeloos et al., PLoS Pathog 2013). Consequently, the E3-NSs-L* polyprotein may not function as intended in human cells. To address this limitation, future work will focus on developing constructs that incorporate inhibitors specific to human RNase L, thereby facilitating more effective translation of our findings to human systems.

(3) The ML336 was only tested at one concentration and works mildly well, but would be more impactful if tested in a dose-response curve.

We have now performed a concentration–response analysis for ML336 (Figure 8a and b), which demonstrates its concentration-dependent effects on transgene expression and saRNA elimination. Thank you for the suggestion.

(4) Overall, there is not a cohesive narrative to the story, instead it comes off as we tried these three different approaches, and they worked in different contexts.

We have revised the graphical abstract, results, and discussion to improve the cohesiveness of the manuscript’s narrative and to better integrate the mechanistic rationale linking the different approaches. We appreciate the feedback.

(5) The title is not supported by the data; the saRNA is still somewhat cytotoxic, immunostimulatory and the antiviral minimally controls transgene expression; suggest making this reflect the data.

We have revised the title to better reflect the scope of the data and the mechanistic focus of the study. The updated title emphasizes the pathways targeted and the outcomes demonstrated, while avoiding overstatement. Thank you for this helpful recommendation.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

A) The presentation of the paper must be strengthened. Inconsistencies, mislabelling, duplicated text, typos, and inappropriate colour code should be changed.

We spotted and corrected several inconsistencies and mislabelling issues throughout the text and figures. Thanks!  

B) Some claims are not supported by the data. For example, the sentence that says that "adolescent mice showed lower discrimination performance than adults (l.22) should be rewritten, as the data does not show that for the easy task (Figure 1F and Figure 1H).

We carefully reviewed the specific claims and fixed some of the wording so it adheres to the data shown.

C) In Figure 7 for example, are the quantified properties not distinct across primary and secondary areas?

We now carried out additional analysis to test this. We found that while AUDp and AUDv exhibit distinct tuning properties, they show similar differences between adolescent and adult neurons (see Supplementary Table 6, Fig. S7-1a-h). Note that TEa and AUDd could not be evaluated due to low numbers of modulated neurons in this protocol.

D) Some analysis interpretations should be more cautious. (..) A lower lick rate in general could reflect a weaker ability to withhold licking- as indicated on l.164, but also so many other things, like a lower frustration threshold, lower satiation, more energy, etc).

That is a fair comment, and we refined our interpretations. Moreover, we also addressed whether impulsiveness impacted lick rates. In the Educage, we found that adolescent mice had shorter ITIs only after FAs (Fig. S2-1). In the head-fixed setup, we examined (1) the proportion of ITIs where licks occurred (Fig. S3-1c) and (2) the number of licks in these ITIs (Fig. S3-1d). We found no differences between adolescents and adults, indicating that the differences observed in the main task are not due to general differences in impulsiveness (Fig. S2-1, Fig. S3-1c, d). Finally, we note that potential differences in satiation were already addressed in the original manuscript by carefully examining the number of trials completed across the session. See also Review 3, comment #1 below.

Reviewer #2 (Public review):

A) For some of the analyses that the authors conducted it is unclear what the rationale behind them is and, consequently, what conclusion we can draw from them.

We reviewed the manuscript carefully and revised the relevant sections to clarify the rationale behind the analyses. See detailed responses to all the reviewer’s specific comments.

B) The results of optogenetic manipulation, while very interesting, warrant a more in-depth discussion.

We expanded our discussion on these experiments (L495-511) and also added an additional analysis to strengthen our findings (Fig. S3-2e).

Reviewer #3 (Public review):

(1) The authors report that "adolescent mice showed lower auditory discrimination performance compared to adults" and that this performance deficit was due to (among other things) "weaker cognitive control". I'm not fully convinced of this interpretation, for a few reasons. First, the adolescents may simply have been thirstier, and therefore more willing to lick indiscriminately. The high false alarm rates in that case would not reflect a "weaker cognitive control" but rather, an elevated homeostatic drive to obtain water. Second, even the adult animals had relatively high (~40%) false alarm rates on the freely moving version of the task, suggesting that their behavior was not particularly well controlled either. One fact that could help shed light on this would be to know how often the animals licked the spout in between trials. Finally, for the head-fixed version of the task, only d' values are reported. Without the corresponding hit and false alarm rates (and frequency of licking in the intertrial interval), it's hard to know what exactly the animals were doing.

irst, as requested, we added the Hit rates and FA rates for the head-fixed task (Fig. S3-1a). Second, as requested by the reviewr, we performed additional analyses in both the Educage and head-fixed versions of the task. Specifically, we analyzed the ITI duration following each trial outcome. We found that adolescent mice had shorter ITIs only after Fas (Fig. S2-1). In the head-fixed setup, we examined (1) the proportion of ITIs during which licks occurred (Fig. S3-1c) and (2) the number of licks in these ITIs (Fig. S3-1d). We found no differences between adolescents and adults, indicating that the differences observed in the main task are not due to general differences in impulsiveness (Fig. S2-1, Fig. S3-1c, d). See also comment #D of reviewer #1 above.

B) There are some instances where the citations provided do not support the preceding claim. For example, in lines 64-66, the authors highlight the fact that the critical period for pure tone processing in the auditory cortex closes relatively early (by ~P15). However, one of the references cited (ref 14) used FM sweeps, not pure tones, and even provided evidence that the critical period for this more complex stimulus occurred later in development (P31-38). Similarly, on lines 72-74, the authors state that "ACx neurons in adolescents exhibit high neuronal variability and lower tone sensitivity as compared to adults." The reference cited here (ref 4) used AM noise with a broadband carrier, not tones.

We carefully checked the text to ensure that each claim is accurately supported by the corresponding reference.

C) Given that the authors report that neuronal firing properties differ across auditory cortical subregions (as many others have previously reported), why did the authors choose to pool neurons indiscriminately across so many different brain regions?

We appreciate the reviewer’s concern. While we acknowledge that pooling neurons across auditory cortical subregions may obscure region-specific effects, our primary focus in this study is on developmental differences between adolescents and adults, which were far more pronounced than subregional differences.

To address this potential limitation: (1) We analyzed firing differences across subregions during task engagement (see Fig. S4-1, S4-2, S4-3; Supplementary Tables 2 and 3). (2) We have now added new analyses for the passive listening condition in AUDp and AUDv (Fig. S7-1; Supplementary Table 6).

These analyses support our conclusion that developmental stage has a greater impact on auditory cortical activity than subregional location in the contexts examined. For clarity and cohesion, the main text emphasizes developmental differences, while subregional analyses are presented in the Supplement.

D) And why did they focus on layers 5/6? (Is there some reason to think that age-related differences would be more pronounced in the output layers of the auditory cortex than in other layers?)

We agree that other cortical layers, particularly supragranular layers, are important for auditory processing and plasticity. Our focus on layers 5/6 was driven by both methodological and biological considerations. Methodologically, our electrode penetrations were optimized to span multiple auditory cortical areas, and deeper layers provided greater mechanical stability for chronic recordings. Biologically, layers 5/6 contain the principal output neurons of the auditory cortex and are well-positioned to influence downstream decision-making circuits. We acknowledge the limitation of our recordings to these layers in the manuscript (L268; L464-8).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The presentation of the paper must be strengthened. As it is now, it makes it difficult to appreciate the strengths of the results. Here are some points that should be addressed:

a) The manuscript is full of inconsistencies that should be fixed to improve the reader's understanding. For example, the description on l.217 and the Figure. S3-1b, the D' value of 0 rounded to 0.01 on l. 735 (isn't it rather the z-scored value that is rounded? A D' of 0 is not a problem), the definition of lick bias on l. 750 and the values in Fig.2, the legend of Figure 7F and what is displayed on the graph (is it population sparseness or responsiveness?), etc.

We adjusted the legend and description of former Fig. S3-1b (now Fig. S3-2b).

We now clarify that the rounded values refer to z-scored hit and false alarm rates that we used in the d’ calculation. We adjusted the definition of the lick bias in Fig. 2 and Fig. S3-1b (L804).

We replaced ‘population responsiveness’ with ‘population sparseness’ throughout the figures, legend and the text.

b) References to figures are sometimes wrong (for example on l. 737,739).

c) Some text is duplicated (for example l. 814 and l. 837).

d) Typos should be corrected (for example l. 127, 'the', l. 787, 'upto').

We deleted the incorrect references of this section, removed the duplicated text, and corrected the typos.

e) Color code should be changed (for example the shades of blue for easy and hard tasks - they are extremely difficult to differentiate).

After consideration, we decided to retain the blue color code (i.e., Fig. 1d, Fig. 3d, Fig. 4e-g, Fig. 5c, Fig. 6d–g), where the distinction between the shades of blue appears sufficiently clear and maintains visual consistency and aesthetic appeal. We did however, made changes in the other color codes (Fig. 4, Fig. 5, Fig. 6, Fig. 7).

f) Figure design should be improved. For example, why is a different logic used for displaying Figure 5A or B and Figure 1E?

We adjusted the color scheme in Fig. 5. We chose to represent the data in Fig. 5 according to task difficulty, as this arrangement best illustrates the more pronounced deficits in population decoding in adolescents during the hard task.

f) Why use a 3D representation in Figure 4G? (2)

The 3D representation in Fig. 4g was chosen to illustrate the 3-way interactions between onset-latency, maximal discriminability, and duration of discrimination.

g) Figure 1A, lower right panel- should "response" not be completed by "lick", "no lick"?

We changed the labels to “Lick” and “No Lick” in Fig. 1a.

h) l.18 the age mentioned is misleading, because the learning itself actually started 20 days earlier than what is cited here.

Corrected.

i) Explain what AAV5-... is on l.212.

We added an explanation of virus components (see L216-220).

(2) The comparison of CV in Figure 2 H-J is interesting. I am curious to know whether the differences in the easy and hard tasks could be due to a decrease in CV in adults, rather than an increase in CV in adolescents? Also, could the difference in J be due to 3 outliers?

We agree that the observed CV differences may reflect a reduction in variability in adults rather than an increase in adolescents. We have revised the Results section accordingly to acknowledge this interpretation.

Regarding the concern about potential outliers in Fig. 2J, we tested the data for outliers using the isoutlier function in MATLAB (defining outliers as values exceeding three standard deviations from the mean) and found no such cases.

(3) Figure 2c shows that there is no difference in perceptual sensitivity between adolescents and adults, whereas the conclusion from Figure 4 is that adolescents exhibit lower discriminability in stimulus-related activity. Aren't these results contradictory?

This is a nuanced point. The similar slopes of the psychometric functions (Fig. 2c) indicating comparable perceptual sensitivity and the lower AUC observed in the ACx of adolescents (Fig. 4) do not necessarily contradict each other. These two measures capture related but distinct issues: psychometric slopes reflect behavioral output, which integrates both sensory encoding and processing downstream to ACx, while the AUC analysis reflects stimulus-related neural activity in ACx, which may still include decision-related components.<br /> Note that stimulus-related neural discriminability outside the context of the task is not different between adolescent and adult experts (Fig. 7h; p = 0.9374, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). This suggests that there are differences that emerge when we measure during behavior. Also note that behavior may rely on processing beyond ACx, and it is possible that downstream areas compensate for weaker cortical discriminability in adolescents — but this issue merits further investigation.

(4) Why do you think that the discrimination in hard tasks decreases with learning (Figure 6D vs Figure 6F)?

This is another nuanced point, and we can only speculate at this stage. While it may appear counterintuitive that single-neuron discriminability (AUC) for the hard task is reduced after learning (Fig. 6D vs. 6F), we believe this may reflect a shift in sensory coding in expert animals. In a recent study (Haimson et al., 2024; Science Advances), we found that learning alters single-neuron responses in the easy versus hard task in complex and distinct ways, which may account for this result. It is also possible that, in expert mice, top-down mechanisms such as feedback from higher-order areas act to suppress or stabilize sensory responses in auditory cortex, reducing the apparent stimulus selectivity of single neurons (e.g., AUC), even as behaviorally relevant information is preserved or enhanced at the population level.

Reviewer #2 (Recommendations for the authors):

This is very interesting work and I enjoyed reading the manuscript. See below for my comments, queries and suggestions, which I hope will help you improve an already very good paper.

We thank the reviewer for the meticulous and thoughtful review.

(1) Line 107: x-axis of panel 1e says 'pre-adolescent'.

(2) Line 130: replace 'less' with 'fewer'.

(3) Line 153: 'both learned and catch trials': I find the terminology here a bit confusing. I would typically understand a catch trial to be a trial without a stimulus but these 'catch' trials here have a stimulus. It's just that they are not rewarded/punished. What about calling them probe trials instead?

We corrected the labelling (1), reworded to ‘fewer’ and ‘probe trials’ (2,3).

(4) Line 210: The results of the optogenetics experiments are very interesting. In particular, because the effect is so dramatic and much bigger than what has been reported in the literature previously, I believe. Lick rates are dramatically reduced suggesting that the mice have pretty much stopped engaging in the task and the authors very rightly state that the 'execution' of the behavior is affected. I think it would be worth discussing the implications of these results more thoroughly, perhaps also with respect to some of the lesion work. Useful discussions on the topic can be found, for instance, in Otchy et al., 2015; Hong et al., 2018; O'Sullivan et al., 2019; Ceballo et al., 2019 and Lee et al., 2024. Are the mice unable to hear anything in laser trials and that is why they stopped licking? If they merely had trouble distinguishing them then we would perhaps expect the psychometric curves to approach chance level, i.e. to be flat near the line indicating a lick rate of 0.5. Could the dramatic decrease in lick rate be a motor issue? Can we rule out spillover of the virus to relevant motor areas? (I understand all of the 200nL of the virus were injected at a single location) Or are the effects much more dramatic than what has been reported previously simply because the GtACR2 is much more effective at silencing the auditory cortex? Could the effect be down to off-target effects, e.g. by removing excitation from a target area of the auditory cortex, rather than the disruption of cortical processing?

We have now expanded the discussion in the manuscript to more thoroughly consider alternative interpretations of the strong behavioral effect observed during ACx silencing (L495–511). In particular, we acknowledge that the suppression of licking may reflect not only impaired sensory discrimination but also broader disruptions to arousal, motivation, or motor readiness. We also discuss the potential impact of viral spread, circuit-level off-target effects, and the potency of GtACR2 as possible contributors. We highlight the need for future work using more graded or temporally precise manipulations to resolve these issues.

(5) Line 226: Reference 19 (Talwar and Gerstein 2001) is not particularly relevant as it is mostly concerned with microstimulation-induced A1 plasticity. There are, however, several other papers that should be cited (and potentially discussed) in this context. In particular, O'Sullivan et al., 2019 and Ceballo et al., 2019 as these papers investigate the effects of optogenetic silencing on frequency discrimination in head-fixed mice and find relatively modest impairments. Also relevant may be Kato et al., 2015 and Lee et al., 2024, although they look at sound detection rather than discrimination.

We changed the references and pointed the reader to the (new section) Discussion.

(6) Line 253: 'engaged [in] the task.

(7) Figure 4: It appears that panel S4-1d is not referred to anywhere in the main text.

Fixed.

(8) Line 260: Might be useful to explain a bit more about the motivation behind focusing on L5/L6. Are there mostly theoretical considerations, i.e. would we expect the infragranular layers to be more relevant for understanding the difference in task performance? Or were there also practical considerations, e. g. did the data set contain mostly L5/L6 neurons because those were easier to record from given the angle at which the probe was inserted? If those kinds of practical considerations played a role, then there is nothing wrong with that but it would be helpful to explain them for the benefit of others who might try a similar recording approach.

There were no deep theoretical considerations for targeting L5/6.  Our focus on layers 5/6 was driven by both methodological and biological considerations. Methodologically, our electrode penetrations were optimized to span multiple auditory cortical areas, and deeper layers provided greater mechanical stability for chronic recordings. Biologically, layers 5/6 contain the principal output neurons of the auditory cortex and are well-positioned to influence downstream decision-making circuits. We acknowledge the limitation of our recordings to these layers in the manuscript (L268; L463–467). See also comment D of reviewer 3.

(9) Supplementary Table 2: The numbers in brackets indicate fractions rather than percentages.

Fixed.

(10) Figure S4-3: The figure legend implies that the number of neurons with significant discriminability for the hard stimulus and significant discriminability for choice was identical. (adolescent neurons = 368, mice = 5, recordings = 10; adult n = 544, mice = 6, recordings = 12 in both cases). Presumably, that is not actually the case and rather the result of a copy/paste operation gone wrong. Furthermore, I think it would be helpful to state the fractions of neurons that can discriminate between the stimuli and between the choices that the animal made in the main text.

Thank you for spotting the mistake. We corrected the n’s and added the percentage of neurons that discriminate stimulus and choice in the main text and the figure legend.

(11) Line 301: 'We used a ... decoder to quantify hit versus correct reject trial outcomes': I'm not sure I understand the rationale here. For the single unit analysis hit and false alarm trials were compared to assess their ability to discriminate the stimuli. FA and CR trials were compared to assess whether neurons can encode the choice of the mice. But the hit and CR trials which are contrasted here differ in terms of both stimulus and behavior/choice so what is supposed to be decoded here, what is supposed to be achieved with this analysis?

Thank you for this important point. You're correct that comparing hit and CR trials captures differences in both stimulus and choice, or task-related differences. We chose this contrast for the population decoding analysis to achieve higher trial counts per session and similar number of trials which are necessary for the reliability of the analysis. While this approach does not isolate stimulus from choice encoding, it provides an overall measure of how well population activity distinguishes task-relevant outcomes. We explicitly acknowledge this issue in L313-314.

(12) Line 332: What do you mean when you say the novice mice were 'otherwise fully engaged' in the task when they were not trained to do the task and are not doing the task?

By "otherwise fully engaged," we mean that novice mice were actively participating in the task environment, similar to expert mice — they were motivated by thirst and licked the spout to obtain water. The key distinction is that novice mice had not yet learned the task rules and likely relied on trial-and-error strategies, rather than performing the task proficiently.

(13) Line 334: 'regardless of trial outcome': Why is the trial outcome not taken into account? What is the rationale for this analysis? Furthermore, in novice mice a substantial proportion of the 'go' trials are misses. In expert mice, however, the proportion of 'miss trials' (and presumably false alarms) will by definition be much smaller. Given this, I find it difficult to interpret the results of this section.

This approach was chosen to reliably decode a sufficient number of trials for each task difficulty (i.e. expert mice predominantly performed CRs on No-Go trials and novice mice often showed FAs). Utilizing all trial outcomes ensured that we had enough trials for each stimulus type to accurately estimate the AUCs. This approach avoids introducing biases due to uneven trial numbers across learning stages.

(14) Line 378: 'differences between adolescents and adults arise primarily from age': Are there differences in any of the metrics shown in 7e-h between adolescents and adults?

We confirm that differences between adolescents and adults are indeed present in some metrics but not others in Figure 7e–h. Specifically, while tuning bandwidth was similar in novice animals, it was significantly lower in adult experts (Fig. 7e; novice: p = 0.0882; expert: p = 0.0001 Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). The population sparseness was similar in both novice and expert adolescent and adult neurons (Fig. 7f; novice: p = 0.2873; expert: p = 0.1017, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). The distance to the easy go stimulus was similar in novice animals, but lower in adult experts (Fig. 7g; novice: p = 0.7727; expert: p = 0.0001, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). The neuronal d-prime was similar in both novice and expert adolescent and adult neurons (Fig. 7h; novice: p = 0.7727; expert: p = 0.0001, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript).

(15) Line 475: '...well and beyond...': something seems to be missing in this statement.

(16) Line 487: 'onto' should be 'into', I think.

(17) Line 610 and 613: '3 seconds' ... '2.5 seconds': Was the response window 3s or 2.5s?

(18) Line 638: 'set' should be 'setup', I believe.

All the mistakes mentioned above, were fixed. Thanks.

(19) Line 643: 'Reward-reinforcement was delayed to 0.5 seconds after the tone offset': Presumably, if they completed their fifth lick later than 0.5 seconds after the tone, the reward delivery was also delayed?

Apologies for the lack of clarity. In the head-fixed version, there was no lick threshold. Mice were reinforced after a single lick. If that lick occurred after the 0.5-second reinforcement delay following tone offset, the reward or punishment was delivered immediately upon licking.

(20) Line 661: 'effect [of] ACx'.

(21) Line 680: 'a base-station connected to chassis'. The sentence sounds incomplete.

(22) Line 746: 'infliction', I believe, should say 'inflection'.

(23) Line 769: 'non-auditory responsive units': Shouldn't that simply say 'non-responsive units'? The way it is currently written I understand it to mean that these units were responsive (to some other modality perhaps) but not to auditory stimulation.

(24) Line 791: 'bins [of] 50ms'.

(25) Line 811: 'all of' > 'of all'.

(26) Line 814: Looks like the previous paragraph on single unit analysis was accidentally repeated under the wrong heading.

(27) Line 817: 'encoded' should say 'calculated', I believe.

All the mistakes mentioned above were fixed. Thanks.

(28) Line 869: 'bandwidth of excited units': Not sure I understand how exactly the bandwidth, i.e. tuning width was measured.

We acknowledge that our previous answer was unclear and expanded the Methods section. To calculate bandwidth, we identified significant tone-evoked responses by comparing activity during the tone window to baseline firing rates at 62 dB SPL (p < 0.05). For each neuron, we counted the number of contiguous frequencies with significant excitatory responses, subtracting isolated false positives to correct for chance. We then converted this count into an octave-based bandwidth by multiplying the number of frequency bins by the octave spacing between them (0.1661 octaves per step).

(29) Line 871: 'population sparseness': Is that the fraction of tone frequencies that produced a significant response? I would have thought that this measure is very highly correlated to your measure of bandwidth, to the point of being redundant, but I may have misunderstood how one or the other is calculated. Furthermore, the Y label of Figure 7f says 'responsiveness' rather than sparseness and that would seem to be the more appropriate term because, unless I am misunderstanding this, a larger value here implies that the neuron responded to more frequencies, i.e. in a less sparse manner.

We have clarified the use of the term "population sparseness" and updated the Y-axis label in Figure 7f to better reflect this measure. This metric reflects the fraction of tone–attenuation combinations that elicited a significant excitatory response across the entire population of neurons, not within individual units.

While this measure is related to bandwidth, it captures a distinct property of the data. Bandwidth quantifies how broadly or narrowly a single neuron responds across frequencies at a fixed intensity, whereas population sparseness reflects how distributed responsiveness is across the population as a whole. Although the two measures are related, since broadly tuned neurons often contribute to lower population sparseness, they capture distinct aspects of neural coding and are not redundant.

(30) Line 881: I think this line should refer to Figure 7h rather than 7g.

Fixed.

Reviewer #3 (Recommendations for the authors):

(1) In the Educage, water was only available when animals engaged in the task; however, there is no mention of whether/how animal weight was monitored.

In the Educage, mice had continuous access to water by voluntarily engaging in the task, which they could perform at any time. Although body weight was not directly monitored, water access was essentially ad libitum, and mice performed hundreds of trials per day, thereby ensuring sufficient daily intake. This approach allowed us to monitor hydration (ad libitum food is supplied in the home cage). The 24/7 setup, including automated monitoring of trial counts and water consumption, was reviewed and approved by our institutional animal care and use committee (IACUC).

(2) In Figure 2B-C and Figure 2E, the y-axis reads "lick rate". At first glance, I took this to mean "the frequency of licking" (i.e. an animal typically licks at a rate of 5 Hz). However, what the authors actually are plotting here is the proportion of trials on which an animal elicited >= 5 licks during the response window (i.e. the proportion of "yes" responses). I recommend editing the y-axis and the text for clarity.

We replaced the y-label and adjusted the figure legend (Fig. 2).

(3) I didn't see any examples of raw (filtered) voltage traces. It would be worth including some to demonstrate the quality of the data.

We have added an example of a filtered voltage trace aligned to tone onset in Fig. S4-1a to illustrate data quality. In addition, all raw and processed voltage traces, along with relevant analysis code, are available through our GitHub repository and the corresponding dataset on Zenodo.

(4) The description of the calculation of bias (C) in the methods section (lines 749-750) is incorrect. The correct formula is C = -0.5 * [z(hit rate) + z(fa rate)]. I believe this is the formula that the authors used, as they report negative C values. Please clarify or correct.

Thanks for spotting this. It is now corrected.

(5) The authors use the terms 'naïve' and 'novice' interchangeably. I suggest sticking with one term to avoid potential confusion.

(6) Multiple instances: "less trials/day" should be "fewer trials/day"

(7) Supplementary Table 2: The values reported are proportions, not percentages. Please correct.

(8) Line 270: Table 2 does not show the number of neurons in the dataset categorized by region. Perhaps the authors meant Supplementary Table 2?

Fixed. Thank you for pointing these mistakes out.

(9) Figure 5C: the data from the hard task are entirely obscured by the data from the easy task. I recommend splitting it into two different plots.

We agree and split the decoding of the easy and the hard task into two graphs (left: easy task; right: hard task). Thank you!

(10) How many mice contributed to each analyzed data set? Could the authors provide a breakdown in a table somewhere of how many neurons were recorded in each mouse and which ones were included in which analyses?

We added an overview of the analyzed datasets in supplementary Table 7. Please note that the number of mice and neurons used in each analysis is also reported in the main text and legends. Importantly, all primary analyses were conducted using LME models, which explicitly account for hierarchical data structure and inter-mouse variability, thereby addressing potential concerns about data imbalance or bias.

Author Response:

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

Reviewer #1 (Public Review):

Summary:

In this study, Floedder et al report that dopamine ramps in both Pavlovian and Instrumental conditions are shaped by reward interval statistics. Dopamine ramps are an interesting phenomenon because at first glance they do not represent the classical reward prediction errors associated with dopamine signaling. Instead, they seem somewhat to bridge the gap between tonic and phasic dopamine, with an intense discussion still being held in the field about what is their actual behavioral role. Here, in tests with head-fixed mice, and dopamine being recorded with a genetically encoded fluorescent sensor in the nucleus accumbens, the authors find that dopamine ramps were only present when intertrial intervals were relatively short and the structure of the task (Pavlovian cue or progression in a VR corridor) contained elements that indicated progression towards the reward (e.g., a dynamic cue). The authors show that these findings are well explained by their previously published model of Adjusted Net Contingency of Causal Relation (ANCCR).

Strengths:

This descriptive study delineates some fundamental parameters that define dopamine ramps in the studied conditions. The short, objective, and to-the-point format of the manuscript is great and really does a service to potential readers. The authors are very careful with the scope of their conclusions, which is appreciated by this reviewer.

We thank the reviewer for their overall support of the formatting and scope of the manuscript. 

Weaknesses:

The discussion of the results is very limited to the conceptual framework of the authors' preferred model (which the authors do recognize, but it still is a limitation). The correlation analysis presented in panel l of Figure 3 seems unnecessary at best and could be misleading, as it is really driven by the categorical differences between the two conditions that were grouped for this analysis. There are some key aspects of the data and their relationship with each other, the previous literature, and the methods used to collect them, that could have been better discussed and explored.

We agree with the reviewer that a weakness of the discussion was the limited framing of the results within the ANCCR model. To address this, we have expanded our introduction and discussion sections to provide a more thorough explanation of our model and possible leading alternatives.

We thank the reviewer for pointing out that Figure 3l may be misleading for readers; we removed this panel from the revised Figure 4.

We have further addressed the specific concerns raised by the reviewer in their comments to the authors. Indeed, we agree with the reviewer that the original manuscript was narrow in its focus regarding relationships between different aspects of the data. To more thoroughly explore how key variables – including dopamine ramp slope and onset response as well as licking behavior slope – could relate to each other, we have added Extended Data Figure 8. In this figure, we show that no correlations exist between any of these key variables in either dynamic tone condition; it is our hope that this additional analysis highlights the significance of the clear relationship between dopamine ramp slope and ITI duration. 

Reviewer #2 (Public Review):

In this manuscript by Floeder et al., the authors report a correlation between ITI duration and the strength of a dopamine ramp occurring in the time between a predictive conditioned stimulus and a subsequent reward. They found this relationship occurring within two different tasks with mice, during both a Pavlovian task as well as an instrumental virtual visual navigation task. Additionally, they observed this relationship only in conditions when using a dynamic predictive stimulus. The authors relate this finding to their previously published model ANCCR in which the time constant of the eligibility trace is proportionate to the reward rate within the task.

The relationship between ITI duration and the extent of a dopamine ramp which the authors have reported is very intriguing and certainly provides an important constraint for models for dopamine function. As such, these findings are potentially highly impactful to the field. I do have a few questions for the authors which are written below.

We thank the reviewer for their interest in our findings and belief in their potential to be impactful in the field. 

(1) I was surprised to see a lack of counterbalance within the Pavlovian design for the order of the long vs short ITI. Ramping of the lick rate does increase from the long-duration ITIs to the short-duration ITI sessions. Although of course, this increase in ramping of the licking across the two conditions is not necessarily a function of learning, it doesn't lend support to the opposite possibility that the timing of the dynamic CS hasn't reached asymptotic learning by the end of the long-duration ITI. The authors do reference papers in which overtraining tends to result in a reduction of ramping, which would argue against this possibility, yet differential learning of the dynamic CS would presumably be required to observe this effect. Do the authors have any evidence that the effect is not due to heightened learning of the timing of the dynamic CS across the experiment?

We appreciate the reviewer expressing their surprise regarding the lack of counterbalance in our Pavlovian experimental design. We previously did not explicitly do this because the ramps disappeared in the short ITI/fixed tone condition, indicating that their presence is not just a matter of total experience in the task. However, we agree that this is incidental, but not direct evidence. To address this drawback, we repeated the Pavlovian experiment in a new cohort of animals with a revised training order, switching conditions such that the short ITI/dynamic tone (SD) condition preceded the long ITI/dynamic tone (LD) condition (see revised Figure 2a). Despite this change in the training order, the main findings remain consistent: positive dLight slopes (i.e., dopamine ramps) are only observed in the SD condition (Figure 2b-d). 

We thank the reviewer for raising these questions regarding licking behavior and learning and their relationship with dopamine ramps. Indeed, a closer look at the average licking behavior reveals subtle differences across conditions (Figure 1f and Extended Data Figure 5a). While the average lick rate during the ramp window does not differ across conditions (Extended Data Figure 5c), the ramping of the lick rate during this window is higher for dynamic tone conditions compared to fixed tone conditions (Extended Data Figure 5d). Despite these differences, we still believe that the main comparison between the dopamine slope in the SD vs LD condition remains valid given their similar lick ramping slopes. Furthermore, our primary measure of learning is not lick slope, but anticipatory lick rate during the 1 s trace preceding reward delivery, which is robustly nonzero across cohorts and conditions (Figure 1g and Extended Data Figure 5b). 

Taken together, we hope that the results from our counterbalanced Pavlovian training and more rigorous analysis of lick behavior across conditions provide sufficient evidence to assuage concerns that the differences in ramping dopamine simply reflect differences in learning. 

(2) The dopamine response, as measured by dLight, seems to drop after the reward is delivered. This reduction in responding also tends to be observed with electrophysiological recordings of dopamine neurons. It seems possible that during the short ITI sessions, particularly on the shorter ITI duration trials, that dopamine levels may still be reduced from the previous trial at the onset of the CS on the subsequent trial. Perhaps the authors can observe the dynamics of the recovery of the dopamine response following a reward delivery on longer-duration ITIs in order to determine how quickly dopamine is recovering following a reward delivery. Are the trials with very short ITIs occurring within this period that dopamine is recovering from the previous trial? If so, how much of the effect may be due to this effect? It should be noted that the lack of observance of a ramp on the condition of shortduration ITIs with fixed CSs provides a potential control for this effect, yet the extent to which a natural ramp might occur following sucrose deliveries should be investigated.

We thank the reviewer for highlighting the possibility that ramps may be due to the dopamine response recovery following reward delivery. Given that peak reward dopamine responses tend to be larger in long ITI conditions, however, we felt that it was inappropriate to compare post-reward dopamine recovery times across conditions. Instead, we decided to directly compare the dLight slope 2s before cue onset (“pre-cue window,” a proxy for recovery from previous trial) with the dLight slope during our ramp window from 3 to 8s after cue onset (Extended Data Figure 6a). There were no significant differences in pre-cue dLight slope across conditions (Extended Data Figure 6b); this suggests that the ramping slopes seen in the SD condition, but not other conditions, is not simply due to the natural dopamine recovery response following reward delivery. Furthermore, if the dopamine ramps observed in the SD condition were a continuation of the post-reward dopamine recovery from the previous trial, we would expect to see a positive correlation between the dLight slope before and during the cue. However, there is no such correlation between the dLight slopes in the ramp window vs. pre-cue window in the SD condition (Extended Data Figure 6c-d). We believe that this observation, along with the builtin control of the SF condition mentioned by the reviewer, serves as evidence against the possibility of our ramp results being due to a natural ramp after reward delivery.

(3) The authors primarily relate the finding of the correlation between the ITI and the slope of the ramp to their ANCCR model by suggesting that shorter time constants of the eligibility trace will result in more precisely timed predictors of reward across discrete periods of the dynamic cue. Based on this prediction, would the change in slope be more gradual, and perhaps be more correlated with a broader cumulative estimate of reward rate than just a single trial?

To clarify, we do not propose that a smaller eligibility trace time constant results in more precise timing per se. Instead, we believe that the rapid eligibility trace decay from smaller time constants gives greater causal predictive power for later periods in the dynamic cue (see Extended Data Figure 1) since the memory of the earlier periods of the cue is weaker. 

We appreciate the reviewer’s curiosity regarding the influence of a broader cumulative estimate of reward vs. only the immediately preceding ITI on dopamine ramp slopes. Indeed, in several instrumental tasks (e.g., Krausz et al., Neuron, 2023), recent reward rate modulates the magnitude of dopamine ramps, making this an important variable to investigate. We chose to use linear regression for each mouse separately to analyze the relationship between the trial dopamine slope and the average previous ITI for the past 1 through 10 most recent trials. In the SD condition, as reported in our earlier manuscript, there was a significantly negative dependence of trial dopamine slope with the single previous ITI (i.e., if the previous ITI was long, the next trial tends to have a weaker ramp). This negative dependence, however, only held for a single previous trial; there was no clear relationship between the per-trial dopamine slope and the average of the past 2 through 10 ITIs (Extended Data Figure 7a). For the LD condition, on the other hand, there is no clear relationship between the per-trial dopamine slope and the average previous ITI for any of the past 1 through 10 trials, with one exception: there is a significantly negative dependence of trial dopamine slope with the average ITI of the previous 2 trials (Extended Data Figure 7b). This longer timescale relationship in the LD condition suggests that the adaptation of the eligibility trace time constant is nuanced and depends on the general ITI length. 

In general, though we reason that the eligibility trace time constant should depend on overall event rates, we do not currently propose a real-time update rule for the eligibility trace time constant depending on recent event rates. Accordingly, we are currently agnostic about the actual time scale of history of recent event rate calculation that mediates the eligibility trace time constant. Our experimental results suggest that when the ITI is generally short for Pavlovian conditioning, the eligibility trace time constant adapts to ITI on a rapid timescale. However, only a small fraction of the variability of this rapid fluctuation is captured by recent ITI history. A more thorough investigation of this real-time update rule would need to be done in the future.

Reviewer #3 (Public Review):

Summary:

Floeder and colleagues measure dopamine signaling in the nucleus accumbens core using fiber photometry of the dLight sensor, in Pavlovian and instrumental tasks in mice. They test some predictions from a recently proposed model (ANCCR) regarding the existence of "ramps" in dopamine that have been seen in some previous research, the characteristics of which remain poorly understood.

They find that cues signaling a progression toward rewards (akin to a countdown) specifically promote ramping dopamine signaling in the nucleus accumbens core, but only when the intertrial interval just experienced was short. This work is discussed in the context of ongoing theoretical conceptions of dopamine's role in learning.

Strengths:

This work is the clearest demonstration to date of concrete training factors that seem to directly impact whether or not dopamine ramps occur. The existence of ramping signals has long been a feature of debates in the dopamine literature and this work adds important context to that. Further, as a practical assessment of the impact of a relatively simple trial structure manipulation on dopamine patterns, this work will be important for guiding future studies. These studies are well done and thoughtfully presented.

We thank the reviewer for recognizing the context that our study adds to the dopamine literature and the potential for our experiments to guide future work. 

Weaknesses:

It remains somewhat unclear what limits are in place on the extent to which an eligibility trace is reflected in dopamine signals. In the current study, a specific set of ITIs was used, and one wonders if the relative comparison of ITI/history variables ("shorter" or "longer") is a factor in how the dopamine signal emerges, in addition to the explicit length ("short" or "long") of the ITI. Another experimental condition, where variable ITIs were intermingled, could perhaps help clarify some remaining questions.

Though we used ITIs of fixed means, due to the exponential nature of their distribution, we did intermingle ITIs of various durations in both our long and short ITI conditions. The distribution of ITI durations is visualized in Figure 1c for Pavlovian conditioning and Extended Data Figure 9b for VR navigation. 

The relative comparison between consecutive ITIs was not something we originally explored, so we thank the reviewer for wondering how it impacts the dopamine signal. To investigate this, we quantified both the change in ITI (+ or - Δ ITI for relatively longer or shorter, respectively) and the change in dopamine ramp slope between consecutive trials in the SD condition (Figure 3d). Across each mouse separately, we found a significantly negative relationship between Δ slope and Δ ITI (Figure 3e-f). Also, the average Δ slope was significantly greater for consecutive trials with a Δ ITI below -1 s compared to trials with a Δ ITI above +1 s (Figure 3g). Altogether, these findings suggest that relative comparison of ITIs does correlate with changes in the dopamine signal; a relatively longer ITI tends to have a weaker ramp, which fits in nicely with the expected inverse relationship between ITI and dopamine ramp slope from our ANCCR model.

In both tasks, cue onset responses are larger, and longer on long ITI trials. One concern is that this larger signal makes seeing a ramp during the cue-reward interval harder, especially with a fluorescence method like photometry. Examining the traces in Figure 1i - in the long, dynamic cue condition the dopamine trace has not returned to baseline at the time of the "ramp" window onset, but the short dynamic trace has. So one wonders if it's possible the overall return to baseline trend in the long dynamic conditions might wash out a ramp.

This is a good point, and we thank the reviewer for raising it. Certainly, the cue onset response is significantly larger in long ITI conditions (see Figure 1i-j and Figure 4h-j). To avoid any bleed over effect, we intentionally chose ramp window periods during later portions of the trial (in line with work from others e.g., Kim et al., Cell, 2020). While the cue onset dopamine pulse seems to have flatlined by the start of the ramp window period, the dopamine levels clearly remain elevated relative to pre-cue baseline. This type of signal has been observed with fiber photometry in other Pavlovian conditioning paradigms with long cue durations (e.g., Jeong et al., Science, 2022). Because of the persistently elevated dopamine levels, it is certainly possible that a ramping signal during the cue is getting washed out; with the bulk fluorescence photometry technique we employed in this study, this possibility is unfortunately difficult to completely rule out. However, the long ITI/fixed tone (LF) condition could serve as a potential control given the overall similarity in the dopamine signal between the LF and LD conditions: both conditions have large cue onset responses with elevated dopamine throughout the duration of the cue (see Extended Data Figures 2c and 3c). Critically, the LD condition lacks a noticeable ramp despite the dynamic tone providing information on temporal proximity to reward, which is thought to be necessary for dopamine ramps to occur. Importantly, regardless of whether a ramp is masked in the long ITI dynamic condition, most studies investigate such a condition in isolation and would report the absence of dopamine ramps. Thus, at a descriptive level, we believe it remains true that observable dopamine ramps are only present when the ITI is short. 

Not a weakness of this study, but the current results certainly make one ponder the potential function of cue-reward interval ramps in dopamine (assuming there is a determinable function). In the current data, licking behavior was similar on different trial types, and that is described as specifically not explaining ramp activity.

We agree that this work naturally raises the question of the function of dopamine ramps. However, selective and precise manipulation of only the dopamine ramps without altering other features such as phasic responses, or inducing dopamine dips, is highly technically challenging at this moment; due to this challenge, we intentionally focused on the conditions that determine the presence or absence of dopamine ramps rather than their function. We agree with the reviewer that studying the specific function of dopamine ramps is an interesting future question. 

Reviewing Editor:

The reviewers felt the results are of considerable and broad interest to the neuroscience community, but that the framing in terms of ANCCR undermined the scope of the findings as did the brief nature of the formatting of the manuscript. In addition, the reviewers felt that the relationship between ramp dynamics, behavior, and ITI conditions requires more in-depth analyses. Relatedly, the lack of counterbalancing of the ITI durations was considered to be a drawback and needs to be addressed as it may affect the baseline. Addressing these issues in a satisfactory manner would improve the assessment of the manuscript to important/convincing.

We truly appreciate the valuable feedback provided on this manuscript by all three reviewers and the reviewing editor. Based on this input, we have significantly revised the manuscript to address the issues brought up by the reviewers. Firstly, we have conducted additional experiments to counterbalance the ITI conditions for Pavlovian conditioning; this strengthened our results by confirming our original findings that ITI duration, rather than training order, is the key variable controlling the presence or absence of dopamine ramps. Secondly, we completed more rigorous analyses to further explore the relationship between dopamine dynamics, animal behavior, and ITI duration; we generally found no significant correlations between these variables, with a notable exception being our main finding between ITI duration and dopamine ramp slope. Finally, we revised and expanded our writing to both explain predictions from our ANCCR model in less technical language and explore how alternative theoretical frameworks could potentially explain our findings. In doing so, we hope that our manuscript is now more accessible and of interest to a broad audience of neuroscience readers.

Reviewer #1 (Recommendations For The Authors):

The study could be improved if the authors performed a more detailed comparison of how other theoretical frameworks, beyond ANCCR could account for the observed findings. Also, the correlation analysis presented in the panel I of Figure 3 seems unnecessary and potentially spurious, as the slope of the correlation is clearly mostly driven by the categorical differences between the two ITI conditions, which were combined for the analysis - it's not clear what is the value of this analysis beyond the group comparison presented in the following panel.

Again, we thank the reviewer for elaborating on their concern regarding Figure 3l – we have removed it from the revised Figure 4. 

The relationship between ramp dynamics with the behavior and the large differences in cue onset responses between short and long ITI conditions could have been better explored. If I understand correctly the overarching proposal of this and other publications by this group, then the differences in cue responses is determined by the spacing of rewards in a somewhat similar way that the ramps are. So, is there a trial-by-trial correlation between the amplitude of the cue responses and the slope of the ramps? Is there a correlation between any of these two measures with the licking behavior, and if so, does it change with the ITI condition? A more thorough exploration of these relationships would help support the proposal of the primacy of inter-event spacing in determining the different types of dopamine responses in learning.

There are certainly interesting relationships between dopamine dynamics, behavior, and ITI that we failed to explore in our original manuscript – we appreciate the reviewer bringing them up. We found no correlation between dopamine ramp slope and cue onset response in either the SD or LD condition (Extended Data Fig 8a-b). Moreover, we found no correlation between either of these variables and the trial-by-trial licking behavior (Extended Data Fig 8c-f). Finally, there is no relationship between licking behavior and previous ITI duration (Extended Data Fig 8g-h), suggesting that behavioral differences do not account for differences in the dopamine ramp slope. Together, the lack of significant relationships between these other variables highlights the specific, clear relationship between ITI duration and dopamine ramp slope. 

Finally, another issue I feel could have been better discussed is how the particular settings of both tasks might be biasing the results. For example, there is an issue to be considered about how the dopamine ramp dynamics reported here, especially the requirement of a dynamic cue for ramps to be present, square with the previous published results by one of the authors - Mohebi et al, Nature, 2019. In that manuscript, rats were executing a bandit task where, to this reviewer's understanding, there was no explicit dynamic cue aside from the standard sensory feedback of the rats moving around in the behavior boxes to approach a nose poke port. Is the idea that this sensory feedback could function as a dynamic cue? If that's the case, then this short-scale, movement-related feedback should also function as a dynamic cue in a freely moving Pavlovian condition, when the animals must also move towards a reward delivery port, right? Therefore, could it be that the experimental "requirement" of a dynamic cue is only present in a head-fixed condition? One could phrase this in a different way to Steelman and potentially further the authors' proposal: perhaps in any slightly more naturalistic setting, the interaction of the animals with their environment always functions as a dynamic cue indicating proximity to reward, and this relationship was experimentally isolated by the use of head fixation (but not explicitly compared with a freely moving condition) in the present study. I think that would be an interesting alternative to consider and discuss, and perhaps explore experimentally at some point.

We thank the reviewer for raising this important point regarding the influence of our experimental settings on our results. At first glance, it could appear that our results demonstrating the necessity of a dynamic cue for ramps in a head-fixed setting do not fit neatly with other results in a freely moving setup (e.g., Collins et al., Scientific Reports, 2016; Mohebi et al., Nature, 2019). Exactly as the reviewer states though, we believe that sensory feedback from the environment in freely moving preparations serves the same function as a dynamic progression of cues. We have considered the implications of methodological differences between head-fixed and freely moving preparations in the discussion section. 

Reviewer #2 (Recommendations For The Authors):

This comment relates indirectly to comment 3, in that the authors intermix theory throughout the manuscript. I think this would be fine if the experiment was framed directly in terms of ANCCR, but the authors specifically mention that this experiment wasn't developed to distinguish between different theories. As such, it seems difficult to assess the scope of the comments regarding theory within the paper because they tend to be specifically related to ANCCR. For instance, the last comment has broad implications of how the ramp might be related to the overall reward rate, an interesting finding that constrains classes of dopamine models rather than evidence just for ANCCR. Perhaps adding a discussion section that allows the authors to focus more on theory would be beneficial for this manuscript.

We appreciate this suggestion by the reviewer. We have updated both our introduction and discussion sections to elaborate more thoroughly on theory.

Reviewer #3 (Recommendations For The Authors):

The paper could potentially benefit from the use of more accessible language to describe the conceptual basis of the work, and the predictions, and a bit of reformatting away from the brief structure with lots of supplemental discussion.

For example, in the introduction, the line - "Varying the ITI was critical because our theory predicts that the ITI is a variable controlling the eligibility trace time constant, such that a short ITI would produce a small time constant relative to the cue-reward interval (Supplementary Note 1)". As far as I can tell, this is meant to get across the notion that dopamine represents some aspect of the time between rewards - dopamine signals will differ for cues following short vs long intervals between rewards.

As written, the language of the paper takes a fair bit of parsing, but the notions are actually pretty simple. This is partly due to the brief format the paper is written in, where familiarity with the previous papers describing ANCCR is assumed.

From a readability standpoint, and the potential impact of the paper on a broad audience, perhaps this could be considered as a point for revision.

We thank the reviewer for pointing out the drawbacks of our technical language and brief formatting. To address this, we have removed the majority of the supplementary notes and expanded our introduction and discussion sections. In doing so, we hope that the conceptual foundations of this work, and potential alternative theoretical explanations, are accessible and impactful for a broad audience of readers.

Reviewer #2 (Public review):

Summary:

This is the first study to show how a L-R bias in the relationship between numerical magnitude and space depends on brain lateralisation, and moreover, how this is modulated by in ovo conditions.

Strengths:

Novel methodology for investigating the innateness and neural basis of a L-R bias in the relationship between number and space.

Weaknesses:

I would query the way the experiment was contextualised. They ask whether culture or innate pre-wiring determines the 'left-to-right orientation of the MNL [mental number line]'.<br /> The term, 'Mental Number Line' is an inference from experimental tasks. One of the first experimental demonstrations of a preference or bias for small numbers in the left of space and larger numbers in the right of space, was more carefully described as the spatial-numerical association of response codes - the SNARC effect (Dehaene, S., Bossini, S., & Giraux, P. (1993). The mental representation of parity and numerical magnitude. Journal of Experimental Psychology: General, 122, 371-396).<br /> This has meant that the background to the study is confusing. First, they note correctly that many other creatures, including insects can show this bias, though in none of these has neural lateralisation been shown to be a cause. Second, their clever experiment shows that an experimental manipulation creates the bias. If it were innate and common to other species, the experimental manipulation shouldn't matter. There would always be a L-R bias. Third, they seem to be asserting that humans have a left-to-right (L-R) MNL. This is highly contentious, and in some studies, reading direction affects it, as the original study by Dehaene et al showed; and in others, task affects direction (e.g. Bachtold, D., Baumüller, M., & Brugger, P. (1998). Stimulus-response compatibility in representational space. Neuropsychologia, 36, 731-735, not cited). Moreover, a very careful study of adult humans, found no L-R bias (Karolis, V., Iuculano, T., & Butterworth, B. (2011), not cited). Mapping numerical magnitudes along the right lines: Differentiating between scale and bias. Journal of Experimental Psychology: General, 140(4), 693-706). Indeed, Rugani et al claim, incorrectly, that the L-R bias was first reported by Galton in 1880. There are two errors here: first, Galton was reporting what he called 'visualised numerals' and are typically referred to now as 'number forms' - spontaneous and habitual conscious visual representations - not an inference from a number line task. Second, Galton reported right-to-left, circular, and vertical visualised numerals, and no simple left-to-right examples (Galton, F. (1880). Visualised numerals. Nature, 21, 252-256.). So in fact did Bertillon, J. (1880). De la vision des nombres. La Nature, 378, 196-198, and more recently Seron, X., Pesenti, M., Noël, M.-P., Deloche, G., & Cornet, J.-A. (1992). Images of numbers, or "When 98 is upper left and 6 sky blue". Cognition, 44, 159-196, and Tang, J., Ward, J., & Butterworth, B. (2008). Number forms in the brain. Journal of Cognitive Neuroscience, 20(9), 1547-1556.

If the authors are committed to chicks' MN Line they should test a series of numbers showing that the bias to left is greater for 2 and 3 than for 4 etc.

What does all this mean? I think that the experiment should absolutely be published in eLife, but the paper should be shorn of its misleading contextualisation, including the term 'Mental Number Line'. The authors also speculate, usefully, on why chicks and other species might have a L-R bias. I don't think the speculations are convincing, but at least if there is an evolutionary basis for the bias, it should at least be discussed.

In fact, I think it would make a very interesting special issue to bring up to date how and why the L-R bias exists, and where and why it does not.

Karolis, V., Iuculano, T., & Butterworth, B. (2011). Mapping numerical magnitudes along the right lines: Differentiating between scale and bias. Journal of Experimental Psychology: General, 140(4), 693-706. doi:10.1037/a0024255

Review of the revised version:

The background and terminology in the text have been significantly altered and clarified: Spatial Numerical Association (SNA) instead of Mental Number Line (MNL) in the text, but with a discussion about how SNA might be the basis of MNL. This entails a link from SNA - a bias - to mental representation of a sequence of numerical magnitudes, which will need to be spelt out in subsequent work with a sequence of numbers rather than a single number, in this case 4. Could the effect be generalised to much larger numbers?

Although the relationship between number and space seems fundamental, the key question is why the L-R SNA bias should exist at all. The authors take on this challenge and make important arguments for the evolutionary advantage of the bias is (see lines 138ff, 375ff, 444ff), though this is likely still to be controversial.

Subsequent work may clarify its interaction of brain lateralisation with culture, notably reading and writing direction (e.g. Dehaene, S., Bossini, S., & Giraux, P. (1993). The mental representation of parity and numerical magnitude. Journal of Experimental Psychology: General, 122, 371-396), though this relationship has exceptions and challenges (e.g. Karolis, V., Iuculano, T., & Butterworth, B. (2011). Mapping numerical magnitudes along the right lines: Differentiating between scale and bias. Journal of Experimental Psychology: General, 140(4), 693-706).

For example, would humans with more lateralised brains show a stronger bias? Would humans with reverse lateralisation show a R-L SNA?

Author response:

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

Reviewer #1 (Public review):

Functional lateralization between the right and left hemispheres is reported widely in animal taxa, including humans. However, it remains largely speculative as to whether the lateralized brains have a cognitive gain or a sort of fitness advantage. In the present study, by making use of the advantages of domestic chicks as a model, the authors are successful in revealing that the lateralized brain is advantageous in the number sense, in which numerosity is associated with spatial arrangements of items. Behavioral evidence is strong enough to support their arguments. Brain lateralization was manipulated by light exposure during the terminal phase of incubation, and the left-to-right numerical representation appeared when the distance between items gave a reliable spatial cue. The light-exposure induced lateralization, though quite unique in avian species, together with the lack of intense inter-hemispheric direct connections (such as the corpus callosum in the mammalian cerebrum), was critical for the successful analysis in this study. Specification of the responsible neural substrates in the presumed right hemisphere is expected in future research. Comparable experimental manipulation in the mammalian brain must be developed to address this general question (functional significance of brain laterality) is also expected.

We sincerely appreciate the Reviewer's insightful feedback and his/her recognition of the key contributions of our study.

Reviewer #2 (Public review):

Summary:

This is the first study to show how a L-R bias in the relationship between numerical magnitude and space depends on brain lateralisation, and moreover, how is modulated by in ovo conditions.

Strengths:

Novel methodology for investigating the innateness and neural basis of an L-R bias in the relationship between number and space.

We would like to thank the Reviewer for their valuable feedback and for highlighting the key contributions of our study.

Weaknesses:

I would query the way the experiment was contextualised. They ask whether culture or innate pre-wiring determines the 'left-to-right orientation of the MNL [mental number line]'.

We thank the Reviewer for raising this point, which has allowed us to provide a more detailed explanation of this aspect. Rather than framing the left-to-right orientation of the mental number line (MNL) as exclusively determined by either cultural influences or innate pre-wiring, our study highlights the role of environmental stimulation. Specifically, prenatal light exposure can shape hemispheric specialization, which in turn contributes to spatial biases in numerical processing. Please see lines 115-118.

The term, 'Mental Number Line' is an inference from experimental tasks. One of the first experimental demonstrations of a preference or bias for small numbers in the left of space and larger numbers in the right of space, was more carefully described as the spatial-numerical association of response codes - the SNARC effect (Dehaene, S., Bossini, S., & Giraux, P. (1993). The mental representation of parity and numerical magnitude. Journal of Experimental Psychology: General, 122, 371-396).

We have refined our description of the MNL and SNARC effect to ensure conceptual accuracy in the revised manuscript; please see lines 53-59.

This has meant that the background to the study is confusing. First, the authors note, correctly, that many other creatures, including insects, can show this bias, though in none of these has neural lateralisation been shown to be a cause. Second, their clever experiment shows that an experimental manipulation creates the bias. If it were innate and common to other species, the experimental manipulation shouldn't matter. There would always be an L-R bias. Third, they seem to be asserting that humans have a left-to-right (L-R) MNL. This is highly contentious, and in some studies, reading direction affects it, as the original study by Dehaene et al showed; and in others, task affects direction (e.g. Bachtold, D., Baumüller, M., & Brugger, P. (1998). Stimulus-response compatibility in representational space. Neuropsychologia, 36, 731-735, not cited). Moreover, a very careful study of adult humans, found no L-R bias (Karolis, V., Iuculano, T., & Butterworth, B. (2011), not cited, Mapping numerical magnitudes along the right lines: Differentiating between scale and bias. Journal of Experimental Psychology: General, 140(4), 693-706). Indeed, Rugani et al claim, incorrectly, that the L-R bias was first reported by Galton in 1880. There are two errors here: first, Galton was reporting what he called 'visualised numerals', which are typically referred to now as 'number forms' - spontaneous and habitual conscious visual representations - not an inference from a number line task. Second, Galton reported right-to-left, circular, and vertical visualised numerals, and no simple left-to-right examples (Galton, F. (1880). Visualised numerals. Nature, 21, 252-256.). So in fact did Bertillon, J. (1880). De la vision des nombres. La Nature, 378, 196-198, and more recently Seron, X., Pesenti, M., Noël, M.-P., Deloche, G., & Cornet, J.-A. (1992). Images of numbers, or "When 98 is upper left and 6 sky blue". Cognition, 44, 159-196, and Tang, J., Ward, J., & Butterworth, B. (2008). Number forms in the brain. Journal of Cognitive Neuroscience, 20(9), 1547-1556.

We sincerely appreciate the opportunity to discuss numerical spatialization in greater detail. We have clarified that an innate predisposition to spatialize numerosity does not necessarily exclude the influence of environmental stimulation and experience. We have proposed an integrative perspective, incorporating both cultural and innate factors, suggesting that numerical spatialization originates from neural foundations while remaining flexible and modifiable by experience and contextual influences. Please see lines 69–75.

We have incorporated the Reviewer’s suggestions and cited all the recommended papers; please see lines 47–75.

If the authors are committed to chicks' MN Line they should test a series of numbers showing that the bias to the left is greater for 2 and 3 than for 4, etc.

What does all this mean? I think that the paper should be shorn of its misleading contextualisation, including the term 'Mental Number Line'. The authors also speculate, usefully, on why chicks and other species might have a L-R bias. I don't think the speculations are convincing, but at least if there is an evolutionary basis for the bias, it should at least be discussed.

In the revised version of the manuscript, we have resorted to adopt the Spatial Numerical Association (SNA). We thank the Reviewer for this valuable comment.

We appreciated the Reviewer’s suggestion regarding the evolutionary basis of lateralization and have included considerations of its relevance in chicks and other species; please see lines 143-151 and 381-386.

This paper is very interesting with its focus on why the L-R bias exists, and where and why it does not.

We wish to thank the Reviewer again for his/her work.

Reviewer #1(Public review)

(1) Introduction needs to be edited to make it much more concise and shorter. Hypotheses (from line 67 to 81) and predictions (from line 107 to 124) must be thoroughly rephrased, because (a) general readers are not familiar with the hypotheses (emotional valence and BAFT), (b) the hypotheses may or may not be mutually exclusive, and therefore (c) the logical linkage between the hypotheses and the predicted results are not necessarily clear. Most general readers may be embarrassed by the apparently complicated logical constructs of this study. Instead, it is recommended that focal spotlight should be given to the issue of functional contributions of brain lateralization to the cognitive development of number sense.

We thank the Reviewer for these comments, which allowed us to improve the clarity of our hypotheses and predictions. We thoroughly rephrased them to ensure they are accessible to general readers and specified that the models may or may not be mutually exclusive. Additionally, we highlighted the functional contributions of brain lateralization to the cognitive development of number sense, addressing the suggested focal point. While we have shortened the introduction, we opted to retain essential background information to ensure readers are well-informed about the relevant scientific literature. Please review the entire introduction, particularly lines 84–118 and 218.

(2) In relation to the above (a), abbreviations need to be reexamined. MNL (mental number line) appears early on lines 27 and 49, whereas the possibly related conceptual term SNA appeared first on line 213, without specification to "spatial numerical association".

We thank the Reviewer for bringing this to our attention. We have addressed the suggestions, and the term SNA has been used specifically to refer to numerical spatialization in non-human animals. Please see lines 27-30.

(3) By the way, what difference is there between MNL and SNA? Please specify the difference if it is important. If not important, is it possible that one of these two is consistently used in this report, at least in the Introduction?

We clarified the distinction between MNL and SNA and have consistently used SNA in this report; please see lines 47-75.

(4) In relation to the above (a and b), clarification of the hypotheses and their abbreviations in the form of a table or a graphical representation will strongly reinforce the general readers' understanding. It is also possible that some of these hypotheses are discussed later in the Discussion, rather than in Introduction.

We appreciated this suggestion and have now clarified the hypotheses, also providing a table/graphical representation, aiming to enhance accessibility for general readers; please see lines 110-118, and 218.

(5) Figures 1 and 2 are transparent and easily understandable; however, the statistical details in the Results may bother the readers as the main points are doubly represented in Figures 1, 2, and Table 1. These (statistics and Table 1) may go to the supplementary file, if the editor agrees.

We would prefer to keep Table 1 and the statistical details as part of the main article to provide readers with a comprehensive overview of the experimental results. However, if the editors also suggest to move them to the supplementary file, we are open to making this adjustment.

(6) In Figure 1D and E, and text lines 139-140. Figure 1D shows that the chick is looking monocularly by the right eye, but the text (line 139) says "left eye in use. Is it correct?

We thank the reviewer for pointing out this incongruity. We have corrected the text to align with Figure 1D and E; please see lines 180-181.

(7) Methods. The behavioral experiment was initiated on Wednesday (8 a.m.; line 479), but at what age? At what post-hatch day was the experiment terminated? A simple graphical illustration of the schedule will be quite helpful.

We have added the requested details, specifying that experiments began on the third post-hatch day and ended on the fifth day; please see lines 533-539.

Additionally, we have included a graphical illustration of the schedule to enhance clarity; please see line 666.  

(8) Methods. How many chicks were excluded from the study in the course of Pre-training (line 525) and Training (line 535-536)? Was the exclusion rate high, or just negligible?

We appreciate the reviewer's suggestion. We have now included the number of subjects excluded during the training phase; please see lines 593-597.

We wish to thank the Reviewer again for his/her work.

Author response:

The following is the authors’ response to the original reviews

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

This work integrates two timepoints from the Adolescent Brain Cognitive Development (ABCD) Study to understand how neuroimaging, genetic, and environmental data contribute to the predictive power of mental health variables in predicting cognition in a large early adolescent sample. Their multimodal and multivariate prediction framework involves a novel opportunistic stacking model to handle complex types of information to predict variables that are important in understanding mental health-cognitive performance associations. 

Strengths: 

The authors are commended for incorporating and directly comparing the contribution of multiple imaging modalities (task fMRI, resting state fMRI, diffusion MRI, structural MRI), neurodevelopmental markers, environmental factors, and polygenic risk scores in a novel multivariate framework (via opportunistic stacking), as well as interpreting mental health-cognition associations with latent factors derived from partial least squares. The authors also use a large well-characterized and diverse cohort of adolescents from the ABCD Study. The paper is also strengthened by commonality analyses to understand the shared and unique contribution of different categories of factors (e.g., neuroimaging vs mental health vs polygenic scores vs sociodemographic and adverse developmental events) in explaining variance in cognitive performance 

Weaknesses: 

The paper is framed with an over-reliance on the RDoC framework in the introduction, despite deviations from the RDoC framework in the methods. The field is also learning more about RDoC's limitations when mapping cognitive performance to biology. The authors also focus on a single general factor of cognition as the core outcome of interest as opposed to different domains of cognition. The authors could consider predicting mental health rather than cognition. Using mental health as a predictor could be limited by the included 9-11 year age range at baseline (where many mental health concerns are likely to be low or not well captured), as well as the nature of how the data was collected, i.e., either by self-report or from parent/caregiver report. 

Thank you so much for your encouragement.

We appreciate your comments on the strengths of our manuscript.

Regarding the weaknesses, the reliance on the RDoC framework is by design. Even with its limitations, following RDoC allows us to investigate mental health holistically. In our case, RDoC enabled us to focus on a) a functional domain (i.e., cognitive ability), b) the biological units of analysis of this functional domain (i.e., neuroimaging and polygenic scores), c) potential contribution of environments, and d) the continuous individual deviation in this domain (as opposed to distinct categories). We are unaware of any framework with all these four features.

Focusing on modelling biological units of analysis of a functional domain, as opposed to mental health per se, has some empirical support from the literature. For instance, in Marek and colleagues’ (2022) study, as mentioned by a previous reviewer, fMRI is shown to have a more robust prediction for cognitive ability than mental health. Accordingly, our reasons for predicting cognitive ability instead of mental health in this study are motivated theoretically (i.e., through RDoC) and empirically (i.e., through fMRI findings). We have clarified this reason in the introduction of the manuscript.

We are aware of the debates surrounding the actual structure of functional domains where the originally proposed RDoC’s specific constructs might not fit the data as well as the data-driven approach (Beam et al., 2021; Quah et al., 2025). However, we consider this debate as an attempt to improve the characterisation of functional domains of RDoC, not an effort to invalidate its holistic, neurobiological and basicfunctioning approach. Our use of a latent-variable modelling approach through factor analyses moves towards a data-driven direction. We made the changes to the second-to-last paragraph in the introduction to make this point clear:

“In this study, inspired by RDoC, we a) focused on cognitive abilities as a functional domain, b) created predictive models to capture the continuous individual variation (as opposed to distinct categories) in cognitive abilities, c) computed two neurobiological units of analysis of cognitive abilities: multimodal neuroimaging and PGS, and d) investigated the potential contributions of environmental factors. To operationalise cognitive abilities, we estimated a latent variable representing behavioural performance across various cognitive tasks, commonly referred to as general cognitive ability or the gfactor (Deary, 2012). The g-factor was computed from various cognitive tasks pertinent to RDoC constructs, including attention, working memory, declarative memory, language, and cognitive control. However, using the g-factor to operationalise cognitive abilities caused this study to diverge from the original conceptualisation of RDoC, which emphasises studying separate constructs within cognitive abilities (Morris et al., 2022; Morris & Cuthbert, 2012). Recent studies suggest an improvement to the structure of functional domains by including a general factor, such as the g-factor, in the model, rather than treating each construct separately (Beam et al., 2021; Quah et al., 2025). The g-factor in children is also longitudinally stable and can forecast future health outcomes (Calvin et al., 2017; Deary et al., 2013). Notably, our previous research found that neuroimaging predicts the g-factor more accurately than predicting performance from separate individual cognitive tasks (Pat et al., 2023). Accordingly, we decided to conduct predictive models on the g-factor while keeping the RDoC’s holistic, neurobiological, and basic-functioning characteristics.”

Reviewer #2 (Public review):

Summary: 

This paper by Wang et al. uses rich brain, behaviour, and genetics data from the ABCD cohort to ask how well cognitive abilities can be predicted from mental-health-related measures, and how brain and genetics influence that prediction. They obtain an out-ofsample correlation of 0.4, with neuroimaging (in particular task fMRI) proving the key mediator. Polygenic scores contributed less. 

Strengths: 

This paper is characterized by the intelligent use of a superb sample (ABCD) alongside strong statistical learning methods and a clear set of questions. The outcome - the moderate level of prediction between the brain, cognition, genetics, and mental health - is interesting. Particularly important is the dissection of which features best mediate that prediction and how developmental and lifestyle factors play a role. 

Thank you so much for the encouragement. 

Weaknesses: 

There are relatively few weaknesses to this paper. It has already undergone review at a different journal, and the authors clearly took the original set of comments into account in revising their paper. Overall, while the ABCD sample is superb for the questions asked, it would have been highly informative to extend the analyses to datasets containing more participants with neurological/psychiatric diagnoses (e.g. HBN, POND) or extend it into adolescent/early adult onset psychopathology cohorts. But it is fair enough that the authors want to leave that for future work. 

Thank you very much for providing this valuable comment and for your flexibility.

For the current manuscript, we have drawn inspiration from the RDoC framework, which emphasises the variation from normal to abnormal in normative samples (Morris et al., 2022). The ABCD samples align well with this framework.

We hope to extend this framework to include participants with neurological and psychiatric diagnoses in the future. We have begun applying neurobiological units of analysis for cognitive abilities, assessed through multimodal neuroimaging and polygenic scores (PGS), to other datasets containing more participants with neurological and psychiatric diagnoses. However, this is beyond the scope of the current manuscript. We have listed this as one of the limitations in the discussion section:

“Similarly, our ABCD samples were young and community-based, likely limiting the severity of their psychopathological issues (Kessler et al., 2007). Future work needs to test if the results found here are generalisable to adults and participants with stronger severity.”

In terms of more practical concerns, much of the paper relies on comparing r or R2 measures between different tests. These are always presented as point estimates without uncertainty. There would be some value, I think, in incorporating uncertainty from repeated sampling to better understand the improvements/differences between the reported correlations. 

This is a good suggestion. We have now included bootstrapped 95% confidence intervals in all of our scatter plots, showing the uncertainty of predictive performance.

The focus on mental health in a largely normative sample leads to the predictions being largely based on the normal range. It would be interesting to subsample the data and ask how well the extremes are predicted. 

We appreciate this comment. Similar to our response to Reviewer 2’s Weakness #1, our approach has drawn inspiration from the RDoC framework, which emphasises the variation from normal to abnormal in normative samples (Morris et al., 2022). Subsampling the data would make us deviate from our original motivation. 

Moreover, we used 17 mental healh variables in our predictive models: 8 CBCL subscales, 4 BIS/BAS subscales and 5 UPSS subscales. It is difficult to subsample them. Perhaps a better approach is to test the applicability of our neurobiological units of analysis for cognitive abilities (multimodal neuroimaging and PGS) in other datasets that include more extreme samples. We are working on this line of studies at the moment, and hope to show that in our future work. 

Reviewer 2’s Weakness #4

A minor query - why are only cortical features shown in Figure 3? 

We presented both cortical and subcortical features in Figure 3. The cortical features are shown on the surface space, while the subcortical features are displayed on the coronal plane. Below is an example of these cortical and subcortical features from the ENBack contrast. The subcortical features are presented in the far-right coronal image.

We separated the presentation of cortical and subcortical features because the ABCD uses the CIFTI format (https://www.humanconnectome.org/software/workbenchcommand/-cifti-help). CIFTI-format images combine cortical surface (in vertices) with subcortical volume (in voxels). For task fMRI, the ABCD parcellated cortical vertices using Freesurfer’s Destrieux atlas and subcortical voxels using Freesurfer’s automatically segmented brain volume (ASEG).

Due to the size of the images in Figure 3, it may have been difficult for Reviewer 2 to see the subcortical features clearly. We have now added zoomed-in versions of this figure as Supplementary Figures 4–13.

Recommendations for the authors:

Reviewer #1 (Recommendations for the autors):

(1) In the abstract, could the authors mention which imaging modalities contribute most to the prediction of cognitive abilities (e.g., working memory-related task fMRI)? 

Thank you for the suggestion. Following this advice, we now mention which imaging modalities led to the highest predictive performance. Please see the abstract below.

“Cognitive abilities are often linked to mental health across various disorders, a pattern observed even in childhood. However, the extent to which this relationship is represented by different neurobiological units of analysis, such as multimodal neuroimaging and polygenic scores (PGS), remains unclear. 

Using large-scale data from the Adolescent Brain Cognitive Development (ABCD) Study, we first quantified the relationship between cognitive abilities and mental health by applying multivariate models to predict cognitive abilities from mental health in children aged 9-10, finding an out-of-sample r\=.36 . We then applied similar multivariate models to predict cognitive abilities from multimodal neuroimaging, polygenic scores (PGS) and environmental factors. Multimodal neuroimaging was based on 45 types of brain MRI (e.g., task fMRI contrasts, resting-state fMRI, structural MRI, and diffusion tensor imaging). Among these MRI types, the fMRI contrast, 2-Back vs. 0-Back, from the ENBack task provided the highest predictive performance (r\=.4). Combining information across all 45 types of brain MRI led to the predictive performance of r\=.54. The PGS, based on previous genome-wide association studies on cognitive abilities, achieved a predictive performance of r\=.25. Environmental factors, including socio-demographics (e.g., parent’s income and education), lifestyles (e.g., extracurricular activities, sleep) and developmental adverse events (e.g., parental use of alcohol/tobacco, pregnancy complications), led to a predictive performance of r\=.49. 

In a series of separate commonality analyses, we found that the relationship between cognitive abilities and mental health was primarily represented by multimodal neuroimaging (66%) and, to a lesser extent, by PGS (21%). Additionally, environmental factors accounted for 63% of the variance in the relationship between cognitive abilities and mental health. The multimodal neuroimaging and PGS then explained 58% and 21% of the variance due to environmental factors, respectively. Notably, these patterns remained stable over two years. 

Our findings underscore the significance of neurobiological units of analysis for cognitive abilities, as measured by multimodal neuroimaging and PGS, in understanding both a) the relationship between cognitive abilities and mental health and b) the variance in this relationship shared with environmental factors.”

(2) Could the authors clarify what they mean by "completing the transdiagnostic aetiology of mental health" in the introduction? (Second paragraph). 

Thank you. 

We intended to convey that understanding the transdiagnostic aetiology of mental health would be enhanced by knowing how neurobiological units of cognitive abilities, from the brain to genes, capture variations due to environmental factors. We realise this sentence might be confusing. Removing it does not alter the intended meaning of the paragraph, as we clarified this point later. The paragraph now reads:

“According to the National Institute of Mental Health’s Research Domain Criteria (RDoC) framework (Insel et al., 2010), cognitive abilities should be investigated not only behaviourally but also neurobiologically, from the brain to genes. It remains unclear to what extent the relationship between cognitive abilities and mental health is represented in part by different neurobiological units of analysis -- such as neural and genetic levels measured by multimodal neuroimaging and polygenic scores (PGS). To fully comprehend the role of neurobiology in the relationship between cognitive abilities and mental health, we must also consider how these neurobiological units capture variations due to environmental factors, such as sociodemographics, lifestyles, and childhood developmental adverse events (Morris et al., 2022). Our study investigated the extent to which a) environmental factors explain the relationship between cognitive abilities and mental health, and b) cognitive abilities at the neural and genetic levels capture these associations due to environmental factors. Specifically, we conducted these investigations in a large normative group of children from the ABCD study (Casey et al., 2018). We chose to examine children because, while their emotional and behavioural problems might not meet full diagnostic criteria (Kessler et al., 2007), issues at a young age often forecast adult psychopathology (Reef et al., 2010; Roza et al., 2003). Moreover, the associations among different emotional and behavioural problems in children reflect transdiagnostic dimensions of psychopathology (Michelini et al., 2019; Pat et al., 2022), making children an appropriate population to study the transdiagnostic aetiology of mental health, especially within a framework that emphasises normative variation from normal to abnormal, such as the RDoC (Morris et al., 2022).“

(3) It is unclear to me what the authors mean by this statement in the introduction: "Note that using the word 'proxy measure' does not necessarily mean that the predictive model for a particular measure has a high predictive performance - some proxy measures have better predictive performance than others". 

We added this sentence to address a previous reviewer’s comment: “The authors use the phrasing throughout 'proxy measures of cognitive abilities' when they discuss PRS, neuroimaging, sociodemographics/lifestyle, and developmental factors. Indeed, the authors are able to explain a large proportion of variance with different combinations of these measures, but I think it may be a leap to call all of these proxy measures of cognition. I would suggest keeping the language more objective and stating these measures are associated with cognition.” 

Because of this comment, we assumed that the reviewers wanted us to avoid the misinterpretation that a proxy measure implies high predictive performance. This term is used in machine learning literature (for instance, Dadi et al., 2021). We added the aforementioned sentence to ensure readers that using the term 'proxy measure' does not necessarily mean that the predictive model for a particular measure has high predictive performance. However, it seems that our intention led to an even more confusing message. Therefore, we decided to delete that sentence but keep an earlier sentence that explains the meaning of a proxy measure (see below).

“With opportunistic stacking, we created a ‘proxy’ measure of cognitive abilities (i.e., predicted value from the model) at the neural unit of analysis using multimodal neuroimaging.”

(4) Overall, despite comments from reviewers at another journal, I think the authors still refer to RDoC more than needed in the intro given the restructuring of the manuscript. For instance, at the end of page 4 and top of page 5, it becomes a bit confusing when the authors mention how they deviated from the RDoC framework, but their choice of cognitive domains is still motivated by RDoC. I think the chosen cognitive constructs are consistent with what is in ABCD and what other studies have incorporated into the g factor and do not require the authors to further justify their choice through RDoC. Also, there is emerging work showing that RDoC is limited in its ability to parse apart meaningful neuroimaging-based patterns; see for instance, Quah et al., Nature 2025 (https://doi.org/10.1038/s41467-025-55831-z). 

Thank you very much for your comment. We have addressed it in our Response to Reviewer 1’s summary, strengths, and weaknesses above. We have rewritten the paragraph to clarify the relevance of our work to the RDoC framework and to recent studies aiming to improve RDoC constructs (including that from Quah and colleagues).

(5) I am still on the fence about the use of 'proxy measures of cognitive abilities' given that it is defined as the predictive performance of mental health measures in predicting cognition - what about just calling these mental health predictors? Also, it would be easier to follow this train of thought throughout the manuscript. But I leave it to the authors if they decide to keep their current language of 'proxy measure of cognition'. 

Thank you so much for your flexibility. As we explained previously, this ‘proxy measures’ term is used in machine learning literature (for instance, Dadi et al., 2021). We thought about other terms, such as “score”, which is used in genetics, i.e., polygenic scores (Choi et al., 2020). and has recently been used in neuroimaging, i.e., neuroscore (Rodrigue et al., 2024). However, using a ‘score’ is a bit awkward for mental health and socio-demographics, lifestyle and developmental adverse events. Accordingly, we decided to keep the term ‘proxy measures’.

(6) It is unclear which cognitive abilities are being predicted in Figure 1, given the various domains that authors describe in their intro. Is it the g-factor from CFA? This should be clarified in all figure captions. 

Yes, cognitive abilities are operationalised using a second-order latent variable, the g-factor from a CFA. We now added the following sentence to Figure 1, 2, 4 to make this point clearer. Thank you for the suggestion:

“Cognitive abilities are based on the second-order latent variable, the g-factor, based on a confirmatory factor analysis of six cognitive tasks.”

(7) I think it may also be worthwhile to showcase the explanatory power cognitive abilities have in predicting mental health or at least comment on this in the discussion. Certainly, there may be a bidirectional relationship here. The prediction direction from cognition to mental health may be an altogether different objective than what the paper currently presents, but many researchers working in psychiatry may take the stance (with support from the literature) that cognitive performance may serve as premorbid markers for later mental health concerns, particularly given the age range that the authors are working with in ABCD. 

Thank you for this comment. 

It is important to note that we do not make a directional claim in these cross-sectional analyses. The term "prediction" is used in a machine learning sense, implying only that we made an out-of-sample prediction (Yarkoni & Westfall, 2017). Specifically, we built predictive models on some samples (i.e., training participants) and applied our models to test participants who were not part of the model-building process. Accordingly, our predictive models cannot determine whether mental health “causes” cognitive abilities or vice versa, regardless of whether we treat mental health or cognitive abilities as feature/explanatory/independent variables or as target/response/outcome variables in the models. To demonstrate directionality, we would need to conduct a longitudinal analysis with many more repeated samples and use appropriate techniques, such as a cross-lagged panel model. It is beyond the scope of this manuscript and will need future releases of the ABCD data.

We decided to use cognitive abilities as a target variable here, rather than a feature variable, mainly for theoretical reasons. This work was inspired by the RDoC framework, which emphasises functional domains. Cognitive abilities is the functional domain in the current study. We created predictive models to predict cognitive abilities based on a) mental health, b) multimodal neuroimaging, c) polygenic scores, and d) environmental factors. We could not treat cognitive abilities as a functional domain if we used them as a feature variable. For instance, if we predicted mental health (instead of cognitive abilities) from multimodal neuroimaging and polygenic scores, we would no longer capture the neurobiological units of analysis for cognitive abilities.

We now made it clearer in the discussion that our use of predictive models cannot provide the directional of the effects

“Our predictive modelling revealed a medium-sized predictive relationship between cognitive abilities and mental health. This finding aligns with recent meta-analyses of case-control studies that link cognitive abilities and mental disorders across various psychiatric conditions (Abramovitch et al., 2021; East-Richard et al., 2020). Unlike previous studies, we estimated the predictive, out-of-sample relationship between cognitive abilities and mental disorders in a large normative sample of children. Although our predictive models, like other cross-sectional models, cannot determine the directionality of the effects, the strength of the relationship between cognitive abilities and mental health estimated here should be more robust than when calculated using the same sample as the model itself, known as in-sample prediction/association (Marek et al., 2022; Yarkoni & Westfall, 2017). Examining the PLS loadings of our predictive models revealed that the relationship was driven by various aspects of mental health, including thought and externalising symptoms, as well as motivation. This suggests that there are multiple pathways—encompassing a broad range of emotional and behavioural problems and temperaments—through which cognitive abilities and mental health are linked.”

(8) There is a lot of information packed into Figure 3 in the brain maps; I understand the authors wanted to fit this onto one page, and perhaps a higher resolution figure would resolve this, but the brain maps are very hard to read and/or compare, particularly the coronal sections. 

Thank you for this suggestion. We agree with Reviewer 1 that we need to have a better visualisation of the feature-importance brain maps. To ensure that readers can clearly see the feature importance, we added a Zoom-in version of the feature-importance brain maps as Supplementary Figures 4 – 13.

(9) It would be helpful for authors to cluster features in the resting state functional connectivity correlation matrices, and perhaps use shorter names/acronyms for the labels. 

Thank you for this suggestion. 

We have now added a zoomed-in version of the feature importance for rs-fmri as Supplementary Figure 7 (for baseline) and 12 (for follow-up).

(10) Figures 4a) and 4b): please elaborate on "developmental adverse" in the title. I am assuming this is referring to childhood adverse events, or "developmental adversities". 

Thank you so much for pointing this out. We meant ‘developmental adverse events’. We have made changes to this figure in the current manuscript.

(11) For the "follow-up" analyses, I would recommend the authors present this using only the features that are indeed available at follow-up, even if the list of features is lower, otherwise it becomes a bit confusing with the mix of baseline and follow-up features. Or perhaps the authors could make this more clear in the figures by perhaps having a different color for baseline vs follow-up features along the y-axis labels. 

Thank you for this advice. We have now added an indicator in the plot to show whether the features were collected in the baseline or follow-up. We also added colours to indicate which type of environmental factors they were. It is now clear that the majority of the features that were collected at baseline, but were used for the followup predictive model, were developmental adverse events.

(12) Minor: Makowski et al 2023 reference can be updated to Makowski et al 2024, published in Cerebral Cortex. 

Thank you for pointing this out. We have updated the citation accordingly. 

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Author response:

The following is the authors’ response to the original reviews

We thank the reviewers and editors for their careful consideration of our work and pointing out areas where the current version lacked clarity or necessary experiments. Based on the reviews we have made the following significant changes to the revised version:

(1) Revised the text to focus on the distinct pathogen responses to indole in isolation versus fecal material.

We believe the key takeaway from this work is that the native context of a given effector, in this case indole, can elicit markedly different bacterial responses compared to the pure compound in isolation. This is because natural environments contain multiple, often conflicting, stimuli that complicate predictions of overall chemotactic behavior. For example, while indole has been proposed to mediate chemorepulsion and contribute to colonization resistance against enteric pathogens, our findings challenge this model. We provide evidence that feces, the intestinal source of indole, actually induces attraction, and that indole taxis may in fact benefit the pathogen through prioritizing niches with low microbial competition. Put another way, the biological reservoir of indole, fecal material, generates an attraction response but indole regulated the degree of attraction.

Most current understanding of chemotaxis is based on responses to individual, purified effectors. Our study highlights the need to investigate chemotactic responses in the presence of native mixtures, which better reflect the complexity of natural environments and may reveal new functional insights relevant for disease.

Reviewer comments indicated that these core points above were not clearly conveyed in the previous version, and that the manuscript's logical flow needed improvement. In this revised version, we have substantially rewritten the text and removed extraneous content to sharpen the focus on these central findings. We have also aligned our discussion more closely with the experimental data. While we appreciated the reviewers’ thoughtful suggestions, we chose not to expand on topics that fall outside the scope of our current experiments.

(2) Provide new chemotaxis data with mixtures of fecal effectors (Fig. 5).

Related to the above, the reviewers and editors brought up concerns that our discovery of pathogen fecal attraction was underexplored. Although we showed Tsr to be important for mediating fecal attraction, even the tsr mutant showed attraction to a lesser degree, and the reviewers noted that we did not identify what other fecal attractants could be involved.

Fecal material is a complex biological material (as noted by Reviewer 3) and contains effectors already characterized as chemoattractants and chemorepellents. It would be ideal to be able to perform some experiment where individual effectors are removed from fecal material and then quantify chemotaxis. We considered methods to do this but ultimately found this approach unfeasible. Instead, we employed a reductionist approach and developed a synthetic approximate of fecal material containing a mixture of known chemoeffectors at fecal-relevant concentrations (Fig. 5). We used this defined system as a way to test the specific roles of the Tsr effectors L-Ser (attractant) and indole (repellent) in relation to glucose, galactose, and ribose (sensed through the chemoreceptor Trg), and L-Asp (sensed through the chemoreceptor Tar). We chose these effectors as they have reasonable structure-function relationships established in prior work, and had information available about their concentrations in fecal material. We present these data as a new Figure 5, and also provide videos clearly showing the responses to each treatment (Movies 7-10).

This defined system provided several new insights that help understand and model indole taxis amidst other fecal effectors. First, the complete effector mixture, like fecal treatment, elicits attraction. Second, L-Ser is able to negate indole chemorepulsion in cotreatments of the two effectors, and also other chemoattractants in the absence of L-Ser also negate this repulsion, albeit to a lesser degree, helping to explain why the tsr mutant still shows attraction to fecal material. Lastly, we also show that the degree of attraction in this system is controlled by indole, with mixtures containing greater indole showing less attraction. We feel this is an important addition to the study because it provides a new view on how indole-taxis functions in pathogen colonization; rather than causing the pathogen to swim away (like pure indole does) indole helps the pathogen rank and prioritize its attraction to fecal effector mixtures, biasing navigation toward lower indolecontaining niches.

We also acknowledge that this defined system does not capture all possible interactions. Indeed, there are even a few chemoreceptors in Salmonella for which the sensing functions remain poorly understood. Nonetheless, we believe the data offer mechanistic context for understanding fecal attraction and suggest that factors beyond Tsr, L-Ser, and indole also contribute to the observed behaviors, aligning with other data we present.

(3) Provide new data that show that E. coli MG1655, and disease-causing clinical isolate strains of the Enterobacteriaceae Tsr-possessing species E. coli, Citrobacter koseri, and Enterobacter cloacae exhibit fecal attraction (Fig. 4).

An important new finding from this study is our direct test of whether indole-rich fecal material elicits repulsion. Contrary to expectations, given that for E. coli indole is a wellcharacterized strong chemorepellent, we show that fecal material instead elicits attraction in non-typhoidal Salmonella.

Reviewers raised the question of whether our observations regarding indole taxis and attraction to indole-rich feces in Salmonella are similar or relevant to E. coli. While a full dissection of indole taxis in E. coli is beyond the scope of this study and has been the focus of extensive prior research, we sought to address this point by examining whether other enteric pathogens respond similarly to the native indole reservoir, fecal material. To this end, we present new data demonstrating that, like S. Typhimurium, E. coli and other representative enteric pathogens and pathobionts possessing Tsr are also attracted to indole-rich feces (Fig. 4, Movies 4–6, Fig. S4).

Notably, these new results represent some of the first characterizations of chemotactic behavior in the clinical isolates we examined, including E. coli NTC 9001 (a urinary tract infection isolate), Citrobacter koseri, and Enterobacter cloacae, adding another element of novelty to this work.

(4) Repeated all of the explant Salmonella Typhimurium infection studies and added a new experimental control competition between WT and an invasion-deficient mutant (invA).

Although our new colonic explant system was noted as a novelty and strength of this work, it was also seen as a weakness in that some of the results were surprising and difficult to link to chemotactic behavior. Reviewer 3 also brought up the need to be clear about our usage of the term ‘invasion’ in reference to S. Typhimurium entering nonphagocytic host cells, and requested we test an invasion-inhibited mutant (which we do in new experiments, now Fig. S1). We also note that some of the interpretations of these data were made challenging by result variability.

To help address these issues we performed additional replicates for all of our explant experiments (contained within Figure 1, Fig. S1-S2, and Data S1), to provide greater power for our analyses. These new data provide a clearer view of this system that revise our interpretations from the prior version of this study. While treatment with indole alone does suppress the WT advantage over chemotactic mutants for both total colonization and cellular invasion, essentially all other treatments have a similar result with a timedependent increase in both colonization and invasion, dependent on chemotaxis and Tsr. A remaining unique feature of fecal treatment is an increase in the cellular invaded population of the cells at 3 h post-infection. As requested by Reviewer 3, we provide new experimental data showing that in competitions between WT and an invasion-deficient mutant (invA), with fecal material pretreatment, we see the WT has an advantage only for the gentamicin-treated qualifications, providing some support that our model selects for the invaded sub-population. Although we note that the invA still can invade through alternative mechanisms (as discussed in earlier work such as here: https://doi.org/10.1111/1574-6968.12614), so the relative amount of presumed cellular invasion is less than WT, and not zero, in our experiments (Fig. S1).

One point of confusion in the previous version of the text was the assay design for the explant experiments, which is important to understand in order to interpret the results. During the explant infection bacteria are not immersed in the effector treatment solution, rather the tissue is soaked in the effector solution beforehand and then exposed to a 300 µl buffer solution containing the bacteria. This means that the bacteria experience only the residue of that treatment at concentrations far lower. We have added clarity about this through revising Fig. 1 to include a conceptual diagram of the assay (Fig. 1C), and added a new supplementary Fig. S5 that summarizes the explant data in this same conceptual model. We provide detail on the method in the text in lines 115-137. In describing the results, and synthesizing them in the discussion, we now state:

Line 112: “This establishes a chemical gradient which we can use to quantify the degree to which different effector treatments are permissive of pathogen association with, and cellular invasion of, the intestinal mucosa (Fig. 1C).”

And, a new section in the discussion devoted to describing the explant infections:

Line: 366: “Our explant experiments can be thought of as testing whether a layer of effector solution is permissive to pathogen entry to the intestinal mucosa, and whether chemotaxis provides an advantage in transiting this chemical gradient to associate with, and invade, the tissue (Fig. 1C, Fig. S5).”

As mentioned above, we have honed the text to focus on the disparity between the effects of indole alone versus treatments with indole-rich feces to help clarify how these data advance our understanding of the indole taxis in directing pathogenesis. While our explant studies still confirm the role of factors other than L-Ser, indole, and Tsr in directing Salmonella infection and cellular invasion, we now include further analyses of other fecal effectors (described above) that provide some insights into how fecal effectors have some redundancy in their impact.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study shows, perhaps surprisingly, that human fecal homogenates enhance the invasiveness of Salmonella typhimurium into cells of a swine colonic explant. This effect is only seen with chemotactic cells that express the chemoreceptor Tsr. However, two molecules sensed by Tsr that are present at significant concentrations in the fecal homogenates, the repellent indole and the attractant serine, do not, either by themselves or together at the concentrations in which they are present in the fecal homogenates, show this same effect. The authors then go on to study the conflicting repellent response to indole and attractant response to serine in a number of different in vitro assays.

Strengths:

The demonstration that homogenates of human feces enhance the invasiveness of chemotactic Salmonella Typhimurium in a colonic explant is unexpected and interesting. The authors then go on to document the conflicting responses to the repellent indole and the attractant serine, both sensed by the Tsr chemoreceptor, as a function of their relative concentration and the spatial distribution of gradients.

Thank you for your summary and acknowledgement of the strengths of this work. We hope the revised text and additional data we provide further improve your view of the study.

Weaknesses:

The authors do not identify what is the critical compound or combination of compounds in the fecal homogenate that gives the reported response of increased invasiveness. They show it is not indole alone, serine alone, or both in combination that have this effect, although both are sensed by Tsr and both are present in the fecal homogenates. Some of the responses to conflicting stimuli by indole and serine in the in vitro experiments yield interesting results, but they do little to explain the initial interesting observation that fecal homogenates enhance invasiveness.

Thank you for noting these weaknesses. We have provided new data using a defined mixture of fecal effectors to further investigate the roles of L-Ser, indole, and other effectors present in feces that we did not initially study. We have refined our discussion of these results to hopefully improve the clarity of our conclusions. We show now both in explant studies (Fig. 1I) and chemotaxis responses to a defined fecal effector system (Fig. 5) that L-Ser is able to abolish both the suppression of indole-mediated WT advantage and also indole chemorepulsion, respectively. We also show the latter can be accomplished by other fecal chemoattractants (Fig. 5). This is in line with our earlier finding that Tsr, the sensor of indole and L-Ser, is an important mediator of fecal attraction but not the sole mediator.

As this reviewer points out, there are indeed other factors mediating invasion that we do not elucidate here, but we do note these possibilities in the text (lines: 125-127):

“This benefit may arise from a combination of factors, including sensing of host-emitted effectors, redox or energy taxis, and/or swimming behaviors that enhance infection [5,30,31,35].”

Reviewer #2 (Public review):

Summary:

The manuscript presents experiments using an ex vivo colonic tissue assay, clearly showing that fecal material promotes Salmonella cell invasion into the tissue. It also shows that serine and indole can modulate the invasion, although their effects are much smaller. In addition, the authors characterized the direct chemotactic responses of these cells to serine and indole using a capillary assay, demonstrating repellent and attractant responses elicited by indole and serine, respectively, and that serine can dominate when both are present. These behaviors are generally consistent with those observed in E. coli, as well as with the observed effects on cell invasion.

Strengths:

The most compelling finding reported here is the strong influence of fecal material on cell invasion. Also, the local and time-resolved capillary assay provides a new perspective on the cell's responses.

Thank you for acknowledging these aspects of the study.

Weaknesses:

The weakness is that indole and serine chemotaxis does not seem to control the fecal-mediated cell invasion and thus the underlying cause of this effect remains unclear.

In addition, the fact that serine alone, which clearly acts as a strong attractant, did not affect cell invasion (compared to buffer) is somewhat puzzling. Additionally, wild-type cells showed nearly a tenfold advantage even without any ligand (in buffer), suggesting that factors other than chemotaxis might control cell invasion in this assay, particularly in the serine and indole conditions. These observations should probably be discussed.

Addressed above.

Final comment. As shown in reference 12, Tar mediates attractant responses to indole, which appear to be absent here (Figure 3J). Is it clear why? Could it be related to receptor expression?

Thank you for noting this. We now mention this in the discussion. In the course of this work, we encountered a number of apparent inconsistencies, or differences, between what we were observing with S. Typhimurium and what had been reported previously in studies of Tsr function in E. coli. We indeed noted that some studies had investigated a role of Tar for indole taxis (in E. coli), hence why we determined whether, and confirmed, that Tsr is required for indole taxis for S. Typhimurium (Fig. 6).

We do not know the reason for this apparent difference between the two bacteria, but we have previously shown with our same strain of S. Typhimurium IR715, under the same growth assay, and preparation protocol, that L-Asp is a strong chemoattractant for both WT and the tsr mutant (see Glenn et al. 2024, eLife, Fig. 5G: https://iiif.elifesciences.org/lax:93178%2Felife-93178-fig5-v1.tif/full/1500,/0/default.jpg).

This supports that this strain of Salmonella indeed has a functional Tar present and is expressed at a level sufficient for sensing L-Asp. So, if Tar generally mediates indole sensing we do not know why we would not see that in Salmonella. Hence, we do not see any role for Tar in indole chemorepulsion in our strain of study, which is different than reported for E. coli, but we cannot confirm the reason.

Reviewer #3 (Public review):

Summary:

In this manuscript, Franco and colleagues describe careful analyses of Salmonella chemotactic behavior in the presence of conflicting environmental stimuli. By doing so, the authors describe that this human pathogen integrates signals from a chemoattractant and a chemorepellent into an intermediate "chemohalation" phenotype.

Strengths:

The study was clearly well-designed and well-executed. The methods used are appropriate and powerful. The manuscript is very well written and the analyses are sound. This is an interesting area of research and this work is a positive contribution to the field.

Thank you for your comments.

Weaknesses:

Although the authors do a great job in discussing their data and the observed bacterial behavior through the lens of chemoattraction and chemorepulsion to serine and indole specifically, the manuscript lacks, to some extent, a deeper discussion on how other effectors may play a role in this phenomenon. Specifically, many other compounds in the mammalian gut are known to exhibit bioactivity against Salmonella. This includes compounds with antibacterial activity, chemoattractants, chemorepellers, and chemical cues that control the expression of invasion genes. Therefore, authors should be careful when making conclusions regarding the effect of these 2 compounds on invasive behavior.

Thank you for this comment, and we agree with your point. We hope we have revised the text and provided new data to address your concern. We have also chosen for clarity to keep our text close to our experimental data and so have refrained from speculating about some topics, even though you are absolutely correct about the immense complexity of these systems.

It is important that the word invasion is used in the manuscript only in its strictest sense, the ability displayed by Salmonella to enter non-phagocytic host cells. With that in mind, authors should discuss how other signals that feed into the control of Salmonella invasion can be at play here.

Thank you for your recommendation. We have revised the text to hopefully be clearer on our meaning of invasion in regard to Salmonella entering non-phagocytic host cells, essentially changing our usage to ‘cellular invasion’ throughout.

It is also a commonly-used phrase in reference to enteric infections and the colonization resistance conferred by the microbiome to refer to ‘invading pathogens’ (i.e. invasion in the sense of a new microbe colonizing the intestines), For instance, this recent review on Salmonella makes use of the term invading pathogen (https://www.nature.com/articles/s41579-021-00561-4). We acknowledge the confusion by this dual use of the term. We have mostly removed our statements using invasion in this context. We hope our language is clearer in this revised version.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

It was difficult to understand the true intent or importance of the study described in this manuscript. The first figure in the paper showed that a Salmonella Typhimurium strain lacking either CheY, and thus incapable of any chemotaxis, or the Tsr chemoreceptor, and thus incapable of sensing serine or indole, was modestly inferior to the wild-type version of that strain in invading the cells of a swine colonic explant. It then showed that, in the presence of a human fecal homogenate, the wild-type strain had a much greater advantage in invading the colonic cells. Thus, the presence of the fecal homogenate significantly increased invasiveness in a way that depends on chemotaxis and the Tsr chemoreceptor.

As human feces were determined to contain 882 micromolar indole and 338 micromolar serine, the effects of those concentrations of either indole or serine alone or in combination were tested. The somewhat surprising finding was that neither indole nor serine alone nor in combination changed the result from the experiment done with just buffer in the colonic explant.

The clear conclusion of this initial study is that both chemotaxis in general and chemotaxis mediated by Tsr improve the invasiveness of S. Typhimurium. They provide a much bigger advantage in the presence of human feces. However, two molecules present in the feces that are sensed by Tsr, serine, and indole, seem to have no effect on invasiveness either alone or in combination.

At this point, the parsimonious interpretation is that there is something else in human feces that is responsible for the increased invasiveness, and the authors acknowledge this possibility. However, they do not take what appears to be the obvious approach: to look for additional factors in human feces that might be responsible, either by themselves or in combination with indole and/or serine, for the increased invasiveness. Instead, they carry out a detailed examination of the counteracting effects of indole as a repellent and of serine as an attractant as a function of their relative concentrations and their spatial distributions.

Thank you for your comments. In our revised version, we have undertaken some additional studies of other fecal effectors that help better understand the relationship between L-Ser and indole, but also the roles of other chemoattractants (glucose, galactose, ribose, L-Asp) in mediating fecal attraction (Fig. 5). We agree with the reviewer and conclude that fecal attraction and the cell invasion phenotype mediated by fecal treatment are influenced by factors other than only Tsr, indole, and L-Ser. Our new data do show that L-Ser is sufficient to block both the invasion suppression effects of indole (negating the WT advantage) and also indole chemorepulsion, therefore making our detailed examination of the counteracting effects more relevant for understanding this system.

What they find is what other studies have shown, primarily with S. Typhimurium's relative, the gamma-proteobacterium Escherichia coli.

At high indole and low serine concentrations, the repulsion by indole wins out. At low indole and high serine concentrations, attraction by serine wins out. What is perhaps novel is what happens at an intermediate ratio of concentrations. Repulsion by indole dominates at short distances from the source, so there is a zone of clearing. At longer distances, attraction by serine dominates, so there is an accumulation of cells in a "halo" around the zone of clearing. Thus, assuming that serine and indole diffuse equally, the repulsive effect of indole dominates until its concentration falls below some critical level at which the concentration of serine is still high enough to exert an attractive effect.

They go on to show, using ITC, that serine binds to the periplasmic ligand-binding domain (LBD) of Tsr, something that has been studied extensively with very similar E. coli Tsr.

They also show that indole does not bind to the Tsr LBD, which also is known for E. coli Tsr.

This would be newsworthy only if the results were different for S. Typhimurium than for E. coli. As it is, it is merely confirmatory of something that was already known about Tsr of enteric bacteria.

An idea that the authors introduce, if I understand it correctly, is that a repellent response to something in feces, perhaps indole, drives S. Typhimurium chemotactically competent cells out of the colonic lumen and promotes invasion of the bacteria into the cells of the colonic lining. If the feces contain both an attractant and a repellent, bacteria might be attracted by the feces to the lining of the intestine and then enter the colonic cells to escape a repellent, perhaps indole. That is an interesting proposition.

In summary, I think that the initial experimental approach is fine. I do not understand the failure to follow up on the effect of the fecal homogenates in promoting invasion by chemotactic bacteria possessing Tsr. It seems there must be something else in the homogenates that is sensed by Tsr. Other amino acids and related compounds are also sensed by Tsr. Perhaps it is energy or oxygen taxis, which is partially mediated by Tsr, as the authors acknowledge.

Much of the work reported here is quasi-repetitive with work done with E. coli Tsr. Minimally, previous work on E. coli Tsr should be explained more thoroughly rather than dealt with only as a citation.

Thank you for your comments.

We would like to confirm our agreement that E. coli and S. enterica indeed possess similarities. They are Gammaproteobacteria and inhabit/infect the gut. But also we note they diverged evolutionarily during the Jurassic period (ca. 140 million years ago, see: PMC94677). In the context of colonizing humans, the former is a pathobiont, indoleproducer, and a native member of the microbiome, whereas the latter is a frank pathogen and does not produce indole. Hence, there are many reasons to believe one is not an approximate of the other, especially when it comes to causing disease.

We agree that much of what is known about indole taxis has come from excellent studies in well-behaved laboratory strains of E. coli, a powerful model. We believe that expanding this work to include clinically relevant pathogens is important for understanding its role in human disease. In this study, we contribute to that broader understanding by providing new mechanistic insights into Tsr-mediated indole taxis in S. Typhimurium, along with data demonstrating fecal attraction in other enteric pathogens and pathobionts. These findings help define a more general role for Tsr in enteric colonization and disease. While some of our results indeed confirm and extend prior findings, we respectfully believe that such confirmation in relevant pathogenic strains adds value to the field.

Regarding our ITC studies, to our knowledge no other study has investigated, using ITC whether indole does or does not bind the LBD (which we show it does not), nor investigated whether it interferes with L-Ser sensing (which we show it does not). Hence, these are not duplicate findings, although we do acknowledge this leaves the mechanism of indolesensing undiscovered. If we are incorrect in this regard, please provide us a citation and we will be happy to include it and revise our comments.

We now clarify in the text on lines 378-381: “While these leave the molecular mechanism of indole-sensing unresolved, it does eliminate two possibilities that have not, to our knowledge, been tested previously. Overall, our data add support to the hypothesis that a non-canonical sensing mechanism is employed by Tsr to respond to indole [8,18,69].”

Lastly, as noted by the reviewer, and which we mention in the text, essentially all prior studies on indole taxis were conducted in E. coli, and this is not what is new and novel about the work we present, which is focused on S. Typhimurium and testing the prediction that fecal indole protects against pathogen invasion. We have added in a few additional points of comparisons between our results and prior studies. While we appreciate that much understanding has come from E. coli as a model for indole taxis, we feel discussing prior work in extensive detail would be more suitable for a review and would occlude our new findings about Salmonella, and other enterics.

In an earlier version of the manuscript, we included more background on E. coli indole taxis. However, we found that the historical literature in this area was somewhat inconsistent, with different assays using varying time points and indole concentrations, often leading to results that were difficult to reconcile. Providing sufficient context to explain these discrepancies required considerable space and, ultimately, detracted from the focus of our current study. Hence, we have only brought in comparisons with E. coli where most relevant to the present work. Also, we provide new data that E. coli also exhibits fecal attraction, and so there is reason to believe the mechanisms we study here are also relevant to that system.

Some minor points

(1) Hyphens are not needed with constructs like "naturally occurring" or "commonly used".

Thank you. Revisions made throughout.

(2) The word "frank" as in "frank pathogen" seems odd. It seems "potent" would be better.

Thank you for this comment. Per your recommendation, we have removed this term.

The term ‘frank pathogen’ is standard usage in the field of bacterial pathogenesis in reference to a microbe that always causes disease in its host (in this case humans) and causes disease in otherwise healthy hosts (example: https://www.sciencedirect.com/science/article/pii/S1369527420300345). We actually used this specific term to distinguish an aspect of novelty of our study because E. coli can, sometimes, be a pathogen (i.e. a pathobiont) and of course E. coli indole taxis has been previously studied. Ours is the first study of indole taxis in a frank pathogen.

(3) It is unnecessary to coin a new word, chemohalation, to describe a phenomenon that is a simple consequence of repulsion by higher concentrations of a repellent and attraction by lower concentrations of attractant to generate a halo pattern of cell distribution.

Thank you for your opinion on this. We have softened our statements on this point, and in the newly revised version of the text less space is devoted to this idea. We now state in line 304-307:

“There exists no consensus descriptor for taxis of this nature, and so we suggest expanding the lexicon with the term “chemohalation,” in reference to the halo formed by the cell population, and which is congruent with the commonly-used terms chemoattraction and chemorepulsion.”

We appreciate the reviewer’s perspective and agree that the behavior we describe can be viewed as the result of competing attractant and repellent cues. However, we find that the traditional framework of “chemoattraction” and “chemorepulsion” is often insufficient to describe the spatial positioning behaviors we observe in our system. In our experience presenting and discussing this work, especially with audiences outside the chemotaxis field, it has been challenging to convey these dynamics clearly using only those two terms.

For this reason, we introduced the term chemohalation to describe this more nuanced behavior, which appears to reflect a balance of signals rather than a simple unidirectional response. More bacteria enter the field of view, but they are clearly positioned differently than regular ‘chemoattraction.’ We also note that Reviewers 2 and 3 did not raise concerns about the term, and after careful consideration, we have opted to retain it in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

Lines 143-156 seem somewhat overcomplicated and may be confusing. For example: in line 143: "However, when colonic tissue was treated with purified indole at the same concentration, the competitive advantage of WT over the chemotactic mutants was abolished compared to fecaltreated tissue...". But indole was tested alone, so it did not abolish the response; rather the absence of fecal material did.

We appreciate your point. We have made revisions throughout to help improve the clarity of how we discuss the explant infection data and provide new visuals to help explain the experiment and data (Fig. 1C, Fig. S5).

Reviewer #3 (Recommendations for the authors):

(1) Line 46 - Are references 9-11 really about topography?

Thank you. You are correct. Revised and eliminated this statement.

(2) Lines 87-89 - It seems to me that a bit more information on this would be helpful to the reader.

In our revision of the text, to make it more centered on our primary findings of the differences between indole taxis when indole is the sole effector versus amidst other effectors, we have removed this section.

(3) Line 112 - When mentioning the infection of the cecum and colon, authors should specify that this is in mice.

Thank you for this comment. In our revised version we provide references both for animal model infections and work in human patients (ex: https://www.sciencedirect.com/science/article/abs/pii/S0140673676921000)

We have revised our statement to be (Line 99-100: “Salmonella Typhimurium preferentially invades tissue of the distal ileum but also infects the cecum and colon in humans and animal models [42–46].”

(4) Lines 122-123 - Authors state that "This experimental setup simulates a biological gradient in which the effector concentration is initially highest near the tissue and diffuses outward into the buffer solution.". Was this experimentally demonstrated? If not, authors should tone this down.

We have removed this comment and instead present a conceptual diagram illustrating this idea (Fig. 1C). Also, addressed by above.

(5) When looking at the results in Figure 1, I wonder what the results of this experiment would be if the authors tested an invasion mutant of Salmonella. In a strain that is able to perform chemotaxis (attraction and repulsion) but unable to actively invade, would there be a phenotype here? Is it possible that the fecal material affects cellular uptake of Salmonella, independently of active invasion? I don't think the authors necessarily need to perform this experiment, but I think it could be informative and this possibility should at least be discussed.

Thank you for your comments and suggestions. We have included new data of an explant co-infection experiment with WT and an invasion-deficient mutant invA (Fig. S1). Under these conditions, WT exhibits an advantage in the gentamicin-treated homogenate, but not the untreated homogenate, suggestive of an advantage in cellular invasion.

However, we did not repeat all experiments with this genetic background. We felt that would be outside the scope of this work, and would probably require dual chemotaxis/invA deletions to assess the impact of each, which also could be difficult to interpret. The hypothesis mentioned by the Reviewer is possible, but we were not able to devise a way to test this idea, as it seems we would need to deactivate all other mechanisms of Salmonella invasion.

(6) Lines 137-140 - Because this is a competition experiment and results are plotted as CI, the reader can't readily assess the impact of human feces on invasion by WT Salmonella.

Thank you for pointing this out. We want to mention that the data are plotted as CI in the main text, but the supplemental contains the disaggregated CFU data (Fig. S1-2) and the numerical values (Data S1).

Please include the magnitude of induction in this sentence, compared to the buffer control.

The text of this section has been changed to account for new data.

Additionally, although unlikely, the presence of the chemotaxis mutants in the same infection may be a confounding factor. In order to irrefutably ascertain that feces induces invasion, I suggest authors perform this experiment with the wildtype strain (and mutant) alone in different conditions.

Thank you for this suggestion, although after careful consideration we have decided not to repeat these explant studies with monoinfections. Coinfections are a common tool in Salmonella pathogenesis studies, including prior chemotaxis studies which our work builds upon (ex: https://pmc.ncbi.nlm.nih.gov/articles/PMC3630101/). The explant experiments, even controlling as many aspects as we did, still show lots of variability and one way to mitigate this is through competition experiments so that each strain experiences the same environment.

We agree that a cost of this approach is that one strain may affect the other, or may alter the environment in a way that impacts the other. Thus, the resulting data must also be understood through this lens. We have revised the text to stay closer to the competitive advantage phenotype.

(7) Line 150 - Authors state that bacterial loads are similar. However, authors should perform and report statistical analyses of these comparisons, at least in the supplementary data.

We have removed this statement as requested. We do note, however, that the mean CFU values across treatments at identical time points appear qualitatively similar, which is an observation that does not require statistical testing.

(8) Lines 154-154 - This seems incorrect, as the effect observed with the mixture of indole and serine is very similar to the addition of serine alone. Therefore, there was no "neutralization" of their individual effects.

We have revised this statement.

(9) Line 159-161 - I strongly suggest authors reword this sentence. I don't think this is the best way to describe these results. The stronger phenotype observed was with the fecal material. Therefore, it is the indole (alone) condition that does not "elicit a response". Focusing on indole too much here ignores everything else that is present in feces and also the fact that there was a drastic phenotype when feces were used.

Thank you for your opinion on this. We believe this is one of the ways in which our earlier draft was unclear. It was actually a primary motivation of this work to test whether there were differences in pathogen infection, mediated by chemotaxis, in the presence of indole as a singular effector or in its near-native context in fecal material, and our revised text centers our study around this question. We believe this distinction is important for the reasons mentioned earlier.

Relative to buffer treatment, indole changes the behavior of the system, eliminating the WT advantage, and this is the effect we refer to. We have made many revisions to the text of these sections and hope it better conveys this idea. We expect we may still have differences regarding the interpretation of these results, but regardless, thank you for your suggestions and we have tried to implement them to improve the clarity of the text.

(10) Line 162 - Again, I disagree with this. Indole does not have an effect to be cancelled out by serine.

Addressed above, and this text has been changed. Also, we provide new chemotaxis data that at fecal-relevant concentrations of indole and L-Ser, indole chemorepulsion is overridden (Fig. 5).

(11) Lines 166-168 - Again, this is a skewed analysis. Indole and serine could not possibly provide an "additive effect" since they do not provide an effect alone. There is nothing to be added.

This text has been deleted.

(12) Lines 168-170 - Most of the citations provided to this sentence are inadequate. Our group has previously shown that the mammalian gut harbors thousands of small molecules (Antunes LC et al. Antimicrob Agents Chemother 2011). You obviously do not have to cite our work, but there is significant literature out there about the complexity of the gut metabolome.

Thank you for this comment. We have revised this particular text, but do make mention of potential other effectors driving these effects, which was also requested by the other reviewers.

Your work and others indeed support there being thousands of molecules in the gut, but our work centers on chemotaxis, and bacteria have a small number of chemoreceptors and only sense a very tiny fraction of these molecules as effectors. Since the impacts of infection of the explants depends on chemotaxis, we keep our comments restricted to those, but agree that there are likely many interactions involved, such as those impacting gene expression.

Please note our more detailed description of the explant infection assay (and shown in Fig. 1C) that may change your view on the significance of non-chemotaxis effects. The bacteria only experience the effectors at low concentration, not the high concentration that is used to soak and prepare the tissue prior to infection.

(13) Figure 2 - The letter 'B' from panel B is missing.

Thank you very much for bringing this oversite to our attention. We have fixed this.

(14) Legend of Figure 3 - Panel J is missing a proper description. Figure legends need improvement in general, to increase clarity.

Thank you for noting this. This is now Fig. 6E. We have provided an additional description of what this panel shows. We have edited the legend text to read: “E. Shows a quantification of the relative number of cells in the field of view over time following treatment with 5 mM indole for a competition experiment with WT and tsr (representative image shown in F).”

We also have made other edits to figure legends to improve their clarity and add additional experimental details and context. By breaking up larger figures into smaller figures, we also hope to have improved the clarity of our data presentation.

(15) Lines 264-265 - Maybe I am missing something, but I do not see the ITC data for serine alone.

We have clarified in the text that this was measured in our previous study https://elifesciences.org/articles/93178). The present study is a ‘Research Advance’ article format, and so builds on our prior observation.

We have revised the text to read: “To address these possibilities, we performed ITC of 50 μM Tsr LBD with L-Ser in the presence of 500 μM indole and observed a robust exothermic binding curve and KD of 5 µM, identical to the binding of L-Ser alone, which we reported previously (Fig. 6H) [36].”

(16) Lines 296-297 - What is the effect of these combinations of treatments on bacterial cells? I commend the authors for performing the careful growth assays, but I wonder if bacterial lysis could be a factor here. I am not doubting the effect of chemotaxis, but I am wondering if toxic effects could be a confounding factor. For instance, could it be that the "avoidance" close to the compound source and subsequent formation of a halo suggest bacterial death and lysis? I suggest the authors perform a very simple experiment, where bacteria are exposed to the compounds at various concentrations and combinations, and cells are observed over time to ensure that no bacterial lysis occurs.

Thank you for mentioning this possibility. If we understand correctly, the Reviewer is asking if the chemohalation effect we report could be from the bacteria lysing near the source. Our data actually argue against this possibility through a few lines of evidence.

First, if this were the case in experiments with the cheY mutant, we would also see an effect near the source. But actually, in experiments with either the cheY mutant or the tsr mutant, neither of which can sense indole, the bacteria just ignore the stimulus and show an even distribution (see current Fig. 6F).

Second, our calculations suggest that in the chemotaxis assay (CIRA), the bacteria only experience rather low local concentration of indole, mostly I the nM concentration range, because as soon as the effector treatment is injected into the greater volume, it is immediately diluted. This means the local concentration is far below what we see inhibits growth of the cells in the long run and may not be toxic (Fig. 7, Fig. S3).

Lastly, in the representative video presented we can observe individual cells approach and exit the treatment (Movie 11). Due to the above we have not performed additional experiments to test for lysis.

(17) Lines 310-311 - Isn't this the opposite of the model you propose in Figure 5? The higher the concentration of indole in the lumen the more likely Salmonella is to swim away from it and towards the epithelium, favoring invasion, no?

We appreciate the opportunity to clarify this point and apologize for any confusion caused. In response, we have revised the text to place less emphasis on chemohalation, and the specific statement and model in question have now been removed. Instead, we provide a summary of our explant data in light of the other analyses in the study (Fig. S5).

What we meant here was in relation to the microscopic level, not whether or not a host/intestine is colonized. To put it another way, we think our data supports that the pathogen colonizes and infects the host regardless of indole presence, but it uses indole as a means to prioritize which tissues are optimal for colonization at the microscopic level. The prediction made by others was that bacteria swim away from indole source and therefor this could prevent or inhibit pathogen colonization of the intestines, which our data does not support.

(18) Lines 325-326 - Maybe, but feces also contain several compounds with antibacterial activity, as well as other compounds that could elicit chemorepulsion. This should be stated and discussed.

We have removed this statement since we did not explicitly test the growth of the bacteria with fecal treatments. We have refrained from speculating further in the text since we do not have direct knowledge of how that relationship with differing effectors could play out.

We agree with the reviewer that the growth assays are reductionist and give insight only into the two effectors studied. We provide evidence from several different types of enterics that they all exhibit fecal attraction, and it seems unlikely the bacteria would be attracted to something deleterious, but we have not confirmed.

(19) Lines 371-374 - How preserved (or not) is the mucus layer in this model? The presence of an inhibitory molecule in the lumen does not necessarily mean that it will protect against invasion. It is possible that by sensing indole in the lumen Salmonella preferentially swims towards the epithelium, thus resulting in enhanced evasion.

The text in question has been removed. However, we acknowledge the reviewer’s point, and that these explant tissues do not fully model an in vivo intestinal environment. Other than a gentle washing with PBS to remove debris prior to the experiment the tissue is not otherwise manipulated, and feasibly the mucus layer is similar to its in vivo state.

In mentioning this hypothesis about indole, which our data do not support, we were echoing a prediction from the field, proposed in the studies we cite. We agree with the reviewer that there were other potential outcomes of indole impacting chemotaxis and invasion, and indeed our data supports that.

(20) Lines 394-395 - The authors need to remember that the ability to invade the intestinal epithelium is not only a product of chemoattraction and repulsion forces. Several compounds in the gut are used by Salmonella as cues to alter invasion gene expression. See PMID: 25073640, 28754707, 31847278, and many others.

Thank for you for this point, and we now include these citations. We have revised the text in question, stating:

“In addition to the factors we have investigated, it is already well-established in the literature that the vast metabolome in the gut contains a complex repertoire of chemicals that modulate Salmonella cellular invasion, virulence, growth, and pathogenicity [79–81].”

Our intent is not to diminish the role of other intestinal chemicals but rather to put our new findings into the context of bacterial pathogenesis. We do provide evidence that specific chemoeffectors present in fecal material alter where bacteria localize through chemotaxis, which is one method of control over colonization.

(21) Line 408 - I think it could be hard to observe this using your experimental approach.

Because you need to observe individual cells, the number of cells you observe is relatively small. If, in a bet-hedging strategy, the proportion of cells that were chemoattracted to indole was relatively low you likely would not be able to distinguish it from an occasional distribution close to the repellent source. You may or may not want to discuss this.

Thank you for this observation. It is indeed challenging to both observe large scale population behaviors and also the behaviors of individual cells in the same experiment. Our ability to make this distinction is similar to the approach used in the study we cite, so that is our comparison.

But, if there was a subpopulation that was attracted we would predict a ‘bull’s-eye’ population structure, with some cells attracted and other avoiding the source, which we do not see - we see the halo. So, we find no evidence of the bet-hedging response seen in a different study using E. coli and using different time scales than we have.

(22) Lines 410-411 - What could the other attractants be? Would it be possible/desirable to speculate on this?

We have changed the text here, but we present new data that examines some of these other attractants (Fig. 5).

(23) Line 431 - What exactly do you mean by "running phenotype"? Please, provide a brief explanation.

We have removed this text, but a running phenotype means the swimming bacteria rarely make direction changes (i.e. tumbles), which has been associated with promoting contact with the epithelium, described in the references we cite. Hence, this type of swimming behavior could contribute to the effects we observe in the explant studies, potentially explaining some of the Tsr-mediated advantage that was not dependent on L-Ser/indole.

(24) Line 441 - Other work has shown that feces contain inhibitors of invasion gene expression. The authors should integrate this knowledge into their model. In fact, indole has been shown to repress host cell invasion by Salmonella, so it is important that authors understand and discuss the fact that the impact of indole is multifaceted and not only a reflection of its action as a chemorepellent. PMID: 29342189, 22632036.

We agree with the reviewer about this point, and mention this in the text (lines 55-57): “Indole is amphipathic and can transit bacterial membranes to regulate biofilm formation and motility, suppress virulence programs, and exert bacteriostatic and bactericidal effects at high concentrations [16–18,20–22].”

We have added in the references suggested.

What we test here is the specific hypothesis made by others in the field about indole chemorepulsion serving to dissuade pathogens from colonizing.

For instance, the statement from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190613

“Since indole is also a chemorepellent for EHEC [23], it is intriguing to speculate that in addition to attenuating Salmonella virulence, indole also attenuates the recruitment and directed migration of Salmonella to its infection niche in the GI tract.”

And from: https://doi.org/10.1073/pnas.1916974117

“We propose that indole spatially segregates cells based on their state of adaptation to repel invaders while recruiting beneficial resident bacteria to growing microbial communities within the GI tract.”

And

“Thus, foreign ingested bacteria, including invading pathogens such as E. coli O157:H7 and S. enterica, are likely to be prevented by indole from gaining a foothold in the mucosa.”

As shown by others, indole certainly does have many roles in controlling pathogenesis, and there are other chemicals we do not investigate that control invasion and bacterial growth, but we keep our statements here restricted to chemotaxis since that is what are experiments and data show.

(25) Line 472 - "until fully motile". How long did this take, how variable was it, and how was it determined?

Thank you for asking for this clarification. We have added that the time was between 1-2 h, and confirmed visually. Our methods are similar to those described in earlier chemotaxis studies (ex: 10.1128/jb.182.15.4337-4342.2000).

(26) Line 487 - I worry that the fact fecal samples were obtained commercially means that compound stability/degradation may be a factor to consider here. How long had the sample been in storage? Is this information available?

Thank you for this question. We agree that the fecal sample we used serves as a model system and we cannot rule out that handling by the supplier could potentially alter its contents in some way that would impact bacterial chemosensing. However, we note that the measurements of L-Ser and indole we obtained are in the appropriate range for what other studies have shown.

The fecal sample used for all work in the study were from a single healthy human donor, obtained from Lee Biosolutions (https://www.leebio.com/product/395/fecal-stool-samplehuman-donor-991-18). The supplier did not state the explicit date of collection, nor indicated any specific handline or storage methods that would obviously degrade its native metabolites, but we cannot rule that out. In our hands, the fecal sample was collected and kept frozen at -20 C. For research purposes, portions were extracted and thawed as needed, maintaining the frozen state of the original sample to limit degradation from freeze-thaws.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors aimed to enhance the effectiveness of PARP inhibitors (PARPi) in treating high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC) by inhibiting PRMT1/5 enzymes. They conducted a drug screen combining PARPi with 74 epigenetic modulators to identify promising combinations.

Zhang et al. reported that protein arginine methyltransferase (PRMT) 1/5 inhibition acts synergistically to enhance the sensitivity of Poly (ADP-ribose) polymerase inhibitors (PARPi) in high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC) cells. The authors are the first to perform a drug screen by combining PARPi with 74 well-characterized epigenetic modulators that target five major classes of epigenetic enzymes. Their drug screen identified both PRMT1/5 inhibitors with high combination and clinical priority scores in PARPi treatment. Notably, PRMT1/5 inhibitors significantly enhance PARPi treatment-induced DNA damage in HR-proficient HGSOC and TNBC cells through enhanced maintenance of gene expression associated with DNA damage repair, BRCAness, and intrinsic innate immune pathways in cancer cells. Additionally, bioinformatic analysis of large-scale genomic and functional profiles from TCGA and DepMap further supports that PRMT1/5 are potential therapeutic targets in oncology, including HGSOC and TNBC. These results provide a strong rationale for the clinical application of a combination of PRMT and PARP inhibitors in patients with HR-proficient ovarian and breast cancer. Thus, this discovery has a high impact on developing novel therapeutic approaches to overcome resistance to PARPi in clinical cancer therapy. The data and presentation in this manuscript are straightforward and reliable.

Strengths:

(1) Innovative Approach: First to screen PARPi with a large panel of epigenetic modulators.

(2) Significant Results: Found that PRMT1/5 inhibitors significantly boost PARPi effectiveness in HR-proficient HGSOC and TNBC cells.

(3) Mechanistic Insights: Showed how PRMT1/5 inhibitors enhance DNA damage repair and immune pathways.

(4) Robust Data: Supported by extensive bioinformatic analysis from large genomic databases.

Weaknesses:

(1) Novelty Clarification: Needs clearer comparison to existing studies showing similar effects.

(2) Unclear Mechanisms: More investigation is needed on how MYC targets correlate with PRMT1/5.

(3) Inconsistent Data: ERCC1 expression results varied across cell lines.

(4) Limited Immune Study: Using immunodeficient mice does not fully explore immune responses.

(5) Statistical Methods: Should use one-way ANOVA instead of a two-tailed Student's t-test for multiple comparisons.

We sincerely thank Reviewer #1 for the insightful and constructive feedback, as well as for the kind acknowledgment of the significance of our work: “These results provide a strong rationale for the clinical application of a combination of PRMT and PARP inhibitors in patients with HR-proficient ovarian and breast cancer. Thus, this discovery has a high impact on developing novel therapeutic approaches to overcome resistance to PARPi in clinical cancer therapy. The data and presentation in this manuscript are straightforward and reliable.” We greatly appreciate the reviewer #1’s thoughtful comments, which have significantly improved the quality of our manuscript. In response, we conducted additional experiments and analyses, and made comprehensive revisions to the text, figures, and supplementary materials. In the “Recommendations for the authors” sections, we have provided point-by-point responses to each of the reviewer’s comments, which were immensely helpful in guiding our revisions. We believe these updates have substantially strengthened the manuscript and have fully addressed all reviewer concerns.

Reviewer #2 (Public Review):

Summary:

The authors show that a combination of arginine methyltransferase inhibitors synergize with PARP inhibitors to kill ovarian and triple-negative cancer cell lines in vitro and in vivo using preclinical mouse models.

PARP inhibitors have been the common targeted-therapy options to treat high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC). PRMTs are oncological therapeutic targets and specific inhibitors have been developed. However, due to the insufficiency of PRMTi or PARPi single treatment for HGSOC and TNBC, designing novel combinations of existing inhibitors is necessary. In previous studies, the authors and others developed an "induced PARPi sensitivity by epigenetic modulation" strategy to target resistant tumors. In this study, the authors presented a triple combination of PRMT1i, PRMT5i and PARPi that synergistically kills TNBC cells. A drug screen and RNA-seq analysis were performed to indicate cancer cell growth dependency of PRMT1 and PRMT5, and their CRISPR/Cas9 knockout sensitizes cancer cells to PARPi treatment. It was shown that the cells accumulate DNA damage and have increased caspase 3/7 activity. RNA-seq analysis identified BRCAness genes, and the authors closely studied a top hit ERCC1 as a downregulated DNA damage protein in PRMT inhibitor treatments. ERCC1 is known to be synthetic lethal with PARP inhibitors. Thus, the authors add back ERCC1 and reduce the effects of PRMT inhibitors suggesting PRMT inhibitors mediate, in part, their effect via ERCC1 downregulation. The combination therapy (PRMT/PARP) is validated in 2D cultures of cell lines (OVCAR3, 8 and MDA-MB-231) and has shown to be effective in nude mice with MDA-MB-231 xenograph models.

Strengths and weaknesses:

Overall, the data is well-presented. The experiments are well-performed, convincing, and have the appropriate controls (using inhibitors and genetic deletions) and statistics.

They identify the DNA damage protein ERCC1 to be reduced in expression with PRMT inhibitors. As ERCC1 is known to be synthetic lethal with PARPi, this provides a mechanism for the synergy. They use cell lines only for their study in 2D as well as xenograph models.

We sincerely thank Reviewer #2 for the insightful and constructive feedback, as well as for the kind acknowledgment of the significance of our work: “Overall, the data are well-presented. The experiments are well-performed, convincing, and supported by appropriate controls (using inhibitors and genetic deletions) and statistics.” We greatly appreciate the reviewer #2’s thoughtful comments, which have significantly improved the quality of our manuscript. In response, we conducted additional experiments and analyses, and made comprehensive revisions to the text, figures, and supplementary materials. In the “Recommendations for the authors” sections, we have provided point-by-point responses to each of the reviewer’s comments, which were immensely helpful in guiding our revisions. We believe these updates have substantially strengthened the manuscript and have fully addressed all reviewer concerns.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Recent studies have revealed promising synergistic effects between PRMT inhibitors and chemotherapy, as well as DDR-targeting drugs (ref. 89-92). In the discussion, the authors should highlight what is novel in this study compared to the reported studies.

We thank the reviewer for this important comment and fully agree that prior studies have demonstrated the potential of PRMT inhibitors to enhance the efficacy of DNA damage-targeting agents and certain chemotherapies[1-4]. In response to the reviewer’s constructive suggestion, we have now revised the discussion to highlight the novel aspects of our study compared to previously reported findings. Specifically, our work presents several key advances that go beyond prior studies. Below, we would like to emphasize the novelty of our current study as follows:

In the clinic, a strategy termed “induced PARP inhibitor (PARPi) sensitivity by epigenetic modulation” is being evaluated to sensitize homologous recombination (HR)-proficient tumors to PARPi treatments. Together with other groups, we reported that repression of BET activity significantly reduces the expression levels of essential HR genes by inhibiting their super-enhancers[5]. This preclinical discovery is now being assessed in a Phase 1b/2 clinical trial combining the BET inhibitor ZEN-3694 with the PARPi talazoparib for the treatment of patients with metastatic triple-negative breast cancer (TNBC) who do not carry germline BRCA1/2 mutations. Promising anti-tumor activity has been observed in this ongoing trial[6]. Importantly, gene expression profiles from paired tumor biopsies demonstrated robust target engagement, evidenced by repression of BRCA1 and RAD51 mRNA expression, consistent with our preclinical findings in xenograft models. Based on these encouraging results, the trial is being expanded to a Phase 2b stage to enroll additional TNBC patients. Moreover, other combination strategies[7-13] based on this “induced PARPi sensitivity by epigenetic modulation” approach have also shown promising clinical responses in both intrinsic and acquired HR-proficient settings. Notably, these clinical studies indicate that the strategy is well-tolerated, likely due to cancer cells being particularly sensitive to epigenetic repression of DNA damage response (DDR) genes, compared with normal cells.

However, two key clinical challenges remain for broader application of this strategy in oncology: 1) which clinically actionable epigenetic drugs can produce the strongest synergistic effects with PARPi? and 2) can a BRCA-independent approach be developed? To address these questions, we performed a drug screen combining the FDA-approved PARPi olaparib with a panel of clinically relevant epigenetic drugs. This panel includes 74 well-characterized epigenetic modulators targeting five major classes of epigenetic enzymes, comprising 7 FDA-approved drugs, 14 agents in clinical trials, and 54 in preclinical development. Notably, both type I PRMT inhibitors (PRMTi) and PRMT5 inhibitors (PRMT5i) achieved high combination and clinical prioritization scores in the screen. Functional assays demonstrated that PRMT inhibition markedly enhances PARPi-induced DNA damage in HR-proficient cancer cell lines. In line with a strong positive correlation between PRMT and DDR gene expression across primary tumors, we observed that PRMT activity supports the transcription of DDR genes and maintains a BRCAness-like phenotype in cancer cells. These findings provide strong rationale for clinical development of PRMT/PARPi combinations in patients with HR-proficient ovarian or breast cancers. Mechanistic characterization from our study further supports PRMTi clinical development by elucidating mechanisms of action, identifying rational combinations, defining predictive biomarkers, and guiding dosing strategies.

We believe our studies will be of significant interest to the cancer research community for several reasons. First, they address major clinical challenges in women’s cancers, specifically, high-grade serous ovarian cancer (HGSOC) and TNBC, both of which are aggressive malignancies with limited therapeutic options. Second, they offer a novel solution to overcome PARPi resistance. Our earlier discovery of “induced PARPi sensitivity by epigenetic modulation” has already shown promising clinical results and represents a new path to overcome both primary and acquired resistance to PARPi and platinum therapies. Third, they focus on a clinically translatable drug class. Selective and potent PRMT inhibitors have been developed by leading pharmaceutical companies, with more than ten currently in advanced clinical trials. Fourth, they support mechanism-driven combination strategies. Preclinical evaluation of PRMTi-based combinations with other therapeutic agents is urgently needed for future clinical success. Finally, our work highlights understudied but therapeutically relevant mechanisms in cancer biology. In-depth mechanistic analysis of the PRMT regulome is essential, and our studies provide important new insights into how PRMTs regulate transcription, RNA splicing, DNA damage repair, and anti-tumor immune responses in the context of HGSOC and TNBC.

In summary, our study identifies PRMT1 and PRMT5 as key epigenetic regulators of DNA damage repair and shows that their inhibition sensitizes HR-proficient tumors to PARP inhibitors by repressing transcription and altering splicing of BRCAness genes. Distinct from prior strategies, dual inhibition of type I PRMT and PRMT5 exhibits strong synergy, allowing for lower-dose combination treatments that may reduce toxicity. Our findings also nominate ERCC1 as a potential predictive biomarker and suggest that MYC-driven tumors may be particularly responsive to this approach. Collectively, these results offer a mechanistic rationale and translational framework to broaden the clinical application of PARP inhibitors.

(2) In Figures 3H-J, MYC targets were likely to correlate with the expression levels of PRMT1/PRMT5 in various public datasets, supporting previous reports that the Myc-PRMT loop plays critical roles during tumorigenesis (ref. 45). "Myc-targets" signatures were also the most significant signatures correlated with the expression of PRMT1 and PRMT5. The authors suggest that under MYC-hyperactivated conditions, tumors may be extremely sensitive to PRMT inhibitors or PRMTi/PARPi combination. However, the underlying mechanism remains unclear.

We sincerely thank the reviewer for the critical and insightful comments. We fully agree that more direct evidence is needed to establish the regulatory relationship between MYC and PRMT1/5. To investigate the effect of c-Myc on PRMT1 and PRMT5 expression, we analyzed RNA-seq data from P493-6 Burkitt lymphoma cells, which harbor a tetracycline (Tet)-repressible MYC transgene. In this system, MYC expression can be suppressed to very low levels and then reactivated, enabling a gradual increase in c-Myc protein levels[14]. Upon Tet removal to induce MYC expression, we observed a robust upregulation of both PRMT1 (4.3-fold) and PRMT5 (3.6-fold) RNA levels within 24 hours, as measured by RNA-seq. These findings indicate that MYC activation can transcriptionally upregulate PRMT1 and PRMT5. To determine whether this regulation is directly driven by MYC, we further analyzed MYC ChIP-seq profiles from the same cell line following 24 hours of MYC induction. Consistently, we observed remarkably increased MYC binding at the promoter regions of both PRMT1 and PRMT5 genes. Interestingly, MYC’s regulatory influence was not limited to PRMT1 and PRMT5, we also observed transcriptional upregulation of other PRMT family members, including PRMT3, PRMT4, and PRMT6, in response to MYC activation. Together with the data presented in Figure 3H, these new results strongly suggest that MYC directly upregulates the expression of PRMT family genes by binding to their promoter regions. Consequently, increased PRMT expression may facilitate MYC’s regulation of target gene expression and splicing in cancer cells. In cancers with MYC hyperactivation, this feed-forward loop may be amplified, creating a potential therapeutic vulnerability. In response to the reviewer’s insightful suggestion, we have further explored how MYC regulates PRMT1/5 and whether this regulation modulates the efficacy of PRMT inhibitors in oncology. These unpublished observations are currently being prepared for a separate manuscript, and we have now incorporated a discussion of these unpublished findings into the revised version of this manuscript. We thank the reviewer again for the thoughtful and constructive comments regarding the MYC–PRMT regulatory axis.

(3) In Figure 5F, ERCC1 expression was unlikely to be reduced in cells treated with GSK025, especially in OVCAR8 cells, although other cells, including TNBC cells, are dramatically changed after treatment.

We sincerely thank the reviewer for the critical and insightful comments. We agree with the reviewer that in Figure 5F, although GSK025 treatment reduced ERCC1 expression, the loading control Tubulin also showed a notable decrease in the OVCAR8 cell line. This may be because Tubulin expression is not specifically affected by the chemical inhibitor GSK025 in this particular cell line, or it may be secondarily reduced as a consequence of PRMT inhibitor-induced cell death. As the reviewer pointed out, this phenomenon was not observed in other cell lines, suggesting that the effect on Tubulin is not specific to PRMT inhibition. To further investigate, we employed CRISPR/Cas9-mediated knockout of PRMT1 or PRMT5 in OVCAR8 cells, a more specific genetic approach to inhibit PRMT activity. In both cases, ERCC1 expression was significantly reduced, whereas Tubulin levels remained stable (Figure 5G). These results support the conclusion that PRMT1 and PRMT5 specifically regulate ERCC1 expression in OVCAR8 cells. The inconsistent effect on Tubulin is likely due to nonspecific cellular responses to chemical inhibition, which are generally more variable and less precise than those induced by genetic perturbation.

(4) In Figure 7H-L, MDA-MB-231 cells were implanted subcutaneously in nude immunodeficient mice to confirm the synergistic therapeutic action of the PRMTi/PARPi combination in vivo. Although PRMT inhibition activates intrinsic innate immune pathways in cancer cells, suggesting that PRMTi treatments may enhance intrinsic immune reactions in tumor cells, the use of nude immune deficient mice means that changes in the tumor immune microenvironment remain unknown.

We sincerely thank the reviewer for the critical and insightful comments. We fully agree with the reviewer that our in vivo experiments using the human cancer cell line MDA-MB-231 in immunodeficient nude mice limit our ability to assess changes in the tumor immune microenvironment. We thank the reviewer for highlighting this important limitation. While the primary goal of the current study was to investigate the therapeutic synergy between PRMT inhibition and PARP inhibition in cancer cells, we would like to take this opportunity to share additional unpublished data that further support and extend the reviewer’s point regarding the immunomodulatory effects of PRMT inhibitors. In syngeneic mouse tumor models, we have observed that the combination of PRMT inhibition and PARP inhibition leads to a more robust anti-tumor immune response compared to either treatment alone. Specifically, we found increased infiltration of CD8⁺ cytotoxic T cells within the tumor microenvironment, suggesting enhanced immune activation and tumor immunogenicity. Furthermore, we have also obtained preliminary evidence that PRMT inhibition can potentiate immune checkpoint blockade therapy. Mechanistically, this may be mediated through the activation of the STING1 pathway and the upregulation of splicing-derived neoantigens, both of which have been implicated in promoting tumor immune visibility. These findings indicate that beyond enhancing DNA damage response, PRMT inhibition may have a broader impact on tumor-immune interactions and could serve as a promising strategy to sensitize tumors to immunotherapy. A separate manuscript detailing these results is currently in preparation and will be submitted for publication as an independent research article. In light of the reviewer’s thoughtful suggestions and in consideration of feedback from Reviewer #2, who recommended removing Figure 6 from the manuscript, we have carefully reevaluated the overall organization of the manuscript. Given the scope and focus of the current work, as well as the desire to maintain a concise and coherent narrative, we decided to move the content originally presented in Figure 6 to the supplementary materials. This figure is now included as Supplementary Figure S5 in the revised version of the manuscript. We believe this change helps streamline the main text while still making the additional data available for interested readers.

(5) In Figures 6-7, a two-tailed Student's t-test was used to determine the statistical differences among multiple comparisons, which should be performed by one-way ANOVA followed by a post hoc test.

We thank the reviewer for this thoughtful and important comment regarding the choice of statistical method. We fully agree with the reviewer that one-way ANOVA followed by a post hoc test is one of the standard approaches for multiple group comparisons. In response to the suggestion, we have performed one-way ANOVA on our data and found that the statistical conclusions are consistent with those obtained from the two-tailed Student’s t-tests. For example, in the first panel of Figure 6A (OVCAR8 treated with GSK715), one-way ANOVA (p = 1.1 × 10^-6), followed by Tukey’s HSD test, confirmed significant differences between control and Olaparib (p = 0.000165), control and GSK715 (p = 0.000145), control and combination (p = 6.067 × 10^-7), Olaparib and combination (p = 0.0003523), and GSK715 and combination (p = 0.0004015), consistent with the conclusions from the two-tailed t-test shown in Figure 6H. Additionally, we would like to explain why two-tailed Student’s t-tests were used in our current study. When comparisons are predefined and conducted pairwise (i.e., two groups at a time), a two-tailed Student’s t-test is statistically equivalent to one-way ANOVA for those comparisons. In our study, each comparison involved only two groups, and we therefore chose t-tests for hypothesis-driven, specific comparisons rather than exploratory multiple testing. This approach aligns with valid statistical principles. All statistical analyses presented in Figures 6-7 were designed to evaluate specific, biologically meaningful comparisons (e.g., treatment vs. control or treatment A vs treatment B). The study was hypothesis-driven, not exploratory, and did not involve simultaneous comparisons across multiple groups. In such cases, the t-test provides a more direct and interpretable result for targeted comparisons. The use of Student’s t-tests reflects the focused nature of the analysis, where each test directly addresses a specific biological question rather than a global group comparison. We sincerely appreciate the reviewer’s thoughtful comments on the statistical methods.

Reviewer #2 (Recommendations for the authors):

(1) If the authors kept the tumors of various sizes in Figure 7I, it would be important to assess the protein and/or mRNA level of ERCC1 to further support their mechanism.

We sincerely thank the reviewer for the insightful comments. We fully agree that evaluating ERCC1 expression in drug-treated tumor samples is critical to support the proposed mechanism. Due to the limited volume of tumor specimens and extensive necrosis observed after three weeks of treatment in the condition used for Figure 7I, we were unable to obtain sufficient material for expression analysis in the original cohort. To address this, we conducted an additional experiment using xenograft-bearing mice (MDA-MB-231 model), initiating treatment when tumors reached approximately 200 mm³ to ensure adequate tissue collection. We also shortened the treatment duration to 7 days to assess early molecular responses to therapy, rather than downstream effects. Consistent with our in vitro results, both GSK715 and GSK025 significantly reduced ERCC1 RNA expression (0.79 ± 0.17, p = 0.03; 0.82 ± 0.11, p = 0.02, respectively), and the combination treatment further decreased ERCC1 expression (0.49 ± 0.20, p = 0.0003), as determined by qRT-PCR. A two-tailed Student’s t-test was used for statistical analysis. In this experiment, we used the same dosing regimen as in the three-week treatment shown in Figure 7I. Importantly, the shorter treatment period and moderate tumor size at treatment initiation minimized necrosis and did not significantly affect tumor growth, allowing for reliable molecular evaluation. We sincerely thank the reviewer for highlighting this important point.

(2) Figure 2G: please explain why two bands remain for sgPRMT1.

We greatly appreciate the reviewer for raising this insightful and important question. As the reviewer pointed out, an additional band appeared after PRMT1 knockdown in OVCAR8 cells using two sequence-independent gRNAs. Notably, this band was not observed in MDA-MB-231 cells. The antibody used to detect PRMT1 (clone A33, #2449, Cell Signaling Technology) is widely adopted in PRMT1 research, with over 65 citations supporting its specificity. Interestingly, previous studies[15] have identified seven PRMT1 isoforms (v1–v7), generated through alternative splicing and exhibiting tissue-specific expression patterns. Of these, three isoforms are detectable using the A33 antibody. We believe the additional band observed upon sgRNA treatment likely represents a PRMT1 isoform that is normally expressed at low levels in OVCAR8 cells. Upon knockdown of the major isoforms by CRISPR/Cas9, expression of this minor isoform may have increased as part of a compensatory feedback mechanism, rendering it detectable by immunoblotting. Because PRMT1 isoform expression is largely tissue-type specific, it is not surprising that the same band was absent in MDA-MB-231 cells, which are derived from a different lineage than OVCAR8 cells. The reviewer raised an important question regarding the role of PRMT1 isoforms in regulating DNA damage response in cancer. We agree this is an intriguing direction and will investigate it further in future studies.

(3) Figure 4D: Please correct the figure legend so the description matches the color in the figure. Red and blue are absent.

We sincerely thank the reviewer for the critical and insightful comments. The figure legend for Figure 4D has been corrected in the revised version of the manuscript to accurately match the colors shown in the figure. We thank the reviewer for pointing out this issue.

(4) Figure 7A and B: please indicate the cell lines used.

We sincerely thank the reviewer for the critical and insightful comments. In Figure 7A and 7B, human embryonic kidney 293T (HEK293T) cells were used due to their high transfection efficiency and widespread application in reporter assays. This information has been incorporated into the figure legend for Figures 7A and 7B.

(5) What is the link with ERCC1 splicing because reduced overall ERCC1 expression is clear?

We sincerely thank the reviewer for the critical and insightful comments. As the reviewer pointed out, although the direct impact of ERCC1 alternative splicing on its protein expression remains to be fully elucidated, it is likely that PRMT inhibition induces aberrant splicing events that result in the production of alternative ERCC1 isoforms with impaired or altered function. These splicing changes may compromise ERCC1’s role in DNA repair pathways. Furthermore, as shown in Figure 4G, we observed a reduction in the total ERCC1 mRNA reads following PRMTi treatment. This decrease may be attributed, at least in part, to the instability of the alternatively spliced ERCC1 transcripts, which could be more prone to degradation. In combination with the transcriptional downregulation of ERCC1 induced by PRMT inhibition, these alternative splicing events may lead to a further reduction in functional ERCC1 protein levels. This dual impact on ERCC1 expression, through both decreased transcription and the generation of unstable or non-functional isoforms, likely contributes to the enhanced cellular sensitivity to PARP inhibitors observed in our study. We believe this represents an important mechanistic insight into how PRMT inhibition modulates the DNA damage response in cancer cells, and further studies are warranted to investigate the precise role of ERCC1 splicing regulation in this context. We thank the reviewer for pointing out this interesting future research direction.

(6) Figure 7J: From the graph, it seems like Olaparib+G715 and G715+G025 have a similar effect on tumor volume (two curves overlap). Please discuss.

We sincerely thank the reviewer for the critical and insightful comments. In the current study, the doses used for single-agent treatments were selected based on prior publications. For example, the dose of GSK715 was guided by a recent study from the GSK group[16]. Our in vitro and in vivo findings, together with previously published data, consistently demonstrate that GSK715 is more potent than both GSK025 and Olaparib. Notably, treatment with GSK715 alone led to significantly greater inhibition of tumor growth compared to either GSK025 or Olaparib administered individually. This higher potency of GSK715 also explains the comparable levels of tumor suppression observed in the combination groups, including GSK715 plus Olaparib and GSK715 plus GSK025. These results suggest that GSK715 is likely the primary driver of efficacy in the two drug combination settings. Importantly, this observation provides a valuable opportunity to further refine and optimize the dosing strategy for GSK715. Specifically, because GSK715 is highly potent, its dose may be reduced when used in combination regimens without compromising therapeutic efficacy. This approach could significantly improve the safety profile of GSK715 by minimizing potential dose-related toxicities, thereby enhancing its suitability for future clinical development in combination therapy contexts.

(7) Discussion: "PRMT5i increased global sDMA levels"-> "... aDMA levels.".

We sincerely thank the reviewer for the critical and insightful comments. In response, we have corrected the sentence in the discussion from “PRMT5i increased global sDMA levels, which suggested that type I PRMT and PRMT5 share a substrate (i.e., MMA) and/or their functions are compensatory” to “PRMT1i increased global sDMA levels, which suggested that type I PRMT and PRMT5 share a substrate (i.e., MMA) and/or their functions are compensatory.” We apologize for the misstatement and have corrected this error in the revised version of the manuscript.

(8) In addition to the methods, add that nude mice were used in the body of the results and the figure legend for Figure 7J.

We sincerely thank the reviewer for the critical and insightful comments. In the revised version of the manuscript, we have added that immunodeficient nude mice were used in both the body of the Results section and the figure legend for Figure 7J, in addition to the Methods section. We thank the reviewer for this helpful suggestion.

(9) Figure 6 can be deleted to focus the manuscript. It does not add to the PARP inhibition story, but only suggests a link to immunotherapy where this has been reported previously PMID: 35578032 and 32641491.

We sincerely thank the reviewer for the critical and insightful comments. Reviewer #1 also raised a related concern regarding the relevance of this section to the main focus of the manuscript. In consideration of both reviewers’ comments, we have decided to move the data previously shown in Figure 6 to the supplementary section as Supplementary Figure S5. This revision allows us to streamline the main text and maintain a clear focus on the core findings related to PARP inhibition. At the same time, we believe the immunotherapy-related observation may still be of interest to some readers. By presenting these results in the supplementary materials, we ensure that this potentially relevant link remains accessible without distracting from the primary narrative of the manuscript. We greatly appreciate the reviewers’ guidance in helping us improve the clarity and focus of our work. We thank the reviewer for the thoughtful suggestion.

References

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(2) O'Brien, S., et al. Inhibiting PRMT5 induces DNA damage and increases anti-proliferative activity of Niraparib, a PARP inhibitor, in models of breast and ovarian cancer. BMC Cancer 23, 775 (2023).

(3) Carter, J., et al. PRMT5 Inhibitors Regulate DNA Damage Repair Pathways in Cancer Cells and Improve Response to PARP Inhibition and Chemotherapies. Cancer Res Commun 3, 2233-2243 (2023).

(4) Li, Y., et al. PRMT blockade induces defective DNA replication stress response and synergizes with PARP inhibition. Cell Rep Med 4, 101326 (2023).

(5) Yang, L., et al. Repression of BET activity sensitizes homologous recombination-proficient cancers to PARP inhibition. Sci Transl Med 9(2017).

(6) Aftimos, P.G., et al. A phase 1b/2 study of the BET inhibitor ZEN-3694 in combination with talazoparib for treatment of patients with TNBC without gBRCA1/2 mutations. Journal of Clinical Oncology 40, 1023-1023 (2022).

(7) Karakashev, S., et al. BET Bromodomain Inhibition Synergizes with PARP Inhibitor in Epithelial Ovarian Cancer. Cell Rep 21, 3398-3405 (2017).

(8) Sun, C., et al. BRD4 Inhibition Is Synthetic Lethal with PARP Inhibitors through the Induction of Homologous Recombination Deficiency. Cancer Cell 33, 401-416 e408 (2018).

(9) Johnson, S.F., et al. CDK12 Inhibition Reverses De Novo and Acquired PARP Inhibitor Resistance in BRCA Wild-Type and Mutated Models of Triple-Negative Breast Cancer. Cell Rep 17, 2367-2381 (2016).

(10) Iniguez, A.B., et al. EWS/FLI Confers Tumor Cell Synthetic Lethality to CDK12 Inhibition in Ewing Sarcoma. Cancer Cell 33, 202-216 e206 (2018).

(11) Shan, W., et al. Systematic Characterization of Recurrent Genomic Alterations in Cyclin-Dependent Kinases Reveals Potential Therapeutic Strategies for Cancer Treatment. Cell Rep 32, 107884 (2020).

(12) Muvarak, N.E., et al. Enhancing the Cytotoxic Effects of PARP Inhibitors with DNA Demethylating Agents - A Potential Therapy for Cancer. Cancer Cell 30, 637-650 (2016).

(13) Abbotts, R., et al. DNA methyltransferase inhibitors induce a BRCAness phenotype that sensitizes NSCLC to PARP inhibitor and ionizing radiation. Proc Natl Acad Sci U S A 116, 22609-22618 (2019).

(14) Lin, C.Y., et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56-67 (2012).

(15) Goulet, I., Gauvin, G., Boisvenue, S. & Cote, J. Alternative splicing yields protein arginine methyltransferase 1 isoforms with distinct activity, substrate specificity, and subcellular localization. J Biol Chem 282, 33009-33021 (2007).

(16) Fedoriw, A., et al. Anti-tumor Activity of the Type I PRMT Inhibitor, GSK3368715, Synergizes with PRMT5 Inhibition through MTAP Loss. Cancer Cell 36, 100-114 e125 (2019).

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf049), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer: Zexuan Zhu

The paper presents an encoding of the VCF data using Zarr to enable fast retrieving subsets of the data. A vcf2arr conversion was provided and validated on both simulated and real-world data sets. The topic of this work is interesting and of good values, however, the experimental studies and contributions should be considerable improved.1. The proposed method is simply a conversion from VCF to Zarr format. Since both are existing formats, the contributions and originality of this work are not impressive.2. The compression and query performance is the main concern of this work. The method should be compared with other state-of-the-art queriable VCF compressors like GTC, GBC, and GSC.Danek A, Deorowicz S. GTC: how to maintain huge genotype collections in a compressed form. Bioinformatics, 2018;34(11):1834-1840.Zhang L, Yuan Y, Peng W, Tang B, Li MJ, Gui H,etal. GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species. Genome Biology, 2023;24(1):1-22.Luo X, Chen Y, Liu L, Ding L, Li Y, Li S, Zhang Y, Zhu Z. GSC: efficient lossless compression of VCF files with fast query. Gigascience, 2024; 2;13:giae046.3. The method should be evaluated on more real VCF data sets.

Moet aangepast worden

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Reply to the reviewers

Reviewer #1 (Evidence, Reproducibility, and Clarity)

Reviewer comment: This is a very well conceived study of responses to plasma membrane stresses in yeast that signal through the conserved TORC2 complex. Physical stress through small molecular intercalators in the plasma membrane is shown to be independent of their biochemistry and then studies for its effect on plasma membrane morphology and the distribution of free ergosterol (the yeast equivalent of cholesterol), with free being the pool of cholesterol that is available to probes and/or sterol transfer proteins. Experiments nicely demonstrate a negative feedback loop consisting of: stress -> increased free sterol and TORC2 inhibition -> activation of LAM proteins (as demonstrated by Relents and co-workers previously) -> removal of free sterol -> return to unstressed state of PM and TORC2.

Author response: We thank the reviewer for their positive and encouraging feedback. We are pleased to submit our revised manuscript and have addressed all points raised below.

Comment: Fig 2A: Is detection of PIP/PIP2/PS linear for target, or possibly just showing availability that is increased due to local positive curvature?

Response: This is an excellent and fundamental question. While FLARE signal likely reflects lipid availability, its detection is indeed influenced by factors such as membrane curvature and lipid composition, due to varying insertion depths of the lipid-binding domains. For example, studies using NMR suggest that the PLCδ PH domain partially inserts into membranes, potentially conferring curvature sensitivity (Flesch et al., 2005; Uekama et al., 2009). Similarly, curvature influences lactadherin binding, though it's unclear if this extends to its isolated C2 domain (Otzen et al., 2012; Shao et al., 2008; Shi et al., 2004). We could not find direct evidence for curvature sensitivity of P4C(SidC), but assume some influence exists.

To avoid overinterpreting these limitations, we now describe our data based solely on the FLAREs used, rather than inferring enrichment of specific lipid species. We refer to these PM structures as "PI(4,5)P₂-containing", consistent with prior literature (Riggi et al., 2018) and have revised our manuscript accordingly.

Comment: Can any marker be identified for the D4H spots at 2 minutes? In particular, are they early endosomes (shown by brief pre-incubation with FM4-64)?

Response: We appreciate the reviewer's suggestion and have now added new data (Fig. S2E-H). We tested colocalization of D4H spots with FM4-64 (early endosomes), GFP-VPS21 (early endosome marker), and LipidSpot{trade mark, serif} 488 (lipid droplets), but found no overlap. This later observation was not unexpected given that D4H does not recognize Sterol esters. D4H foci also did not overlap with ER (dsRED-HDEL), though they were frequently adjacent to it. While their exact identity remains unknown, we agree this is an intriguing direction for future investigation.

Comment: Is there any functional (& direct) link between Arp inhibition (as in the Pombe study of LAMs by the lab of Sophie Martin) and PM disturbance by amphipathic molecules?

Response: We have explored this connection and now present new data (see final paragraph of Results). Briefly, we show that CK-666 induces internalization of PM sterols in a Lam2/4-dependent manner, and that TORC2 activity is more strongly reduced in lam2Δ lam4Δ cells compared to WT. These findings support the idea that, like PalmC, Arp2/3 inhibition triggers a PM stress that is counteracted by sterol internalization.

Minor Comment: Fig 2A: Labels not clear. Say for each part what FP is used for pip2.

Response: As noted above, we revised image labels to clarify which FLAREs were used, and refer to data accordingly throughout.

Minor Comment: Move fig s2d to main ms. The 1 min and 2 min data are integral to the story.

Response: We agree and have incorporated the 1-min and 2-min data into the main figures. Vehicle-treated controls were moved to Fig. S2.

Minor Comment: The role of Lam2 and Lam4 in retrograde sterol transport has in vivo only been linked to one of their two StART domains not both, as mentioned in the text.

Response: Thank you for pointing this out. We have corrected the text to:

"[...]Lam2 and Lam4[...] contain two START domains, of which at least one has been demonstrated to facilitate sterol transport between membranes (Gatta et al., 2015; Jentsch et al., 2018; Tong et al., 2018)."

Minor Comment: Throughout, images of tagged D4H should be labelled as such, not as "Ergosterol".

Response: We have updated all relevant figure labels and text to refer to "D4H" rather than "Ergosterol", in line with this recommendation.

Reviewer #1 (Significance):

These results in budding yeast are likely to be directly applicable to a wide range of eukaryotic cells, if not all of them. I expect this paper to be a significant guide of research in this area. The paper specifically points out that the current experiments do not distinguish the precise causation among the two outcomes of stress: increased free sterol and TORC2 inhibition. Of these two outcomes which causes which is not yet known. If data were added that shed light on this causation that would make this work much more signifiant, but I can understand 100% that this extra step lies beyond - for a later study for which the current one forms the bedrock.

Response:

We thank the reviewer for their generous assessment. We agree that understanding the causality between increased free sterol and TORC2 inhibition is a critical next step.

Based on our current data, we believe the increase in free ergosterol precedes TORC2 inhibition. For example, TORC2 inhibition alone (e.g., via pharmacological means) does not initially increase free sterol, while it does enhance Lam2/4 activity, promoting sterol internalization (Fig. 3A). Baseline TORC2 activity also inversely correlates with free PM sterol levels in lam2Δ lam4Δ versus LAM2T518A LAM4S401A cells (Figs. 2D, S2C).

Additionally, during sterol depletion, we observe an initial increase in TORC2 activity before growth inhibition occurs, after which activity declines-likely due to compromised PM integrity (Fig. S2M). We now also show that adaptation to several other stresses (e.g., osmotic shock, heat shock, CK-666) partially depends on sterol internalization, which correlates with TORC2 activation (Fig. 4, S4B).

While these findings strengthen the model that PM stress perturbs sterol availability and secondarily impacts TORC2, we cannot yet definitively demonstrate causality. As suggested by Reviewer 3, we tested cholesterol-producing yeast (Souza et al., 2011), but found their response to PalmC indistinguishable from WT, making it difficult to draw mechanistic conclusions (Rebuttal Fig. 2).

Taken together, we favour a model where sterols affect PM properties sensed by TORC2, probably lipid-packing, rather than acting as direct effectors. We hope our revised manuscript more clearly conveys this model and serves as a strong foundation for future mechanistic studies.

Reviewer #2 (Evidence, Reproducibility, and Clarity)

Reviewer comment: This manuscript describes multiple effects of positively-charged membrane-intercalating amphipaths (palmitoylcarnitine, PalmC, in particular) on TORC2 in yeast plasma membranes. It is a "next step" in the Loewith laboratory's characterization of the effect of this agent on this system. The study confirms the findings of Riggi et al.(2018) that PalmC inhibits TORC2 and drives the formation of membrane invaginations that contain phosphatidylinositol-bis-phosphate (PIP2) and other anionic phospholipids. It also demonstrates that PalmC intercalates into the membrane, acts directly (rather than through secondary metabolism) and is representative of a class of cationic amphipaths. The interesting finding here is that PalmC causes a rapid initial increase in the plasma membrane ergosterol accessible to the DH4 sterol probe followed by a decrease caused by its transfer to the cytoplasm through its transporter, LAM2/4. TORC2 is implicated in these processes. Loewith et al. have pioneered in this area and this study clearly shows their expertise. Several of the findings reported here are novel. However, I am concerned that PalmC may not be revealing the physiology of the system but rather adding tangential complexity. (This concern applies to the precursor studies using PalmC to probe the TORC2 system.) In particular, I am not confident that the data justify the authors' conclusions "...that TORC2 acts in a feedback loop to control active sterol levels at the PM and [the results] introduce sterols as possible TORC2 signalling modulators."

Author response:

We thank Reviewer #2 for the constructive and critical evaluation of our work. We appreciate the acknowledgment of the novelty and technical strength of several of our findings, and we understand the concern that PalmC could be eliciting non-physiological effects. Our study was designed precisely to use PalmC and similar membrane-active amphipaths as tools to strongly perturb the plasma membrane (PM) in a controlled and tractable way. We now state this intention explicitly in both the Introduction and Discussion sections. To address concerns about the specificity and physiological relevance of PalmC, we have expanded our dataset to include additional PM stressors (hyperosmotic shock, Arp2/3 inhibition, and heat shock), all of which reproduce key features observed with PalmC-namely, TORC2 inhibition, PM invaginations, and retrograde sterol transport (Fig. 4, S4).

We hope this more comprehensive dataset, along with revised discussion and clarified claims, addresses the reviewer's concerns regarding physiological interpretation and artifact.

Major issues 1 and 2: 1. The invaginations induced by PalmC may not be physiologic but simply the result of the well-known "bilayer couple" bending of the bilayer due to the accumulation of cationic amphipaths in the inner leaflet of the plasma membrane bilayer which is rich in anionic phospholipids. Such unphysiological effects make the observed correlation of invagination with TORC2 inhibition etc. hard to interpret.

Electrostatic/hydrophobic association of PIP2 with PalmC could sequester the anionic phospholipid(s). Such associations could also drive the accumulation of PIP2 in the invaginations. This could explain PalmC inhibition of TORC2 through a simple physical rather than biological process. So, it is difficult to draw any physiological conclusion about PIP2 from these experiments.

Response to major issues 1 and 2:

We agree that amphipath-induced bilayer stress, including via the bilayer-couple mechanism, may contribute to PM curvature changes. However, the reviewer's assumption that PalmC inserts preferentially into the inner leaflet appears inconsistent with both literature and our observations. PalmC is zwitterionic, not cationic, and is unlikely to electrostatically sequester anionic lipids such as PIP2. For clarification, we included a short summary of our proposed mechanism of PalmC in the context of the current literature in our Discussion:

"[...] study it was also demonstrated that addition of phospholipids to the outer PM leaflet causes an excess of free sterol at the inner PM leaflet, and its subsequent retrograde transport to lipid droplets (Doktorova et al., 2025). Although we cannot exclude that it is the substrate of a flippase or scramblase, PalmC is not a metabolite found in yeast, nor, given its charged headgroup, is it likely to spontaneously flip to the inner leaflet (Goñi, Requero and Alonso, 1996). Thus, we propose that PalmC accumulates in the outer leaflet, disrupts the lipid balance with the inner leaflet which is, similarly to the mammalian cell model (Doktorova et al., 2025), rectified by sterol mobilization, flipping and internalization (Fig. 5B)."

While we agree that PM invaginations per se are not the central focus of this study, they are indeed a reproducible and biologically intriguing phenomenon. We emphasize that similar invaginations occur not only during PalmC treatment but also in response to other physiological stresses, such as hyperosmotic shock and Arp2/3 inhibition (Fig. 4), and have been reported independently by others (Phan et al., 2025). Furthermore, related structures have been documented in yeast mutants with altered PIP2 metabolism or TORC2 hyperactivity (Rodríguez-Escudero et al., 2018; Sakata et al., 2022; Stefan et al., 2002), and even in mammalian neurons with SJ1 phosphatase mutations (Stefan et al., 2002). These observations support our interpretation that the observed invaginations represent an exaggerated manifestation of a physiologically relevant stress-adaptive process. In our previous study we indeed proposed that PI(4,5)P2 enrichment in PM invaginations was important for PalmC-induced TORC2 inactivation, using the heat sensitive PI(4,5)P2 kinase allele mss4ts - a rather blunt tool (Riggi et al., 2018). We have now come to the conclusion that different mechanisms other than, or in addition to, PIP2 changes drive TORC2 inhibition in our system. In this study, we use the 2xPH(PLC) FLARE exclusively as a generic PM marker, not as a readout of PIP2 biology. Rather, we propose that sterol redistribution and/or the biophysical impact that this has on the PM are central drivers, with TORC2 acting as a signaling node that senses and adjusts PM composition accordingly.

We now clarify these arguments in the revised Discussion and have reframed our use of PalmC as a probe to explore the capacity of the PM to adapt to acute stress via dynamic lipid rearrangements.

Major issue 3:

As the authors point out, a large number of intercalated amphipaths displace sterols from their association with bilayer phospholipids. This unphysiologic mechanism can explain how PalmC causes the transient increase in the availability of plasma membrane ergosterol to the D4H probe and its subsequent removal from the plasma membrane via LAM2/4. TORC2 regulation may not be involved. In fact, the authors say that "TORC2 inhibition, and thereby Lam2/4 activation, cannot be the only trigger for PalmC induced sterol removal." Furthermore, the subsequent recovery of plasma membrane ergosterol could simply reflect homeostatic responses independent of the components studied here.

Response:

We agree that increased free sterols in the inner leaflet likely initiate retrograde transport. Our results suggest that TORC2 inhibition facilitates this process by disinhibiting Lam2/4, allowing more efficient clearance of ergosterol from the PM (Fig. 3A, S2C). However, the process is not exclusively dependent on TORC2, and we state this explicitly.

We do not observe recovery of PM ergosterol on the timescales measured, while TORC2 activity recovers, suggesting that restoration likely occurs later via biosynthetic or anterograde trafficking pathways, which are outside the scope of this study. These points are clarified in the revised Discussion.

Major issue 3a:

The data suggest that LAM2/4 mediates the return of cytoplasmic ergosterol to the plasma membrane. To my knowledge, this is a nice finding that not been reported previously and is worth confirming more directly.

Response:

We thank the reviewer for this observation but would like to clarify a misunderstanding: our data do not suggest that Lam2/4 mediates anterograde sterol transport. Our results and prior work (Gatta et al., 2015; Roelants et al., 2018) show that Lam2/4 mediate retrograde transport from the PM to the ER, and TORC2 inhibits this process. We now clarify this point in the revised manuscript, stating:

"In vivo, Lam2/4 seem to predominantly transport sterols from the PM to the ER, following the concentration gradient (Gatta et al., 2015; Jentsch et al., 2018; Tong et al., 2018)."

Major issue 4:

I agree with the authors that "It is unclear if the excess of free sterols itself is part of the inhibitory signal to TORC2..." Instead, the inhibition of TORC2 by PalmC may simply result from its artifactual aggregation of the anionic phospholipids (especially, PIP2) needed for TORC2 activity. This would not be biologically meaningful. If the authors wish to show that accessible ergosterol inhibits TORC2 activity or vice versa, they should use more direct methods. For example, neutral amphipaths that do not cause the aforementioned PalmC perturbations should still increase plasma membrane ergosterol and send it through LAM2/4 to the ER.

Response:

We now provide evidence that three orthologous treatments (hyperosmotic shock, heat shock and Arp2/3 inhibition) similarly cause sterol mobilization and, in the absence of sterol clearance from the PM, prolonged TORC2 inhibition. These results do not support the reviewer's contention that the inhibition of TORC2 by PalmC is simply resulting from its artifactual aggregation of the anionic phospholipids. Furthermore, PalmC is zwitterionic, and its interaction with anionic lipids should be somewhat limited.

In our experimental setup, neutral amphipaths did not trigger TORC2 inhibition or D4H redistribution While this differs from prior in vitro work (Lange et al., 2009), we attribute this in part to a discrepancy to experimental setup differences, including flow chamber artifacts that we discuss in the methods section.

Importantly, only amphipaths with a charged headgroup, including zwitterionic (PalmC) and positively charged analogs, produced robust effects. A negatively charged derivative also seemed to have a minor effect on TORC2 activity and PM sterol internalization (Palmitoylglycine (Fig. 1D, Rebuttal Fig. 1). This suggests that in vivo, charge-based membrane perturbation is required to alter PM sterol distribution and TORC2 activity.

Major issue 5.:

The mechanistic relationship between TORC2 activity and ergosterol suggested in the title, abstract, and discussion is not secure. I agree with the concluding section of the manuscript called "Limitations of the study". It highlights the need for a better approach to the interplay between TORC2 and ergosterol.

Response:

This may have been true of the previous submission, but we now demonstrate that provoking PM stress in four orthogonal ways triggers mobilization of sterols, which left uncleared, prevents normal (re)activation of TORC2 activity. We thus conclude that free sterols, directly or more likely indirectly, inhibit TORC2. The role that TORC2 plays in sterol retrotranslocation has been demonstrated previously (Roelants et al., 2018). We believe our expanded data and clarified framework make a compelling case for a stress-adaptive role of sterol retrograde transport that is supervised and modulated-but not fully driven-by TORC2 activity.

Thus, we feel in the present version of this manuscript that the title is now justified.

Minor issue: Based on earlier work using the reporter fliptR, the authors claim that PalmC reduces membrane tension. They should consider that this intercalated dye senses many variables including membrane tension but also lipid packing. I suspect that, by intercalating into and thereby altering the bilayer, PalmC is affecting the latter rather than the former.

Response:

We thank the reviewer for this important point regarding the multifactorial sensitivity of intercalating dyes such as Flipper-TR®, including to membrane tension and lipid packing.

We respectfully note, however, that our current study does not include any new data generated using Flipper-TR®. We referred to earlier work (Riggi et al., 2018) for context, where Flipper-TR® was used as a membrane tension reporter.

We fully agree that the response of such "smart" membrane probes integrates multiple biophysical parameters-including tension, packing, and hydration-which are themselves interrelated as consequences of membrane composition (Colom et al., 2018; Ragaller et al., 2024; Torra et al., 2024). Indeed, this interconnectedness is central to our interpretation of PalmC's pleiotropic effects on the plasma membrane (PM). In our previous study, we observed that PalmC treatment not only reduced apparent PM tension (as measured by Flipper-TR®) but also increased membrane order ((Riggi et al., 2018); see laurdan GP, Fig. 6C), and here we show that it promotes the redistribution of free sterol away from the PM.

Furthermore, PalmC's effect on membrane tension was supported by orthogonal in vitro data: its addition to giant unilamellar vesicles (GUVs) led to a measurable increase in membrane surface area and decreased tension, as shown by pipette aspiration ((Riggi et al., 2018), Fig. 3F). This provides complementary evidence that the membrane tension reduction is not merely an artifact of Flipper-TR® reporting.

That said, we agree with the reviewer that in the case of TORC2 inhibition or hyperactivation, the observed changes in PM tension are based solely on Flipper-TR® data, without additional orthogonal validation. To address this concern, we have revised the relevant text in the manuscript to more cautiously reflect this complexity. The revised sentence now reads:

"Consistent with this role, data generated with the lipid packing reporter dye Flipper-TR® suggest that acute chemical inhibition of TORC2 increases PM tension, while Ypk1 hyperactivation decreases it."

This revised phrasing acknowledges both the utility and the limitations of Flipper-TR® as a probe of membrane biophysics.

Reviewer #2 Significance:

This is an interesting topic. However, use of the exogenous probe, palmitoylcarnitine, could be causing multiple changes that complicate the interpretation of the data.

Reviewers #1 and #3 were much more impressed by this study than I was. I am not a yeast expert and so I may have missed or confused something. I would therefore welcome their expert feedback regarding my comments (#2). Ted Steck

Response:

Thank you for your constructive feedback.

We believe that the manuscript is now much improved, and we hope to have convinced you that the mechanisms that we've elucidated using PalmC represent a general adaptation response to physiological PM stressors.

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

Reviewer comment: The authors describe the effects of surfactant-like molecules on the plasma membrane (PM) and its associated TORC2 complex. Addition of the surfactants with a positively-charged headgroup and a hydro-carbon tail of at least 16 caused the rapid clustering of PI-4,5P2 together with PI-4P and phosphatidylserine in large membrane invaginations. The authors convincingly demonstrate that this effect of the surfactants on the PM is likely caused by a direct disturbance of the PM organization and/or lipid composition. Interestingly, upon PalmC treatment, free ergosterol of the PM was found to first concentrate in the clusters, but within The kinetics of the changes in free ergosterol levels and the changes in TORC2 activity do not match. Ergosterol is rapidly depleted after PalmC treatment (The Lam2/4 data support the idea that ergosterol transport plays a role in the TORC2 recovery, but what role this is, is not clear to me. I think the data fit better with a model in which PalmC causes low tension of the PM which in turn disrupts normal lipid organization and thus causes TORC2 to shut down, maybe not by changes in free ergosterol but by changes, for instance, in lipid raft formation (which is in part effected by ergosterol levels). The transport of ergosterol is only one mechanism that is involved in restoring PM tension and TORC2 activity. However, sensing free ergosterol alone is most likely not the mechanism explaining how TORC2 senses PM tension.

Therefore, I recommend that the model is revised (or supported by more data), reflecting the fact that free ergosterol levels do not directly correlate with the TORC2 activity, but instead might be only one of the PM parameters that regulate TORC2.

Author response:

We thank the reviewer for their thoughtful assessment and constructive suggestions. As described in more detail above, we have included in our revised version of this manuscript a variety of new data, including the sterol-internalization dependent adaptation of the PM and regulation of TORC2 during additional stresses. We think that these data vastly improve on our previous manuscript version. We have addressed each point risen by the reviewer below and revised the manuscript accordingly, including a rewritten discussion and updated model to better reflect the limitations of our current understanding of how TORC2 senses changes in the plasma membrane (PM). It is true that the appearance of PM invaginations tracks well with TORC2 inhibition, but it is not clear to us if they are upstream of this inhibition or merely another symptom of the preceding PM perturbation (PalmC-induced free sterol increase can be observed after 10s (Fig. S2A), but PM invaginations become visible only after ~1 min - meanwhile we can observe near complete TORC2 inhibition after 30s). In this study, we are mostly interested in the role of PM sterol redistribution in stress response. Indeed we think that the role of free sterol clearance during stresses is to adapt the PM to these stresses - thus restoring PM parameters which in turn reactivates TORC2. This can be seen for hyperosmotic stress and the newly introduced PM stressors, Arp2/3 inhibition and heat shock response (Fig. 4). We have therefore softened our model and updated discussion and final figure (Fig. 5) to reflect that TORC2 likely responds to broader changes in PM organization or tension, with sterol redistribution representing one of several contributing factors rather than the sole signal.

Comment: - If TORC2 is indeed inhibited by free ergosterol, the addition of ergosterol to the growth medium should be able to trigger similar effects as PalmC. If this detection of free ergosterol is very specific (e.g. if TORC2 has a binding pocket for ergosterol) we would expect that addition of other sterols such a cholesterol or ergosterol precursors should not inhibit TORC2.

Response:

We appreciate this suggestion and agree that testing whether exogenous ergosterol can mimic PalmC effects would help assess specificity. However, yeast do not readily take up sterols under aerobic conditions, which renders artificial sterol enrichment at the yeast PM rather difficult. We have now included additional data characterizing our Lam2/4 mutants (see below), and pharmacological sterol synthesis inhibition, showing that a depletion of free sterols from the PM correlates with lower TORC2 activity (Fig. 2D, S2C). Additionally, as suggested, we tried to probe if ergosterol directly interacts with TORC2 through a specific binding pocket, by treating a yeast strain expressing cholesterol rather than ergosterol (Souza et al., 2011) with PalmC. However, the response of TORC2 activity in these cells was very similar to that of WT cells (Rebuttal Fig. 2). In conclusion, we agree that at present we do not know mechanistically how sterols affect TORC2 activity, although it does indeed seem more likely to be through an indirect mechanism linked to changes in PM parameters. The nature of such a mechanism will be subject to further studies. We hope that the introduced changes to the manuscript adequately reflect these considerations.

Rebuttal Fig. 2: WT yeast cells which produce ergosterol as main sterol, and mutant cells which produce cholesterol instead were treated with 5 µM PalmC, and TORC2 activity was assessed by relative phosphorylation of Ypk1 on WB. One representative experiment out of two replicates.

Comment: - The experiment in Figure 1C is not controlled for differences in membrane intercalation of the different compounds. For instance, does C16 choline and C16 glycine accumulate at the same rate in the PM (measure similar to experiment in Figure 1B). Maybe the positive charge at the headgroup of the surfactants increases the local concentration at the PM and therefore can explain the difference in effect on the PM.

Response:

We agree with the reviewer that the effects of the various PalmC derivatives are not directly controlled for differences in membrane intercalation. Our structure-activity screen was intended to demonstrate the general biophysical mode of action of PalmC-like compounds and to define minimal structural requirements for activity.

We now note in the manuscript that differential membrane insertion could contribute to the observed variation in efficacy, particularly in relation to tail length. While we considered this additional suggested experiment, it was ultimately judged to be outside the scope of this study due to its complexity and limited impact on the central conclusions.

A clarifying sentence has been added to the relevant results section to explicitly acknowledge this limitation:

"We did not control for differences in PM intercalation efficiency."

We also include a discussion here to further clarify our interpretation. Prior in vitro studies have shown that while intercalation is necessary, it is not sufficient for PM perturbation. For example, palmitoyl-CoA intercalates into membranes but does not induce the same biophysical effects as PalmC (Goñi et al., 1996; Ho et al., 2002). Thus, we believe that intercalation is only part of the story, and that the intrinsic propensity of different headgroups to perturb the PM plays a key role in the disruption of PM lipid organization.

Comment: - Are the intracellular ergosterol structures associated (or in close proximity) with lipid droplets (ergosterol being modified and delivered into a lipid droplet)?

Response:

We thank the reviewer for raising this point. We now include additional data (Fig. S2H) showing that intracellular D4H-positive structures do not reside near or colocalize with lipid droplets. The latter is not entirely unexpected as D4H does not recognize esterified sterols. However, we do observe an increase in overall LD volume following PalmC treatment, consistent with the idea that internalized PM sterols may be stored in LDs as sterol esters over time - although we did not test if this increase in LD volume is Lam2/4 dependent. This increase is mentioned in the revised results text. An increase in cellular LDs has also been recently reported during hyperosmotic shock (Phan et al., 2025).

For more attempts to identify a marker for intracellular D4H foci, see reply to reviewer 1.

Comment:

• How does the AA and DD mutations in Lam2/4 change the localization of the ergosterol sensor (before and after PalmC treatment).

Response:

We thank the reviewer for this question, as in the course of generating these data we realized that our "inhibited" DD mutant was in fact not phosphomimetic but displayed the same D4H distribution as the "hyperactive" AA mutant, i.e. a marked inwards shift of D4H signal away from the PM to internal structures due to increased PM-ER retrograde transport of sterols (Fig. S2C). This led us to critically re-evaluate and ultimately repeat our TORC2 activity WB experiments for PalmC treatment in LAM2/4 mutants. In this new set of experiments, the faster TORC2 recovery after PalmC treatment in the LAM2T518A LAM4S401A mutant did unfortunately not repeat robustly. It is possible that such differences can be observed under specific conditions. Nevertheless, the improved overall quality of the Western blot data allowed us to make the observation that baseline activity was already slightly different in these strains. The Lam2/4 centered part of the results section has subsequently been updated in the manuscript:

"Using a phosphospecific antibody, we did not observe an increase in baseline TORC2 activity in lam2Δ lam4Δ cells, which had been previously reported by electrophoretic mobility shift (Murley et al., 2017). Instead, baseline TORC2 activity was consistently slightly decreased in these cells (Fig. 2D). Ypk1, activated directly by TORC2, inhibits Lam2 and Lam4 through phosphorylation on Thr518 and Ser401, respectively (Roelants et al., 2018; Topolska et al., 2020). We substituted these residues with alanine, generating a strain in which Lam2/4 were no longer inhibited by phosphorylation (Roelants et al., 2018). In these cells, yeGFP-D4H showed that free sterols were constitutively shifted away from the PM to intracellular structures (Fig. S2C, bottom panel). Intriguingly, in opposition to lam2Δ lam4Δ cells, basal TORC2 activity was increased in LAM2T518A LAM4S401A cells (Fig. 2D). This suggests that a decrease in free PM sterols stimulates TORC2 activity [...]"

"In LAM2T518A LAM4S401A cells, TORC2 activity recovers with similar kinetics as the WT (Fig. 2D, bottom blot), suggesting that Lam2/4 release from TORC2 dependent inhibition during PalmC treatment is a fast and efficient process in WT cells, not further expedited by these constitutively active Lams."

As suggested, we also observed D4H localization in LAM2T518A LAM4S401A after PalmC treatment, and implemented these data to further demonstrate that PalmC causes an increase in the fraction of free ergosterol at the PM, which is subsequently removed:

"PalmC addition to LAM2T518A LAM4S401A cells likewise resulted first in a transient increase and then a further decrease in PM yeGFP-D4H signal (Fig. 3C, S3D)."

Comment: - Does Lam2/4 localize to ER-PM contact sites near the large PM invaginations, which could allow for efficient transport of the free ergosterol that accumulates in these structures.

Response:

We were curious about this too, and have now added the requested data in our supplementary material and added a sentence in our results:

"Indeed, in cells expressing GFP-Lam2 we observed that PalmC induced PM invaginations often formed at sites with preexisting GFP-Lam2 foci (Fig. S2K, cyan arrow), although GFP-Lam2 foci did not always colocalize with invaginations (Fig. S2K, yellow arrow) and vice versa. "

Additionally, in the effort to characterize intracellular D4H foci during PalmC as requested by reviewer 1, we also looked at the localization of these foci relative to ER, and found that

"During early timepoints, intracellular foci are usually in close vicinity to ER (Fig. S2E)"

Reviewer #3 (Significance (Required)): The manuscript describes the effects of small molecule surfactants on the PM organization and on TORC2 activity. This is an important set of observation that helps understanding the response of cells to environmental stressors that affect the PM. This field of study is very challenging because of the limited tools available to directly observe lipids and their movements. I consider the data and most of its interpretations of high importance, but I am not convinced of the larger model that tries to link the ergosterol data with TORC2 activity. With adjustments of the model or additional experimental support, this manuscript will be of general interest for cell biologists, especially for researchers studying membrane stress response pathways.

Response:

We thank the reviewer for highlighting the importance of studying PM stress responses and acknowledging the technical challenges involved. We hope the applied changes and additional data succeed in softening our claims about TORC2 regulation while convincing the reviewer that free sterol levels at the PM are one of several contributing factors that correlate with changes in TORC2 activity.

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Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study by Cao et al. provides a compelling investigation into the role of mutational input in the rapid evolution of pesticide resistance, focusing on the two-spotted spider mite's response to the recent introduction of the acaricide cyetpyrafen. This well-documented introduction of the pesticide - and thus a clearly defined history of selection - offers a powerful framework for studying the temporal dynamics of rapid adaptation. The authors combine resistance phenotyping across multiple populations, extensive resequencing to track the frequency of resistance alleles, and genomic analyses of selection in both contemporary and historical samples. These approaches are further complemented by laboratory-based experimental evolution, which serves as a baseline for understanding the genetic architecture of resistance across mite populations in China. Their analyses identify two key resistance-associated genes, sdhB and sdhD, within which they detect 15 mutations in wild-collected samples. Protein modeling reveals that these mutations cluster around the pesticide's binding site, suggesting a direct functional role in resistance. The authors further examine signatures of selective sweeps and their distribution across populations to infer the mechanisms - such as de novo mutation or gene flow-driving the spread of resistance, a crucial consideration for predicting evolutionary responses to extreme selection pressure. Overall, this is a well-rounded, thoughtfully designed, and well-written manuscript. It shows significant novelty, as it is relatively rare to integrate broad-scale evolutionary inference from natural populations with experimentally informed bioassays, however, some aspects of the methods and discussion have an opportunity to be clarified and strengthened.

Strengths:

One of the most compelling aspects of this study is its integration of genomic time-series data in natural populations with controlled experimental evolution. By coupling genome sequencing of resistant field populations with laboratory selection experiments, the authors tease apart the individual effects of resistance alleles along with regions of the genome where selection is expected to occur, and compare that to the observed frequency in the wild populations over space and time. Their temporal data clearly demonstrates the pace at which evolution can occur in response to extreme selection. This type of approach is a powerful roadmap for the rest of the field of rapid adaptation.

The study effectively links specific genetic changes to resistance phenotypes. The identification of sdhB and sdhD mutations as major drivers of cyetpyrafen resistance is well-supported by allele frequency shifts in both field and experimental populations. The scope of their sampling clearly facilitated the remarkable number of observed mutations within these target genes, and the authors provide a careful discussion of the likelihood of these mutations from de novo or standing variation. Furthermore, the discovered cross-resistance that these mutations confer to other mitochondrial complex II inhibitors highlights the potential for broader resistance management and evolution.

Weaknesses:

(1) Experimental Evolution:

- Additional information about the lab experimental evolution would be useful in the main text. Specifically, the dose of cyetpyrafen used should be clarified, especially with respect to the LD50 values. How does it compare to recommended field doses? This is expected to influence the architecture of resistance evolution. What was the sample size? This will help readers contextualize how the experimental design could influence the role of standing variation.

The experimental design involved sampling approximately 6,000 individuals from the wild population ZJSX1, which were subsequently divided into two parallel cohorts under controlled laboratory conditions. The selection group (LabR) was subjected to continuous selection pressure using cyetpyrafen, while the control group (LabS) was maintained under identical laboratory conditions without exposure to acyetpyrafen. A dynamic selection regime was implemented wherein the acaricide dosage was systematically adjusted every two generations to maintain a consistent selection intensity, achieving a mortality rate of 60% ± 10% in the LabR population. This adaptive dosage strategy ensured sustained evolutionary pressure while preventing population collapse. The LC₅₀ values were tested at F1, F32, F54, F60, F62, and F66 generations using standardized bioassay protocols to quantify resistance development trajectories and optimize dosage for subsequent selection cycles. We provided the additional information in subsection 4.1 of the materials and methods section.

- The finding that lab-evolved strains show cross-resistance is interesting, but potentially complicates the story. It would help to know more about the other mitochondrial complex II inhibitors used across China and their impact on adaptive dynamics at these loci, particularly regarding pre-existing resistance alleles. For example, a comparison of usage data from 2013, 2017, and 2019 could help explain whether cyetpyrafen was the main driver of resistance or if previous pesticides played a role. What happened in 2020 that caused such rapid evolution 3 years after launch?

Although the introduction of the other two SDHI acaricides complicates the story, we would like to provide a complete background on the usage of acaricides with this mode of action in China. Although cyflumetofen was released in 2013 before cyetpyrafen, and cyenopyrafen was released in 2019 after cyetpyrafen, their market share is minor (about 3.2%) compared to cyetpyrafen (about 96.8%, personal communication). Since cross-resistance is reported among SDHIs, we could not exclude the contribution of cyflumetofen to the initial accumulation of resistance alleles, but the effect should be minor, both because of their minimal market share and because of the independent evolution of resistance in the field as found in our study. Although the contribution of cyflumetofen and cyenopyrafen cannot be entirely excluded, the rapid evolution of resistance seems likely to be mainly explained by the intensive application of cyetpyrafen. To clarify this issue, we added relevant information in the first paragraph of the discussion section.

(2) Evolutionary history of resistance alleles:

- It would be beneficial to examine the population structure of the sampled populations, especially regarding the role of migration. Though resistance evolution appears to have had minimal impact on genome-wide diversity (as shown in Supplementary Figure 2), could admixture be influencing the results? An explicit multivariate regression framework could help to understand factors influencing diversity across populations, as right now much is left to the readers' visual acuity.

The genetic structure of the populations was examined by Treemix analysis. We detected only one migration event from JXNC to SHPD (no resistance data available for these two populations), suggesting a limited role for migration to resistance evolution. The multiple regression analysis revealed that overall genetic diversity and Tajima’s D across the genome were not significantly associated with resistance levels, genetic structure or geographic coordinates (P > 0.05), which all support a limited role of migration in resistance development.

- It is unclear why lab populations were included in the migration/treemix analysis. We might suggest redoing the analysis without including the laboratory populations to reveal biologically plausible patterns of resistance evolution.

Thank you for the constructive suggestion. The Treemix analysis was redone by removing laboratory populations and is now reported.

- Can the authors explore isolation by distance (IBD) in the frequency of resistance alleles?

Thank you for the constructive suggestion. No significant isolation-by-distance pattern was detected for resistance allele frequencies across all surveyed years (2020: P=0.73; 2021: P=0.52; 2023: P=0.16; Mantel test). We added these results to the text.

- Given the claim regarding the novelty of the number of pesticide resistance mutations, it is important to acknowledge the evolution of resistance to all pesticides (antibiotics, herbicides, etc.). ALS-inhibiting herbicides have driven remarkable repeatability across species based on numerous SNPs within the target gene.

We appreciate this comment, which highlights the need to place our findings within the broader evolutionary context of pesticide resistance. We have investigated references relevant to the evolution of resistance to diverse pesticides. As far as we can tell, the 15 target mutations in eight amino acid residues are among the highest number of pesticide resistance mutations detected, especially within the context of animal studies. We have added relevant text to the second paragraph of the discussion.

- Figure 5 A-B. Why not run a multivariate regression with status at each resistance mutation encoded as a separate predictor? It is interesting that focusing on the predominant mutation gives the strongest r2, but it is somewhat unintuitive and masks some interesting variation among populations.

We conducted a multiple regression analysis to explore the influence of multiple mutations on resistance levels of field populations. However of 15 putative resistant mutations, only five were detected in more than three populations where bioassay data are available, i.e. I260T, I260V, D116G, R119C, R119L. The frequency of three of these mutations, I260T (P = 0.00128), I260V (P = 0.00423) and D116G (P = 0.00058), are significantly correlated with the resistance level of field populations. This has been added.

(3) Haplotype Reconstruction (Line 271-):

- We are a bit sceptical of the methods taken to reconstruct these haplotypes. It seems as though the authors did so with Sanger sequencing (this should be mentioned in the text), focusing only on homozygous SNPs. How many such SNPs were used to reconstruct haplotypes, along what length of sequence? For how many individuals were haplotypes reconstructed? Nonetheless, I appreciated that the authors looked into the extent to which the reconstructed haplotypes could be driven by recombination. Can the authors elaborate on the calculations in line 296? Is that the census population size estimate or effective?

Because haplotypes could not be determined when more than two loci were heterozygous, we detected haplotypes from sequencing data with at most one heterozygous locus. In total 844 individuals and 696 individuals were used to detect haplotypes of sdhB and sdhD. We detected 11 haplotypes (with 8 SNPs) and 24 haplotypes (with 11 SNPs) along 216 bp of the sdhB and 155 bp of the sdhD genes, respectively. Please see the fifth paragraph of subsection 2.4. We used ρ = 4 × Ne × d (genetic distance) (Li and Stephens, 2003) to calculate the number of effective individuals for one recombination event.

(4) Single Mutations and Their Effect (line 312-):

- It's not entirely clear how the breeding scheme resulted in near-isogenic lines. Could the authors provide a clearer explanation of the process and its biological implications?

To investigate the effect of single mutations or their combination on resistance levels, we isolated the females and males with the same homozygous/ hemizygous genotypes for creating homozygous lines. Females from these lines were not near-isogenic, but homozygous for the critical mutations. We revised the description in the methods section to clearly define these lines.

- If they are indeed isogenic, it's interesting that individual resistance mutations have effects on resistance that vary considerably among lines. Could the authors run a multivariate analysis including all potential resistance SNPs to account for interactions between them? Given the variable effects of the D116G substitution (ranging from 4-25%), could polygenic or epistatic factors be influencing the evolution of resistance?

We couldn’t conduct multivariate analysis because most lines have only one resistant SNP. The four lines homozygous for 116G were from the same population. The variable mortality may reflect other unknown mechanisms but these are beyond the scope of this study.

- Why are there some populations that segregate for resistance mutations but have no survival to pesticides (i.e., the green points in Figure 5)? Some discussion of this heterogeneity seems required in the absence of validation of the effects of these particular mutations. Could it be dominance playing a role, or do the authors have some other explanation?

We didn’t investigate the degree of dominance of each mutation. The mutation I260V shows incompletely dominant inheritance (Sun, et al. 2022). To investigate survival rate of different populations, the two-spotted spider mite T. urticae was exposed to 1000 mg/L of cyetpyrafen, higher than the recommended field dose of 100 mg/L. Such a high concentration may lead to death of an individual heterozygous for certain mutations, such as I260V.

- The authors mention that all resistance mutations co-localized to the Q-site. Is this where the pesticide binds? This seems like an important point to follow their argument for these being resistance-related.

Yes. We revised Fig. 3c to show the Q-site.

(5) Statistical Considerations for Allele Frequency Changes (Figure 3):

- It might be helpful to use a logistic regression model to assess the rate of allele frequency changes and determine the strength of selection acting on these alleles (e.g., Kreiner et al. 2022; Patel et al. 2024). This approach could refine the interpretation of selection dynamics over time.

Thank you for this suggestion. A logistic regression model was used to track allele frequencies trajectories. The selection coefficient of each allele and their joint effects were estimated.

Reviewer #2 (Public review):

Summary:

This paper investigates the evolution of pesticide resistance in the two-spotted spider mite following the introduction of an SDHI acaricide, cyatpyrafen, in China. The authors make use of cyatpyrafen-naive populations collected before that pesticide was first used, as well as more recent populations (both sensitive and resistant) to conduct comparative population genomics. They report 15 different mutations in the insecticide target site from resistant populations, many reported here for the first time, and look at the mutation and selection processes underlying the evolution of resistance, through GWAS, haplotype mapping, and testing for loss of diversity indicating selective sweeps. None of the target site mutations found in resistant populations was found in pre-exposure populations, suggesting that the mutations may have arisen de novo rather than being present as standing variation, unless initially present at very low frequencies; a de novo origin is also supported by evidence of selective sweeps in some resistant populations. Furthermore, there is no significant evidence of migration of resistant genotypes between the sampled field populations, indicating multiple origins of common mutations. Overall, this indicates a very high mutation rate and a wide range of mutational pathways to resistance for this target site in this pest species. The series of population genomic analyses carried out here, in addition to the evolutionary processes that appear to underlie resistance development in this case, could have implications for the study of resistance evolution more widely.

Strengths:

This paper combines phenotypic characterisation with extensive comparative population genomics, made possible by the availability of multiple population samples (each with hundreds of individuals) collected before as well as after the introduction of the pesticide cyatpyrafen, as well as lab-evolved lines. This results in findings of mutation and selection processes that can be related back to the pesticide resistance trait of concern. Large numbers of mites were tested phenotypically to show the levels of resistance present, and the authors also made near-isogenic lines to confirm the phenotypic effects of key mutations. The population genomic analyses consider a range of alternative hypotheses, including mutations arising by de novo mutation or selection from standing genetic variation, and mutations in different populations arising independently or arriving by migration. The claim that mutations most likley arose by multiple repeated de novo mutations is therefore supported by multiple lines of evidence: the direct evidence of none of the mutations being found in over 2000 individuals from naive populations, and the indirect evidence from population genomics showing evidence of selective sweeps but not of significant migration between the sampled populations.

Weaknesses:

As acknowledged within the discussion, whilst evidence supports a de novo origin of the resistance-associated mutations, this cannot be proven definitively as mutations may have been present at a very low frequency and therefore not found within the tested pesticide-naive population samples.

We agree that we could not definitively exclude the presence of a very low incidence of favoured mutations before the introduction of this novel acaricide.

Near-isofemale lines were made to confirm the resistance levels associated with five of the 15 mutations, but otherwise, the genotype-phenotype associations are correlative, as confirmation by functional genetics was beyond the scope of this study.

We hope that future functional studies will validate the effects of these mutations on resistance in both the two-spotted spider mite T. urticae and other spider mite species. This could be done by creating near-isogenic female lines or using CRISPR-Cas9 technology, as gene knockouts have recently been established for T. urticae.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Could the authors elaborate on the environmental context (e.g., climate, geography) of the sampled populations to give more nuance to the analysis of genetic differentiation and resistance evolution?

We have explored the influence of geographic isolation on the frequency of resistance alleles by Mantel tests (isolation by distance). We didn’t investigate the influence of climate, because most of the samples were from greenhouses, where the climate to which the pest is exposed is unclear.

(2) Line 161: is this supposed to be one R and one S?

Yes, we added this information (LabR and LabS).

(3) Line 207: variation is not saturated at the first two sites because the different combinations are not seen. This is a bit misleading.

What we wanted to indicate was that the two codon positions are saturated, rather than their combinations. We revised this sentence by adding “of each codon position”.

(4) Line 376: continuous selection did not "result in a new mutation arising". Rather, the mutation arose and was subsequently selected on.

We revised the expression of this de novo mutation and selection process.

(5) Line 402: can the authors explore what Ne would be necessary to drive the number of mutational origins they observe, as in (Karasov et al. 2010)?

It is challenged to estimate Ne, especially when mutation rate data from the two-spotted spider mite T. urticae is unavailable. We observed 2.7 resistant mutations per population in samples collected in 2024, seven years after the release of cyetpyrafen. The estimated mutation rate (Θ) is  0.0193, given 20 generations per year for T. urticae. An effective population size (Ne) of 2.29*10⁶ would be necessary to reach the number of de novo mutations observed in this study, given Θ  =  3Neμ (haplodiploid sex determination of T. urticae) and a mutation rate of μ  =  2.8*10^-9 per base pair per generation as estimated for Drosophila melanogaster (Keightley et al., 2014). The high reproductive capacity of T. urticae (> 100 eggs per female) and short generation time makes it easier to reach such a population size in the field as we now note.

(6) Line 482: how did the authors precisely kill 60% of samples with their selection? What was the applied rate? In general, listing the rates of insecticide used in dose response would be useful to decipher if LD50s are projected outside of the doses used (seems like they are). In this case, authors should limit their estimates to those > the highest rate used in the dose response.

It is difficult to control mortality precisely. We applied cyetpyrafen every two generations but did not determine the LC₅₀ every two generations. When mortality was lower than 60%, another round of spraying was applied by increasing the dosage of the pesticide. The LC₅₀ values were tested at F₁, F₃₂, F₅₄, F₆₀, F₆₂, and F₆₆ generations to establish the trajectories around resistance.

(7) The light pink genomic region in Figure 2 was distracting. Why is it included if there is no discussion of genomic regions outside the sdh genes? Generally, there was a lot going on in this figure, and some guiding categories (i.e., lab selected vs wild population) on the figure itself could help orient the reader.

We included chromosome 2 colored in light pink/ red to show the selection signal across a wider genomic region. In the figure legend, we added a description of the lab selected, field resistant and field susceptible populations. Very little common selection signal was detected among resistant populations on chromosome 2, indicating this region was less likely to be involved in resistance evolution of T. urticae to cyetpyrafen. We also described the result briefly in the figure legend.

Reviewer #2 (Recommendations for the authors):

(1) The most significant aspect of this study is the use of multiple pest population samples taken before as well as after the introduction of a class of pesticides, allowing a thorough comparative population genomics study in a species where a range of resistance mutations have appeared within a few years. I would prefer to see a title conveying this significance, rather than the current study, which focuses on the total number of mutations and claimed notoriety of the (at that point unnamed) study species. Similarly, I would prefer an abstract that relies less on superlative claims and includes more details: the scientific name of the study species; the number of years in which resistance evolved; the number of historical specimens; how the resistance levels for single mutations were shown.

(1) The title was changed by adding “the two-spotted spider mite Tetranychus urticae” and removing the “unprecedented number” to emphasize that “recurrent mutations drive rapid evolution”, i.e., “Recurrent Mutations Drive the Rapid Evolution of Pesticide Resistance in the Two-spotted Spider Mite Tetranychus urticae.”

(2) The scientific name of the study species was added.

(3) The number of years in which resistance evolved was added.

(4) The number of historical specimens was added (2666).

(5) Because we used homozygous lines but not iso-genic lines or gene-edited lines, our bioassay data could not provide direct evidence on the level of resistance conferred by each mutation. We revised our description of the results and removed this content from the abstract.

Line 29: if you want to claim the number is unprecedented, please specify the context: unprecedented for a pesticide target in an arthropod pest? (more resistance mutations may have been found in bacteria/fungi...).

We revised the sentence by adding “in an arthropod pest”.

Line 30: rather than a claim of notoriety, it may be better to specify what damage this pest causes.

Revised by describing it as an arthropod pest.

Line 34: please clarify, was this all in different haplotypes, or were some mutations found in combination?

Done: We identified 15 target mutations, including six mutations on five amino acid residues of subunit sdhB, and nine mutations on three amino acid residues of subunit sdhD, with as many as five substitutions on one residue.

(2) The introduction begins by framing the context as resistance evolution in invertebrate pests. However, the evolutionary processes examined in the study are applicable to resistance in other systems, and potentially to other cases of rapid contemporary evolution. The authors could show wider significance for their work beyond the subfield of invertebrate pests by including more of this wider context in their introduction and discussion: even if this means they can no longer claim novelty based on the number of mutations alone, the study is a strong example of the use of population genomics combined with functional and phenotypic characterisation to investigate the evolutionary processes underlying the emergence of resistance, so could have wider importance than within its current framing.

The background was revised as mentioned above to take this into account.

For example, in lines 48-50, please clarify what is meant by pesticides here (insects/arthropods? weeds and pathogens too?) In lines 69-73, the opposite is sometimes seen in fungal pathogens, with large numbers of mutations generated in lab-evolved strains.

We extended pesticides to those targeting arthropods, weeds and pathogens. We still emphasize the situation mainly with respect to arthropod pests.

(3) Lines 91-93: how many modes of action? How recently were SDHI acaricides introduced?

Added: at least 11 groups of acaricides based on their modes of action. SDHI was launched in 2007.

(4) Line 98-102: Use in China is a useful background for the study populations, but the global context should be included too.

Yes, four SDHI acaricides developed around the globe were introduced.

(5) Line 113: They show diverse mutations, but all within the mechanism of target-site point mutations.

We agree to your suggestion. This sentence has been removed as it repeats information stated above it.

(6) Line 115-116: Yes, agreed; I think this is the main strength of the current study and should be emphasised sooner.

Thanks.

(7) Line 158: Selective sweep signals were clear in half of the resistant populations but not in the others. The suggestion that the others had undergine soft sweeps, with multiple mutations increasing in frequency simultaneously but no one reaching fixation, seems reasonable; but the authors could compare the populations that did show a sweep with those that did not (for example, was there greater diversity or evenness of genotypes in those that did not?).

Five resistant populations with selection signals identified by PBE analysis (Figure 2b) showed corresponding decreases in π and Tajima’s D near the two SDH genes but not across the genome (Figure S1).

(8) Line 313: please clarify "in combination with other mutations" within a mixed population or combined in one individual/haplotype? Also, the phrase "characterised the function" may be a little misleading, as this is a correlative analysis, not functional confirmation.

None of the combinations of different resistant mutations was observed in a single haplotype. Here, we examine resistance levels associated with a single mutation or two mutations on sdhB and sdhD in one individual, i.e. sdhB_I260V and sdhD_R119C. We revised the sentences to avoid any implication of functional confirmation.

(9) Line 358: again, please clarify the context: among arthropod pests?

Done.

(10) Line 360-363: please give some background on when and where these related compounds were introduced.

Added.

(11) Line 410: yes fitness costs may be a factor, but you could also give an example of a cost expressed in the absence of any pesticides, as well as the given example of negative cross-resistance.

We added the example of the H258Y mutation which causes both fitness costs and negative cross-resistance.

(12) Lines 419-438: this is one aspect where the situation for insecticides is in contrast with some other resistance areas.

Yes, we restricted these statements to arthropod pests.

(13) Line 466: some more detail could be given here: for example, SNP-specific monitoring would be less effective, but amplicon sequencing would be more suitable.

Yes, revised.

(14) Lines 472-475: Please list the numbers of field/lab, pre/post exposure, and sensitive/resistant populations within the main text.

Done. The number of sensitive/resistant populations was reported in the result section.

(15) Line 483: randomly selected individuals?

Yes, added randomly selected individuals.

(16) Line 556: Sanger sequencing to characterise populations? Or a number of individuals from each population?

Revised.

(17) References: there are some duplicate entries, please check this.

Checked.

(18) Figure 1e: consider a log(10) scale to better show large fold changes and avoid multiple axis breaks.

Thanks for your suggestions. However we didn’t scale the LC₅₀ value, because we wanted to show the specific impact of 1,000 mg/L. The breaks in the Y axis around 30 mg/L -1,000 mg/L reveal that the LC50s of the resistant populations were all greater than 1000 mg/L, while those of the susceptible populations were all below 30 mg/L. This justified the use 1000 mg/L as a discriminating dose to investigate resistance status and level in subsequent work.

Author response:

The following is the authors’ response to the original reviews

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

There are four main areas that need further clarification:

(1) Further and more complete assessment of senescence and the fibroblasts must be done to support the claims. 

We sincerely appreciate the Reviewing Editor's valuable suggestion regarding the addition of cellular senescence detection markers. In the revised manuscript, we have incorporated additional detection markers for cellular senescence, such as H3K9me3 and SA-β-gal staining, in healthy and periodontitis gingival samples to further validate our findings (Figure 1A, B in revised manuscripts).

(2) Confusion between ageing and senescence throughout the manuscript.

We fully understand the concerns raised by the Reviewing Editor and reviewers regarding the confusion between the concepts of ageing and senescence in the manuscript. Cellular senescence is a manifestation of ageing at the cellular level. In the revised manuscript, we have given priority to the term ‘senescence’ to describe the cell condition instead of ‘aging’.

(3) The lipid metabolism mechanistic claims are very speculative and largely unsupported by experimental data. 

We greatly appreciate the Reviewing Editor and reviewers for pointing out the incorrect statements regarding the role of lipid metabolism in regulating cellular senescence. Since the mechanism by which cellular metabolism regulates cellular senescence is not the core focus of this manuscript, we have moved the results of the metabolic analysis from the sc-RNA sequencing data to the figure supplement (Figure 4-figure supplement 1) and revised the related statements in the revised manuscript (Page 7-8, Line 186-194).

(4) Concerns about the use of Metformin as a senotherapy vs other pleiotropic effects in periodontitis and the suggestion of using an alternative Senolytic drug (Bcl2 inhibitors, etc.). 

We fully understand the concerns of the Reviewing Editor and reviewers regarding metformin as an anti-aging therapy. In the revised manuscript, we have included additional experiments using other senolytic drugs ABT-263, a Bcl2 inhibitor, in the ligature-induced periodontitis mouse model. The corresponding results could be found in the Figure 6. and Page 9-10, Line 248-264 in the revised manuscripts.

Reviewer #1 (Recommendations For The Authors):

While most of the experiments are elegantly designed and the procedures well conducted there are several critical weaknesses that temper my enthusiasm for this solid and timely work. Considering my main points, I would recommend the following:

(1) Potentiate the senescent assessment in vitro and, most importantly, in vivo. E.g. SABgal with fresh tissue, other senescent biomarkers like SAHFs (HP1g or H3K9me3), etc.

We sincerely appreciate the reviewers' suggestion to potentiate the assessment of cellular senescence. In the revised manuscript, we performed SA-β-gal staining on fresh frozen samples, revealing a significantly higher number of SA-β-gal positive cells in the gingival tissue of periodontitis, particularly in the lamina propria, while few SA-β-gal positive cells were observed in healthy gingival tissue (Figure. 1A). Additionally, we assessed the protein level changes of H3K9me3, a marker of senescence-associated heterochromatin foci (SAHF), in gingival tissues from healthy individuals and periodontitis patients. The results showed a notable increase in the number of H3K9me3 positive cells in periodontitis tissues, approximately double that found in healthy gingiva (Figure. 1B). This trend aligns with our previous findings of elevated p16 and p21 levels. Collectively, these results further confirm that periodontitis gingival tissues contain a greater number of senescent cells compared to healthy gingiva.  

(2) Claims on disturbances in lipid metabolism as a driver of CD81+ fibroblast senescence require appropriate functional/mechanistic validations and experiments of metabolism rewiring.

We sincerely appreciate the reviewers' suggestion for more experimental evidence regarding the role of lipid metabolism in driving CD81+ fibroblast senescence. The influence and mechanisms of lipid metabolism on cellular senescence is a complex and important scientific issue, and it is not the central focus of this manuscript. Therefore, to avoid causing confusion for the reviewers and readers, we have removed the metabolism analysis in the Figure 4-figure supplement 1 and revised the presentation of the relevant results in the revised manuscript to ensure a more rigorous interpretation of our findings (Page 7-8, Line 186-194). 

(3) Do LPS-stimulated HGFS implementing the senescent programme secrete C3? Detection of complement C3 at the protein level (e.g. by ELISA) would reinforce the proposed mechanism.

This is indeed a very interesting question. In response to the reviewers' suggestion, we measured the levels of C3 protein secreted by human gingival fibroblasts induced by Pg-LPS, which is one of the markers of the senescence-associated secretory phenotype (SASP). The results indicated that, compared to untreated fibroblasts, those induced by Pg-LPS exhibited significantly higher levels of C3 secretion, approximately 1.5 times that of the control group (Figure. 5G). Additionally, we also found that primary gingival fibroblasts derived from periodontitis tissues secreted more complement C3 compared to those derived from healthy tissues (Figure. 5F). These findings suggest that the increased secretion of complement C3 by gingival fibroblasts in periodontitis tissues may be related to Pg-LPS-induced cellular senescence.

(4) The mechanism of Metformin to impair senescence and/or the SASP is not fully validated and Metformin can produce other pleiotropic effects. A key experiment (including therapeutic implications) is using a senolytic drug (e.g. Navitoclax) to causally connect the eradication of senescent CD81+ fibroblasts with the recruitment of neutrophils. If the hypothesis of the authors is correct this approach should result in reduced levels of gingival CD81 and C3 positivity, prevention of neutrophils infiltration (reduced MPO positivity), and ameliorate bone damage in ligationinduced periodontitis murine models.

We fully understand the reviewers' concerns regarding the role of metformin in alleviating cellular senescence and the possibility of it acting through non-senescent pathways. To clarify the role of cellular senescence in the recruitment of neutrophils by CD81+ fibroblasts through C3 in periodontitis, we treated a ligature-induced periodontitis mouse model with ABT-263, also known as Navitoclax. The results showed that after ABT-263 treatment, the number of p16-positive or H3K9me3-positive senescent cells in the periodontitis mice significantly decreased. Additionally, we observed reductions in the quantities of CD81+ fibroblasts, C3 protein levels, neutrophil infiltration, and osteoclasts to varying degrees in the LIP model after ABT263 treatment (Figure. 6). These results further support our hypothesis that the eradication of senescent CD81+ fibroblasts could reduce neutrophil infiltration and alveolar bone resorption. 

(5) Have the authors considered using any of the available C3/C3aR inhibitors to validate the involvement of neutrophils and the inflammatory response in periodontitis? A C3/C3aR inhibitor would be an elegant treatment group in parallel with the senolytic approach.

Thank you very much for the reviewers' suggestion to investigate neutrophil infiltration and inflammatory responses after treating periodontitis with C3/C3aR inhibitors. In a clinical study by Hasturk et al. in 2021 (Reference 1), it was found that using the C3 inhibitor AMY-101 effectively alleviated gingival inflammation levels in periodontitis patients. This was reflected in significant decreases in clinical indicators such as the modified gingival index and bleeding on probing, as well as a marked reduction in inflammatory tissue destruction markers, including MMP-8 and MMP-9. In addition, Tomoki Maekawa et al. (Reference 2) demonstrated that a peptide inhibitor of complement C3 effectively reduced inflammation levels and the extent of bone resorption in periodontitis. Moreover, research by Guglietta et al. (Reference 3) clarified that the C3 complement promotes neutrophil recruitment and the formation of neutrophil extracellular traps (NETs) via C3aR. And neutrophil extracellular traps are considered key pathological factors in causing sustained chronic inflammation in periodontitis (References 4 and 5). In summary, existing studies have clearly indicated that C3/C3aR inhibitors likely reduce neutrophil recruitment and inflammation in periodontitis. 

Reference

(1) Hasturk, H., Hajishengallis, G., Forsyth Institute Center for Clinical and Translational Research staff, Lambris, J. D., Mastellos, D. C., & Yancopoulou, D. (2021). Phase IIa clinical trial of complement C3 inhibitor AMY-101 in adults with periodontal inflammation. The Journal of clinical investigation, 131(23), e152973.

(2) Maekawa, T., Briones, R. A., Resuello, R. R., Tuplano, J. V., Hajishengallis, E., Kajikawa, T., Koutsogiannaki, S., Garcia, C. A., Ricklin, D., Lambris, J. D., & Hajishengallis, G. (2016). Inhibition of pre-existing natural periodontitis in non-human primates by a locally administered peptide inhibitor of complement C3. Journal of clinical periodontology, 43(3), 238–249.

(3) Guglietta, S., Chiavelli, A., Zagato, E., Krieg, C., Gandini, S., Ravenda, P. S., Bazolli, B., Lu, B., Penna, G., & Rescigno, M. (2016). Coagulation induced by C3aR-dependent NETosis drives protumorigenic neutrophils during small intestinal tumorigenesis. Nature communications, 7, 11037.

(4) Kim, T. S., Silva, L. M., Theofilou, V. I., Greenwell-Wild, T., Li, L., Williams, D. W., Ikeuchi, T., Brenchley, L., NIDCD/NIDCR Genomics and Computational Biology Core, Bugge, T. H., Diaz, P. I., Kaplan, M. J., Carmona-Rivera, C., & Moutsopoulos, N. M. (2023). Neutrophil extracellular traps and extracellular histones potentiate IL-17 inflammation in periodontitis. The Journal of experimental medicine, 220(9), e20221751.

(5) Silva, L. M., Doyle, A. D., Greenwell-Wild, T., Dutzan, N., Tran, C. L., Abusleme, L., Juang, L. J., Leung, J., Chun, E. M., Lum, A. G., Agler, C. S., Zuazo, C. E., Sibree, M., Jani, P., Kram, V., 6 Martin, D., Moss, K., Lionakis, M. S., Castellino, F. J., Kastrup, C. J., … Moutsopoulos, N. M. (2021). Fibrin is a critical regulator of neutrophil effector function at the oral mucosal barrier. Science (New York, N.Y.), 374(6575), eabl5450.

Other comments

(1) Figure 1. The authors report upregulation of the aging pathway in bulk RNAseq analyses. What about the upregulation of senescence-related pathways and differential expression of SASP-related genes in this experiment?

Thanks for this interesting question. Through further analysis of the bulk RNA sequencing results of gingival tissues from LIP mice model, we found significant alterations in multiple senescence-associated secretory phenotype (SASP) genes and several cellular senescencerelated pathways. SASP genes, such as Icam1, Mmp3, Nos3, Igfbp7, Igfbp4, Mmp14, Timp1, Ngf, Il6, Areg, and Vegfa, were markedly upregulated in the periodontitis samples of ligature-induced mice (Figure 1-figure supplement 2A). Moreover, we observed a significant reduction in oxidative phosphorylation levels and the tricarboxylic acid (TCA) cycle in the periodontitis group, suggesting that the occurrence of cellular senescence may be related to mitochondrial dysfunction (Figure 1figure supplement 2B and C.).

Additionally, we noted the activation of the PI3K-AKT and MAPK pathways in LIP model (Figure 1-figure supplement 2D and E), both of which can induce cellular senescence by activating the tumor suppressor pathway TP53/CDKN1A, leading to cell cycle arrest (References 1, 2). Furthermore, the NF-κB signaling pathway was also significantly enriched in LIP model (Figure 1-figure supplement 2F), which is closely associated with the secretion of SASP factors (Reference 3).

In summary, our bulk RNA sequencing results suggest enrichment of cellular senescencerelated pathways in the periodontitis group, including mitochondrial metabolic dysregulation, senescence-related pathways, and alterations in the SASP. Related results were added into Page 56 of the revised manuscripts.

Reference

(1) Tang Q, Markby GR, MacNair AJ, Tang K, Tkacz M, Parys M, Phadwal K, MacRae VE, Corcoran BM. TGF-β-induced PI3K/AKT/mTOR pathway controls myofibroblast differentiation and secretory phenotype of valvular interstitial cells through the modulation of cellular senescence in a naturally occurring in vitro canine model of myxomatous mitral valve disease. Cell Prolif. 2023 Jun;56(6):e13435. doi: 10.1111/cpr.13435.

(2) Sayegh S, Fantecelle CH, Laphanuwat P, Subramanian P, Rustin MHA, Gomes DCO, Akbar AN, Chambers ES. Vitamin D3 inhibits p38 MAPK and senescence-associated inflammatory mediator secretion by senescent fibroblasts that impacts immune responses during ageing. Aging Cell. 2024 Apr;23(4):e14093.

(3) Raynard C, Ma X, Huna A, Tessier N, Massemin A, Zhu K, Flaman JM, Moulin F, Goehrig D, Medard JJ, Vindrieux D, Treilleux I, Hernandez-Vargas H, Ducreux S, Martin N, Bernard D. NF-κB-dependent secretome of senescent cells can trigger neuroendocrine transdifferentiation of breast cancer cells. Aging Cell. 2022 Jul;21(7):e13632.

(2) I wonder whether the authors could clarify how the semi quantifications for p21, p16, Masson's trichrome, C3, or MPO were done in Figures 1, 2, and 6.

Thank you very much for the reviewer's suggestion. We have added the semi-quantitative methods for p21, p16, Masson's trichrome, C3, and MPO in the Methods section. Specifically, for semi-quantification of protein expressions, the mean optical density (MOD) of positive stains for p21, p16, and C3 was measured using the ImageJ2 software (version 2.14.0, National Institutes of Health, Bethesda, MD). The number of MPO-positive cells and collagen volume fractions (stained blue) for individual sections were also measured using the ImageJ2 software. (Page 19, Line 537-541 in the revised manuscripts).  

(3) Figure 2. It is unclear whether N=6 refers to 6 mice, maxilla, or fields per group.

Thank you very much for the reviewer's question. To avoid any misunderstandings for the reviewer and readers, we have added a definition of the sample size in the description of the micro-CT analysis method. Specifically, in the micro-CT quantitative analysis, the sample size n for each group consists of 6 mice, with the average value of the BV/TV of the bilateral maxillary alveolar bone taken as one sample for statistical analysis (Page 17-18, Line 488-490 in the revised manuscripts).  

(4)  igure 4K. Please provide separated staining for p16, VIM, and CD81, and not only the Merge. It is difficult to identify the triple-positive cells. Also, the arrows are difficult to observe.

Thank you very much for the reviewer's suggestion. In the revised manuscript, we have included separated staining for p16, VIM, and CD81, and the triple-positive cells are indicated with white arrows (Figure 5-figure supplement 1). 

(5) Overall, improve the magnifications in the IF experiments and show where the magnified areas come from.

Thank you very much for the reviewer's suggestion. We have enlarged the fluorescence result images.

(6) Refer to the original datasets of the scRNAseq results in figure legends.

Thank you very much for the reviewer's suggestion. We have indicated the source of the raw single-cell sequencing data in the figure legend.

(7) Check English grammar and writing.

Thank you for the reviewer's suggestion. We checked the grammar and writing in the revised manuscript assisted by a native English speaker and AI tools like Chat-GPT.

Reviewer #2 (Recommendations For The Authors):

(1) When the authors refer to accelerated aging and/or senescence, they are doing so in comparison to what?

Thank you for the reviewer's question, which allows me to further clarify the concepts of accelerated aging and/or senescence. In sections 2.1 and Figure 1 of this manuscript, we referred to accelerated aging and/or senescence. This indicates that the gingival tissues of periodontitis patients exhibit a higher number of senescent cells and elevated levels of senescence-related markers compared to healthy gingival tissues. In the title of this manuscript, we describe CD81+ fibroblasts as a unique subpopulation with accelerated cellular senescence. This means that CD81+ fibroblasts display higher expression levels of senescence-related genes, cell cycle inhibitor p16, and SASP factors compared to other fibroblast subpopulations. To avoid any misunderstanding, we have deleted the text ‘accelerated senescence’ in the revised manuscripts. 

(2) In general, the main text does not describe the results using exact and reproducible terminology. Phrases like "X was most active", "a significant increase was observed", "the highest proportion was", and "the level of aging increased" should be supported by adding quantification details and by detailing what these comparisons are made to, to improve the reproducibility of the results.

Thank you for the reviewer's suggestion. To improve the reproducibility of the results, we have added quantification details in the results section and clarified what comparisons are being made through the whole manuscript.

(3) In some sections of the main text and figure legends, it is not entirely clear which sequencing experiments were conducted by the authors, which analyses were conducted by the authors on publicly available sequencing data, and which analyses were conducted on their mouse sequencing data.

Thank you for the valuable feedback from the reviewer. To further clarify the source of the sequencing data, we have clearly indicated the data source in both the results section and the figure legends. 

(4) In Figure 3H, the images showing SA-beta-gal staining on LPS-treated fibroblasts do not show convincingly the difference between treatments that are represented in the graph.

Thank you for the reviewer's suggestion. To further clearly show the differences between treatments, we have enlarged the partial image of SA-β-gal staining shown in Figure 2-figure supplement 2 of the revised manuscripts. 

(5) The choice of colors for Figure 4K is far from ideal as it is very difficult to tell apart red from purple channels and thus to visualize triple positive cells. A different LUT should be chosen, and separate individual channels should be shown to clearly identify triple-positive cells from others. Arrows also do not currently point at triple-positive cells.

Thank you for the reviewer's suggestion. In the revised manuscript, we have included separated staining for p16, VIM, and CD81, and the triple-positive cells are marked with white arrows shown in Figure 5-figure supplement 1 of the revised manuscripts.  

(6) The authors state that treatment with metformin "alleviated.... inflammatory cell infiltration (Figure 2C), and collagen degradation (Figure 2D) as observed through H&E and Masson staining." However, I cannot find a description of how the "relative fraction of collagen" in Figure 2Gc was calculated and how the H&E image they provide shows evidence of a reduction in inflammatory cells at that magnification.

Thank you for the reviewer's suggestion. In the revised manuscript, we have added details in the methods section regarding the calculation of the "relative fraction of collagen" (Page 19, Line 539-541). Specifically, the collagen volume fractions (stained blue) for individual sections were measured using ImageJ2 software. Additionally, we have marked the infiltrating inflammatory cells in the gingiva in the H&E images with black arrows shown in Figure 7-figure supplement 1B of the revised manuscripts.

(7) It appears that the in vivo experiment for metformin treatment was conducted with 6 animals per group, but this is not clear in the figures, main text, and methods.

Thank you for the reviewer's suggestion. In the revised manuscript, we have included the number of mice in each group for the in vivo experiments, specifying that there are 6 mice per group in the figures, main text, and methods sections.

(8) The methodology described for the bulk RNA-sequencing experiment in mice should describe the sequencing library characteristics and some reference to quality control thresholds that were implemented (mapped and aligned reads, sequencing depth and coverage, etc.).

In the bulk RNA-sequencing experiment, the sequencing library characteristics and quality control thresholds were listed as follows:

Sequencing Library Characteristics: We utilized the Illumina TruSeq RNA Library Construction Kit, generating libraries with an insert fragment length of approximately 400-500 bp.

Quality Control Standards include the following:

Alignment and Mapping Rates: The read data for all samples underwent preliminary quality control using FastQC (v0.11.9) and were aligned using HISAT2 (v2.2.1). The average mapping rate for each sample was over 90%.

Sequencing Depth and Coverage: Each sample had a sequencing depth of 30M-40M paired reads to ensure sufficient transcript coverage. Detailed alignment statistics have been provided in the supplementary materials.

Other Quality Control Measures: During the analysis, we also utilized RSeQC (v3.0.1) to evaluate the transcript coverage and GC bias of the sequencing data.

The corresponding method description and reference were added in the Page 19-20, Line 546-558 of the revised manuscripts.

(9) Patients with periodontitis are labeled as diagnosed with "chronic periodontitis". I would like to know how the authors defined this chronic state of the disease in their inclusion criteria.

Thank you very much for the reviewer’s question, which gives us the opportunity to further clarify the definition and diagnosis of chronic periodontitis. The diagnostic criteria for patients with chronic periodontitis in this study are based on the 1999 International Workshop for a Classification of Periodontal Diseases and Conditions (Reference 1). Chronic periodontitis is a type of periodontal disease distinct from aggressive periodontitis, and it is not diagnosed based on the rate of disease progression. Clinically, the diagnosis of chronic periodontitis is primarily based on clinical attachment loss (CAL) ≥ 4 mm or probing depth (PD) ≥ 5 mm as one of the criteria for diagnosis.

Reference

(1) Armitage G. C. (2000). Development of a classification system for periodontal diseases and conditions. Northwest dentistry, 79(6), 31–35.

(10) There is no detail about the age and sex of the donors for the healthy gingival fibroblast experiments. Are they some of the patients mentioned in Supplementary Table 1? Please clarify the source and number of independent primary cultures.

Thank you very much to the reviewer for allowing us to further clarify the source and number of independent primary cultures. In the cell experiments, we used gingival fibroblasts derived from gingival tissue of two healthy volunteers and two patients with periodontitis as experimental subjects. This information has been listed in the Supplementary Table 1. 

(11) Can the authors explain why their age inclusion criteria were different for the healthy and periodontitis groups according to their methods (healthy 18-50 years old: periodontitis 18-35 years old?)

Thank you very much to the reviewer for pointing this out. We noticed that there was an error in the age range indicated for the healthy and periodontitis groups in the inclusion criteria. Based on the original inclusion criteria information, we have corrected the age range of the included population. 18-65 years old individuals were included into the both healthy and periodontitis groups. (Page 14-15, Line 396-404 in the revised manuscripts)

(12) The methodology for inclusion is confusing and does not reflect the actual information of the recruited patients and samples thus analyzed. In the text, the healthy group appears to have included 8 young adult individuals and 8 middle-aged individuals. However, the list of recruited patients shows all healthy patients were in the young adult range (below 35 years of age) while all chronic periodontitis patients were middle-aged (above 50 years of age). Please clarify.

Thank you very much to the reviewer for pointing out the issues in the article. This study included 8 healthy periodontal patients and 8 patients with periodontitis (Page 14, Line 396-398 and Supplementary Table 1 in the revised manuscripts). Since periodontitis has a higher prevalence in middle-aged and elderly populations, the periodontitis samples included in this study were mostly from this demographic. In contrast, the healthy gingival samples were sourced from patients undergoing wisdom tooth extraction, which primarily involves younger individuals. Therefore, due to the limited sample size, we could not enforce strict age matching. To address this, we repeated the relevant experiments in more consistent mouse models, which confirmed the increase in senescent cells in periodontal tissues (Figure 1D in the revised manuscripts). In summary, although the clinical samples were limited, the experimental results from the mouse models still support our conclusions.

(13) The number of biological replicates for each group used in the bulk RNA-sequencing experiment is unclear. The methods state:" For those with biological duplication, we used DESeq2 [8] (version: 1.34.0) to screen differentially expressed gene sets between two biological conditions; for those without biological duplication, we used edgeR". Please clarify the number of mouse samples sequenced and the description of the groups.

Thank you very much to the reviewer for pointing out the errors in the article. In the transcriptome sequencing, we collected gingival tissues from 3 healthy mice and gingival tissues from 3 ligature-induced periodontitis mice. Therefore, we used the DESeq2 (version: 1.34.0) method to filter for differentially expressed genes. The corresponding descriptions were revised in Page 20, Line 554-555 in the revised manuscripts.

(14) Cluster group labels are misaligned in Figure 4C.

Thank you very much for the reviewer's suggestion. The cluster group labels in Figure 3C of the revised manuscripts have been aligned.

Reviewer #3 (Recommendations For The Authors):

Major Comments for the Authors:

(1) I do not find the immunohistochemical staining of p16 and p21 shown in Figures 2E and F to be particularly compelling. Especially as other stains of these markers used later in the manuscript are of higher quality (i.e. Figures 3F and G). Can this staining be improved to better reflect the quantifications in Figure 2G?

Thank you very much for the reviewer's suggestion. In the revised manuscript, we have provided more representative images in Figure 7C in the revised manuscripts to reflect the effect of metformin treatment on the number of p16-positive cells in periodontitis. In Figure 7-figure supplement 1D of the revised manuscripts, we have marked p21-positive cells with black arrows to help readers better identify the p21-positive cells. Additionally, we have also assessed the H3K9me3 marker, which is more specific, and the results similarly indicate that metformin treatment can alleviate the formation of senescent cells in periodontitis (Figure 7-figure supplement 1E of the revised manuscript).

(2) On line 140, Supplementary Figure 2C, D is quoted to show "...an increase in senescence characteristics of fibroblasts with the severity of periodontitis." This figure panel does not appear to support this statement. Please revise.

Thank you very much for pointing out the errors in the manuscript. In the revised version, we have corrected this part of the description and added that “The results showed a decline in fibroblast proportion along with increasing disease severity (Figure 2-figure supplement 1C and D)” (Page 6, Line 153-154 of the revised manuscript)

(3) I do not find the Western Blot experiment in Figure 4L to be particularly convincing. The text states that p21, p16, and CD81 increase in a context-dependent manner upon LPS stimulation, which doesn't appear to be very evident. I recommend repeating this experiment and showing both a representative blot alongside a blot density quantification where the bars have the error shown between experiments.

Thank you very much for the reviewer’s suggestion regarding this result. During subsequent repeated experiments, we found that the result was not reproducible, and we have removed the related results.

(4) The results state that metabolic profiling of senescent fibroblasts shows an increase in the biosynthesis of Linoleic acid, linolenic acid, arachidonic acid, and steroid. However, in Figure 5B only arachidonic acid and steroid biosynthesis appear to be elevated in CD81+ Fibroblasts, while Linoleic and linolenic acid appear to be decreased. Can the authors comment on this discrepancy? Moreover, in Figure 5C steroid biosynthesis is unchanged between healthy and periodontitis samples, contrary to the claimed increased trend in the results text. Please revise this section. Also, in Figures 5 B and C some of the terms are highlighted in a red or blue box. This is not discussed in the figure legend. Could the significance of this be explained or could these highlights be removed from the figure?

Thank you very much for the reviewer’s correction regarding the errors in the manuscript. In the Page 7-8, Line 186-194 of the revised manuscripts, “Pathways related to fatty acid biosynthesis, arachidonic acid metabolism, and steroid biosynthesis were significantly upregulated in CD81+ fibroblasts (Figure 4-figure supplement 1A)” was re-wrote. Moreover, we have removed the results from Figure 5C, and the highlights in Figures 5B and C of the previous manuscripts. Since the mechanism by which cellular metabolism regulates cellular senescence is not the core focus of this manuscript, we have moved the results of the metabolic analysis from the sc-RNA sequencing data to the figure supplement (Figure 4-figure supplement 1) and revised the related statements in the revised manuscript (Page 7-8, Line 186-194).

(5) The authors state that arachidonic acid can be converted to prostaglandins and leukotrienes through COXs (which are expressed in their CD81+ Fibroblasts), accentuating inflammatory responses. Have the authors profiled for the expression of prostaglandins and leukotrienes in their CD81+ Fibroblasts or between healthy and periodontitis samples? Such data would be a great inclusion in the manuscript.

Thank you very much for the reviewer’s suggestion. Our results indicated that CD81+ gingival fibroblasts expressed higher levels of PTGS1 and PTGS2 compared to other fibroblast subpopulations. These genes encode proteins that are COX-1 and COX-2, which are key enzymes in prostaglandin biosynthesis (Figure 4-figure supplement 1 of the revised manuscript). Additionally, previous studies have reported high levels of prostaglandins and leukotrienes in periodontal tissues, and these pro-inflammatory mediators contribute to tissue destruction in periodontitis (Reference 1 and 2).

Reference

(1) Van Dyke, T. E., & Serhan, C. N. (2003). Resolution of inflammation: a new paradigm for the pathogenesis of periodontal diseases. Journal of dental research, 82(2), 82–90.

(2) Hikiji, H., Takato, T., Shimizu, T., & Ishii, S. (2008). The roles of prostanoids, leukotrienes, and platelet-activating factor in bone metabolism and disease. Progress in lipid research, 47(2), 107–126.

(6) Lines 199 and 200 state "...the cellular senescence of CD81+ fibroblasts could be attributed to disturbances in lipid metabolism". While altered lipid metabolic profiles are shown in Figure 5 to correlate with senescent fibroblasts/periodontitis tissue, no evidence is shown to suggest that they are the driver or cause of fibroblast senescence. Could this sentence be amended to better reflect the conclusions that can be drawn from the data presented?

Thank you very much for the reviewer’s suggestion. We have revised the related statements and believed that “lipid metabolism might play a role in cellular senescence of the gingival fibroblasts” in the Page 7, Line 189 of the revised manuscripts.  

Minor Comments for the Authors:

(1) There are some sentences without references that I feel would warrant referencing: - Line 112 - "Metformin, an anti-aging drug has shown potential in inhibiting cell senescence in various disease models (REFERENCE)."

Thank you for the reviewer's suggestion. We have included the relevant references in the Page10, Line 267-271 of the revised manuscripts.

Reference

(1) Soukas, A. A., Hao, H., & Wu, L. (2019). Metformin as Anti-Aging Therapy: Is It for Everyone?. Trends in endocrinology and metabolism: TEM, 30(10), 745–755.

(2) Kodali, M., Attaluri, S., Madhu, L. N., Shuai, B., Upadhya, R., Gonzalez, J. J., Rao, X., & Shetty, A. K. (2021). Metformin treatment in late middle age improves cognitive function with alleviation of microglial activation and enhancement of autophagy in the hippocampus. Aging cell, 20(2), e13277.

- Line 210 - "Previous studies have demonstrated the importance of sustained neutrophil infiltration in the progression of periodontitis (REFERENCE)."

Thank you for the reviewer's suggestion. We have included the relevant references in the Page 8, Line 211-214 of the revised manuscripts.

Reference

(1) Song, J., Zhang, Y., Bai, Y., Sun, X., Lu, Y., Guo, Y., He, Y., Gao, M., Chi, X., Heng, B. C., Zhang, X., Li, W., Xu, M., Wei, Y., You, F., Zhang, X., Lu, D., & Deng, X. (2023). The Deubiquitinase OTUD1 Suppresses Secretory Neutrophil Polarization And Ameliorates Immunopathology of Periodontitis. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 10(30), e2303207.

(2) Kim, T. S., Silva, L. M., Theofilou, V. I., Greenwell-Wild, T., Li, L., Williams, D. W., Ikeuchi, T., Brenchley, L., NIDCD/NIDCR Genomics and Computational Biology Core, Bugge, T. H., Diaz, P. I., Kaplan, M. J., Carmona-Rivera, C., & Moutsopoulos, N. M. (2023). Neutrophil extracellular traps and extracellular histones potentiate IL-17 inflammation in periodontitis. The Journal of experimental medicine, 220(9), e20221751.

(3) Ando, Y., Tsukasaki, M., Huynh, N. C., Zang, S., Yan, M., Muro, R., Nakamura, K., Komagamine, M., Komatsu, N., Okamoto, K., Nakano, K., Okamura, T., Yamaguchi, A., Ishihara, K., & Takayanagi, H. (2024). The neutrophil-osteogenic cell axis promotes bone destruction in periodontitis. International journal of oral science, 16(1), 18.

(2) To improve the quality of several of the authors' claims I would recommend some further quantification of their experimental analyses. Namely:

- Figures 3 F and G

- Figures 4 I, J and K

- Figures 6 F and G

- Supplementary Figures 4 A, B, and C

Thank you for the reviewer's suggestion. We have supplemented the quantitative analysis results for some images based on the reviewer's recommendations, specifically in Figure. 2G, Figure. 3G, Figure 5-figure supplement 1A, B, Figure 5-figure supplement 2A and Figure 7figure supplement 3A-D in the revised manuscripts. 

(3) Figure 1L has missing x-axis annotation.

Thank you for the reminder from the reviewer. The X-axis label has been added in Figure 1-figure supplement 1D for the GO term annotation. 

(4) Line 117 is missing a reference for the experimental schematic shown in Figure 2A.

Thank you for the reminder from the reviewer. The experimental schematic shown in Figure 7A has been referenced in Page 10, Line 275-277.

(5) The "BV/TV ratio" and "CEJ-ABC distance" should be briefly explained in the results test (Lines 118 and 119).

Thank you for the reviewer's suggestion. We have added the explanation of "BV/TV ratio" and "CEJ-ABC distance." In Page 10-11, Line 279-281 in the revised manuscripts.

(6) Figure 2 could be improved by having some annotation for the anatomical regions shown.

Thank you for the reviewer’s valuable suggestion. We have labeled the relevant anatomical structures to enhance clarity in Figure 7 in the revised manuscripts. 

(7) The positive signal for p16 and p21 is difficult to interpret in Figure 2. Could the clarity of this be improved either by using more evident images or annotation with arrowheads indicating positive cells?

Thank you for the reviewer's suggestion. In the revised manuscript, we have provided more representative images in Figure. 7C in the revised manuscripts to reflect the effect of metformin treatment on the number of p16-positive cells in periodontitis. In Figure 7-figure supplement 1D of the revised manuscripts, we have marked p21-positive cells with black arrows to help readers better identify the p21-positive cells. Additionally, we have also assessed the H3K9me3 marker, which is more specific, and the results similarly indicate that metformin treatment can alleviate the formation of senescent cells in periodontitis (Figure 7-figure supplement 1E of the revised manuscript).

(8) Figure 2Gc, d, and e are not mentioned in the results text. Please include references to these panels at the appropriate points.

Thank you for the reminder. In the revised manuscripts, Figures 2G c, d, and e in the previous manuscripts have been mentioned in the text in the Page 11, Line 284-289 of the revised manuscript. 

(9) Scale bars are missing in Supplementary Figure 2E.

Thank you for the suggestion. The scale bar has been added in the Figure 7-figure supplement 2B in the revised manuscripts. 

(10) The order of the figure panels is not always mentioned in the order they are referred to in the text. For example, Figure 3 is presented in the order of A, B, D then C. Could this be changed to reflect the order in the results text?

Thank you for the feedback. We have renumbered the figures according to the order mentioned in the original manuscript (Page 6, Line 146-149, Figure 2 in the revised manuscripts).

(11) To improve reader clarity it would be good to briefly introduce the gene expression datasets analysed, such as GSE152042. I.e. what the experimental condition is from which it is derived.

Thank you for the suggestion. We have included a brief description of the information and sources of the samples from GSE152042 in Page 6, Line 140-142 of the revised manuscripts. 

(12) To improve reader clarity I would recommend signifying clearly in the figure if the data shown is from mouse or human samples. For example in Figure 3F and G.

Thank you for the suggestion. We have moved all the results from the mouse experiments to the figures supplement (Figure 5-figure supplement 1 and 2 in the revised manuscripts).

(13) The images shown in Figure 3H for SA-beta-Gal do not seem very convincing. Could this be improved?

Thank you for the suggestion. To further illustrate the differences in SA-beta-Gal results between the groups, we have provided images at higher magnification in the Figure 2-figure supplement 2 of the revised manuscripts.  

(14) Supplementary Figure 2E would benefit from small experimental schematics that would allow the reader to appreciate the timings of the treatment for this experiment.

Thank you for the suggestion. We have added a schematic diagram in Figure 7-figure supplement 2A of the revised manuscripts to illustrate the LPS treatment, metformin treatment, and the timing of the assessments. 

(15) Figure 4K would benefit from showing the merged image and single channels of each of the stains to better assess the degree of colocalisation.

Thank you for the suggestion. We have included each individual fluorescence channel in Figure 5-figure supplement 1C of the revised manuscripts. 

(16) The writing on the X-axis of Figure 6B is almost illegible to me, although this may just be a compression artefact. This makes the interpretation of the data quite difficult. Also, for Figures 6 B and C, the meaning of the (H) and (P) annotations should be clear on either the figure or figure legend. I surmise that they represent "Healthy" and "Periodontic" samples respectively.

Thank you for the suggestion. In the revised manuscript, we have enlarged Figure 6B in the previous manuscripts to better display the X-axis as shown in the Figure 5B of the revised manuscripts. Additionally, we have fully labeled "Healthy" and "Periodontitis" in Figure 5C of the revised manuscripts.

(17) MPO-positive cells are introduced on line 216, however, no explanation is provided for what population or state the expression of this protein marks. I surmise the authors are using it to detect Neutrophil populations. If so, could the authors briefly state this the first time it is used?

Thank you for the suggestion. In the revised manuscript, we have added an introduction to MPO. MPO, or myeloperoxidase, is considered one of the markers for neutrophils. (Page 9, Line 240-242 of the revised manuscripts)

(18) Supplementary Figure 3D does not appear to be mentioned or discussed in the results text.

Thank you for the reminder. We have referenced Supplementary Figure 3D in the previous manuscripts in Page 9, Line 240-242 shown as Figure 5-figure supplement 2C of the revised manuscript.  

(19) Figure 6E showing increased C3 expression in periodontic samples is not very convincing and differences in expression are not evident. Can the authors provide an image that more convincingly matches their quantification?

Thank you for the suggestion. In the revised manuscript, we have provided more representative images shown in Figure 5E of the revised manuscript.

(20) Figure 6I shows the expression of CD81 and SOD2 in healthy and periodontic tissue. The associated results texts (Lines 220 to 223) discuss the spatial coincidence of CD81 and MPO. Can the authors address this discrepancy in either the results text or the figure panel? Moreover, can Figure 6H and I be annotated to show the location of the gingival lamina propria to improve clarity?

Thank you for the reminder. We have revised the relevant statements in the text: "Interestingly, spatial transcriptomic analysis of gingival tissue revealed that the regions expressing CD81 and SOD2, a neutrophil marker, in periodontitis overlapped in the gingival lamina propria, showing a high spatial correlation" in Page 9, Line 223-226 of the revised manuscripts. Additionally, we have labeled the gingival lamina propria (LP) in Figure 5H of the revised manuscripts.

(21) I am confused about the purpose of Supplementary Figure 3E and what evidence it provides. Can the authors comment on this?

Thank you for the reminder. To avoid any potential misunderstanding by readers, we have deleted Supplementary Figure 3 image in the revised manuscripts

Author response:

The following is the authors’ response to the original reviews

eLife Assessment 

The authors utilize a valuable computational approach to exploring the mechanisms of memorydependent klinotaxis, with a hypothesis that is both plausible and testable. Although they provide a solid hypothesis of circuit function based on an established model, the model's lack of integration of newer experimental findings, its reliance on predefined synaptic states, and oversimplified sensory dynamics, make the investigation incomplete for both memory and internal-state modulation of taxis.  

We would like to express our gratitude to the editor for the assessment of our work. However, we respectfully disagree with the assessment that our investigation is incomplete, if the negative assessment is primarily due to the impact of AIY interneuron ablation on the chemotaxis index (CI) which was reported in Reference [1]. It is crucial to acknowledge that the CI determined through experimental means incorporates contributions from both klinokinesis and klinotaxis [1]. It is plausible that the impact of AIY ablation was not adequately reflected in the CI value. Consequently, the experimental observation does not necessarily diminish the role of AIY in klinotaxis. Anatomical evidence provided by the database (http://ims.dse.ibaraki.ac.jp/ccep-tool/) substantiates that ASE sensory neurons and AIZ interneurons, which have been demonstrated to play a crucial role in klinotaxis [Matsumoto et al., PNAS 121 (5) e2310735121], have the much higher number of synaptic connections with AIY interneurons. These findings provide substantial evidence supporting the validity of the presented minimal neural network responsible for salt klinotaxis.

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

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

We would like to express our gratitude for the time and consideration you have dedicated to reviewing our manuscript.

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

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

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

We would like to thank the reviewer for the appropriate summary of our work. 

Strengths and weaknesses: 

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

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

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

We would like to thank the reviewer for recognizing the usefulness of our approach. In the revised version, we improved the explanation according to your suggestions.  

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

We would like to express our gratitude for the constructive feedback we have received. We concur with some of your assertions. In fact, our model is the minimal network for salt klinotaxis, which includes solely the interneurons that are connected to each other via the highest number of synaptic connections. It is important to note that our model does not consider redundant interneurons that exhibit overlapping roles. Consequently, the model is not applicable to the study of the impact of interneuron ablation. In the reference [1], the influence of interneuron ablations on the chemotaxis index (CI) has been investigated. The experimentally determined CI value incorporates the contributions from both klinokinesis and klinotaxis. Consequently, it is plausible that the impact of AIY ablation was not significantly reflected in the CI value. The experimental observation does not necessarily diminish the role of AIY in klinotaxis. 

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

As the reviewer has noted, our model does not incorporate the context-dependent response of the ASER. Instead, the impact of the salt concentration-dependent glutamate release from the ASER [S. Hiroki et al. Nat Commun 13, 2928 (2022)] as the result of the ASER responses was in detail examined in the present study.

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

We would like to express our gratitude to the reviewer for their constructive feedback. As you correctly noted, the hypothesized synaptic reversal between ASER and AIY is not explicitly modeled in terms of the sensitivity of the receptors in the AIY and the glutamate basal levels by the ASER. On the other hand, in the present study, under considering a substantial difference in the sensitivity of the two glutamate receptors on the AIY, we sought to endeavored to elucidate the impact of salt-concentration-dependent glutamate basal levels on klinotaxis. To this end, we conducted a comprehensive examination of the full range gradual change in the ASER-to-AIY connection from inhibitory to excitatory, as illustrated in Figures S4 and S5.

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

We might not understand the substance of your assertions. However, we understand that the oscillator dynamics were not intrinsically generated by the oscillator neural circuit that is explicitly incorporated into our modeling. On the other hand, the present study focuses on how the sensory input and resulting interneuron dynamics regulate the oscillatory behavior of SMB motor neurons to generate klinotaxis. The neuron dynamics via gap junctions results from the equilibration of the membrane potential yi of two neurons connected by gap junctions rather than the zi. We added this explanation in the revised manuscript as follows.

“The hyperpolarization signals in the AIZL are transmitted to the AIZR via the gap junction (Figs. S1d and S1f and Fig. 3d). This is because the neuron dynamics via gap junctions results from the equilibration of the membrane potential y_i of two neurons connected by gap junctions rather than the z_i.”

In the limitation, we added the following sentence:

“In the present study, the oscillator components of the SMB are not intrinsically generated by an oscillator circuit but are instead externally imposed via 𝑧_i^OSC. Furthermore, the complex and context-dependent responses of ASER {Luo:2014et} were not taken into consideration. It should be acknowledged as a limitation of this study that these omitted factors may interact with circuit dynamics in ways that are not captured by the current simplified implementation.”

Appraisal: 

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

We would like to thank the reviewer for assessing our work.

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

We agree with your opinion. The present hypothesis should be verified by experiments.

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

We would like to express our gratitude for the constructive feedback we have received. In the reference [1], the influence of interneuron ablations on the chemotaxis index (CI) has been investigated. It is important to acknowledge that the experimentally determined CI value encompasses the contributions of both klinokinesis and klinotaxis. It is plausible that the impact of AIY ablation was not reflected in the CI value. Consequently, these experimental observations do not necessarily diminish the role of AIY in klinotaxis. The neural circuit model employed in the present study constitutes a minimal network for salt klinotaxis, encompassing solely interneurons that are connected to each other via the highest number of synaptic connections. Anatomical evidence provided by the database (http://ims.dse.ibaraki.ac.jp/cceptool/) substantiates that ASE sensory neurons and AIZ interneurons, which have been demonstrated to play a crucial role in klinotaxis [Matsumoto et al., PNAS 121 (5) e2310735121], have the much higher number of synaptic connections with AIY interneurons. Our model does not take into account redundant interneurons with overlapping roles, thus rendering it not applicable to the study of the effects of interneuron ablation.

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

As the reviewer noted, the experimentally identified neural circuit that inhibits the SMB motor neurons in starved individuals is not incorporated in our model. Instead of incorporating this circuit explicitly, we examined whether our minimal network model could reproduce dispersal behavior under starvation conditions solely due to the experimentally demonstrated inhibitory effect of SMB motor neurons.

Impact: 

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

We would like to express our gratitude for useful suggestion. We added this argument in Discussion of the revised manuscript as follows.    

“The principle of operation, in which the dependence of motor responses to sensory inputs on the phase of motor oscillation, appears to be a convergent solution for taxis and navigation across species. In fact, analogous mechanisms have been postulated in the context of chemotaxis in Drosophila larvae chemotaxis {Wystrach:2016bt} and phototaxis in zebrafish {Wolf:2017ei}. Consequently, the synaptic reversal mechanism highlighted in this study offers the framework for understanding how the behaviors that are adaptive to the environment are embedded within sensorimotor systems and how experience shapes these neural circuits across species.”

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

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

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

We would like to acknowledge the appropriate recognition of our work.

Additional Context 

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

We would like to express our gratitude for the insightful commentary. In the revised manuscript, we incorporated the argument that the similar oscillator mechanism with stimulus-driven perturbations has been observed for zebrafish phototaxis [3] and Drosophila larvae chemotaxis [2] into Discussion.

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

We would like to acknowledge your thoughtfully articulated comment. As the reviewer pointed out, the anatomical database (http://ims.dse.ibaraki.ac.jp/ccep-tool/) shows that that the neural circuits underlying weathervaning and pirouettes in C. elegans are predominantly distinct but exhibit partial overlap. When we restrict our search to the neurons that are connected to each other with the highest number of synaptic connections, we identify the projections from the neural circuit of weathervaning to the circuit of pirouettes; however we observed no reversal projections. This finding suggests that the neural circuit of weathervaning, namely, our minimal neural network, is not likely to be affected by that of pirouettes, which consists of AIB interneurons and interneurons and motor neurons the downstream. 

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

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

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

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

Reviewer #2 (Public review): 

Summary: 

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

We would like to express our gratitude for the time and consideration the reviewer has dedicated to reviewing our manuscript.

Strengths: 

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

We would like to thank the reviewer for appreciating our research. 

Weaknesses: 

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

We would like to thank the reviewer for the constructive feedback. As the reviewer noted, the fundamental assumptions posited in the study have yet to be substantiated by biological validation, and consequently, these assumptions must be directly assessed by biological experimentation. The model performance for salt klinotaxis has been evaluated by multiple factors, including not only a chemotaxis index but also the curving rate vs. bearing (Fig. 4a, the bearing is defined in Fig. A3) and the curving rate vs. normal gradient (Fig. 4c). These two parameters work to characterize the trajectory during salt klinotaxis. In the revised version, we meticulously revised the manuscript according to the reviewer’s suggestions. We would like to express our sincere gratitude for your insightful review of our work.

Recommendations for the authors:  

Reviewer #1 (Recommendations for the authors): 

An interesting and engaging methodology combining theoretical and computational approaches. Overall I found the manuscript up to discussion a difficult read, and I would suggest revising it. I would also recommend introducing the general operating principle of the oscillator with sensory perturbations before jumping into the implementation details of signal propagation specific to C.

elegans.  

In order to elucidate the relation between the general operating principle of the oscillator with sensory perturbations and the results shown by the two graphs from the bottom in Fig. 3d, the following statement was added on page 12.

“It is remarkable that this regulatory mechanism derived via the optimization of the CI has been observed in the context of chemotaxis in Drosophila larvae chemotaxis {Wystrach:2016bt} and phototaxis in zebrafish {Wolf:2017ei}. The principle of operation, in which the dependence of motor responses to sensory inputs on the phase of motor oscillation, therefore, may serve as a convergent solution for taxis and navigation across species.”

The abstract could benefit from a clarification of terms to benefit a broader audience:  The term "salt klinotaxis" is used without prior introduction or definition. It would be beneficial to briefly explain this term, as it may not be familiar to all readers. 

Due to the limitation of the word number in the abstract, the explanation of salt klinotaxis could not be included.

Although ASER is introduced as a right-side head sensory neuron, AIY neurons are not similarly introduced. It may also benefit to introduce here that ASER integrates memory with current salt gradients, tuning its output to produce context-appropriate behaviour.  

Due to the limitation of the word number in the abstract, we could add no more the explanations. 

"it can be anticipated that the ASER-AIY synaptic transmission will undergo a reversal due to alterations in the basal glutamate Release": Where is this expectation drawn from? Is it derived from biophysical or is it a functional expectation to explain the network's output constraints?  

As delineated before this sentence, it is derived from a comprehensive consideration of the sensitivity of excitatory/inhibitory glutamate receptors expressed on the postsynaptic AIY interneurons, in conjunction with varying the basal level of glutamate transmission from ASER.

The statement that the model "revealed the modular neural circuit function downstream of ASE" could be more explicit. What specific insights about the downstream circuit were uncovered?

Highlighting one or two key findings would strengthen the impact.  

Due to the limitation of the word number in the abstract, no more details could be added here, while the sentence was revised as “revealed that the circuit downstream of ASE functions as a module that is responsible for salt klinotaxis.” This is because the salt-concentration dependent behaviors in klinitaxis can be reproduced through the modulation of the ASRE-AIY synaptic connections alone, despite the absence of alterations in the neural circuit downstream of AIY.

I believe the authors should cite Luo et al. 2014, which also studies how chemotactic behaviours arise from neural circuit dynamics, including the dynamic encoding of salt concentration by ASER, and the crucial downstream interaction with AIY for chemotactic actions. 

We would like to express our gratitude for useful suggestion. We cited Luo et al. 2014 in the discussion on the limitation of our work. 

The introduction could also be improved for clarity. Specifically in the last paragraph authors should clarify how the observed synchrony of ASER excitation to the AIZ (Matsumoto et al., 2024), validates the resulting network.  

We would like to express our gratitude for useful suggestion. We added the following explanation in the last paragraph of the introduction.

“Specifically, the synchrony of the excitation of the ASER and AIZ {Matsumoto:2024ig} taken together with the experimentally identified inhibitory synaptic transmission between the AIY and AIZ revealed that the ASER-AIY synaptic connections should be inhibitory, which was consistent with the network obtained from the most evolved model.”

In addition, we added the following explanation after “It was then hypothesized that the ASER-AIY inhibitory synaptic connections are altered to become excitatory due to a decrease in the baseline release of glutamate from the ASER when individuals are cultured under C_cult < C_test.”

This is due to the substantial difference in the sensitivity of excitatory/inhibitory glutamate receptors expressed on the postsynaptic AIY interneurons.

I would also strongly recommend replacing the term "evolved model", with "Optimized Model" or "Best-Performing Model" to clarify this is a computational optimization process with limitations - optimization through GAs does not guarantee finding global optima.  

We revised "evolved model" as "optimized model" in the main and SI text.

The text overall would benefit from editing for clarity and expression.  

According to the revisions mentioned above, we revised “best optimized model” as “most optimized model” in the main and SI text.

The font size on the plot axis in Figures 3 c&d should be increased for readability on the printed page. Label the left/right panel to indicate unconstrained / constrained evolution.  

As you noted, the font size of the subscript on the vertical axis in Figs 3c and 3d was too small. We have revised the font size of the subscript in Figs. 3c and 3d and also in Fig. 5e. At your suggestion, “unconstrained” and “constrained” have been added as labels to the left and right panels in Fig. 3.

There is no input/transmission to AIYR to step input in either model shown in Figure 3? 

As shown in Fig. S1e and S1f, there are the transmissions to the AIYR from the ASEL and ASER. 

Supplementary Figure 1 attempts to explain the interactions. There are inconsistent symbols used for inhibition and excitation between network schema (colours) and the z response plots (arrows vs circles), combined with different meanings for red/blue making it very confusing. 

We could not address the inconsistency in the color of arrows and lines with an ending between Figs. S1c and S1d and Figs. S1a and S1b. On the other hand, Figs. S1e and S1f were revised so that the consistent symbols were used for inhibition, excitation, and electrical gap connections in Figs. S1c-S1f. The same revisions were made for Fig. S7c-S7f.

Model parameters are given to 15 decimal precision, which seems excessive. Is model performance sensitive to that order? We would expect robustness around those values. The authors should identify relevant orders and truncate parameters accordingly. 

We examined the influence of the parameter truncation on the trajectory and decided that the parameters with four decimal places were appropriate. According to this, we revised Table A4.

Figure 3 caption typo "step changes I the salt concentration".  

The typo was revised in Fig. 3 caption. 

Reviewer #2 (Recommendations for the authors): 

(1) Overall, the language of the paper is not properly organized, making the paper's logic and purpose hard to follow. In the Results Section, many observations or findings lack explicit interpretation. To address this issue, the authors should consider (1) adopting the contextcontent-conclusion scheme, (2) optimizing the logic flow by clearly identifying the context and goals prior to discussing their results and findings, (3) more explicitly interpreting their results, especially in a biological context.  

We would like to express our gratitude for helpful suggestion. According to your suggestion listed below, we revised the main and SI texts.

(2) In Figure 2, trajectories from the model with AIY-AIZ constraints show a faster convergence than those from the constraint-free model. However, in the corresponding texts in the Results section, the authors claimed no significant difference. It seems that the authors made this argument only based on CI (Chemotaxis Index). Therefore, in order to address such inconsistency, the authors need more explanation on why only relying on CI, which is an endpoint metric, instead of the whole navigation.  

I would like to thank you for the helpful comment. In the present study, not only the CI but also the curving rate shown in Fig. 4 were applied to characterize the behavior in klinotaxis.

According to your comments, we revised the related description in the main text as follows:

“The difference between these CI values is slight, while the model optimized with the constraints exhibits a marginally accelerated attainment of the salt concentration peak, as shown by the trajectories. The slightly higher chemotaxis performance observed in the constrained model is not essentially attributed to the introduction of the AIY-AIZ synaptic constraints but rather depends on the specific individuals selected from the optimized individuals obtained from the evolutionary algorithm. In fact, even when the AIY-AIZ constraints are taken into consideration, the model retains a significant degree of freedom to reproduce salt klinotaxis due to the presence of a substantial parameter space. Consequently, the impact of the AIY-AIZ constraints on the optimization of the CI is expected to be negligible.”

(3) In Figures 3a and b, some inter-neuron connections are relatively weak (e.g., AIYR to AIZR in Figure 3a) - thus it is unclear whether the polarity of such synapses would significantly influence the behavioral outcome or not. The authors could consider plotting the change of the connection strengths between neurons over the course of model optimization to get a sense of confidence in each inter-neuron connection. 

In the evolutional algorithm, the parameters of individuals are subject to discontinuous variation due to the influence of selection, crossover, and mutations. Consequently, it is not straightforward to extract information regarding parameter optimization from parameter changes due to the non-systematic nature of parameter variation..

(4) In Figure 3, the order of individual figure panels is incorrect: in the main text, Figure 3 a and b were mentioned after c and d. Also, the caption of Figure 3c "negative step changes I the" should be "in".  

The main text underwent revision, with the description of Figures 3a and 3b being presented prior to that of Figures 3c and 3d. The typo was revised.

(5) In Figure 4, the order of individual figure panels is messed up: in the main text, Figure 4 a was mentioned after b.  

The main text underwent revision, with the description of Figure 4a being presented prior to that of Figure 4b.

(6) Also in Figure 4, the authors need to provide a definition/explanation of "Bearing" and "Translational Gradient". In Figure 4d, the definition of positive and negative components is not clear.  

Normal and Translational Salt Concentration Gradient in METHOD was referenced for the definition and explanation of the bearing and the translational gradient. We added the following explanation on the positive and negative components.

“The positive and negative components of the curving rate are respectively sampled from the trajectory during leftward turns (as illustrated in Fig. 4b) and rightward turns, respectively.”

(7) Figure 5: the authors need to explain why c has an error bar and how they were calculated, as this result is from a computational model. Figure 5d is experimental results - the authors need to add error bars to the data points and provide a sample size. 

As explained in Analysis of the Salt Preference Behavior in Klinotaxis in METHOD, the ensemble average of these quantities was determined by performing 100,000 sets of the simulation with randomized initial orientation for a simulation time of T_sim=200 sec. The error bars for the experimental data were added in Figs. 5c, 6a, and S9a.

(8) On Page 14, the authors said, "To this end, this end, we used the best evolved network with the constraints, in which we varied the synaptic connections between ASER and AIY from inhibitory to excitatory." How did the model change the ASER-AIY signaling specifically? The authors should provide more explanation or at least refer to the Methods Section.  

The caption of Fig. S4 was referred as the explanation on the detailed method. 

(9) Page 15: "a subset a subset exhibited a slight curve...". This observation from the model simulation is contradictory to experiments. However, their explanation of that is hard to understand.  

I would like to thank you for the helpful comment. To improve this, we added the following explanation:

“In the case of step increases in 𝑧OFF as illustrated in the second right panel from the bottom in Fig.3d, the turning angle φ is increased from its ideal oscillatory component to a value close to zero, causing the model worm to deviate from the ideal sinusoidal trajectory and gradually turn toward lower salt concentrations. On the other hand, in the case of step increases in 𝑧ON as illustrated in the second left panel from the bottom in Fig.3d, the turning angle φ is again increased from its ideal oscillatory component to a value close to zero, causing the model worm to deviate from the ideal sinusoidal trajectory and gradually turn toward higher salt concentrations. The behaviors that are consistent with these analyses are observed in the trajectory illustrated in Fig. S8b.”

(10) Last result session: inhibited SMB in starved worms is due to a mechanism unrelated to their neural network model upstream to SMB. Therefore, their results recapitulating the worms' dispersal behaviors cannot strengthen the validity of their model.  

We agree with your opinion. We think that the findings from the study of starved worms do not provide evidence to validate the neural network model upstream of SMB.   

(11) Discussion: "in contrast, the remaining neurons...". This argument lacks evidence or references.  

This argument is based on the results obtained from the present study. This sentence was revised as follows:

“This regulatory process enables the reproduction of salt concentration memory-dependent reversal of preference behavior in klinotaxis, despite the remaining neurons further downstream of the ASER not undergoing alterations and simply functioning as a modular circuit to transmit the received signals to the motor systems. Consequently, the sensorimotor circuit allows a simple and efficient bidirectional regulation of salt preference behavior in klinotaxis.”

(12) To increase the predictive power of their model, can the authors perform simulations on mutant worms, like those with altered glutamate basal level expression in ASER?  

We would like to express our gratitude for useful suggestion. The simulations, in which the weight of the ASER-AIY synaptic connection is increased from negative (inhibitory connection) to positive (excitatory connection), as illustrated in Figure S4, provide valuable insights into the relationship between varying glutamate basal levels from ASER and behavior in klinotaxis, such as the chemotaxis index.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

In the present study, Chen et al. investigate the role of Endophilin A1 in regulating GABAergic synapse formation and function. To this end, the authors use constitutive or conditional knockout of Endophilin A1 (EEN1) to assess the consequences on GABAergic synapse composition and function, as well as the outcome for PTZ-induced seizure susceptibility. The authors show that EEN1 KO mice show a higher susceptibility to PTZ-induced seizures, accompanied by a reduction in the GABAergic synaptic scaffolding protein gephyrin as well as specific GABAAR subunits and eIPSCs. The authors then investigate the underlying mechanisms, demonstrating that Endophilin A1 binds directly to gephyrin and GABAAR subunits, and identifying the subdomains of Endophilin A1 that contribute to this effect. Overall, the authors state that their study places Endophilin A1 as a new regulator of GABAergic synapse function.

Strengths:

Overall, the topic of this manuscript is very timely, since there has been substantial recent interest in describing the mechanisms governing inhibitory synaptic transmission at GABAergic synapses. The study will therefore be of interest to a wide audience of neuroscientists studying synaptic transmission and its role in disease. The manuscript is well-written and contains a substantial quantity of data.

Weaknesses:

A number of questions remain to be answered in order to be able to fully evaluate the quality and conclusions of the study. In particular, a key concern throughout the manuscript regards the way that the number of samples for statistical analysis is defined, which may affect the validity of the data analysed. Addressing this weakness will be essential to providing conclusive results that support the authors' claims.

We would like to thank the reviewer for appreciation of the value of our study and careful critics to help us improve the manuscript. We will correct the way that the number of samples for statistical analysis is defined throughout the manuscript as suggested and update figures, figure legends, and Materials and Methods accordingly. For example, we will average the values for all dendritic segments from one neuron, so that each data point represents one neuron in the graphs.

Reviewer #2 (Public review):

Summary:

The function of neural circuits relies heavily on the balance of excitatory and inhibitory inputs. Particularly, inhibitory inputs are understudied when compared to their excitatory counterparts due to the diversity of inhibitory neurons, their synaptic molecular heterogeneity, and their elusive signature. Thus, insights into these aspects of inhibitory inputs can inform us largely on the functions of neural circuits and the brain.

Endophilin A1, an endocytic protein heavily expressed in neurons, has been implicated in numerous pre- and postsynaptic functions, however largely at excitatory synapses. Thus, whether this crucial protein plays any role in inhibitory synapse, and whether this regulates functions at the synaptic, circuit, or brain level remains to be determined.

New Findings:

(1) Endophilin A1 interacts with the postsynaptic scaffolding protein gephyrin at inhibitory postsynaptic densities within excitatory neurons.

(2) Endophilin A1 promotes the organization of the inhibitory postsynaptic density and the subsequent recruitment/stabilization of GABA A receptors via Endophilin A1's membrane binding and actin polymerization activities.

(3) Loss of Endophilin A1 in CA1 mouse hippocampal pyramidal neurons weakens inhibitory input and leads to susceptibility to epilepsy.

(4) Thus the authors propose that via its role as a component of the inhibitory postsynaptic density within excitatory neurons, Endophilin A1 supports the organization, stability, and efficacy of inhibitory input to maintain the excitatory/inhibitory balance critical for brain function.

(5) The conclusion of the manuscript is well supported by the data but will be strengthened by addressing our list of concerns and experiment suggestions.

We would like to thank the reviewer for their favorable impression of manuscript. We also appreciate the great experiment suggestions to help us improve the manuscript.

Weaknesses:

Technical concerns:

(1) Figure 1F and Figure 1H, Figures 7H,J:

Can the authors justify using a paired-pulse interval of 50 ms for eEPSCs and an interval of 200 ms for eIPSCs? Otherwise, experiments should be repeated using the same paired pulse interval.

We apologize for the confusion. As illustrated by the schematic current traces, the decay time constants of eEPSCs and eIPSCs in hippocampal CA1 neurons are different. The eEPSCs exhibit a faster channel closing rate, corresponding to a smaller time constant Tau. Thus, a shorter inter-stimulus interval (50 ms) was chosen for paired-pulse ratio recordings. In contrast, the eIPSCs display a slower channel closing rate, with a Tau value larger than that of eEPSCs, so a longer inter-stimulus interval (200 ms) was used for PPR. This protocol has been long-established and adopted in previous studies (please see below for examples).

Contractor, A., Swanson, G. & Heinemann, S. F. Kainate receptors are involved in short- and long-term plasticity at mossy fiber synapses in the hippocampus. Neuron 29, 209-216, doi:10.1016/s0896-6273(01)00191-x (2001).

Babiec, W. E., Jami, S. A., Guglietta, R., Chen, P. B. & O'Dell, T. J. Differential Regulation of NMDA Receptor-Mediated Transmission by SK Channels Underlies Dorsal-Ventral Differences in Dynamics of Schaffer Collateral Synaptic Function. Journal of neuroscience 37, 1950-1964, doi:10.1523/JNEUROSCI.3196-16.2017 (2017).

(2) Figures 3G,H,I:

While 3D representations of proteins of interest bolster claims made by superresolution microscopy, SIM resolution is unreliable when deciphering the localization of proteins at the subsynaptic level given the small size of these structures (<1 micrometer). In order to determine the actual location of Endophilin A1, especially given the known presynaptic localization of this protein, the authors should complete SIM experiments with a presynaptic marker, perhaps an active zone protein, so that the relative localization of Endophilin A1 can be gleaned. Currently, overlapping signals could stem from the presynapse given the poor resolution of SIM in this context.

Thanks for your suggestions. It is certainly preferable to investigate the relative localization of endophilin A1 using both presynaptic and postsynaptic markers. For SIM imaging in Figure 3G-I, to visualize neuronal morphology, we immunostained GFP as cell fill, leaving two other channels for detection of immunofluorescent signals of endophilin A1 and another protein. We will try co-immunostaining of endophilin A1, the active zone protein bassoon (presynaptic marker) and gephyrin without morphology labeling. Alternatively, we will do co-staining of endophilin A1 and bassoon in GFP-expressing neurons. We agree that overlapping signals or proximal localization of presynaptic endophilin A1 with gephyrin or GABA_AR γ2 could not be ruled out. To note, if image resolution is improved with the use of a more advanced imaging system, the overlap between two proteins will become smaller or even disappear. With the ~110 nm lateral resolution of SIM microscopy, the degree of overlap between the two proteins of interest is much lower than in confocal microscopy. Given the presynaptic localization of endophilin, most likely we will observe a small overlap (presynatpic) or proximal localization (postsynaptic) of endophilin A1 with bassoon. Nevertheless, we will complete the SIM experiments as suggested to improve the manuscript.

Manuscript consistency:

(1) Figure 2:

The authors looked at VGAT and noticed a reduction of signals in hippocampal regions in their P21 slices, indicating that the proposed postsynaptic organization/stabilization functions of Endophilin A1 extend to the inhibitory presynapse, perhaps via Neuroligin 2-Neurexin. Simultaneously, hippocampal regions in P21 slices showed a reduction in PSD-95 signals, indicating that excitatory synapses are also affected. It would be crucial to also look at excitatory presynapses, via VGLUT staining, to assess whether EndoA1 -/- also affects presynapses. Given the extensive roles of Endophilin A1 in presynapses, especially in excitatory presynapses, this should be investigated.

Thanks for the thoughtful comments. Given that the both VGAT and PSD95 signals are reduced in hippocampal regions in P21 slices, it is conceivable that the proposed postsynaptic organization/stabilization functions of endophilin A1 extend to the inhibitory presynapse via Neuroligin-2-Neurexin and the excitatory presynapse as well during development. Of note, endophilin A1 knockout did not impair the distribution of Neuroligin-2 in inhibitory postsynapses (immunoisolated with anti-GABA_AR α1) in mature mice (Figure 3K), and endophilin A1 did not bind to Neuroligin-2 (Figure 4D), suggesting that endophilin A1 might function via other mechanisms. Nevertheless, as functions of endophilin A family members at the presynaptic site are well-established, the reduction of presynaptic signals in developmental hippocampal regions of EndoA^-/- mice might result from the depletion of presynaptic endophilin A1. The presynaptic deficits can be compensatory by other mechanisms as neurons mature. Certainly, we will do VGLUT staining of EndoA1^-/- brain slices as suggested to assess the role of endophilin A1 in excitatory presynapses in vivo.

(2) Figure 7C:

The authors do not assess whether p140Cap overexpression rescues GABAAR receptor loss exhibited in Endophilin A1 KO, as they did for Gephryin. This would be an important data point to show, as p140Cap may somehow rescue receptor loss by another pathway. In fact, it is mentioned in the text that this experiment was done, "Consistently, neither p140Cap nor the endophilin A1 loss-of-function mutants could rescue the GABAAR clustering phenotype in EEN1 KO neurons (Figure 7C, D)" yet the data for p140Cap overexpression seem to be missing. This should be remedied.

Thanks a lot for the thoughtful comment. We will determine whether p140Cap overexpression also rescues the GABA_AR clustering phenotype in EndoA1^-/- neurons by surface GABA_AR γ2 staining in our revised manuscript.

Reviewer #3 (Public review):

Summary:

Chen et al. identify endophilin A1 as a novel component of the inhibitory postsynaptic scaffold. Their data show impaired evoked inhibitory synaptic transmission in CA1 neurons of mice lacking endophilin A1, and an increased susceptibility to seizures. Endophilin can interact with the postsynaptic scaffold protein gephyrin and promote assembly of the inhibitory postsynaptic element. Endophilin A1 is known to play a role in presynaptic terminals and in dendritic spines, but a role for endophilin A1 at inhibitory postsynaptic densities has not yet been described.

Strengths:

The authors used a broad array of experimental approaches to investigate this, including tests of seizure susceptibility, electrophysiology, biochemistry, neuronal culture, and image analysis.

Weaknesses:

Many results are difficult to interpret, and the data quality is not always convincing, unfortunately. The basic premise of the study, that gephyrin and endophilin A1 interact, requires a more robust analysis to be convincing.

We greatly appreciate the positive comment on our study and the very valuable feedback for us to improve the manuscript. We will conduct additional experiments to improve our data quality and strengthen our evidences according to these great constructive suggestions. To gain strong evidence for the interaction between endophilin A1 and gephyrin, we will perform in vitro pull-down assay with recombinant proteins from bacterial expression system.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) For all of the electrophysiology experiments, only the number of neurons recorded is stated, but not the number of independent animals that these neurons were obtained from. The number of independent animals used should be stated for each panel. At least 3 independent animals should be used in each group, otherwise, more data needs to be added.

We apologize for missing the information in the original manuscript. For all electrophysiological experiments, data were obtained from more than 3 experimental animals. The figure legends were updated to include the number of independent animals used for each panel.

(2) For the cell culture experiments analyzing dendritic puncta at GABAergic synapses, the number of data points analysed appears to be the number of dendritic segments quantified, regardless of whether they originate from the same neuron or not. This analysis method is not valid, since dendritic segments from the same neuron cannot be counted as statistically independent samples. The authors need to average the values for all dendritic segments from one neuron, such that one neuron equals one data point. This alteration should be made for Figures 2B, 2D, 4H, 4J, 5B, 5C, 5E, 5J, 5L, 6B, 6D, 6F, 6H, 6J, 6K,7B, and 7D. In addition, the number of independent cultures from which the neurons were obtained should be stated for each panel. At least 3 independent cultures should be used in each group, otherwise, more data need to be added.

Thanks for the criticism. We reanalyzed the data throughout the manuscript as suggested and updated the figure legends accordingly. Moreover, we increased the number of neurons from independent experiments to further confirm the results in our revised manuscript.

In the revised manuscript, we averaged the values for all dendritic segments from a single neuron and updated the data in Figure 3B, 3D, 4H, 4J, 5B, 5C, 5E, 5K, 5M, 6B, 6D, 6F, 6H, 6J, 6K,7B, and 7D.

Neurons analyzed in each group were derived from at least 3 independent cultures. Due to very low efficiency of sparse transfection in primary cultured hippocampal neurons, multiple experimental repetitions were necessary to obtain the sufficient number of neurons for analysis. We described statistical analysis in “Material and Methods” section in the original manuscript as follows:

“For all biochemical, cell biological and electrophysiological recordings, at least three independent experiments were performed (independent cultures, transfections or different mice).”

(3) Individual data points should be shown on all graphs, particularly in Figures 2C, 2F, 2I, 3F, 3K, and 3L.

Thank you for the suggestion. We replaced the original graphs with scatterplots and mean ± S.E.M. in new Figures.

(4) For each experiment, the authors should state explicitly in the methods section whether that experiment was conducted blind to genotype.

Thank you for the suggestion. We have modified the description of blind analysis for each experiment in methods section to “Seizure susceptibility was measured blindly by rating seizures on a scale of 0 to 7 as follows…”, “Quantification of immunostaining were carried out blindly…” in our revised manuscript.

(5) For each experiment, the authors should state whether they used male or female mice, and what age the mice were at the time of the experiment

Thanks a lot for the suggestion. We usually use male and female mice for neuron culture and behavioral test. We observed no sex-related differences in PTZ-induced behaviors, so the results were pooled together.

For mice ages, P0 pups were used for hippocampal neuron cultures and virus injection in electrophysiological recording assays or FingR probes assays. P14-21 mice were used for electrophysiological recording, immunofluorescent staining and FingR probes detection in brain slice, while adult mice (P60) for behavioral tests, immunofluorescent staining in brain slice and biochemical assays. We have modified the description in genders and ages of mice in methods section to “To evaluate seizure susceptibility, 8-10-week-old male and female EndoA1^+/+ or EndoA1^-/- littermates or EndoA1^fl/fl littermates were intraperitoneally administered… ”, “For virus injection, 8-9-week-old naive male and female littermates were anesthetized…”, “Male and female littermates (P21 or P60) were anesthetized and immediately perfused…”, “Hippocampi of female or male pups (P0) were rapidly dissected under sterile conditions…”, “PSD fractions from adult mouse brain were prepared as previously described…”, “Newborn EndoA1^fl/fl littermates (male or female) were anesthetized on ice for 4-5 min…” in our revised manuscript.

(6) For each experiment involving WT and KO mice, please state whether WTs and KOs were bred as littermates from heterozygous breeders

Sorry for the confusion. In our study, EndoA1^+/+ and EndoA1^-/- mice were bred as littermates from heterozygous breeders. We added the information in methods section as follows in our revised manuscript, “EndoA1^+/+ and EndoA1^-/- mice were bred as littermates from heterozygous breeders…”, “To evaluate seizure susceptibility, 8-10-week-old male and female EndoA1^+/+ or EndoA1^-/- littermates or EndoA1^fl/fl littermates…”, “For virus injection, 8-9-week-old naive male and female littermates were anesthetized…”, “Male and female littermates (P21 or P60) were anesthetized and immediately perfused…”, “For co-IP from brain lysates, the whole brain from 8-10-week-old WT and KO littermates were dissected…”, “Newborn EndoA1^fl/fl littermates (male or female) were anesthetized on ice for 4-5 min…”.

(7) For experiments comparing three or more groups, the authors claim in the methods section to have used a one-way ANOVA for statistical analysis. However, no ANOVA values are given, only the post-hoc tests. Please add the ANOVA values for each experiment before stating the values of the post-hoc analysis.

Sorry for the missing information. We used one-way ANOVA for comparing three or more groups in the original manuscript and have changed to two-way ANOVA for behavior data analysis in our revised manuscript as suggested in Recommendations (18). We added the ANOVA values (F & p values) for each experiment in new figures. For example, see Figure 1C.

(8) In Figure 1A-C, seizure susceptibility was compared in EEN+/+ and EEN-/- mice, but the methods section states that seizure susceptibility was evaluated in 8-10-week-old male C57BL/6N mice (line 513). Was this meant to indicate that the EEN+/+ and EEN-/- mice were on a C57BL/6N background? How does this match with the statement that EEN1 -/- mice were generated on a C57BL/6J background (line 467)?

We apologize for the mistake. In our study, EEN1^-/- mice were generated on a C57BL/6J background, as stated in our previously published papers (Yang et al., 2021; Yang et al., 2018) and in “Animals” in Material and Methods of our original manuscript. We had corrected the statement to “To evaluate seizure susceptibility, 8-10-week-old male and female EndoA1^+/+ or EndoA1^-/- littermates…” in Material and Methods of the revised manuscript.

(9) In the electrophysiology experiments in Figure 1E-O, it is not clear to me which neurons were recorded in the control group. The methods section states that "Whole-cell recordings were performed on an AAV-infected neuron and a neighboring uninfected neuron" (line 736). However, the figure legends states that recordings were obtained from "10 control (Ctrl, mCherry alone) and 10 EEN1 KO (mCherry and Cre) pyramidal neurons" (line 1079), which would indicate that the controls are not uninfected neurons from the same animal, but AAV-mCherry infected neurons from a different animal. Please clarify which of the two descriptions is accurate.

Thanks for catching the error! In all electrophysiological experiments, a neighboring uninfected neuron was used as the control in Figure 1E-O. This was incorrectly stated in the figure legend of the original manuscript. In the revised manuscript, the information has been corrected in figure legends of new Figure 1 (E-F).

(10) The authors show that in Endophilin A1 KO animals, eIPSCs are reduced, but mIPSC frequency and amplitude are unaltered. How do they explain this finding in the context of the fact that gephyrin and GABAAR1.

We apologize for the confusion about the data of electrophysiological recording. Compared with eIPSC, which are recorded in the presence of electrically evoked action potential that elicited a substantial release of neurotransmitter, mIPSCs are small, spontaneous currents recorded in the presence of TTX during patch-clamp experiments, resulting from the release of neurotransmitters from presynaptic terminals in the absence of action potential. The amplitude of mIPSCs typically reflects the quantal release of neurotransmitters, while their frequency can vary depending on synaptic activity and the state of the neuron.

A number of molecules fine-tune presynaptic neurotransmitter release and functions of inhibitory postsynaptic receptors. In our study, inhibitory postsynapses were partially affected in endophilin A1 knockout neurons, while presynaptic endophilin A1 remained intact during electrophysiological recordings. Conceivably, the observed deficits in endophilin A1 knockout mice were mild. Following endophilin A1 depletion, inhibitory postsynaptic receptors appeared sufficient to respond to spontaneous neurotransmitter release but may be inadequate to large amounts of neurotransmitter release evoked by action potential. Meanwhile, spontaneous synaptic activity and the state of the neuron were not obviously affected under basic state by endophilin A1 depletion during postnatal stages. Consequently, mIPSC frequency and amplitude remain unaltered but eIPSCs were reduced compared to the control neurons. This finding was consistent with behavioral experiments, where aggressive epileptic behaviors were induced by PTZ rather than spontaneous epilepsy in endophilin A1 knockout mice.

(11) Distribution of gephyrin, VGAT, and GABAARg2 differs substantially between the different layers of hippocampal area CA1, and the same goes for the other regions of the hippocampus. However, in Figure 2, it is not clear to me from the sample images which layers of each subregion the authors quantified, or indeed whether they paid attention to which layers they included in their analysis. This can lead to a substantial skewing of the data if different layers were preferentially included in the two genotypes. Please clarify which layers were analysed, and how comparability between WTs and KOs was ensured. This is particularly important given the authors' claim that Endophilin A1 acts equally at all subtypes of GABAergic synapses (lines 373- 376).

Thanks for the cautiousness! We distinguished each hippocampal subregion based on the anatomical structure in brain slices. Quantification of fluorescent mean intensity of each synaptic protein in all layers of each subregion, as shown in new Figure 2 and Figure S2A-F, revealed that GABAergic synaptic proteins were impaired in both P21 and P60 KO mice.

We further analyzed the fluorescent signal of core postsynaptic component, gephyrin, in individual layers of each subregion in the hippocampus of mature WT and KO mice, as presented in new Figures S2G-H. Our findings demonstrated a decrease in gephyrin levels across all layers of each subregion in KO mice. Additionally, we examined gephyrin clustering across the soma, axon initial segment (AIS), and dendrites in cultured mature endophilin A1 knockout hippocampal neurons, as shown in new Figure S5E-H. The results showed that gephyrin was affected in all subcellular regions following endophilin A1 knockout.

Collectively, these data suggest that endophilin A1 functions across all subtypes of GABAergic postsynapses.

(12) In Figure 3E-F, the authors state that there was no change in the total level of synaptic neurons in EEN1 KO neurons (line 188). However, there is no quantification of the total level of synaptic neurons shown, and based on the immunoblot in Figure 3E, it looks like there is a substantial reduction in NR1, NL2, and g2. The authors should present a quantification of the total levels of these proteins and adjust their statement accordingly if necessary.

Thanks a lot for your comments. We quantified the total protein levels in Figure 3E and added the result to new Figure 3F, showing that total protein levels were not obviously affected in cultured KO neurons. When normalized to total protein levels, the surface levels of GABA_A receptors were significantly compromised compared to surface GluN1 and NL2. Furthermore, the total protein levels were not affected in brains of KO mice, as shown in Figures 3K (input) and 3L (S1). Collectively, there was no change in the total level of synaptic proteins in KO neurons.

(13) In Figure 3G-I, the authors claim, based on super-resolution images as presented here, that Endophilin A1 colocalizes with gephyrin and g2. However, no quantification of this colocalization is presented. The authors should add this quantification to support their claim and indicate how many GABAergic synapses contain Endophilin A1.

Thank you for the thoughtful comments. The resolution of the images is significantly improved by super-resolution microscopy. As a result, the overlap between the two proteins will become smaller or even disappear. Since no two proteins can occupy the same physical space, they would show lower colocalization and instead exhibit proximal localization. As expected, in Figures 3G and 3H, we observed only small overlap or proximal localization of endophilin A1 with gephyrin or GABA_AR γ2. To further confirm the localization of endophilin A1 in inhibitory synapses, we co-stained endophilin A1 with both pre- and post-synaptic proteins, gephyrin and Bassoon. Then we quantified the colocalization of endophilin A1 with gephyrin or with Bassoon using the method for super-resolution images described in the reference (Andrew D. McCall. Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy. McCall BMC Bioinformatics (2024) 25:55). The percentage of gephyrin or Bassoon puncta that were in close proximity with endophilin A1 was also calculated, as shown in new video 5 and new Figure S4B-G. These data have been added in the revised manuscript as follows, “We further detected the localization of endophilin A1 to inhibitory synapses by co-immunostaining with both pre- and post-synaptic markers (Figure. S4B and Video 5). Quantitative analysis of super-resolution localization maps revealed that ~ 47 % puncta of gephyrin or Bassoon were proximal to endophilin A1 (Figure. S4G, n \= 14), with a mean distance between endophilin A1- and gephyrin-positive pixels of ∼ 120 nm, or between endophilin A1- and Bassoon-positive pixels of ∼ 130 nm (Figure. S4C-F).”

(14) In the quantification shown in Figure 3K-L, there are no error bars in the WT data sets. This presumably means that all values were normalized to WT. However, since this artificially eliminates the variance in the WT group, a t-test is no longer valid, since this assumes a normal distribution and normal variance, which are no longer given. The authors should either change the way they normalize their data to maintain the variance in the WT group or perform a different statistical test that can account for the artificial lack of variance in one of the groups.

Thank you for the suggestions! We modified our analysis approach. Specifically, we used mean value of WTs to normalize data to preserve the variance in the WT group and performed unpaired t-tests to assess statistical significance in Figure 3K-L. Additionally, we replaced the bar graphs with modified graphs showing individual data points. Please see Response to Recommendation (12).

(15) What is the difference between the coIP experiment in Figure 4E and 3J, right panel? In both cases, an Endophilin A1 IP is performed, and gephyrin, GABAARg2, and GABAARa1 are assessed. However, Figure 3J's right panel indicates that Endophilin A1 does interact with the GABAAR subunits, whereas Figure 4E shows that it does not. How do the authors explain this discrepancy? Were these experiments performed more than once?

Sorry for the confusion. Figure 3J and Figure 4E show data from immunoisolation assay and conventional co-immunoprecipitation (co-IP), respectively. Immunoisolation allows for the rapid and efficient separation of subcellular membrane compartments using antibodies conjugated to magnetic beads. In Figure 3J, we used antibodies against GABA_AR α1 subunit or endophilin A1 to isolate the inhibitory postsynaptic membranes or endophilin A1-associated membranous compartments. In contrast, co-immunoprecipitation detects direct protein-protein interactions in detergent-solubilized lysates. For Figure 4E, we applied antibodies against endophilin A1 to precipitate its interaction partners. The results in Figure 3J and Figure 4E demonstrate that endophilin A1 is localized in the inhibitory postsynaptic compartment and directly interacts with gephyrin, but not with GABA_ARs. Detailed information regarding the methods used for co-IP and immunoisolation can be found in “GST-pull down, co-immunoprecipitation (IP), and immunoisolation” in the “Material and Methods” section of original manuscript.

These experiments were repeated multiple times to ensure reliability. In fact, consistent data showing endophilin A1 localization in the inhibitory postsynaptic compartment were observed in Figure 3K, showing the quantified data as well.

(16) For the colocalization analysis in Figure 5A-C, what percentage of gephyrin puncta contain g2 in the WT and Endophilin A1 KO? Currently, only a correlation coefficient is provided, but not the degree of overlap. Please add this information to the figure.

Thanks for the comments on the colocalization analysis. We analyzed the percentage of gephyrin puncta overlapping with GABA_AR γ2 and added the graphs in new Figure 5C.

(17) Figure 6 investigates how actin depolarization affects GABAergic synapse function, but does not assess how Endophilin A1 contributes to this process. The authors then provide an extremely short statement in the discussion, stating that their data are contradictory to a previous study (lines 412 - 417). This section of the discussion should be expanded to address the specific role of Endophilin A1 in the consequences of actin depolymerization.

Thanks a lot for the advice. In the original manuscript, we discussed the specific role of endophilin A1 at inhibitory postsynapses as follows in Discussion:

“As membrane-binding and actin polymerization-promoting activities of endophilin A1 are both required for its function in enhancing iPSD formation and g2–containing GABA_AR clustering to iPSD, we propose that membrane-bound endophilin A1 promotes postsynaptic assembly by coordinating the plasma membrane tethering of the postsynaptic protein complex and its stabilization with the actin cytomatrix”

Following your advice, we added a statement in the revised manuscript addressing the role of endophilin A1 in actin polymerization at inhibitory postsynapses, shown as follows, “In the present study, the impaired clustering of gephyrin and GABA_A γ2 by F-actin depolymerization underscores the essential role of F-actin in the assembly and stabilization of the inhibitory postsynaptic machinery. Membrane-bound endophilin A1 promotes F-actin polymerization beneath the plasma membrane through its interaction with p140Cap, an F-actin regulatory protein, thereby facilitating and/or stabilizing the clustering of gephyrin and γ2-containing GABA_A ​receptors at postsynapses.”

(18) Which statistical analysis was conducted in Figure 7F? Given the nature of the data, a repeated measures ANOVA would be necessary to accurately assess the statistical accuracy.

Sorry for the confusion. We conducted one-way ANOVA followed by Tukey post hoc test at each time point in original Figure 7F. We have employed the method of repeated measures ANOVA followed by Tukey post hoc test as suggested in new Figure 7F. Meanwhile, we reanalyzed data in new Figure 1C with the same method. We also modified the description in “Statistical analysis” and Figure legends for new Figure1C and 7F in revised manuscript.

Reviewer #2 (Recommendations for the authors):

Data presentation:

(1) Figures 2A, B, D, E, G, H. Figures S2A, B, D:

Add P21 or P60 labels to these figures so that the difference between similarly stained samples (e.g. Figures 2A, B) is obvious to the reader.

Thanks! We added “P21” or “P60” labels in new Figure 2 and Figure S2 as suggested.

(2) Figures 4C, D:

The authors must make their coIP data annotation consistent. In Figure 4C, they use actual microgram amounts when, e.g., describing how much input was present, yet in Figure 4D they use + and -. The authors should pick one.

Thanks for the comments. We labeled the consistent data annotation in new Figure 4C and 4D, we also changed the label in 4F for the consistent data annotation.

(3) Figure 5A

GFP is gray in this figure, but in all other figures, it is blue. Consider changing for presentation reasons.

Thanks a lot for pointing out the problem. We replaced gray with blue color to indicate GFP in new Figure 5A.

(4) Figures 6A, C, E, G

Label graphs as either short-term or long-term drug treatment.

Thanks for the suggestion. We labeled the graphs as 60 min for short-term or 120 min for long-term drug treatment in new Figure 6A, C, E, G for convenient reading.

Annotation, grammar, spelling, typing errors:

(1) Figure 4G:

Merge and GFP labels are seemingly swapped.

Thanks a lot for sharp eye. We corrected the labels in new Figure 4G.

(2) Fig 4I:

The authors use "Gephryin" instead of GPN. They should be consistent and choose one.

Sorry for the mistake. We changed the label consistent with other figures in new Figure 4I and rearranged the images in figures for good looking.

(3) "One-hour or two-hour treatment of mature neurons with nocodazole..."

Thanks for your advice. We modified the sentence to “Treatment of mature neurons with nocodazole, a microtubule depolymerizing reagent, for one hour (short-term) or two hours (long-term), caused…”.

(4) The authors should indicate that one-hour is their short-term treatment and that two-hour is their long-term treatment so that when these terms are used later to describe LatA experiments, it is clearer to the reader.

Thanks for your comments. We modified the statement as seen in Response to Recommendation (3), it is clearer to the reader.

(5) EEA1. The authors should use a more conventional term EndoA1 so that the manuscript can be searched easily.

Thanks a lot for the suggestion. We replaced all of the term “EEN1” with “EndoA1” in the revised manuscript.

Reviewer #3 (Recommendations for the authors):

Major Points

(1) The number of observations for the electrophysiology experiments in Figure 1 (dots are neurons) is very low and it is not clear whether the data shown is derived from different mice. The same criticism applies to the data shown in Figures 7G-K.

We apologize for the low neuron number in electrophysiology experiments. In the patch-clamp experiments, the number of neurons recorded was higher than what is shown in the figures. However, neurons with a membrane resistance (Rm) below 500 MΩ, indicating unstable seals or poor conditions, were excluded from the analysis. Additionally, we added the number of mice from which the data derived in each group in the figure legends for Figure 1, 7 and S1, this point was also raised by Reviewer #1 (Please see Response to Recommendation (1)).

(2) Images in Figure 2 are shown at low magnification, statements on changes in intensity of inhibitory synaptic markers in the hippocampal region are impossible to interpret. Analysis of inhibitory synapses in vivo would require sparse neuronal labeling and 3D reconstruction, for instance using gephyrin-FingRs (Gross et al., Neuron 2013).

Thanks for your insightful suggestion. We obtained pCAG_PSD95.FingR-eGFP-CCR5TC and pCAG_GPN.FingR-eGFP-CCR5TC constructs from Addgene (plasmid # 46295 & #46296). We attempted in utero electroporation (IUE) to introduce the DNAs into cortical neurons or hippocampal neurons at E14.5, unfortunately with no success. Following the repetitive operation for numerous times, we could eventually obtain newborn pups of ICR mice after IUE. However, we failed to obtain any newborn pups of C57BL/6J mice due to abortion following the procedure. Furthermore, pregnant C57BL/6J mice (WTs or KOs) did not survive or remained in a poor state of health after surgery. Therefore, we were unable to analyze synapses through sparse labeling and 3D reconstruction by IUE. Alternatively, we obtained commercial AAVs carrying rAAV-EF1a-PSD95.FingR-eGFP-CCR5TC and rAAV-EF1a-mRuby2-Gephyrin.FingR-IL2RGTC, then injected into the CA1 region of EndoA1^fl/fl mice at P0. Mice were fixed and detected the fluorescent signals in CA1 regions at P21. Consistent with immunostaining with antibodies, decreased mRuby2-Gephyrin.FingR or PSD95.FingR-eGFP was observed in dendrites of KO neurons at P21, as shown in new Figure S3. In combination with electrophysiological recording, PSD fractionation and immunoisolation from brains, these data support our conclusion regarding the effects of endophilin A1 knockout on the inhibitory synapses.

Additionally, we transfected DIV12 cultured hippocampal neurons with pCAG_PSD95.FingR-eGFP-CCR5TC or pCAG_GPN.FingR-eGFP-CCR5TC and observed fluorescent signals on DIV16. Both the signal intensity and number of GPN.FingR-eGFP clusters were also significantly attenuated, with no obvious changes in PSD95.FingR-eGFP clusters in dendrites of mature neurons, as shown in new Figure S5A-D. We are very pleased that the result further strengthened our original conclusion. We have added the new pieces of data in our revised manuscript.

(3) Figure 3: surface labeling of GluA1 or the GABAAR gamma 2 subunit is difficult to interpret: the patterns are noisy and the numerous puncta appear largely non-synaptic although this is difficult to judge in the absence of additional synaptic markers. It appears statistics are done on dendritic segments rather than the number of neurons. The legend does not mention how many independent cultures this data is derived from. In their previous study (Yang et al., Front Mol Neurosci 2018), the authors noted a decrease in surface GluA1 levels in the absence of endophilin A1. How do they explain the absence of an effect on surface GluA1 levels in the current study?

Sorry for the concern and thanks for your comments. First, we assessed changes in the surface levels of excitatory and inhibitory receptors by co-immunostaining in cultured WT and KO hippocampal neurons. Given the very low transfection efficiency of neurons in high density culture, numerous puncta of receptors from adjacent non-transfected neurons were also detected. This approach may contribute to the noisy pattern observed in Figure 3A. Besides, the projections of z-stack for higher magnified dendrites may likely introduced higher background signals. We have now replaced the original images with the newest repeat in new Figure 3A. Moreover, we confirmed a decrease in the surface expression of GABA_AR γ2 by the biotinylation assay, as shown in Figure 3E. Indeed, we agree that some puncta for surface labeling of receptors seemed to be non-synaptic localization. In order to reflect the decrease in synaptic proteins at synapses, we isolated PSD fraction by biochemical assay and found that gephyrin and GABA_AR γ2, two major inhibitory postsynaptic components, were reduced in the PSD fraction from KO brains, as shown in Figure 3L. Their colocalization was also attenuated in the absence of endophilin A1, as shown in Figure 5A-C. Combined with electrophysiological recording, these data from multiple assays indicate GluA1 at synapses was not obviously affected but GABA_AR γ2 at synapses was impaired in endophilin A1 KO neurons in the present study.

We have corrected the way that the number of samples is defined for statistical analysis as suggested. This point was also raised by Reviewer #1 (Recommendation (2)). We averaged the values from all dendritic segments of a single neuron, such that one neuron equaled one data point. We had replaced the original Figure 3B and 3D (please see Response to Recommendation (2) by Reviewer #1). Additionally, we added the number of independent cultures these data were derived from to figure legends in revised manuscript.

Previously, we observed a small decrease in surface GluA1 levels in spines under basal conditions and a more pronounced suppression of surface GluA1 accumulation in spines upon chemical LTP in endophilin A1 KO neurons from EndoA1^-/- mice that knockout endophilin A1 since embryonic development stages (Figure 5C,H. Yang et al., Front Mol Neurosci, 2018). In Figure 3A and B in current study, we analyzed surface receptor levels in GFP-positive dendrites, rather than spines, under basal conditions when endophilin A1 was depleted at the later developmental stage. We found a decrease in surface GABA_AR γ2 levels but no significant effects on surface GluA1 levels in dendrites. These findings indicate that endophilin A1 primarily affects excitatory synaptic proteins in spines during synaptic plasticity and inhibitory synaptic proteins in dendrites under basal conditions in mature neurons.

(4) Super-resolution images in Figure 3G, H, I: endophilin A1 puncta look different in panel 3I compared to 3G and 3H, which are very noisy. It is difficult to interpret how specific these EEN1 puncta are. Previous images showing EEN1 distribution in dendrites look different (Yang et al., Front Mol Neurosci 2018); is the same KO-verified antibody being used here? Colocalization of EEN1 with gephyrin or the GABAAR gamma 2 subunit is difficult to interpret; gephyrin mostly does not seem to colocalize with EEN1 in the example shown.

Sorry for your concerns. As stated previously in Major Points (3), transfection efficiency was very low in cultured neurons and our cultured neurons were at relative high density. As a result, numerous puncta of proteins located in the adjacent non-transfected neurons were also detected, which may contribute to noisy signals observed in Figure 3G-I.

In our previous paper, we confirmed the specificity of the antibody against endophilin A1 (5A,B. Yang et al., Front Mol Neurosci, 2018). We used the same antibody (rabbit anti-endophilin A1, Synaptic Systems GmbH, Germany) in the current study. While the previous images were obtained using confocal microscopy, the current images in Figures 3G, H, and I were acquired using super-resolution microscopy (SIM). The different patterns observed in the dendrites may be attributed to the difference in image resolution, antibodies dilution and reaction time.

Reviewer #1 also points out the quantification of colocalization of gephyrin and GABA_AR γ2 with endophilin A1. Please see Response to Recommendation (13) by Reviewer #1.

(5) The interaction of gephyrin and endophilin A1 is based on coIP experiments in cells and brain tissue. To convincingly demonstrate that these proteins interact, biophysical experiments with purified proteins are necessary.

Thanks a lot for your great suggestions on the interaction of endophilin A1 with gephyrin. To convincingly demonstrate their interaction, we performed pull-down assay with purified recombinant proteins and the result shows that both G and E domains of gephyrin were involved in the interaction with endophilin A1. The data has been added to the revised manuscript as new Figure 5I. We also modified the statement about the data and figure legends in the revised manuscript.

(6) Figure 4G: the gephyrin images are not convincing; the inhibitory postsynaptic element typically looks somewhat elongated; these puncta are very noisy and do not appear to represent iPSDs. The same criticism applies to the images shown in Figures 5 and 7.

Thanks for the comment. The gephyrin puncta in our images exhibited heterogeneous shapes and sizes, with some appearing somewhat elongated. To address this, we compared the puncta pattern of gephyrin with that shown in the reference. As illustrated in the figure from the reference, gephyrin puncta also displayed distinct shapes and sizes, Figure 3A-F, Neuron 78, 971–985, June 19, 2013). Please note that the images were z-stack projections at higher magnification, as described in the "Materials and Methods" section. This approach may likely introduce higher background signals and may contribute to the much more heterogeneous appearance of the puncta in Figures 4, 5, and 7. As mentioned previously, the numerous gephyrin puncta located in the adjacent non-transfected neurons may also contribute to some of the noisy signals observed. We have replaced the original images with new images in new Figure 4G, 5 and 7.

Moreover, in order to confirm the effects of endophilin A1 KO on the gephyrin clustering, we also detected the endogenous clusters of gephyrin or PSD95 visualized by GPN.FingR-eGFP or PSD95.FingR-eGFP in cultured mature neurons. The results were consistent with immunostaining with antibodies against gephyrin. Please see Response to Recommendation (2)

(7) Figure 7E, F: the rescue (Cre + WT) appears to perform better than the control (mCherry + GFP) in the PTZ condition; how do the authors explain this? Mixes of viral vectors were injected, would this approach achieve full rescue?

Thanks for the thoughtful comment. Mixed viruses were injected bilaterally into the hippocampal CA1 regions. The results showed a full rescue effect by WT endophilin A1 in knockout mice during the early days, with even a little bit better rescue effect than the control group in the later days under the PTZ condition, as shown in Figures 7E and 7F. In the current study, overexpression of endophilin A1 increased the clustering of gephyrin and GABA_AR γ2 in cultured neurons, as shown in Figures 4I-J and 5D-E. Presumably, the slightly better rescue effects observed in the behavioral tests was likely attributed to the enhanced clustering and/or stabilization of gephyrin/GABA_AR γ2 by WT endophilin A1 expression in KO neurons in vivo. Moreover, the electrophysiological recording also showed full rescue effects on eIPSC by WT endophilin A1 in KO neurons (Figure 7G-K).

Minor Points

(1) The authors mention that they previously found a decrease in eEPSC amplitude in EEN1 KO mice (Yang et al., Front Mol Neurosci 2018). The data in Fig. 1E suggests a decrease in eEPSC amplitude but is not significant here, likely due to the small number of observations. If both eEPSC and iEPSC amplitude are reduced in the absence of EEN1. Would the E/I ratio still be significantly changed?

We apologize for the confusion. In our previous study, AMPAR-mediated excitatory postsynaptic currents (eEPSCs) were found to be slightly but significantly reduced compared to the control group, while NMDAR-mediated excitatory postsynaptic currents showed no significant difference (Figure 4N,O. Yang et al., Front Mol Neurosci, 2018). In the current study, we adopted a different recording protocol, simultaneously measuring eEPSCs and eIPSCs from the same neuron to calculate the E/I ratio. Unlike previous studies, we did not use inhibitors to suppress GABA receptor activity. As a result, the recorded signals did not distinguish AMPAR-mediated or NMDAR-mediated excitatory postsynaptic currents to reflect total eEPSCs, which may explain the non-significant reduction observed compared to control neurons in this study.

It is possible that the eEPSC amplitude would show a significant reduction if a larger number of neurons were recorded. Nevertheless, the larger suppression of eIPSCs in the absence of endophilin A1 indicates that the E/I ratio is significantly altered.

(2) Page 7: the authors mention they aim to exclude effects on presynaptic terminals of deleting endophilin A1 in cultured neurons, is this because of a sparse transfection approach?

Please clarify.

Sorry for the confusion. In cultured neurons, we always observed sparse transfection due to the very low transfection efficiency (~ 0.5%). Therefore, we could examine the effects of endophilin A1 knockout specifically in the specific CamKIIa promoter-driven Cre-expressing postsynaptic neurons, while endophilin A1 remained intact in the non-transfected presynaptic neurons.

(3) The representative blot of the surface biotinylation experiment (Figure 3E) suggests that loss of endophilin A1 also affects GluN1 and Nlgn2 levels, and error bars in panel 3F (lacking individual data points) suggest these experiments were highly variable.

Sorry for the confusion. Reviewer #1 also raised the question and we quantified the total level of GluN1 and NL2 in Figure 3E. And we replaced the original graphs with scatterplots and means ± S.E.M. Please see the Response to Recommendation (3) & (12) by Reviewer #1.

(4) Have other studies analyzing inhibitory synapse composition identified endophilin A1 as a component? The rationale for this study seems to be primarily based on the presence of epileptic seizures and E/I imbalance.

Thank you for your questions. To date, no other studies investigated endophilin A1 as an inhibitory postsynaptic component. We observed the proximal localization of endophilin A1 with inhibitory postsynaptic proteins using super-resolution microscopy (SIM) and quantification results showed ~ 47% puncta of gephyrin correlated with endophilin A1 (Figure 3G-I and S4B-G). We further immunoisolated the inhibitory postsynaptic fraction using GABA_A receptors and found that endophilin A1 was present in the isolated fraction, and vice versa (Figure 3J). Additionally, we demonstrated that endophilin A1 directly interacted with gephyrin through co-IP and pull-down assays (Figure 5J-I). Together with data from immunolabeling, biochemical assays, electrophysiological recordings, and behavioral tests, these results identified endophilin A1 as an inhibitory postsynaptic component.

(5) Figure 3J: what are S100 and P100 labels? Is Nlgn2 part of the EEN1 complex? If it is, why are Nlgn2 surface levels not affected by EEN1 loss (Figure 3E, F, K)? Why does EEN1 not interact with Nlgn2 in HEK cells (Figure 4D)?

Sorry for the confusion. The detailed information regarding S100 and P100 can be found in the “GST-pull down, co-immunoprecipitation (IP), and immunoisolation” in the “Materials and Methods” section. S100 contains soluble proteins, while P100 refers to the membrane fraction after high speed (100,000xg) centrifugation.

Figures 3J-K and 4C-F showed the data from immunoisolation and conventional co-immunoprecipitation assays, respectively. Immunoisolation, which uses antibodies coupled to magnetic beads, allows for the rapid and efficient separation of subcellular membrane compartments. In Figure 3J-K, we used antibodies against GABA_AR α1 to isolate membrane protein complexes from the inhibitory postsynaptic fraction. In contrast, co-immunoprecipitation typically detects direct interactions between proteins solubilized by detergent treatment. For Figure 4C-F, FLAG beads were used in HEK293 lysates, or antibodies against endophilin A1 were employed in brain lysates to precipitate direct interaction partners. Combined with the results from Figure 3J-L, the data in 4C-F indicated that endophilin A1 was localized in the inhibitory postsynaptic compartment and directly bound to gephyrin but not to either GABA_A receptors or Nlgn2 (NL2). This binding promoted the clustering of gephyrin and GABA_AR γ2 at synapses, facilitating GABA_AR assembly.

Nlgn2 (NL2) is a key inhibitory postsynaptic component but does not directly bind to endophilin A1. Consequently, endophilin A1 failed to co-immunoprecipitate with NL2 in the presence of detergent in HEK293 cell lysates (Figure 4D). Furthermore, the surface levels of NL2 or its distribution in PSD fraction were unaffected by the loss of endophilin A1 (Figure 3E, F, K, L). This suggests that mechanisms independent of endophilin A1 orchestrate the surface expression and synaptic distribution of NL2.

(6) How do the authors interpret the finding that endophilin A1, but not A2 or A3, binds gephyrin? What could explain these differences?

Thanks for the thoughtful comment. Endophilin As contain BAR and SH3 domains. While the amino acid sequences in the BAR and SH3 domains are highly conserved, the intrinsically disordered loop region between BAR and SH3 domains is highly variable. A study by the Verstreken lab revealed that a human mutation in the unstructured loop region of endophilin A1 increases the risk of Parkinson's disease. They also demonstrated that the disordered loop region controls protein flexibility, which fine-tunes protein-protein and protein-membrane interactions critical for endophilin A1 function (Bademosi et al., Neuron 111, 1402–1422, May 3, 2023). Our previous study showed that endophilin A1 and A3, but not A2, bind to p140Cap through their SH3 domains, despite the high sequence homology in the SH3 domains among these proteins (Figure2A,B. Yang et al., Cell Research, 2015). These findings indicate that each endophilin A likely interacts with specific partners due to distinct key amino acids.

Additionally, endophilin A1 is expressed at much higher levels than A2 and A3 in neurons, with distinct distribution of them across different brain regions. Our lab demonstrated that the function of A1 at postsynapses (both excitatory and inhibitory synapses) cannot be compensated by A2 or A3. Therefore, it is reasonable that endophilin A1, rather than A2 or A3, binds to gephyrin, even though the underlying mechanisms remain unclear.

(7) Figure 4G: panels are mislabeled (GFP vs merge).

Thanks for careful reading and sorry for the mistake. We corrected the label in new Figure 4G. Please see Response to Annotation, grammar, spelling, typing errors:(1) by Reviewer #2.

Author response:

The following is the authors’ response to the original reviews

Life Assessment

The authors use a synthetic approach to introduce synaptic ribbon proteins into HEK cells and analyze the ability of the resulting assemblies to cluster calcium channels at the active zone. The use of this ground-up approach is valuable as it establishes a system to study molecular interactions at the active zone. The work relies on a solid combination of super-resolution microscopy and electrophysiology, but would benefit from: (i) additional ultrastructural analysis to establish ribbon formation (in the absence of which the claim of these being synthetic ribbons might not be supported; (ii) data quantification (to confirm colocalization of different proteins); (iii) stronger validation of impact on Ca2+ function; (iv) in depth discussion of problems derived from the use of an over-expression approach.

We thank the editors and the reviewers for the constructive comments and appreciation of our work. Please find a detailed point-to-point response below. In response to the critique received, we have now (i) included an ultrastructural analysis of the SyRibbons using correlative light microscopy and cryo-electron tomography, (ii) performed quantifications to confirm the colocalisation of the various proteins, (iii) discussed and carefully rephrased our interpretation of the role of the ribbon in modulating Ca²⁺ channel function and (iv) discussed concerns regarding the use of an overexpression system. 

Public Reviews:

Reviewer #1 (Public Review):

We would like to thank the reviewer for the comments and advice to further improve our manuscript. We have completely overhauled the manuscript taking the suggestions of the reviewer into account.

(1) Are these truly "synthetic ribbons". The ribbon synapse is traditionally defined by its morphology at the EM level. To what extent these structures recapitulate ribbons is not shown. It has been previously shown that Ribeye forms aggregates on its own. Do these structures look any more ribbonlike than ribeye aggregates in the absence of its binding partners?

We thank reviewer 1 for their constructive feedback and critique of the work. 

We agree that traditionally, ribbon synapses have always been defined by the distinct morphology observed at the EM level. However, since the discovery of the core-components of ribbons (RIBEYE and Piccolino) confocal and super-resolution imaging of immunofluorescently labelled ribbons have gained importance for analysing ribbon synapses. A correspondence of RIBEYE immunofluorescent structures at the active zone to electron microscopy observations of ribbons has been established in numerous studies (Wong et al, 2014; Michanski et al, 2019, 2023; Maxeiner et al, 2016; Jean et al, 2018) even though direct correlative approaches have yet to be performed to our knowledge. We have now analysed SyRibbons using cryo-correlative electron-light microscopy. We observe that GFPpositive RIBEYE spots corresponded well with electron-dense structures, as is characteristic for synaptic ribbons (Robertis & Franchi, 1956; Smith & Sjöstrand, 1961; Matthews & Fuchs, 2010). We could also observe SyRibbons within 100 nm of the plasma membrane (see Fig. 3). We have now added this qualitative ultrastructural analysis of SyRibbons in the main manuscript (lines 272 - 294, Fig. 3 and Supplementary Fig. 3).

(2) No new biology is discovered here. The clustering of channels is accomplished by taking advantage of previously described interactions between RBP2, Ca channels and bassoon. The localization of Ribeye to bassoon takes advantage of a previously described interaction between the two. Even the membrane localization of the complexes required the introduction of a membraneanchoring motif.

We respectfully disagree with the overall assessment. Our study emphasizes the synthetic establishment of protein assemblies that mimic key aspects of ribbon-type active zone, defining minimum molecular requirements. Numerous previous studies have described the role of the synaptic ribbon in organising the spatial arrangement of Ca²⁺ channels, regulating their abundance and possibly also modulating their physiological properties (Maxeiner et al, 2016; Frank et al, 2010; Jean et al, 2018; Wong et al, 2014; Grabner & Moser, 2021; Lv et al, 2016). We would like to highlight that there remain major gaps between existing in vitro and in vivo data; for instance, no evidence for direct or indirect interactions between Ca²⁺ channels and RIBEYE have been demonstrated so far. While we do indeed take advantage of previously known interactions between RIBEYE and Bassoon (tom Dieck et al, 2005); between Bassoon, RBP2 and P/Q-type Ca²⁺ channels (Davydova et al, 2014); and between RBP2 and Ltype Ca²⁺ channels (Hibino et al, 2002), our study tries to bridge these gaps by establishing the indirect link between the synaptic ribbon (RIBEYE) and L-type CaV1.3 Ca²⁺ channels using a bottom-up approach, which has previously just been speculative. Our data shows how even in a synapse-naive heterologous expression system, ribbon synapse components assemble Ca²⁺ channel clusters and even show a partial localisation of Ca²⁺ signal. Moreover, we argue that the established reconstitution approach provides other interesting insights such as laying ground-up evidence supporting the anchoring of the synaptic ribbon by Bassoon. Finally, we expect that the established system will serve future studies aimed at deciphering the role of putative CaV1.3 or CaV1.4 interacting proteins in regulating Ca²⁺ channels of ribbon synapses by providing a more realistic Ca²⁺ channel assembly that has been available in heterologous expression systems used so far. In response to the reviewers comment we have augmented the discussion accordingly.  

(3) The only thing ribbon-specific about these "syn-ribbons" is the expression of ribeye and ribeye does not seem to participate in the localization of other proteins in these complexes. Bsn, Cav1.3 and RBP2 can be found in other neurons.

The synaptic ribbon made of RIBEYE is the key molecular difference in the molecular AZ ultrastructure of ribbon synapses in the eye and the ear. We hypothesize the ribbon to act as a superscaffold that enables AZ with large Ca²⁺ channel assemblies and readily releasable pools. In further support of this hypothesis, the present study on synthetic ribbons shows that CaV1.3 Ca²⁺ channel clusters are larger in the presence of SyRibbons compared to SyRibbon-less CaV1.3 Ca²⁺ channel clusters in tetratransfected HEK cells (Ca²⁺ channels, RBP, membrane-anchored Bassoon, and RIBEYE, Fig. 6). In response to the reviewers comment we now added an analysis of triple-transfected HEK cells (Ca²⁺ channels, RBP, membrane-anchored Bassoon), in which CaV1.3 Ca²⁺ channel clusters again are significantly smaller than at the SyRibbons and indistinguishable from SyRibbon-less CaV1.3 Ca²⁺ channel clusters (Fig. 6E, F).

(4) As the authors point out, RBP2 is not necessary for some Ca channel clustering in hair cells, yet seems to be essential for clustering to bassoon here.

Here we would like to clarify that RBP2 is indeed important in inner hair cells for promoting a larger complement of CaV1.3 and RBP2 KO mice show smaller CaV1.3 channel clusters and reduced whole cell and single-AZ Ca²⁺ influx amplitudes (Krinner et al, 2017). However, a key point of difference we emphasize on is that even though CaV1.3 clusters appeared smaller, they did not appear broken or fragmented as they do upon genetic perturbation of Bassoon (Frank et al, 2010), RIBEYE (Jean et al, 2018) or Piccolino (Michanski et al, 2023). This highlights how there may be a hierarchy in the spatial assembly of CaV1.3 channels at the inner hair cell ribbon synapse (also described in the discussion section “insights into presynaptic Ca²⁺ channel clustering and function”) with proteins like RBP2 regulating abundance of CaV1.3 channels at the synapse and organising them into smaller clusters – what we have termed as “nanoclustering”; while Bassoon and RIBEYE may serve as super-scaffolds further organizing these CaV1.3 nanoclusters into “microclusters”. Observations of fragmented Ca²⁺ channel clusters and broader spread of Ca²⁺ signal seen upon Ca²⁺ imaging in RIBEYE and Bassoon mutants (Jean et al, 2018; Frank et al, 2010; Neef et al, 2018), and the absence of such a phenotype in RBP2 mutants (Krinner et al, 2017) may be explained by such a differential role of these proteins in organising Ca²⁺ channel spatial assembly. The data of the present study on reconstituted ribbon containing AZs are in line with these observations in inner hair cells: RBP2 appears important to tether Ca²⁺ channels to Bassoon and these AZ-like assemblies are organised to their full extent by the presence of RIBEYE. As mentioned in the response to point 3 of the reviewer, we have now further strengthened this point by adding the analysis of SyRibbon-less CaV1.3 Ca²⁺ channel clusters in tripletransfected HEK cells (Ca²⁺ channels, RBP, membrane-anchored Bassoon, Fig. 6E, F). Moreover, we have revised the discussion accordingly. 

(5) The difference in Ca imaging between SyRibbons and other locations is extremely subtle.

We agree with the reviewer on the modest increase in Ca²⁺ signal amplitude seen in the presence of  SyRibbons and provide the following reasoning for this observation: 

(i) It is plausible that due to the overexpression approach, Ca²⁺ channels (along with RBP2 and PalmBassoon) still show considerably high expression throughout the membrane even in regions where SyRibbons are not localised. Indeed, this is evident in the images shown in the lower panel in Fig. 6B, where Ca²⁺ channel immunofluorescence is distributed across the plasma membrane with larger clusters formed underneath SyRibbons (for an opposing scenario, please see the cell in Fig. 6B upper panel with very localised CaV1.3 distribution underneath SyRibbons). This would of course diminish the difference in the Ca²⁺ signals between membrane regions with and without SyRibbons. We note that while the contrast is greater for native synapses, extrasynaptic Ca²⁺ channels have been described in numerous studies alone for hair cells (Roberts et al, 1990; Brandt, 2005; Zampini et al, 2010; Wong et al, 2014).

(ii) Nevertheless, we do not expect a remarkably big difference in Ca²⁺ influx due to the presence of SyRibbons in the first place. Ribbon-less AZs in inner hair cells of RIBEYE KO mice showed normal Ca²⁺ current amplitudes at the whole-cell and the single-AZ level (Jean et al, 2018). However, it was the spatial spread of the Ca2+ signal at the single-AZ level which appeared to be broader and more diffuse in these mutants in the absence of the ribbon, in contrast to the more confined Ca2+ hotspots seen in the wild-type controls. 

So, in agreement with these published observations – it appears that presence of SyRibbons helps in spatially confining the Ca²⁺ signal by super scaffolding nanoclusters into microclusters (see also our response to points 3 and 4 of the reviewer): this is evident from seeing some spatial confinement of Ca²⁺ signals near SyRibbons on top of the diffuse Ca²⁺ signal across the rest of the membrane as a result of overexpression in HEK cells. 

We have now carefully rephrased our interpretation throughout the manuscript and added further explanation in the discussion section.   

(6) The effect of the expression of palm-Bsn, RBP2 and the combination of the two on Ca-current is ambiguous. It appears that while the combination is larger than the control, it probably isn't significantly different from either of the other two alone (Fig 5). Moreover, expression of Ribeye + the other two showed no effect on Ca current (Figure 7). Also, why is the IV curve right shifted in Figure 7 vs Figure 5?

We agree with the reviewer that co-expression of palm-Bassoon and RBP2 seems to augment Ca²⁺ currents, while the additional expression of RIBEYE results in no change when compared to wild-type controls. We currently do not have an explanation for this observation and would refrain from making any claims without concrete evidence. As the reviewer also correctly pointed out, while the expression of the combination of palm-Bassoon and RBP2 raises Ca²⁺ currents, current amplitudes are not significantly different when compared to the individual expression of the two proteins (P > 0.05, Kruskal-Wallis test). In light of this, we have now carefully rephrased our MS. Moreover, we would like to thank reviewer 1 for pointing out the right shift in the IV curve which was due to an error in the values plotted on the x-axis. This has been corrected in the updated version of the manuscript. 

(7) While some of the IHC is quantified, some of it is simply shown as single images. EV2, EV3 and Figure 4a in particular (4b looks convincing enough on its own, but could also benefit from a larger sample size and quantification)

We have now added quantifications for the colocalisations of the various transfection combinations depicted in the above-mentioned figures collectively in Supplementary Figure 7 and added the corresponding results and methods accordingly. 

Reviewer #2 (Public Review):

We would like to thank the reviewer for the comments and advice to further improve our manuscript.

(1) Relies on over-expression, which almost certainly diminishes the experimentally-measured parameters (e.g. pre-synapse clustering, localization of Ca2+ entry).

We acknowledge this limitation highlighted by the reviewer arising from the use of an overexpression system and have carefully rephrased our interpretation and discussed possible caveats in the discussion section. 

(2) Are HEK cells the best model? HEK cells secrete substances and have a studied-endocytitic pathway, but they do not create neurosecretory vesicles. Why didn't the authors try to reconstitute a ribbon synapse in a cell that makes neurosecretory vesicles like a PC12 cell?

This is a valid point for discussion that we also had here extensively. We indeed did consider pheochromocytoma cells (PC12 cells) for reconstitution of ribbon-type AZs and also performed initial experiments with these in the initial stages of the project. PC12 cells offer the advantage of providing synaptic-like microvesicles and also endogenously express several components of the presynaptic machinery such as Bassoon, RIM2, ELKS etc (Inoue et al, 2006) such that overexpression of exogenous AZ proteins would have to be limited to RIBEYE only. 

However, a major drawback of PC12 cells as a model is the complex molecular background of these cells. We have also briefly described this in the discussion section (line 615 – 619). Naïve, undifferentiated PC12 cells show highly heterogeneous expression of various CaV channel types (Janigro et al, 1989); however, CaV1.3, the predominant type in ribbon synapses of the ear, does not seem to express in these cells (Liu et al, 1996). Furthermore, our attempts at performing immunostainings against CaV1.3 and at overexpressing CaV1.3 in PC12 cells did not prove successful and we decided on refraining from pursuing this further (data not shown). 

On the contrary, HEK293 cells being “synapse-naïve” provide the advantage of serving as a “blank canvas” for performing such reconstitutions, e.g. they lack voltage-gated Ca²⁺ channels and multidomain proteins of the active zone. Moreover, an important practical aspect for our choice was the availability of the HEK293 cell line with stable (and inducible) expression of the CaV1.3 Ca²⁺ channel complex. Finally, as described in lines 613 – 614 of the discussion section, even though HEK293 cells lack SVs and the molecular machinery required for their release, our work paves way for future studies which could employ delivery of SV machinery via co-expression (Park et al, 2021) which could then be analyzed by the correlative light and electron microscopy workflow we worked out and added during revision. 

(3) Related to 1 and 2: the Ca channel localization observed is significant but not so striking given the presence of Cav protein and measurements of Ca2+ influx distributed across the membrane. Presumably, this is the result of overexpression and an absence of pathways for pre-synaptic targeting of Ca channels. But, still, it was surprising that Ca channel localization was so diffuse. I suppose that the authors tried to reduce the effect of over-expression by using an inducible Cav1.3? Even so, the accessory subunits were constitutively over-expressed.

We agree with the reviewer on the modest increase in Ca²⁺ signal amplitude seen in the presence of SyRibbons. Yes, we employed inducible expression of the CaV1.3a subunit and tried to reduce the effect of overexpression by testing different induction times. However, we did not observe any major differences in expression and observed large variability in CaV1.3 expression across cells irrespective of induction duration. At all time points, there were cells with diffuse CaV1.3 localisation also in regions without SyRibbons which likely reduced the contrast of the Ca²⁺ signal we observe. We provide the following reasoning for this observation: 

(i) It is plausible that due to the overexpression approach, Ca²⁺ channels (along with RBP2 and PalmBassoon) still show considerable expression along the membrane also in regions where SyRibbons are not localised. Indeed, this is evident in the images shown in the lower panel in Fig. 6B where Ca²⁺ channel immunofluorescence is distributed across the plasma membrane with larger clusters formed underneath SyRibbons. This would of course diminish the difference in the Ca²⁺ signals between membrane regions with and without SyRibbons. We note that while the contrast is greater for native synapses, extrasynaptic Ca²⁺ channels have been described in numerous studies alone for hair cells (Roberts et al, 1990; Brandt, 2005; Zampini et al, 2010; Wong et al, 2014).

(ii) Nevertheless, we do not expect a striking difference in Ca²⁺ influx amplitude due to the presence of SyRibbons in the first place. Ribbon-less AZs in inner hair cells of RIBEYE KO mice showed normal Ca²⁺ current amplitudes at the whole-cell and the single-AZ level (Jean et al, 2018). Instead, it was the spatial spread of the Ca²⁺ signal at the single-AZ level which appeared to be broader and more diffuse in these mutants in the absence of the ribbon, in contrast to the more confined Ca²⁺ hotspots seen in the wildtype controls. 

So, in agreement with these published observations – it appears that presence of SyRibbons helps in spatially confining the Ca²⁺ signal by super scaffolding nanoclusters into microclusters: this is evident from seeing some spatial confinement of Ca²⁺ signals near SyRibbons on top of the diffuse Ca²⁺ signal across the rest of the membrane as a result of overexpression in HEK cells. 

We have now carefully rephrased our interpretation throughout the manuscript and added further explanation in the discussion section.   

Reviewer #3 (Public Review):

We would like to thank the reviewer for the comments and advice to further improve our manuscript.

(1) The results obtained in a heterologous system (HEK293 cells) need to be interpreted with caution. They will importantly speed the generation of models and hypothesis that will, however, require in vivo validation.

We acknowledge this limitation highlighted by Reviewer 3 arising from the use of an overexpression system and have carefully rephrased our interpretation and discussed possible caveats in the discussion section. We employed inducible expression of the CaV1.3a subunit and tried to reduce the effect of overexpression by testing different induction times. However, we did not observe any major differences in expression and observed large variability in CaV1.3 expression across cells irrespective of induction duration. At all time points, there were cells with diffuse CaV1.3 localisation, even in regions without SyRibbons and this could reduce the contrast of the Ca²⁺ signal we observe. We provide the following reasoning for this observation: 

(i) It is plausible that due to the overexpression approach, Ca²⁺ channels (along with RBP2 and PalmBassoon) still show considerable expression along the membrane also in regions where SyRibbons are not localised. Indeed, this is evident in the images shown in the lower panel in Fig. 6B where Ca²⁺ channel immunofluorescence is distributed across the plasma membrane with larger clusters formed underneath SyRibbons. This would of course diminish the difference in the Ca²⁺ signals between membrane regions with and without SyRibbons. We note that while the contrast is greater for native synapses, extrasynaptic Ca²⁺ channels have been described in numerous studies alone for hair cells (Roberts et al, 1990; Brandt, 2005; Zampini et al, 2010; Wong et al, 2014).

(ii) Nevertheless, we do not expect a striking difference in Ca²⁺ influx amplitude due to the presence of SyRibbons in the first place. Ribbon-less AZs in inner hair cells of RIBEYE KO mice showed normal Ca²⁺ current amplitudes at the whole-cell and the single-AZ level (Jean et al, 2018). Instead, it was the spatial spread of the Ca²⁺ signal at the single-AZ level which appeared to be broader and more diffuse in these mutants in the absence of the ribbon, in contrast to the more confined Ca²⁺ hotspots seen in the wildtype controls. 

So, in agreement with these published observations – it appears that presence of SyRibbons helps in spatially confining the Ca²⁺ signal by super scaffolding nanoclusters into microclusters: this is evident from seeing some spatial confinement of Ca²⁺ signals near SyRibbons on top of the diffuse Ca²⁺ signal across the rest of the membrane as a result of overexpression in HEK cells. 

(2) The authors analyzed the distribution of RIBEYE clusters in different membrane compartments and correctly conclude that RIBEYE clusters are not trapped in any of those compartments, but it is soluble instead. The authors, however, did not carry out a similar analysis for Palm-Bassoon. It is therefore unknown if Palm-Bassoon binds to other membrane compartments besides the plasma membrane. That could occur because in non-neuronal cells GAP43 has been described to be in internal membrane compartments. This should be investigated to document the existence of ectopic internal Synribbons beyond the plasma membrane because it might have implications for interpreting functional data in case Ca2+-channels become part of those internal Synribbons.

In response to this valid concern, we have now included the suggested experiment in Supplementary Figure 1. We investigated the subcellular localisation of Palm-Bassoon and did not find Palm-Bassoon puncta to colocalise with ER, Golgi, or lysosomal markers, suggesting against a possible binding with membrane compartments inside the cell. We have added the following sentence in the results section, line 145 : “Palm-Bassoon does not appear to localize in the ER, Golgi apparatus or lysosomes (Supplementary Fig 1 D, E and F).”

(3) The co-expression of RBP2 and Palm-Bassoon induces a rather minor but significant increase in Ca2+-currents (Figure 5). Such an increase does not occur upon expression of (1) Palm-Bassoon alone, (2) RBP2 alone or (3) RIBEYE alone (Figure 5). Intriguingly, the concomitant expression of PalmBassoon, RBP2 and RIBEYE does not translate into an increase of Ca2+-currents either (Figure 7).

We agree with the reviewer that co-expression of palm-Bassoon and RBP2 seems to augment Ca²⁺ currents, while the additional expression of RIBEYE results in no change when compared to wild-type controls. We currently do not have an explanation for this observation and would refrain from making any claims without concrete evidence. We also highlight that, while the expression of the combination of palm-Bassoon and RBP2 raises Ca²⁺ currents, current amplitudes are not significantly different when compared to the individual expression of the two proteins (P > 0.05, Kruskal-Wallis test). In light of this, we have now carefully rephrased our MS. 

(4) The authors claim that Ca2+-imaging reveals increased CA2+-signal intensity at synthetic ribbontype AZs. That claim is a subject of concern because the increase is rather small and it does not correlate with an increase in Ca2+-currents.

Thanks for the comment: please see our response to your first comment and the lines 585 – 610 in the discussion section.

Recommendations for the authors:  

Reviewer #2 (Recommendations For The Authors):

(1) The authors should have a better discussion of problems derived from over-expression.

Done. Please see above. 

(2) Ideally, the authors would repeat the study using a secretory cell line, but this is of course not possible. The idea could be brought forth, though.

As described above in our response to the public review of reviewer 2, we have discussed this idea in the discussion section (refer to lines 615 – 619), emphasizing on both the advantages and the limitations of using a secretory cell line (e.g. PC12 cells) instead of HEK293 cells as a model for performing such reconstitutions. 

Reviewer #3 (Recommendations For The Authors):

(1) There are several figures in which colocalization between different proteins is studied only displaying images but without any quantitative data. This should be corrected by providing such a quantitative analysis.

We have now added quantifications for the colocalisations of the various transfection combinations depicted in the above-mentioned figures collectively in Supplementary Figure 7 and added the corresponding results and methods accordingly. 

(2) The little increase in Ca2+-currents and Ca2+-influx associated to the clustering of Ca2+-channels to Synribbons is a concern. The authors should discuss if such a minor increase (found only when Palm-Bassoon and RBP2 ae co-expressed) would have or not physiological consequences in an actual synapse. They might discuss the comparison of those results and compare with results obtained in genetically modified mice in which Ca2+-currents are affected upon the removal of AZs proteins. On the other hand, they should explain why Ca2+-currents do not increase when the Synribbons are formed by RIBEYE, Palm-Bassoon and RBP2.

Done. Please see above. 

(3) The description of the patch-clamp experiments should be enriched by including representative currents. Did the authors measure tail currents?

We would like to thank the reviewer for the valuable suggestion and have now added representative currents to the figures (see Supplementary Figure 5B). We agree with the reviewer on the importance of further characterizing the Ca²⁺ currents in the presence and absence of SyRibbons by analysis of tail currents for counting the number of Ca²⁺ channels by non-stationary fluctuation analysis but consider this to be out of scope of the current study and an objective for future studies. 

(4) The current displayed in Figure 7 E should be explained better.

Previous studies have shown that Ca²⁺-binding proteins (CaBPs) compete with Calmodulin to reduce Ca²⁺-dependent inactivation (CDI) and promote sustained Ca²⁺ influx in Inner Hair Cells (Cui et al, 2007; Picher et al, 2017). In the absence of CaBPs, CaV1.3-mediated Ca²⁺ currents show more rapid CDI as in the case here upon heterologous expression in HEK cells ((Koschak et al, 2001), see also Picher et al 2017 where co-expression of CaBP2 with CaV1.3 inhibits CDI in HEK293 cells). The inactivation kinetics of CaV1.3 are also regulated by the subunit composition (Cui et al, 2007) along with the modulation via interaction partners and given the reconstitution here we do not find the currents very surprising. 

(5) Is the difference in Ca2+-influx still significantly higher upon the removal of the maximum value measured in positive Syribbons spots (Figure 7, panel K)?

Yes, on removing the maximum value, the P value increases from 0.01 to 0.03 but remains statistically significant. 

(6) In summary, although the approach pioneered by the authors is exciting and provides relevant results, there is a major concern regarding the interpretation of the modulation of Ca2+ channels.

We have now carefully rephrased our interpretation on the modulation of Ca²⁺ channels.  

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We thank the three reviewers for the time and caution taken to assess our manuscript, and for their constructive feedback that will help improve the study. We herewith provide a revision plan, expecting that the additional experiments and corrections will address the key points raised by the reviewers.

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__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

Summary: The manuscript by Delgado et al. reports the role of the actin remodeling Arp2/3 complex in the biology of Langerhans cells, which are specialized innate immune cells of the epidermis. The study is based on a conditional KO mouse model (CD11cCre;Arpc4fl/fl), in which the deletion of the Arp2/3 subunit ArpC4 is under the control of the myeloid cell specific CD11c promoter.

In this model, the assembly of LC networks in the epidermis of ear and tail skin is preserved when examining animals immediately after birth (up to 1 week). Subsequently however LCs from ArpC4-deleted mice start displaying morphological aberrations (reduced elongation and number of branches at 4 weeks of age). Additionally, a profound decline in LC numbers is reported in the skin of both the ear and tail of young adult mice (8-10 weeks).

To explore the cause of such decline, the authors then opt for the complementary in vitro study of bone-marrow derived DCs, given the lack of a model to study LCs in vitro. They report that ArpC4 deletion is associated with aberrantly shaped nuclei, decreased expression of the nucleoskeleton proteins Lamin A/C and B1, nuclear envelop ruptures and increased DNA damage as shown by γH2Ax staining. Importantly, they provide evidence that the defects evoked by ArpC4 deletion also occur in the LCs in situ (immunofluorescence of the skin in 4-week old mice).

Increased DNA damage is further documented by staining differentiating DCs from ArpC4-deleted mice with the 53BP1 marker. In parallel, nuclear levels of DNA repair kinase ATR and recruitment of RPA70 (which recruits ATR to replicative forks) are reduced in the ArpC4-deleted condition. In vitro treatment of DCs with the topoisomerase II inhibitor etoposide and the Arp2/3 inhibitor CK666 induce comparable DNA damage, as well as multilobulated nuclei and DNA bridges. The authors conclude that the ArpC4-KO phenotype might stem, at least in part, from a defective ability to repair DNA damages occurring during cell division.

The study in enriched by an RNA-seq analysis that points to an increased expression of genes linked to IFN signaling, which the authors hypothetically relate to overt activation of innate nucleic acid sensing pathways.

The study ends by an examination of myeloid cell populations in ArpC4-KO mice beyond LCs. Skin cDC2 and cDC2 subsets display skin emigration defects (like LCs), but not numerical defects in the skin (unlike LCs). Myeloid cell subsets of the colon are also present in normal numbers. In the lungs, interstitial and alveolar macrophages are reduced, but not lung DC subsets. Collectively, these observations suggest that ArpC4 is essential for the maintenance of myeloid cell subsets that rely on cell division to colonize or to self-maintain within their tissue of residency (including LCs).

MAJOR COMMENTS

1. ArpC4 and Arp2/3 expression The authors argue that LCs from Arpc4KO mice should delete the Arpc4 gene in precursors that colonize the skin around birth. It would be important to show it to rule out the possibility that the lack of phenotype (initial seeding, initial proliferative burst) in young animals (first week) could be related to an incomplete deletion of ArpC4 expression. Also important would be to show what is happening to the Arp2/3 complex in LCs from Arpc4KO mice.

__Response: __We thank this reviewer for the careful assessment of our manuscript. Regarding this specific comment, we would like to clarify that we do not expect ArpC4 to be deleted in LC precursors, as CD11c is only expressed once the cells have entered the epidermis. Instead, we expect the deletion to take place after birth around day 2-4 (Chorro et al., 2009). For this reason, we performed a deletion PCR of epidermal cells at postnatal day 7 (P7), a time at which the proliferative burst occurs. This analysis revealed CD11c-Cre-driven recombination in the ArpC4 locus (Fig. S2C). This experiment indicates that ArpC4 deletion does not alter LC proliferation and postnatal network formation.

Revision plan: We will revise the manuscript text to more clearly explain when ArpC4 will be deleted during development when using the CD11c-Cre transgene, and better emphasize the rationale for the deletion PCR.

In the in vitro studies with DCs, the level of ArpC4 and Arp2/3 deletion at the protein level is also not documented.

__Response: __We have previously analyzed the expression of ArpC4 in BMDCs in a recent study, confirming its loss in CD11c-Cre;ArpC4fl/fl cells at the protein level: Rivera et al. Immunity 2022; doi: 10.1016/j.immuni.2021.11.008. PMID: 34910930 (Fig. S2D). Therefore, in the current manuscript we only refer to that paper (Results, first paragraph).

The authors explain that surface expression of the CD11c marker, which drives Arpc4 deletion, gradually increased during differentiation of DCs: from 50% to 90% of the cells. Does that mean that loss of ArpC4 expression is only effective in a fraction of the cells examined before day 10 of differentiation (e.g. in the RNA-seq analysis)?

__Response: __The reviewer is correct, there is heterogeneity in CD11c expression, which is inherent of these DC culture model, implying that Arpc4 gene deletion will be partial. However, despite this, we were able to detect significant differences between the transcriptomes of control and CD11c-Cre;ArpC4fl/fl DCs in early phases during differentiation, emphasizing that the phenotype of ArpC4 loss is robust.

Revision Plan: We will include a notion on this heterogeneity in the revised manuscript text.

Intra-nuclear versus extra-nuclear activities of Arp2/3

The authors favor a model whereby intra-nuclear ArpC4 helps maintaining nuclear integrity during proliferation of DCs (and possibly LCs). However, multiple pools of Arp2/3 have been described and accordingly, multiple mechanisms may account for the observed phenotype: i) cytoplasmic pool to drive the protrusions sustaining the assembly of the LC network and its connectivity with keratinocytes ; ii) peri-nuclear pool to protect the nucleus ; iii) Intra-nuclear pool to facilite DNA repair mechanisms e.g. by stabilizing replicative forks (the scenario favored by the authors).

__Response: __The referee is correct, and this is actually discussed in our manuscript (page 11, upper paragraph): we cannot exclude that several pools of branched actin are influencing the phenotype we here describe.

Unfortunately, we have previously tested several antibodies against ArpC4, but in our hands, and despite comprehensive optimization, they did not yield specific signals that would enable us to assess changes in subcellular localization in murine cells. Upon this reviewer's comment, we will now reassess the available tools and found an antibody against ArpC2 (Millipore, Anti-p34-Arc/ARPC2, 07-227-I-100UG) that may work based on published data. We have ordered this product to test it for IF staining of ArpC2, hoping to be able to delineate the subcellular localization of ArpC2 in DCs and potentially LCs.

Revision plan: Upon receipt, we will test the ArpC2 antibody (Millipore, #07-227-I-100UG) both in cultured DCs and in epidermal whole mounts, running various optimization protocols regarding fixation, permeabilization and blocking reagents, next to antibody dilution. That way we hope to be able to detect the subcellular localization of Arp2/3 complex components as requested by this reviewer.

It is recommended that the authors try to gather more supportive data to sustain the intra-nuclear role. Documenting ArpC4 presence in the nucleus would help support the claim. It could be combined with treatments aiming at blocking proliferation in order to reinforce the possibility that a main function of ArpC4 is to protect proliferating cells by favoring DNA repair inside the nucleus.

__Response: __We thank this reviewer for this very helpful comment. As outlined in the previous response, we will aim at obtaining subcellular localization data for Arp2/3 complex components, and along with that study a potential intranuclear localization. Beyond that, in comparison to commonly cultured cell types, however, we face two hurdles addressing the nuclear Arp2/3 role in full: 1) Due to poor transduction rates and epigenetic silencing, we cannot sufficiently express exogenous constructs such as ArpC4-NLS in DCs to assess the subcellular localization of Arp2/3 complex components. 2) We have performed preliminary tests to block proliferation in DCs, using the cyclin D kinase 1 inhibitor RO3306 at different concentrations and incubation times during DC differentiation. Unfortunately, most cells were found dead after treatment. Further lowering the inhibitor concentrations (below 3.5uM) will likely not block the cell cycle, rendering this approach unsuited.

Revision plan: We will test the suitability of additional antibodies directed against Arp2/3 complex components to assess their subcellular localization, with the aim to discriminate peripheral cytoplasmic vs. perinuclear vs. intranuclear localization. In addition, we will add a comment in the discussion, further addressing this point. In the case we remain unable to pinpoint that Arp2/3 resides in the nucleus, we will further tone down our current phrasing in the discussion, also emphasizing the possibility that cytoplasmic or perinuclear pools of the complex may indirectly help maintain the integrity of the genome in LCs.

Nuclear envelop ruptures

The nuclear envelop ruptures are not sufficiently documented (how many cells were imaged? quantification?). The authors employ STED microscopy to examine Lamin B1 distribution. The image shown in Figure 4A does not really highlight the nuclear envelop, but rather the entire content. Whether it is representative is questionable. We would expect Lamin B1 staining intensity to be drastically reduced given the quantification shown in Figure 3D. In addition, although the authors have stressed in the previous figure that Arpc4-KO is associated with nucleus shape aberrations, the example shown in Figure 4A is that of a nucleus with a normal ovoid shape.

It is recommended to quantify the ruptures with Lap2b antibodies (or another staining that would better delineate the envelop) in order to avoid the possible bias due to the reduced staining intensity of Lamin B1.

__Response: __NE ruptures were quantified by imaging NLS-GFP-expressing DCs in microchannels to visualize leakage of their nuclear content (Fig. 4B,C). The STED image mentioned by the referee (Fig. 4A,D) was only shown to further illustrate examples of NE ruptures, here using Lamin B as an immunofluorescence marker for the NE. We do agree with the reviewer that it was not chosen optimally to represent the ArpC4-KO phenotype regarding nuclear shape and Lamin B1.

Revision plan: We will provide representative examples of nuclear illustrations of the ArpC4-KO phenotype vs. control cells. In addition, we will perform STED microscopy of Lap2B immunostained DCs as suggested by the referee.

A missing analysis is that of nuclear envelop ruptures as a function of nucleus deformations.

__Response: __As stated in the manuscript (page 5, third paragraph), the morphology of DCs is quite heterogeneous. As mentioned above, nuclear rupture events were quantified by live-imaging of NLS-GFP expressing DCs, enabling the tracing of rupture events. Live imaging is the only robust manner to measure nuclear membrane rupture events as they are transient due to rapid membrane repair (Raab et al. Science 2016). The NLS-GFP label itself, however, is not accurate enough to also quantify nuclear deformations. The latter therefore was quantified after cell fixation, using DAPI and/or immunostaining for NE envelope markers (Figures 3 and S3).

Revision plan: We will quantify nuclear deformations using Lap2B staining of the nuclear envelope as suggested by the referee.

Fig 4B-C: same frequency of Arpc4-KO and WT cells displaying nuclear envelop ruptures in the 4-µm channels; however image show a rupture for the Arpc4-KO and no rupture for the WT cells (this is somehow misleading). Are ruptures similar in Arpc4-KO and WT cells in this condition?

__Response: __We apologize for choosing an image that better reflects our quantification, our mistake.

Revision plan: We will choose an image that better reflects our quantification.

Fig 4D-E: is their a direct link between nuclear envelop ruptures and ƴH2A.X?

__Response: __At present, we can only correlate the findings of increased gH2Ax and elevated events of nuclear envelope ruptures in ArpC4-KO DCs. Rescue experiments are very difficult to impossible in DCs (e.g. restoring Lamin A/C and B levels in the KOs and subsequently assessing the amount of DNA damage). While we are afraid that we cannot address a potential link between NE ruptures and DNA damage by experiments in a manner feasible within this manuscript's revision, we have discussed this interesting aspect based on observations in immortalized cell culture systems (page 10). However, we would like to note that this was indeed shown for different cell types in Nader et al. Cell 2021. This effect results from access of cytosolic nuclease Trex1 to nuclear DNA.

Revision plan: This point will be clarified in our revised manuscript.

Interesting (but optional) would be to understand what is happening to DNA, histones? Is their evidence for leakage in the cytoplasm?

__Response: __We have not investigated so far. We will attempt to do so.

Revision plan: To address this aspect, we plan to perform immunostainings for double-stranded DNA in the cytoplasm (along with an NE marker). This approach has been used in the field to mark cytoplasmic DNA.

RNA seq analysis

The RNA-seq analysis suffers from a lack of direct connection with the rest of the study. The extracted molecular information is not validated nor further explored. It remains very descriptive. The PCA analysis suggests a « more pronounced transcriptomic heterogeneity in differentiating Arpc4KO DCs ». However it seems difficult to make such a claim from the comparison of 3 mice per group. In addition, such heterogeneity is not seen in the more detailed analysis (Fig 5F). The authors claim that « day 10 control and Arpc4KO DCs showed no to very little differences in gene expression, in contrast to cells at days 7-9 of differentiation ». This is not obvious from the data displayed in the corresponding figure. In addition, it is not expected that cells that may take a divergent differentiation path at days 7-9 may would return to a similar transcriptional activity at day 10.

A point that is not discussed is that before day 10 of DC differentiation, Arpc4 KO is expected to only occur in about 50% of the cell population. This is expected to impact the RNA-seq analysis.

Not all clusters have been exploited (e.g. cluster 3 elevated, cluster 6 partly reduced). I suggest the authors reconsider their analysis and analysis of the RNA-seq analysis (or eventually invest in complementary analysis).

__Response: __Despite a comprehensive analysis of the different transcriptomes of control and ArpC4 mutant cells during DC differentiation, we decided to focus the presentation and discussion of our RNAseq results on the most notable findings. Of these, the elevated innate immune responses in ArpC4-KO DCs (Fig. 5E,H) caught our particular attention, as this seemed highly meaningful in light of DC and LC functions.

Revision plan: As suggested by the referee, in the revised manuscript, we will better connect the RNAseq data to the other cellular and molecular analyses shown, complementing these results by investigating the potential involvement of innate immune responses in the ArpC4-KO phenotype.

What causes the profound numerical drop of LC in the epidermis?

A major open question is what causes the massive drop of LCs. Although differentiating Arpc4KO DCs start accumulating DNA damage upon proliferation, they succeed in progressing through the cell cycle. There is even a slightly elevated expression of cell cycle genes at day 7 of differentiation in the DC model.

Only a trend for increased apoptosis is observed in ear and tail skin. It would be important to provide complementary data documenting increased death (or aberrant emigration?) of LCs in the 4-8 week time window.

__Response: __We agree with the reviewer that this is an important question. We exclude that elevated emigration causes the decline of LCs in ArpC4-KO epidermis, as ArpC4-mutant LCs are significantly reduced (and not increased) in skin-draining lymph nodes (Fig. 7E). To assess whether increased cell death contributed to LC loss, we have tried to identify LCs that are just about to die. As the reviewer noted, we could only observe a trend of apoptosis-positive LCs in mutant epidermis. We assume that this is because of a quick elimination of compromised LCs following DNA damage, with only a short time passing until LCs with impaired genome integrity will be cleared from the system, making it very difficult to detect gH2Ax-positive cells that are positive for markers of cell death.

Revision plan: Despite the abovementioned expected limitations to detect DNA-damage-positive but viable LCs in vivo, for the manuscript revision we will collect 6-week-old mice to analyze LC numbers and apoptosis (cleaved Caspase-3), complementing our data derived from 7-day and 4-week-old mice (Figures S2A,B, S2E,F). Suited mice have been born end of May; we are ready to analyze them at 6-weeks of age, accordingly.

Functional consequences

Although the study reports novel aspects of LC biology, the consequence of ArpC4 deletion for skin barrier function and immunosurveillance are not investigated. It would seem very relevant to test how this model copes with radiation, chemical and/or microorganism challenges.

__Response: __We fully agree with this reviewer that this is a very interesting point. Therefore, next to assessing the steady-state circulation of LCs and DCs, we also addressed the consequence of ArpC4 loss for LC function in chemically challenged skin: we performed skin painting experiments using the contact sensitizer fluorescein isothiocyanate (FITC), diluted in the sensitizing agent dibutyl phthalate (DBP), to detect cutaneous-derived phagocytes within draining lymph nodes. These experiments revealed that migration of Arpc4KO LCs (as well as of Arpc4KO DCs) to skin-draining lymph nodes was impaired (Fig. 7C-E), confirming an in vivo role of ArpC4 for immune cell migration to lymphatic organs following a chemical challenge. Considering the lengthy legal approval procedures for new animal experimentation procedures, additional in vivo challenges -beyond the provided FITC challenge study- would take at least 6 additional months, which would delay excessively the revision of our manuscript.

Revision Plan: We will better explain the FITC painting experiment to highlight its importance.

MINOR COMMENTS:

1- Figure 1D

Gating strategy: twice the same empty plots. The content seems to be missing... Does this need to be shown in the main figure?

__Response: __We apologize for this problem; there might be a problem due to file conversion of PDF reader software. In our PDF versions (including the published bioRxiv preprint) we do see the data points (see below); however, we have earlier experienced incomplete FACS plots during manuscript preparation.

Revision plan: We will take extra care and double-check the results after converting the figures into PDFs. In addition, we will provide JPG files when submitting the revised manuscript, to prevent such problems.

2- Figure 2

Best would be to keep same scale to compare P1 and P7 (tail skin, figure 2A)

Response and revision plan: We will replace the examples with micrographs of comparable scale (already solved, will be provided with manuscript revision).

Overlay of Ki67 and MHC-II does not allow to easily visualize the double-positive cells (Fig 2C)

Response and revision plan: We will provide single-channel image next to the merged view, and improve the visualization of double-positive cells (already solved, will be provided with manuscript revision)

Quality of Ki67 staining different for Arpc4-KO (less intense, less focused to the nuclei): a technical issue or could that reflect something?

Response and revision plan: We thank the reviewer for spotting this. We have re-assessed all Ki67 micrographs and noted that the originally chosen examples indeed are not fully representative. We have meantime selected more representative examples of Ki67-positive cells in control and mutant tissues, reflecting no difference in the principal nature of Ki67 staining (already taken care of, will be provided with manuscript revision).

Fig 2C: Panels mounted differently for ear and tail skin (different order to present the individual stainings, Dapi for tail skin only).

Response and revision plan: We will harmonize the sequence of panels in figure 2 with submission of the revised manuscript.

3- LC branch analysis (Fig 1 and 2)

While Fig 1 indicates that ear skin LCs form in average twice as few branches as tail skin LCs (3-4 versus 8-9 branches per cell), Fig 2 shows the opposite (10-12 versus 6-7 branches per cell).

Is this due to a very distinct pattern between the 2 considered ages (4 weeks versus 8-10 weeks)? Could the author double-check that there is no methodological bias in their analysis?

Response: We thank the reviewer for hinting us on this apparent inconsistency. Indeed, our initial analysis suffered from a bias in detecting LC dendrites, as the tissue cellularity and overall morphology significantly differs between 4-week-old and adult animals: In adult animals, the immunostainings show a higher baseline background signal for the skin epithelium compared to P28. We had noted this beforehand and had adjusted the imaging pipeline accordingly, with a more stringent thresholding to eliminate background signals in the case of adult tissues. While we were able to detect the described ArpC4 phenotype, this strategy resulted in a reduced ability to detect dendrites (both in control and mutant tissues), explaining the seemingly reduced number of dendrites in adult vs. 4-week-old tissues.

Revision plan: We have double-checked both the micrographs and the corresponding quantifications and did not identify errors. Instead, our assumption -that a too high stringency for background reduction in adults caused the discrepancy- turned out correct. At present, we are re-doing the detailled analyses of LC morphology at 4-week and adult stages by confocal microscopy using a 63x objective rather than a 40x objective as done previously. First results confirm that with this approach the number of LC dendrites across these ages are largely comparable, while the phenotypes of ArpC4 loss are retained. We will provide a completely new analysis with revision of the manuscript.

4- Fig 3 E-G

How many animals were examined (n=5)? Reproducible accros animals? Why was it done with 4-week animals (phenotype not complete? Event occurring before loss in numbers...)

Response and revision plan: As mentioned in the figure legend for Fig. 3F we have analysed N = 4 control and N= 5 KO mice (for clarity, we will add this information to Figure 3E and G in the revised document). We chose the 4-week time-point as this was the stage when the loss of LCs first became apparent (even though non-significant at this age). We aimed to learn whether changes in nuclear morphology and nuclear envelope markers represented early molecular and cellular events following ArpC4 loss. Compared to later stages, this strategy poses a reduced risk to detect indirect effects of ArpC4 loss. We will clarify this in the revised manuscript text.

Staining Lamin A/C globally more intense in the Arpc4-KO epidermis (also seems to apply to the masks corresponding to the LCs). Surprising to see that the quantification indicates a major drop of Lamin A/C intensity in the LCs.

Response and revision plan: We again thank the reviewer for this careful assessment. The originally chosen micrographs are indeed not fully representative. As with many tissue stainings, there is inter-sample variability. We have now revisited the micrographs and did not find a significant global reduction of Lamin A/C in the entire epidermis (including keratinocytes/KCs). The drop of Lamin A/C intensity is restricted to ArpC4 LCs -and not KCs- and in line with the reduced Lamin A/C expression data in DCs (Fig. 3C,D). We have selected more representative examples, which will be provided with the revised manuscript.

Legend Fig 4D replace confocal microscopy by STED microscopy

Revision plan: We will replace "confocal microscopy" by "STED microscopy" accordingly.

6- Figure 4F

Intensity/background of γH2Ax staining very distinct between the 2 micrographs shown for WT and Arpc4-KO epidermis.

Response and revision plan: We have revisited the micrographs and now selected more representative examples, which will be provided in the revised manuscript.

7- Figure 7C, F, H

Gating strategies: would be better to harmonize the style of the plots (dot plots and 2 types of contour plots have been used...)

Response and revision plan: We agree and will provide a harmonized plot illustration in the revised manuscript.

8- Figure 7H

Legend of lower gating strategy seems to be wrong (KO and not WT).

Response and revision plan: We thank the reviewer for pointing out this mistake. A corrected figure display will be provided with revision.

Reviewer #1 (Significance (Required)):

Strengths: the general quality of the manuscript is high. It is very clearly written and it contains a very detailed method section that would allow reproducing the reported experiments. This work entails a clear novelty in that it represents the first investigation of the role of ArpC4 in LCs. It opens an interesting perspective about specific mechanisms sustaining the maintenance of myeloid cell subsets in peripheral tissues. This work is therefore expected to be of interest for a large audience of cellular immunologists and beyond. Challenging skin function with an external trigger would lift the relevance for a even wider audience (see main point 6).

__Response: __see point 6.

Limitations: in its current version the manuscript suffers from a lack of solidity around a few analysis (see main points on ArpC4 and Arp2/3 protein expression, nuclear envelop rupture analysis,...). It also tends to formulate a narrative centered on the ArpC4 intra-nuclear function that is not definitely proven.

The field of expertise of this reviewer is: cellular immunology and actin remodeling.

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

SUMMARY This is a study in experimental mice employing both in vitro and, importantly, in vivo approaches. EPIDERMAL LANGERHANS CELLS serve as a paradigm for the maintenance of homeostasis of myeloid cells in a tissue, epidermis in this case. In addition to well known functions of the ACTIN NETWORK in cell migration, chemotaxis, cell adherence and phagocytosis the authors reveal a critical function of actin networks in the survival of cells in their home tissue.

Actin-related proteins (Arp), specifically here the Arp2/3 complex, are necessary to form the filamentous actin networks. The authors use conditional knock-out mice where Arpc4 (an essential component of the Arp2/3 complex) is deleted under the control of CD11c, the most prominent dendritic cell marker which is also expressed on Langerhans cells. In normal mice, epidermal Langerhans cells reside in the epidermis virtually life-long. They initially settle the epidermis around and few days after birth an establish a dense network by a burst of proliferation and then they "linger on" by low level maintenance proliferation. In the epidermis of Arpc4 knock-out mice Langerhans cells also start off with this proliferative burst but, strikingly, they do not stay but are massively reduced by the age of 8-12 weeks.

The analyses of this decline revealed that

-- the shape (number of nuclear lobes) and integrity of cell nuclei was compromised; they were fragile and ruptured to some degree when Arpc4 was knocked out, i.e., the Arp2/3 complex was missing;

-- DNA damage, as detected by staining for gamma-H2Ax or 53BP1 accumulated when Arpc4 was knocked out, i.e., the Arp2/3 complex was missing;

-- recruitment of DNA repair molecules was inhibited when Arpc4 was knocked out, i.e., the Arp2/3 complex was missing;

-- gene signatures of interferon signaling and response were increased when Arpc4 was knocked out, i.e., the Arp2/3 complex was missing;

-- in vivo migration of dendritic cells and Langerhans cells from the skin to the draining lymph nodes in an inflammatory setting (FITC painting of the skin) was impaired when Arpc4 was knocked out, i.e., the Arp2/3 complex was missing;

-- the persistence of the typical dense network of Langerhans cells in the epidermis, created by proliferation shortly after birth, is abrogated when Arpc4 was knocked out, i.e., the Arp2/3 complex was missing. Importantly, this was not the case for myeloid cell populations that settle a tissue without needing that initial burst of proliferation. For instance, numbers of colonic macrophages were not affected when Arpc4 was knocked out, i.e., the Arp2/3 complex was missing.

Thus, the authors conclude that the Arp2/3 complex is essential by its formation of actin networks to maintain the integrity of nuclei and ensure DNA repair thereby ascertaining the maintenance proliferation of Langerhans cells and, as the consequence, the persistence of the dense epidermal netowrk of Langerhans cells.

Up-to-date methodology from the fields of cell biology and cellular immunology (cell isolation from tissues, immunofluorescence, multiparameter flow cytometry, FISH, "good old" - but important - transmission electronmicroscopy, etc.) was used at high quality (e.g., immunofluorescence pictures!). Quantitative and qualitative analytical methods were timely and appropriate (e.g., Voronoi diagrams, cell shape profiling tools, Cre-lox gene-deletion technology, etc.). Importantly, the authors used a clever method, that they had developed several years ago, namely the analysis of dendritic cell migration in microchannels of defined widths. Molecular biology methods such as RNAseq were also employed and analysed by appropriate bioinformatic tools.

MAJOR COMMENTS:

• ARE THE KEY CONCLUSIONS CONVINCING? Yes, they are.

• SHOULD THE AUTHORS QUALIFY SOME OF THEIR CLAIMS AS PRELIMINARY OR SPECULATIVE, OR REMOVE THEM ALTOGETHER? No, I think it is ok as it stands. The authors are wording their claims and conclusions not apodictically but cautiously, as it should be. They point out explicitely which lines of investigations they did not follow up here.

• WOULD ADDITIONAL EXPERIMENTS BE ESSENTIAL TO SUPPORT THE CLAIMS OF THE PAPER? REQUEST ADDITIONAL EXPERIMENTS ONLY WHERE NECESSARY FOR THE PAPER AS IT IS, AND DO NOT ASK AUTHORS TO OPEN NEW LINES OF EXPERIMENTATION. I think that the here presented experimental evidence suffices to support the conclusions drawn. No additional experiments are necessary.

• ARE THE SUGGESTED EXPERIMENTS REALISTIC IN TERMS OF TIME AND RESOURCES? IT WOULD HELP IF YOU COULD ADD AN ESTIMATED COST AND TIME INVESTMENT FOR SUBSTANTIAL EXPERIMENTS. Not applicable.

• ARE THE DATA AND THE METHODS PRESENTED IN SUCH A WAY THAT THEY CAN BE REPRODUCED? Yes, they are.

• ARE THE EXPERIMENTS ADEQUATELY REPLICATED AND STATISTICAL ANALYSIS ADEQUATE? Yes.

__Response: __We thank the reviewer very much for assessing our work, for providing constructive suggestions, and for acknowledging the strength of the study.

MINOR COMMENTS:

• SPECIFIC EXPERIMENTAL ISSUES THAT ARE EASILY ADDRESSABLE. None

• ARE PRIOR STUDIES REFERENCED APPROPRIATELY? Essentially yes. Regarding the reduction / loss of the adult epidermal Langerhans cell network, it may be of some interest to also refer to / discuss to another one of the few examples of this phenomenon. There, the initial burst of proliferation is followed by reduced proliferation and increased apoptosis when a critical member of the mTOR signaling cascade is conditionally knocked out (Blood 123:217, 2014).

__Response and revision plan: __As suggested, we will include into the revised manuscript further examples with related phenotypes regarding the progressive decline of LCs.

• ARE THE TEXT AND FIGURES CLEAR AND ACCURATE? Yes they are. Figures are well arranged for easy comprehension.

• DO YOU HAVE SUGGESTIONS THAT WOULD HELP THE AUTHORS IMPROVE THE PRESENTATION OF THEIR DATA AND CONCLUSIONS?

1. Materials & Methods. The authors write, regarding flow cytometry of epidermal cells: "Briefly, 1cm2 of back skin from 8-14 weeks old female wild-type and knockout littermates was dissociated in 0.25 mg/mL Liberase (Sigma, cat. #5401020001) and 0.5 mg/mL DNase (Sigma, cat.#10104159001) in 1 mL of RPMI (Sigma) and mechanically disaggregated in Eppendorf tubes, FOLLOWED BY INCUBATED for 2 h at 37 {degree sign}C." Followed by what?

__Response and revision plan: __We apologize for this mistake. The text should read: "... followed by blocking and antibody labeling of cells in single cell suspension.". We will provide the correct text in the revised manuscript.

Materials & Methods. BMDC electronmicroscopy. What is "IF". Please specify.

__Response and revision plan: __We also regret this mistake in the method text. It should read: "... For electron microscopy analysis, after PDMS removal, cells were fixed using 2.5% glutaraldehyde ...". We will correct this in the revised manuscript.

RESULTS in gene expression analyses. The authors observe some increase in apoptosis (as detected by cleaved-Caspase-3 staining). Is this observation in immunofluorescence also evident in the RNAseq data (where the IFN changes were seen), i.e., in Figure 5.

__Response and revision plan: __We will check our RNAseq data regarding any changes in apoptosis-related genes and, if so, include these in the revised manuscript.

Figure 7 F and G. Perhaps the authors may want to swap upper and lower panels in F or G, so that macrophage FACS plots and bar graphs are in the same row - ob, obiously, DC plots and bars likewise.

__Response and revision plan: __We agree and will harmonize the panel sequence in the revised manuscript.

Figure 7H. "Gating strategy in ArpC4WT Lung (previously gated in Live CD45+ cells)" - The lower row is knock-out, not WT. This is indicated correctly in the legand, but in the figure both rows are labeled as WT.

__Response and revision plan: __Indeed, the legend information is correct, but the corresponding figure panel is incorrect. We will provide a corrected version with revision.

The reference by Park et al. 2021 is missing in the list.

__Response and revision plan: __We will add the reference to the revised bibliography.

Figure 1D. Sure, the bar graphs are meant to say "CD11c"? The FACS plots show "CD11b".

__Response and revision plan: __We will check the panels and correct where necessary.

As to cDC1. In Figure 1D the FACS plot shows an absence of CD103+ cDC1 cells. In contrast, In Figure 7A-left side panel, there is not difference in cDC1 cells between WT and KO mice. Is therefore the flow cytometry plot in Figure 1D not representative regarding cDC1 cells? Correct?

__Response and revision plan: __The reviewer is correct about this apparent discrepancy. We have not observed differences in the control vs. Aprc4-KO cDC1 population, hence Figure 7 represents our findings. For figure 1, we have by mistake chosen a non-representative plot, with the aim of illustrating the gating strategy. We apologize for this mistake and will provide a corrected an representative FACS plot figure in the revised manuscript.

Reviewer #2 (Significance (Required)):

• DESCRIBE THE NATURE AND SIGNIFICANCE OF THE ADVANCE (E.G. CONCEPTUAL, TECHNICAL, CLINICAL) FOR THE FIELD. This is a conceptual advance. It adds a big step to our understanding of how immune cells in tissues (which all come from the bone marrow or are seeded before birth from embryonal hematopoietic organs such as yolk sac and fetal liver) can remain resident in these tissues. For cell types such as Langerhans cells, which establish their final population density within their tissues of residence, the presented finding convincingly buttress the role of proliferation and thereby the role for the actin-related protein complex 2/3 (Arp2/3).

• PLACE THE WORK IN THE CONTEXT OF THE EXISTING LITERATURE (PROVIDE REFERENCES, WHERE APPROPRIATE). While we know much about actin-related proteins (Arp), as correctly cited by the authors, this knowledge is derived mostly from in vitro studies. The submitted study translates the findings to an in vivo setting for the first time.

• STATE WHAT AUDIENCE MIGHT BE INTERESTED IN AND INFLUENCED BY THE REPORTED FINDINGS. Skin immunologists foremost, but these findings are of interest to the entire community of immunologists, but also cell biologists.

• DEFINE YOUR FIELD OF EXPERTISE. My expertise is in skin immunology, in particular skin dendritic cells including Langerhans cells.

We acknowledge the referee for their positive assessment of our manuscript.

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

Summary:

The manuscript identifies a role of the Arp2/3 complex, the major regulator of actin branching in cells, for controlling the homeostasis of murine Langerhans cells (LCs), a specialized subset of dendritic cells in the skin epidermis. The findings of the study are based on the analysis of CD11c-Cre Arpc4-flox mice, a conditional knockout mouse model, which interferes with Arp2/3 function in Langerhans cells and other CD11c-expressing myeloid cells, e.g. dendritic cell or macrophage subsets. By using immunofluorescence and flow cytometry analysis of epidermis and skin tissues, the authors provide a detailed analysis of LC numbers at different developmental stages (postnatal day 1, 7, 28, and adult mice) and demonstrate that Arpc4-deficiency does not interfere with the establishment of LC networks until postnatal day 28. However, LCs in ear and tail skin are substantially reduced in Arpc4-deficient mice at 8-12 weeks of age. In parallel to their in vivo model, the authors analyze cultures of bone marrow-derived dendritic cells (BMDCs) from control and CD11c-Cre Arpc4-flox mice. Arpc4-deficiency in BMDCs, which develop over 8-10 days in culture, results in nuclear shape and lamina abnormalities, as well as signs of increased DNA damage. Aspects of this phenotype are also detected in Langerhans cells in epidermal preparations. Transcriptomic analysis of BMDCs highlights a gene signature of increased expression of the interferon response pathway and alterations in cell cycle regulation. Arpc4-deficient BMDCs show increased expression of DNA damage markers and reduced expression of certain DNA repair factors. Based on these correlative findings from the BMDC model, the authors conclude that the decline in LC numbers might develop from the accumulation of DNA damage over time, which the authors phrease "pre-mature aging of Langerhans cells". Lastly, the authors show a heterogenous picture how Arp2/3 depletion affects distinct DC populations in CD11c-Cre Arpc4-flox mice. While some tissue-resident DC subsets appear normal in numbers, others are declined in numbers in the tissue. This may be related to their proliferation potential in tissues.

Major comments:

• Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

1) The authors claim that Arpc4 deficiency selectively compromises myeloid cell populations that rely on proliferation for tissue colonization (Figure 7). The presented data might give hints for such a general hypothesis, but solid experimental proof to prove this is lacking. When comparing myeloid cell subsets from foru different irgans, the authors refer to published data that some dendritic cell subsets are more proliferative in tissues than others and that CD11cCre Arpc4-flox mice appear to have reduced cell numbers in these populations. However, the presented data are purely correlative and no functional connection to cell proliferation has been made to the phenotypes. While some dendritic cell subsets (Langerhans cells, alveolar DCs) show reduced cell numbers in CD11cCre Arpc4-flox mice, other myeloid cell cells subsets are unaffected (e.g. dermal cDC1 and 2, colon macrophages).There could be plenty of other reasons that might underly the observed discrepancies between these cell subsets, e.g. Arp2/3 knockout efficiency and myeloid cell turnover in the tissue are just two examples, which have not been taken into consideration. Direct measurement of cell proliferation, e.g. BrdU labeling, and the observed phenotype would be missing to make such claims. The data could either be removed. Experimentally addressing these points could take 3-6 months.

Response and planned revisions: We thank the referee for bringing this point. We agree that these results give hints that support our conclusion but that do not address this question directly. However, we would like to insist on the fact that our conclusion is based on studies from others showing that alveolar macrophages self-maintain themselves through proliferation (Bain et al. Mucosal Immunology 2022). In contrast, it has been reported that most colonic macrophages are derived from monocytes that are being recruited to the gut through life (Bain et al. Mucosal Immunity 2023)

We propose to better explain and discuss these points in our revised manuscripts. In addition, we will stress that we do not exclude that different intracellular Arpc4-dependent processes might contribute to the phenotypes observed (beyond maintenance of DNA integrity). These revisions will help mitigate our conclusions and leave open the potential implication of alternative mechanisms that will be discussed as suggested by the referee.

2) The authors claim that DC subsets (e.g. dermal cDCs), which develop from pre-DCs, are not affected by Arp2/3 depletion (Figure 7, although the FACS plot in Fig. 1D would suggest a different picture for cDC1). This is surprising in light of the data with bone marrow-derived DCs (BMDCs), the major in vitro model of this study, which develop from CDPs that again develop from pre-DCs. BMDCs did show aberrant nuclei and signs of DNA damage. How would the authors then explain the discrepancies of the BMDC model with DC subsets, where the authors feel that the pre-DC origin explains the phenotypic difference? This is a general concern of the data interpretation and conclusions.

__Response: __We thank the referee to bring this point that indeed requires clarification. Two non-exclusive hypotheses could explain this apparent discrepancy:

• The ontogeny of bone-marrow-derived DCs: Depending on the protocol used, there might be variations in the precursors DCs develop from. We use one of the first protocols, which was pioneered by Paola Ricciardi-Castagnoli lab (Winzler et al. J.Exp.Med. 1997). It relies on a supernatant from J558 cells transfected with GMCSF, which contains additional cytokines and mainly generate DC2-like DCs. Langerhans cells are closer to DC2s, which resemble more macrophages than DC1s. We thus chose this protocol rather than the protocols that use Flt3-L, which produce both DC1s and DC2s developed from common dendritic-cell precursors (CDPs). It is thus possible that our BM-derived DCs develop from other precursor cells that are possibly closer to monocyte precursors.
• As shown in Figure 5C, kinetics of acquisition of CD11c expression, and thus deletion of the Arpc4 gene, might be distinct in vivo and in vitro. In vivo, as stated in our manuscript, DCs acquire CD11c as preDCs and undergo few rounds of divisions after. In vitro, as shown by our cycling experiments, BM-derived DCs continuously cycle, so they will keep dividing after having acquire CD11c (around day 7) and deleting the Arpc4 gene. __Revision plan: __We propose to mention these hypotheses in the discussion of our manuscript to explain the apparent contradiction raised by the referee.

3) In line with point 2, the authors never show that BMDCs show reduced proliferation, reduced cell numbers or increased cell death in Arpc4-deficient cell cultures, as a consequence of the detected DNA damage and impaired DNA repair. In fact, Figure 5C even shows that cell growth rates between control and KO are equal. This is a major mismatch in the current study. Since the authors use the BMDC model to explain the declining cell numbers in Langerhans cells (which derive from fetal liver cells), this phenotype is not mirrored by the BMDC culture and it remains open whether the observed changes in nuclear DNA damage and repair are indeed directly linked to the observed phenotype of declining cell numbers in the tissue. These aspects require argumentation why cell growth is unchanged in KO cells. Additional experiments addressing these points with sufficient biological replicates (cultures from different mice) could take 2-3 months, including preparation time.

__Response____: __We thank the referee for bringing this point, which was probably not properly discussed in the first version of our manuscript. Indeed, Arpc4KO BM-derived DCs do not show the premature cell death phenotype observed in LCs in vivo, as stated by the referee. There are at least two putative non-exclusive explanations for this. First, unlike LCs, which are long-lived cells, BM-derived DCs can be kept in culture for only 10-12 days. As DNA damage-induced cell death takes time (LCs only start to die about 3-4 weeks after network establishment), the lifespan of BM-DCs could simply not be long enough to observe this phenotype. Second, in the epidermis, LCs are physically constrained and continuously exposed to diverse signals that might increase their sensitivity to DNA damage and thereby induction of subsequent cell death.

__Revision Plan: __We will clarify this point in our revised manuscript by providing putative explanations for the death phenotype of Arpc4-deficient LCs not being observed in BM-derived DCs. We will further explain that this does not invalidate this cellular model as it was used to raise hypotheses on the putative role played by Arpc4 in myeloid cells, i.e. maintenance of DNA integrity, which was then confirmed in vivo (Arpc4KO LCs do indeed display DNA damage in the epidermis). Without this "imperfect cellular model", we would have probably not been able to uncover this novel function of Arp2/3 in immune cells.

4) The authors refer to a "pre-mature aging" phenotype of Arpc4-deficient BMDCs and LCs, based on reductions in Lamin B, Lamin A and increases in gH2AX and 53BP1. I find this term and overstatement of the current data and suggest that other markers for cell senescence, such as p53, Rb, p21 and b-Galactosidase are then also used to make such strong claim on "aging" and cell senescence. Experimentally addressing this point with sufficient biological replicates could take 2-3 months, including preparation time.

__Revision Plan: __We will assess the expression of these genes and senescence signatures in our RNAseq analysis as well as in Arpc4WT and Arpc4KO-derived DCs, as suggested by the referee.

5) The study does not provide a mechanism how the Arp2/3 complex would mediate the observed effects on DNA damage and repairs has not been addressed in the cell model, and only potential scenarios from other non-myeloid cell lines are discussed. It remains unclear whether the observed phenotypes in Arpc4-depleted myleoid cells relate to the direct nuclear function of Arp2/3 or the cytosolic function of Arp2/3, including its roles in cytoskeletal regulation that may have secondary effects on the nuclear alterations. This is a general concern of the presented data, data on mechanism might require more than 6 months.

__Revision Plan: __The referee is correct: Our manuscript shows that Arp2/3 deficiency in specific myeloid cells impacts on their survival in vivo and proposes that this could result at least in part from impaired maintenance of DNA integrity in these cells. We do not know whether this also applies to non-myeloid cells, which, although very interesting, is beyond the scope of the present study. In addition, we do not have any experimental tool to distinguish whether the DNA damage phenotype of Arpc4KO cells involves the nuclear or cortical pool of F-actin, this is why we have left this question open in the discussion of our manuscript.

6) OPTIONAL: The authors make a strong case arguing that the increased interferon expression signature (based on the transcriptomics data) reflects the nuclear ruptures in Arpc4-deficient cells and adds to the observed phenotype. If this is so, what happens then in STING knockout cells in the presence of CK666 inhibitor?

---

__Revision Plan____: __The referee is correct in that we do not show this point experimentally and should therefore temper this conclusion.

• Are the data and the methods presented in such a way that they can be reproduced?

1) The analyses include quite a number of intensity calculations of immunofluorescence signals (Fig. 3D, E; Fig. 4E, Fig. 5B and 6B)? The background stainings are often variable or very high. In some cases it is even unclear whether stainings are really detecting protein and go beyond background staining (Fig. 6A, Fig. 5F). How were immunofluorescence data acquired and dealt with different background staining intensities?

__Revision Plan: __We will carefully describe the microscopes used for image acquisition as well as the downstream analyses for each experiment, which indeed vary depending on the signals observed with distinct antibodies or construct.

2) It remained unclear to me on which basis the nuclear deformations in Fig. 3G, H were calculated?

__Revision Plan: __We will carefully describe the methods used to quantify nuclear deformations.

3) The detailed phenotype of control mice is a bit unclear. It appears as if these were Cre-negative animals. Did the authors have some proof-of-principle experiments showing that CD11cCre Arpc4 +/+ animals have comparable phenotypes to Cre-negative animals?

• Are the experiments adequately replicated and statistical analysis adequate?

__Revision Plan: __We have never observed any decline in LC numbers in other mouse lines/genotypes (for example in cPLA2flox/flox;CD11c-Cre mice shown in the manuscript, Fig. S6B), excluding a putative role for the Cre in LC death.

For most experiments, the number of biological replicates (mice, or BMDC cultures from different mice) and individual values (n, cells) are indicated. Statistical analysis appears adequate.

Minor comments:

• Prior published studies on Arp2/3 function in immune cells are referenced accordingly. A number of additional pre-print manuscripts on this topic have not been cited and could be considered referencing.

__Revision Plan: __We will fix this point and cite additional, relevant preprints.

• The text is very clearly and very well written. Figures are clear and accurate for most cases. There are some open questions:

• Fig. 1B: The number of dots betwenn graph and legend do not match. The dots are not n=12 for both genotypes. Additionally: What do the symbols in the circles in the graph stand for? This is also in another later figure unclear.

• Fig. 2C: The current IF presentation (overlay MHCII with Ki67) is not very helpful. An additional image that shows only the Ki67 signal in the MHCII mask would be very helpful.

• Fig. 4B: BMDCs of which culture day were used for these experiments?

• Fig. 4A and D shows the same representative cells for two biological messages, which is only moderately convincing regarding a "general" phenotype.

• Fig. 5, B: Scale bars are missing.

__Revision Plan: __We will fix all these points.

Reviewer #3 (Significance (Required)):

Strengths and Advance:

The study provides strong data and a very detailed analysis of how the Arp2/3 complex regulates stages of Langerhans cell development and homeostasis. The role of the Arp2/3 complex as regulator of actin branching, which is involved in many cellular functions, has previously not been reported for this cell type. Previous research in immune cells have already studied the Arp2/3 complex, but studies were focussed on its role in migration and the majority of published phenotypes related to cell migration. While there are already a number of in vitro studies showing that the Arp2/3 complex can regulate aspects of cell cycle control or cell death in non-immune cells, most of these studies were performed with immortalized, non-immune cell lines, which can be more easily manipulated to dissect mechanistic aspects of the cellular phenotype, but are limited in their physiological interpretation. Hence, it is a major strength of this study to investigate the effects of Arp2/3 in a primary immune cell type, directly in the native and physiological environment. This is important because in vitro data from other cell types cannot be easily extrapolated to any other cell type and it is critical for our understanding to collect physiological data from tissues, where the biology really happens. The finding that the Arp2/3 complex regulates the tissue-residency of Langerhans cell through processes that are unrelated to migration are partially unexpected, shifting the view of this protein complex's physiological role to other cell biological processes, e.g. regulation of cell proliferation.

Limitations: The limitations of the study are detailed in the five major points listed above. The study accumulates many experiments that characterize the phenotype of Arpc4-depleted cells, showing signs of DNA damage in Langerhans cells and cultures of BMDCs. How the Arp2/3 complex would mechanistically mediate the observed effects on DNA damage and repairs have not been addressed. It also remains open whether this is due to the effects of the Arp2/3 complex in the nucleus or the cytosol, which would be biologically extremely important to understand. Above that, there are some discrepancies regarding the phenotype of the BMDC model, which does neither entirely match the Langerhans cell phenotype in the tissue (reduced proliferation, LC derive from different progenitors), nor other endogenous DC populations, which should also derive from similar progenitors.

Audience and reviewer background:

In its current form, the manuscript will already be of interest for several research fields: Langerhans cell and dendritic cell homeostasis, immune cell trafficking, actin and cytoskeleton regulation in immune cells, physiological role of actin-regulating proteins. My own field of expertise is immune cell trafficking in mouse models, leukocyte migration and cytoskeletal regulation. I cannot judge the analysis and clustering of the bulk RNA sequencing data.

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.

When verifying the general definition of informatics, it mostly aligns with Health Informatics but needs a few clarifications to fully fit this domain. Health Informatics is not just about transforming data — it’s about using information and technology to improve patient care, safety, and health outcomes. This work relies on tools like electronic health records (EHRs), clinical decision support systems, telehealth, and secure data sharing to make sure information is available and meaningful to clinicians, patients, and public health teams (Sumner et al., 2025). A good definition must also include how Health Informatics depends on standards like HL7 and FHIR to ensure systems can work together. It also involves designing user-friendly systems and following strict privacy rules like HIPAA, plus strong ethical practices around consent and data security (Valenta et al., 2018). It’s important to note the growing role of artificial intelligence and remote monitoring in today’s digital health systems too. Overall, Health Informatics clearly goes beyond general IT — it’s an interdisciplinary field that connects clinical care, data science, and technology to support safer, more efficient, and patient-centered healthcare (Mansurjonovich, 2023). When done well, Health Informatics empowers teams to deliver better evidence-based decisions. It also helps reduce errors and supports health equity by improving access to quality information and care.

Rationale: Including these specific elements makes the definition of Health Informatics more accurate because it shows that this domain is not only about handling data but also about applying it responsibly to real healthcare challenges. It highlights how Health Informatics connects people, technology, and policy to improve patient safety and outcomes, which goes beyond what general IT or computer science do on their own. By focusing on ethical, secure, and patient-centered data use, the definition makes it clear how Health Informatics stands apart as a unique and critical field in modern healthcare (Mansurjonovich, 2023; Sumner et al., 2025).

References:

Mansurjonovich, J. M. (2023). Designing an electronic didactic environment to ensure interdisciplinary integration in the teaching of" Informatics and information technologies" during professional education. Conference, 3(03), 78-82.

Sumner, B., Martin, R., Gladman, T., Wilkinson, T. J., & Grainger, R. (2025). Understanding the gap: A balanced multi-perspective approach to defining essential digital health competencies for medical graduates. BMC medical education, 25(1), 1-11.

Valenta, A. L., Berner, E. S., Boren, S. A., Deckard, G. J., Eldredge, C., Fridsma, D. B., Gadd, C., Gong, Y., Johnson, T., Jones, J., Manos, E. L., Phillips, K. T., Roderer, N. K., Rosendale, D., Turner, A. M., Tusch, G., Williamson, J. J., & Johnson, S. B. (2018). AMIA Board White Paper: AMIA 2017 core competencies for applied health informatics education at the master’s degree level. Journal of the American Medical Informatics Association, 25(12), 1657–1668. https://doi.org/10.1093/jamia/ocy132

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Reply to the reviewers

Revision Plan

June 28, 2025

Manuscript number: RC-2025-02982

Corresponding author(s): Babita Madan, Nathan Harmston, David Virshup

General Statements In Wnt signaling, the relative contributions of ‘canonical (β-catenin dependent) and non- canonical (β-catenin independent) signaling remains unclear. Here, we exploited a unique and highly robust in vivo system to study this. Our study is therefore the first comprehensive analysis of the β-catenin independent arm of the Wnt signaling pathway in a cancer model and illustrates how a combination of cis-regulatory elements can determine Wnt-dependent gene regulation.

We are very pleased with the reviews; it appears we communicated our goal and our findings clearly, and in general the reviewers felt the study provided important information, was well planned and the results were “crystal clear”.

While more experiments could strengthen and extend the results, we feel our results are already very robust due to the use of multiple replicates in the in vivo system.

The Virshup lab in Singapore closed July 1, 2025 and so additional wet lab studies are not feasible.

1. 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.

Below we address the points raised by the reviewers:

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

…

The article has the merit of addressing a yet-unsolved question in the field (if beta-catenin can also repress genes) that only a limited number of studies has tried to tackle, and provides useful datasets for the community. The system employed is elegant, and the PORCN-inhibition bypassed by a ____constitutively active beta-catenin is clean and ingenious. The manuscript is clearly written.

We thank the reviewers for their kind comments on the importance of the data. Our orthotopic model provides the opportunity to exploit robust Wnt regulated gene expression in a more responsive microenvironment than can be achieved in cell culture and simple flank xenograft models.

Here we propose a series of thoughts and comments that, if addressed, would in our opinion improve the study and its description.

1) We wonder why a xenograft model is necessary to induce a robust WNT response in these cells.

The authors describe this set-up as a strength, as it is supposed to provide physiological relevance, yet it is not clear to us why this is the case.

We welcome the opportunity to expand on our choice of an orthotopic xenograft model. It has been long established that cancer cells behave differently in different in vivo locations (Killion et al., 1998). Building on this, we confirmed this in our system that identical pancreatic cancer cells treated with the same PORCN inhibitor had very different responses in vitro, in the flank and in their orthotopic environment (Madan et al., 2018). To quote from our prior paper, “Looking only at genes decreasing more than 1.5-fold at 56 hours, we would have missed 817/1867 (44%) genes using a subcutaneous or 939/1867 (50%) using an in vitro model. Thus, the overall response to Wnt inhibition was reduced in the subcutaneous model and further blunted in vitro. An orthotopic model more accurately represents real biology.

The reason for this is presumably the very different orthotopic microenvironment, including tissue appropriate stroma-tumor, vascular-tumor, lymphatic-tumor, and humoral interactions.

Moreover, as the authors homogenize the tumour to perform bulk RNA-seq, we wonder whether they are not only sequencing mRNA from the cancer cells but also from infiltrating immune cells and/or from the surrounding connective tissue.

In experiments generating RNA-seq data from xenograft models, the resulting sequences can originate from either human (graft) or mouse (host). In order to account for this, following standard practice, we filtered reads prior to alignment using Xenome (Conway et al., 2012). We have added additional text to the methods to highlight this step in our pipeline.

2) If, as the established view implies, Wnt/beta-catenin only leads to gene activation, pathway

inhibition would free up the transcriptional machinery - there is evidence that some of its constituents are rate-limiting. The free machinery could now activate some other genes: the net effect observed would be their increased transcription upon Wnt inhibition, irrespective of beta-catenin's presence. Could this be considered as an alternative explanation for the genes that go up in both control and bcat4A lines upon ETC-159 administration? This, we think, is in part corroborated by the absence of enrichment of biological pathways in this group of genes. The genes that are beta-catenin-dependent and downregulated (D&R) are obviously not affected by this alternative explanation.

This is an interesting suggestion, and we will incorporate this thought into our discussion of potential mechanisms.

3) The authors mention that HPAF-II are Wnt addicted. Do they die upon ETC-159 administration, and is this effect rescued by exogenous WNT addition?

We and several others have previously reported that Wnt-addicted cells differentiate and/or senesce upon Wnt withdrawal in vivo but not in vitro. This is related to the broader changes in gene expression in the orthotopic tumors. The effect of PORCN inhibition has been demonstrated by us and others and is rescued by Wnt addition, downstream activation of Wnt signaling by e.g. APC mutation, and, as we show here, stabilized β-catenin.

4) Line 120: the authors write about Figure 1C: "This demonstrates that the growth of β-cat4A cells in vitro largely requires Wnts to activate β-catenin signaling." The opposite is true: control cells require WNT and form less colony with ETC159, while β-cat4A are independent from Wnt secretion.

We appreciate the reviewer pointing out our mis-statement. This error has now been corrected in the revised manuscript.

5) Lines 226-229: "The β-catenin independent repressed genes were notably enriched for motifs bound by homeobox factors including GSC2, POU6F2, and MSGN1. This finding aligns with the known role of non-canonical Wnt signaling in embryonic development" This statement assumes that target genes, or at least the beta-catenin independent ones, are conserved across tissues, including developing organs. This contrasts with the view that target genes in addition to the usual suspects (e.g., AXIN2, SP5 etc.) are modulated tissue-specifically - a view that the authors (and in fact, these reviewers) appear to support in their introduction.

We agree with the reviewer that a majority of Wnt-regulated genes are tissue specific. Indeed, the β-catenin independent Wnt-repressed genes may also be tissue specific. In other tissues, we speculate that other β-catenin independent Wnt-repressed genes may also have homeobox factor binding sites as well and so the general concept remains valid. We do not have sufficient data in other tissues to resolve this issue.

7) The luciferase and mutagenesis work presented in Figure 5 are crystal-clear. One important aspect that remains to be clarified is whether beta-catenin and/or TCF7L2 directly bind to the NRE sites. Or do the authors hypothesize that another factor binds here? We suggest the authors to show TCF7L2 binding tracks at the NRE/WRE motifs in the main figures.

A major question of the reviewers was, can we provide additional evidence that the NRE is bound by LEF/TCF family members. Our initial analysis of more datasets indicates TCF7L2 peaks are enriched on NREs in Wnt-β-catenin responsive cell lines like HCT116 and PANC1. These analyses appear to further support the model that the NRE binds TCF7L2, but we fully agree these analyses can neither prove nor disprove the model.

In our revision, we will analyze additional cut and run datasets as suggested and look at the HEPG2 datasets suggested by reviewer 1. We are concerned about tissue specificity as some of the genes are not expressed in e.g. HEPG2 or HEK293 cells where datasets are available. However, our data continues to support a functional role for the NRE in the modulation of β-catenin regulated genes. The best analysis would be more ChIP-Seq or Cut and Run assays on tissues, not cells, but these studies are beyond what we can do.

What about other TCF/LEFs and beta-catenin? Are there relevant datasets that could be explored to test whether all these bind here during Wnt activation?

As above, We will analyze additional ChIP and Cut & Run datasets to address this question looking at β-catenin and other LEF/TCF family members. We also reflect on the fact that ChIP-Seq does not necessarily imply that the targeted factor (e.g.,TCF7L2) is bound in the target site in all the cells.